Tuesday, 28 July 2026

Skill-SP frames self-evolution as a reinforcement-learning loop over a bank of discrete 'skills,' each defining a scenario with reliable, verifiable execution feedback, while a dynamic skill controller routes proposer/solver interactions across skills to preserve open-ended task diversity. This resolves the diversity-vs-verifiability tradeoff that plagues both environment-bound RL (precise but narrow) and free-form self-generation (broad but reward-noisy). Practitioners could replicate this by defining a skill schema with built-in verifiers and training a routing policy jointly with the proposer/solver via RL. Expect gains to concentrate in agentic/tool-use domains where per-skill correctness checks are tractable.

The authors train an RL agent in a simplified ATC simulation environment to make sequencing/conflict-resolution decisions, then apply post-hoc or intrinsic explainability techniques to expose the policy's decision rationale to human controllers. The contribution is methodological: demonstrating how XRL (explainable RL) methods can be layered onto a safety-critical decision-making agent as an initial testbed before scaling to realistic ATC settings. Practitioners interested in human-AI teaming for safety-critical control could reuse this environment/methodology as a benchmark for comparing explainability techniques on RL policies.

Across ~6,000 runs on two office-automation benchmarks and three agent harness stacks, the authors decompose skill-augmentation effects into 'regressions' (previously solved tasks now failed) versus 'residual failures' (tasks failed regardless), showing average success-rate improvements mask substantial regression costs — top skill sets win primarily by regressing less. They identify concrete failure mechanisms: 'skill description osmosis' (in-context skill text alters behavior even when unused) and 'grounding displacement' (skills interfering with task-specific grounding), among others. This suggests agent-skill evaluation protocols should report regression/residual breakdowns rather than aggregate success rate, and that skill libraries should be audited for unintended in-context side effects.

PinEqualizer is a full-funnel (retrieval through ranking) debiasing system deployed at Pinterest that corrects for existing-content bias across both search and recommendation surfaces simultaneously, rather than the single-stage cold-start fixes common in prior industry work. It pairs the intervention with a scalable measurement framework designed to give fast short-term experiment signal while still validating long-term effects, addressing the classic explore/exploit tradeoff where naive exploration hurts short-term engagement metrics. Teams building recommender cold-start solutions can take away the architectural pattern (funnel-wide correction) and the dual-horizon evaluation methodology as reusable design principles, even without Pinterest's specific model internals.

Quantum Spectral Models construct the generator (Hamiltonian) of the data-encoding unitary directly from each input matrix's spectral properties, rather than using generic coordinate-wise rotation gates, so the encoded state reflects matrix-level spectral values/subspaces instead of just per-element values. Three variants are studied — symmetric, global block, and non-overlapping patch-local block Hamiltonians — and the model outputs admit truncated Fourier decompositions where input-conditioned spectral gaps act as candidate frequency components and spectral subspaces determine coefficients. This gives a concrete recipe (data reuploading with input-dependent Hamiltonian construction) that QML researchers can implement on simulators or NISQ hardware to test whether matrix-aware inductive bias improves expressivity or sample efficiency over standard angle-encoding baselines.

The authors develop a two-stage stacked model that first uses patient-reported outcome (PRO) survey data as a low-cost screening signal, then combines it with clinical variables in a second stage to predict CTCAE-DIGEST-graded dysphagia risk, aiming to approximate videofluoroscopic imaging results without requiring the imaging itself. This addresses a concrete clinical gap: there's currently no validated threshold for when self-reported symptom burden should trigger further workup in HNC survivorship care. A practitioner could replicate the stacking architecture (PRO-only base model + clinical meta-model) as a template for other late-effect risk-stratification problems where a gold-standard test is too burdensome for routine screening.

The study administers a fixed rubric to Claude, Grok, GPT, and Gemini across four snapshots (Oct 2025-Feb 2026) via both API and web interfaces, scoring credibility assigned to ethnonationalist claims derived from Frank Salter's biosocial framework versus control prompts on settled evolutionary science. Grok's default 'Fast' variant scored credibility at 70-75 versus 15-40 for all other models/variants, a divergence absent on control prompts, implicating deployment-layer configuration (system prompts, RLHF tuning, or interface-specific patches) rather than base model capability as the driver. A key methodological finding is an unannounced overnight behavioral shift in one model variant, demonstrating that credibility audits must track API vs. web deltas and repeat over time, since point-in-time single-interface evaluations can miss silent server-side patches that materially change an LLM's epistemic stance.

CausalForge pairs Causalean — a Lean library with 7,035 machine-checked declarations covering causal inference, built with LM assistance under human review — with CausalSmith, an agentic pipeline that autonomously selects topics, proposes results, formalizes statements in Lean, and constructs machine-checked proofs, replacing unreliable LLM-based paper review with formal verification as the correctness gate. This directly targets the failure mode documented in prior work (e.g., 'Bad Scientist') where LLM reviewers accept fabricated results at near-chance detection rates. Researchers interested in automated theorem-proving-backed research agents can build on the open Causalean library or adapt the CausalSmith topic-selection/proof-construction pipeline to other formally-grounded domains (e.g., statistics or algorithms) where Lean or another proof assistant has adequate library coverage.

Bag-of-waves learns a shift-invariant dictionary of EEG waveform templates via shift-invariant k-means, converting continuous signals into discrete atom-token sequences whose occurrence counts feed a downstream classifier or clustering step, avoiding both hand-crafted spectral features and opaque deep nets. The authors extend the base representation with atom-to-atom transition statistics ('n-grams') to capture temporal dynamics, and generalize from single-channel to regional/cross-channel atoms to capture spatial structure. This gives a fully interpretable, low-data-friendly pipeline where practitioners can inspect the actual learned waveform templates as biomarkers rather than relying on fixed band-power features or uninterpretable embeddings.

CARA extracts domain-grounded risk concepts from accident narrative text, grounds them into video frames via vision-language similarity scoring, and assembles them into evolving concept trajectories over time. These trajectories act as intrinsic supervisory signal that directly guides spatial and temporal attention mechanisms within the collision-anticipation model, rather than serving as post-hoc explanation. The approach targets the interpretability gap in existing feature-driven and post-hoc-explanation collision models, offering a template for concept-bottleneck-style reasoning in dynamic (video) rather than static recognition settings.

The V-fold jackknife estimates variance and constructs confidence intervals/simultaneous confidence bands for regular asymptotically linear estimators of pathwise differentiable parameters using only V leave-fold-out refits, sidestepping the need to derive or evaluate an influence function analytically. Uncertainty is quantified from the empirical dispersion of jackknife pseudo-values rather than resampling thousands of bootstrap replicates, cutting computational cost substantially. This gives semiparametric/ML-based estimators (e.g., from targeted learning or double machine learning pipelines) a theoretically justified inference procedure practitioners can drop in wherever bootstrap validity is currently assumed but unproven.

SUSA introduces complex-valued open-chain and periodic reservoir architectures plus regime-conditioned expert modules that interpret reservoir features across calm/onset/recovery/persistent-stress volatility regimes, layered on a common AR-Ridge anchor with a bounded residual correction trained under the QLIKE loss. The authors also implement open-system q-qubit analogues in Qiskit, testing whether genuine quantum dynamics add value over classical complex-valued reservoirs. Evaluated on 16 U.S. equity/ETF series with chronological train/val/test splits, a 12-step input window, and 5-step horizon, SUSA performs competitively with GARCH — a useful benchmark for anyone exploring reservoir computing or quantum-classical hybrids for financial time series.

κ-LoRA shows empirically that a LoRA weight matrix's condition number — the ratio of its largest to smallest singular value — predicts how much that matrix contributes to adaptation quality: low-condition-number matrices are already well-balanced across singular directions and yield marginal gains from tuning, while high-condition-number matrices have underdeveloped, richer subspaces that drive most performance improvement. This gives a cheap, pre-training diagnostic for selective LoRA updates, letting practitioners skip fine-tuning low-condition matrices to cut compute without materially sacrificing adaptation quality — directly actionable for anyone building parameter-efficient fine-tuning pipelines on large or edge-deployed models.

The method converts closed-loop controller-output and process-variable signals into 2D optimal-transport (OT) images that encode control-loop dynamics, then trains a CNN encoder with a combined objective: cross-entropy classification loss on labeled simulated data plus a Deep CORAL domain-alignment loss that matches feature covariances between simulated and real-loop distributions. This targets the well-known sim-to-real generalization gap in data-driven stiction detection, giving practitioners a concrete recipe — OT imaging plus covariance-alignment fine-tuning — for deploying simulation-trained fault detectors on real industrial control loops without needing large labeled real-world datasets.

The paper reduces singular value soft-thresholding to the matrix polar decomposition, enabling use of GPU-optimized polar decomposition algorithms (e.g., Newton–Schulz iterations) in place of the standard SVD-based computation, yielding a significant empirical GPU speedup. The tradeoff is reduced robustness/accuracy, attributed to the discontinuity of the sign function underlying the reduction, so the authors flag it as suited to low-accuracy regimes (e.g., within iterative optimization loops like proximal or ADMM methods) rather than precision-critical use, leaving formal robustness analysis to future work.

In overparameterized linear regression, negative-ridge regularization corrects for weak spectral directions acting like an implicit ridge penalty, but its stable endpoint is constrained — its pole must stay below the smallest nonzero empirical eigenvalue, causing disproportionate anti-shrinkage of smaller eigenvalues. The paper shows early-stopped gradient descent from a negative shift produces a smooth, mixed-sign-capable filter function: a leading prefix of above-ridgeless directions plus controlled shrinkage/exposure of lower directions, with the stopping time setting the crossover point, avoiding the pole constraint entirely. In a Gaussian spike-plus-flat spectral model, they identify a 'Marchenko-Pastur barrier' — the shift canceling the implicit penalty sits a bulk-width above the smallest empirical eigenvalue — and prove the early-stopped path dominates every admissible negative-ridge endpoint, giving a precise random-matrix-theory explanation for why early stopping can outperform explicit negative-ridge regularization.

ADAPT-GQE trains a transformer-based generative model on ADAPT-VQE-produced reference circuits for molecular ground-state preparation, then uses the trained model both to propose circuit ansätze and to score their quality, enabling RL fine-tuning that surpasses the training distribution's accuracy. This effectively amortizes the per-molecule cost of ADAPT-VQE's greedy operator selection into a learned generative prior. Practitioners could extend this by scaling the training set across more molecules/basis sets or by swapping the RL reward for hardware-specific circuit depth/noise objectives.

The paper studies augmenting training data for learned optimization-initializers by harvesting intermediate iterates from a projected gradient descent (PGD) solver on one-sided box-constrained QPs, rather than only using converged solutions, and derives a Rademacher-complexity-based generalization bound quantifying how the k-neighborhood size and related hyperparameters affect sample efficiency. This gives a theoretically grounded data-augmentation recipe for training generative warm-start models without additional solver runs, illustrated on two example QPs. It's directly applicable to DDDAS-style (dynamic data-driven application systems) pipelines that repeatedly re-solve parametric optimization problems under tight latency budgets.

TRACE-Router reframes LLM routing for agentic workflows: instead of per-call routing decisions that suffer from credit-assignment mismatch under delayed rewards, it uses a contextual bandit to select and pin one backend LLM per task at admission time, then updates its policy from the terminal, task-level reward combining accuracy and latency. This avoids the attribution problem where per-call routers can't tell which of many chained calls caused the final outcome. Teams building multi-step agent pipelines could adopt this admission-time routing pattern plus a bandit policy update loop keyed on end-to-end task success rather than per-call proxies.

This is a qualitative HCI/education research report using trio-ethnography — a reflexive, dialogic method with three participants of differing standpoints — pairing two computing educators against one CS undergraduate across three conversations to surface AI-supported learning processes invisible to classroom observation alone. The findings are process-level: student narratives disclosed previously hidden reasoning and AI-use patterns that reshaped the educators' assumptions about assessment, transparency, and pedagogy design. Educators or researchers designing AI-integrated CS curricula could adopt this dialogic method as a lightweight, ongoing feedback mechanism to complement classroom observation and survey-based studies.

The study probes four pretrained audio foundation models (AST, CLAP, BEATs-bio, BirdNET) with Mantel tests correlating embedding-space distances against phylogenetic distances, using 1,754 recordings across 32 marine mammal species (with a focus on 26 cetaceans) from the Watkins Marine Mammal Sound Database. Foundation model embeddings recover strong phylogenetic signal (CLAP/BEATs-bio r=0.82, AST r=0.74, all p<0.001) versus near-zero correlation for hand-crafted 105-dimensional MFCC features (r=0.040), a gap that persists after PCA projection, suggesting these general-purpose embeddings encode taxon-relevant acoustic structure beyond what domain-specific bioacoustic pretraining alone provides. Researchers could use this Mantel-test probing protocol as a general diagnostic for whether any embedding space captures latent biological/phylogenetic structure, and as evidence favoring general-purpose audio foundation models over hand-crafted features for bioacoustic transfer tasks.

grapheme-kit is an open-source Python library that reimplements standard lexical distance/similarity/evaluation metrics (e.g., edit distance-style measures) to operate on Unicode grapheme clusters rather than raw code points, addressing systematic misrepresentation of errors in scripts like Tamil and Sinhala where one grapheme spans multiple code points. It ships grapheme cluster segmentation plus composition/decomposition utilities specific to those scripts, and the authors validate the approach via an OCR evaluation case study showing grapheme-level metrics better reflect true error rates than code-point-level ones. NLP practitioners working on OCR, spell-checking, or MT evaluation for complex/Indic scripts can drop this in as a more faithful replacement for standard code-point-based metric libraries.

Deformable Triangle Splatting extends 2D-primitive radiance field rendering (Gaussian/triangle splatting) by attaching K control points per triangle edge, each with a single learnable scalar displacement, allowing edges to bow inward or outward and thus represent non-convex/concave geometry with one primitive instead of many convex ones. Differentiable rendering is achieved via a custom rasterization pipeline in barycentric coordinate space with a winding-number inside/outside test per pixel and a learnable sharpness-controlled window function for soft/anti-aliased boundaries, preserving view-consistency. This offers a drop-in primitive upgrade for splatting-based real-time novel-view synthesis pipelines aiming to cut primitive counts on scenes with curved or concave surface detail.

The paper proposes a dynamic least-privilege capability-scoping architecture for enterprise LLM agents built on three sources: static role-based permission ceilings, a task-context classifier that narrows the active permission set to the current task, and policy-derived rules prohibiting specific dangerous capability combinations regardless of individual permissions being allowed. It's framed as a prevention-over-detection security control (an agent cannot misuse a credential absent from its context, independent of its reasoning or evasion ability), and supports both an enforcing mode and an observe-only mode that logs out-of-context permission requests for auditing. Security teams designing agent authorization systems could use the three-source model directly as a policy architecture, with the synthetic dataset mentioned presumably usable for training/evaluating the task-context classifier component.

The paper formalizes an 'angular effective learning rate' for scale-invariant networks, capturing how the angle between successive parameter states evolves as a function of update angle, parameter norm, and update norm, showing standard norm-based learning-rate measures are a special case assuming orthogonality between parameter and update. They decompose each optimizer step into radial (norm-changing) and tangential (angle-changing) components and empirically test whether radial updates drive the angular effective learning rate under Hyperball-style constrained optimization. Results show radial components have limited direct influence, refuting a plausible mechanism for Hyperball's empirical advantage and motivating further mechanistic study. Useful for anyone tuning normalization-constrained optimizers at scale who wants a principled diagnostic beyond norm heuristics.

The method poses graph-constrained correlation matrix generation as a convex optimization problem: projecting an initial candidate matrix onto the elliptope (the set of positive semidefinite matrices with unit diagonal) subject to a sparsity mask dictated by a graph's edge structure, which is a structured instance of matrix completion. Several numerical projection schemes are implemented and compared, and the framework adds a novel degree of freedom — control over the mean of off-diagonal correlation values — that existing elliptope-projection or partial-correlation methods lack. The approach explicitly does not sample uniformly from the feasible set, so it's a controllable generator rather than an unbiased sampler, useful for benchmark/stress-test matrix construction in finance or network science.

The paper develops an identifiability theory for action-conditioned Joint-Embedding Predictive Architectures (JEPAs) with Gaussian latent state-space dynamics under Gaussian behavior policies, addressing when learned latent representations recover the true state and true action-effect dynamics up to benign transformations, rather than confounded or spurious ones. It characterizes how limited conditional action variation in the behavior policy can cause state-dependent transitions and action effects to become statistically confounded, and derives conditions (on policy variation and model structure) sufficient for joint identifiability of state and dynamics. This gives practitioners building JEPA-based world models for control a theoretical checklist — e.g., required action-policy diversity — to trust that latent-space planning is operating on a faithful representation rather than an unidentifiable one.

The work reframes explainable AI for creative practice around 'model bending' — direct manipulation of internal weights/activations — rather than post-hoc technical explanation, implementing an interactive inspection and intervention interface as custom nodes within ComfyUI's node-based workflow system, including layer selection and real-time intervention controls for Stable Diffusion 1.5. They conduct qualitative and quantitative analysis of how bending interventions at specific architectural components (e.g., particular U-Net layers or attention blocks) produce distinct, characterizable visual effects. This provides a reusable interface pattern and empirical component-to-effect mapping that other tool-builders or diffusion researchers could extend to other checkpoints or architectures for practice-based interpretability work.

PRIMS is a physics-aware multimodal Transformer for on-device fluid identification, combining three modules: Physics-based Token Vectorization (converts raw Coriolis and pressure sensor signals into physically meaningful embeddings), a Physical Component Synthesizer (explicitly models viscosity-mediated dependencies among flow rate, pressure, and density), and Physics-guided Fusion (structures cross-modal attention around these physical correlations rather than learning them purely data-driven). This contrasts with domain-agnostic sensor-fusion baselines that ignore governing physical relationships, aiming to improve generalization and interpretability under varying flow/pressure/temperature conditions. Engineers building embedded multimodal sensing systems could adopt the physics-token and physics-guided attention design as a template for injecting domain constraints into Transformer-based sensor fusion.

Reflector combines a hand-designed interval-class oracle — a table of weights scoring pitch-class compatibility between sources — with a neural encoder trained purely on synthetic audio to approximate that oracle's scores via dot products in a 128-dimensional embedding space, enabling interactive-speed retrieval. A sweep-line algorithm over the composer's multi-track timeline detects co-sounding regions as the arrangement evolves, computing oracle-weighted compatibility scores against those live co-sounding contexts rather than a single static reference sample. This lets retrieval adapt dynamically to arrangement changes, and the synthetic-training-plus-oracle-distillation approach is a reusable pattern for other audio compatibility/retrieval tasks needing real-time performance without labeled real-world harmonic data.

LunarFM is a multimodal foundation model that fuses 18 input channels from six instruments across three lunar orbital missions into a shared embedding space, addressing the fragmentation caused by heterogeneous multi-instrument data, sparse labels, and bespoke task-specific pipelines in lunar remote sensing. The shared representation is demonstrated to support diverse downstream applications including similarity search and few-shot learning, implying the model was trained with a self-supervised or contrastive objective capable of aligning disparate sensor modalities into a common latent space. This provides a reusable backbone for lunar science and in-situ resource utilization (ISRU) mapping tasks, letting practitioners fine-tune or query the shared embedding rather than train new single-instrument models for each new mapping or classification problem.

The framework wraps an LLM-driven agentic pipeline with a self-calibration loop where an ARIMA forecaster provides a dynamically updated pseudo-ground-truth signal, substituting for the missing labeled data typical of open-ended agentic deployments. This lets the system detect and correct operational drift without continuous human-in-the-loop supervision. The approach is validated experimentally on edge resource profiling/allocation, a domain chosen for its noisy, non-stationary telemetry. Practitioners building autonomous agent monitoring could adapt the ARIMA-based calibration signal as a lightweight, model-agnostic drift detector for other agentic deployments lacking ground truth.

The paper establishes high-probability generalization guarantees for nonlinear least-squares estimation of the one-step transition/prediction function from a single trajectory of a time-homogeneous, ergodic Markov process, measured against the invariant measure rather than an i.i.d. sample distribution. It shows how trajectory dependence structure modifies standard statistical learning bounds, then extends the analysis to higher-order Markov systems, finite state spaces, and to learning Koopman operators via the same least-squares/concentration machinery. This gives a rigorous statistical foundation for system identification and Koopman-based control/forecasting methods that in practice are trained on a single observed trajectory rather than repeated i.i.d. trials.

SceneActBench evaluates VLM agents across five visually-conditioned 3D action tasks (built from 210 source instances, 520 task cases with paired input conditions), scoring final outputs against hidden ground truth using task-specific geometric metrics rather than text similarity. All tasks run through a single fixed agent-environment loop to control for confounds across model comparisons. Across eleven proprietary VLM configurations, Overall scores ranged only 38.6-50.2, indicating current frontier VLMs struggle broadly with grounded, multi-object 3D manipulation despite strong performance on perception-only benchmarks. This provides a reusable evaluation harness for researchers building or comparing embodied/tool-using VLM agents on scene-level action tasks.

HiKV is an algorithm-hardware co-design for KV cache compression during LLM decoding, applying hierarchical importance filtering: Stage I evicts unimportant tokens under a fixed budget, and Stage II sub-selects the significant elements within each retained token's KV vector, achieving compression ratios beyond what either single-granularity approach reaches alone. The hardware component is a dedicated accelerator built around a reconfigurable importance sorter whose datapath switches between the two stages' distinct sorting requirements, unifying both acceleration stages in one circuit with minimal added overhead. Evaluated on representative LLMs, it reports up to 7.95x compression, offering a template for practitioners designing memory-efficient long-context inference systems combining eviction and intra-token pruning.

LAW encodes watermark bits as antipodal angular positions within the diffusion latent, exploiting the rotation-invariance of isotropic Gaussian distributions so embedding preserves both latent Gaussianity and the i.i.d. structure across latent dimensions, addressing a correlation-degradation failure mode the authors show existing latent watermarks introduce (previously only indirectly observed via FID quality drops). This makes the watermark more robust to normal and adversarial perturbations during latent inversion/removal attacks compared to prior latent-domain schemes that violate these statistical constraints. Practitioners building provenance/watermarking systems for diffusion models can use the angular encoding as a drop-in latent modification requiring no changes to model weights, with the Gaussianity-preservation property serving as a direct, checkable robustness criterion.

AgentRCA is a zero-shot agentic framework for root cause analysis that combines a data-driven digital twin, modeling nominal system dynamics, with a tool-augmented LLM performing inference-time reasoning rather than learning fault-specific mappings from labeled failure data. The agent iteratively queries the twin and other tools to gather statistical evidence, evaluates competing causal hypotheses, and converges on an explained diagnosis, addressing both the black-box opacity and labeled-data scarcity limitations of prior data-driven RCA methods. This offers a template for building interpretable, evidence-grounded diagnostic agents in industrial monitoring settings where fault-labeled data is scarce but a normal-behavior model can be learned or simulated.

Entropic Curvature extends the Lott-Sturm-Villani optimal-transport curvature framework to graphs via displacement convexity of entropy along Wasserstein geodesics, providing a global, transport-based curvature measure in contrast to local edge-level notions like Ollivier-Ricci or Forman curvature. The authors define a tractable Weak Entropic Curvature lower bound and derive from it a Poincaré-type inequality controlling oversmoothing, a transport-entropy generalization bound, and an 'expansion paradox' result relating sparsity, spectral expansion, and positive entropic curvature. This gives GNN researchers a principled, globally-certifiable diagnostic for oversmoothing/oversquashing that could inform architecture or rewiring choices aimed at improving long-range information propagation.

IR275K is a curated infrared multi-frame super-resolution (MFSR) benchmark comprising 594 video sequences and 275,196 frames, with defined sequence-level train/validation/test splits and a reproducible X4 upscaling evaluation protocol, addressing the fragmentation of prior infrared MFSR evaluation across private datasets and ad-hoc protocols that fail to capture thermal contrast, sensor noise, weak texture, and platform-induced frame variation. As an initial architectural probe, the authors benchmark CGMamba, a lightweight state-space (Mamba-based) model with 10.90M parameters, on this dataset. This gives remote-sensing and super-resolution researchers a standardized, reproducible testbed for developing and comparing efficient infrared MFSR methods under realistic satellite constraints.

The pipeline extracts grammatical rules, example sentences, and lexical entries from grammar reference books via LLM parsing, then synthesizes parallel corpora used for fine-tuning rather than in-context prompting at inference. Evaluated on Kalamang, Tuatschin, and Mandan, fine-tuned models beat seed-data baselines in up to 75% of configurations, with ChrF++ gains as high as +8.8. A 96-configuration factorial study varying target part-of-speech, retrieval granularity, and sample volume isolates which generation choices actually drive gains, giving a replicable recipe for other under-resourced languages with grammar documentation.

IDEAgent is a multi-agent system framing automated ideation as quality-diversity (QD) search rather than single-objective optimization. Quality is driven via multi-objective feedback loops for targeted repair/refinement of each idea lineage, while diversity is enforced through a lightweight sequential memory that explicitly compares candidates against the full historical population rather than only siblings. This lineage-based evolutionary structure lets practitioners inspect idea ancestry and tune selection pressure between quality and novelty, offering a more principled alternative to prior independent quality-only or diversity-only ideation pipelines.

The method reformulates support-set selection for few-shot medical image segmentation as a sequential decision problem solved via reinforcement learning, where an agent policy predicts an entire support set jointly rather than scoring candidates independently. This captures complementary information and interaction effects between selected samples that greedy or per-sample active-learning heuristics miss. Given an unlabeled candidate pool, the trained agent directly outputs a support set optimized for downstream adaptation performance, offering a plug-in selection strategy practitioners could pair with existing few-shot segmentation backbones.

The paper formalizes 'protocol validity,' the requirement that a benchmark's evaluation procedure preserves the intended capability as necessary for success, and introduces HackDetect, a post-hoc audit that identifies exploitable exposures (leaked solutions, evaluation artifacts, generator structure, feedback manipulation) in agent transcripts, attributes their use, and quantifies score inflation as the 'Mislead gap' (exploit score minus intended-behavior score). Applied across multiple agent benchmarks, it gives a standardized attribution procedure researchers can adopt to audit their own evaluation pipelines for reward hacking before trusting leaderboard numbers.

The paper introduces 'interior interpretability,' analyzing attention rollout as a row-stochastic Markov-like operator describing feature-token propagation across transformer layers, and applies Doeblin-Dobrushin contraction theory to it. The key result: when the rollout operator's Dobrushin coefficient is small, the operator is provably close to a rank-one stochastic matrix determined by normalized column sums, giving a closed-form structural characterization of the propagation profile rather than requiring layer-by-layer inspection. Demonstrated on tabular transformers trained for metabolomic age prediction, this gives practitioners a quantitative diagnostic for when and how strongly a model's internal attention structure collapses toward a fixed pattern.

The system pairs a 21-DoF dexterous robotic hand with compliant soft skin (engineered to minimize light leakage and produce continuous silhouettes) and a learned shadow self-model that maps hand configurations to their projected shadow shape. This self-model lets the robot plan and execute hand motions optimized for legible shadow expressions rather than for the hand's literal appearance, decoupling communicative signal from physical morphology. The key contribution is treating projected visual abstraction (shadow) as a controllable, learnable output distinct from direct embodiment, which could generalize to other projected/reflected communication modalities. Roboticists working on legible motion or human-robot interaction could build on the self-model architecture for other abstraction-based signaling channels.

ViTacWorld is an action-conditioned world model that jointly models vision and tactile observations for contact-rich manipulation, pretrained on large-scale real tactile datasets plus a purpose-built simulation environment (exploiting tactile signals' smaller sim-to-real gap versus vision), then fine-tuned with real-world policy rollouts. This combines data-scaling strategies from vision-language world models with the physical grounding of touch sensing to overcome the scarcity of real tactile interaction data. Practitioners building manipulation policies for tasks like insertion or grasping under occlusion could use this as a learned dynamics model for model-based planning or policy fine-tuning.

The framework extends ROS control with standardized joint- and Cartesian-space command interfaces (stiffness/damping gains, nullspace targets, feedforward terms) and a plugin architecture that decouples hardware abstraction/kinematics-dynamics computation from control-law implementation, letting variable-impedance and other compliant-control algorithms be written once and loaded at runtime across arbitrary manipulators. This addresses the lack of reusable cross-platform infrastructure in current compliant-control software stacks. Robotics engineers can implement a new impedance-control law as a self-contained plugin and immediately deploy it on any manipulator supported by the generic hardware wrappers, without rewriting robot-specific interfacing code.

Teachy Mini operationalizes 'knowledge-based design' (KBD) requirements for generative social robots on the Reachy Mini platform, combining system prompting, retrieval-augmented generation, and stateful prompt orchestration to constrain LLM outputs to sourced material and maintain coherent multi-turn tutoring state. A preliminary N=24 study had participants complete a robot-guided lesson on research methodologies, comparing conditions to assess learning and perceived reliability. This provides a concrete implementation reference and a KBD requirement checklist for researchers building LLM-driven physical tutoring agents where factual grounding and transparency are safety-critical design constraints.

The pipeline first runs a Faster R-CNN detector to extract per-component geometric features from images, then feeds these into a transformer that predicts a candidate adjacency matrix over detected parts. This matrix conditions a Siamese network built on an attentional graph convolutional network (aGCN), which performs message passing between part-pairs to classify assembly relationships without relying on semantic class labels. The key claim is data efficiency: because the representation is purely geometric rather than semantic, the method generalizes from very small training sets, unlike typical scene-graph approaches that need large annotated corpora. Practitioners could adapt this for automated assembly-instruction generation or as a perception front-end for robotic assembly planning.

The authors introduce an asymmetric origami bending (AOB) pattern and asymmetric dual-chamber (ADC) actuator design, combining single-chamber (AOB-S) and dual-chamber (AOB-D) pneumatic units into one monolithic silicone/origami structure that forms both finger and palm actuators. Motion characteristics are controlled by an 'asymmetric ratio' parameter governing fold and chamber geometry, allowing bio-inspired coupled finger-palm kinematics from a single fabrication step rather than multi-part assembly. This addresses the classic soft-robotics tradeoff between manufacturing simplicity and functional performance (grip force, dexterity) by encoding multifunctionality directly into geometry rather than added mechanisms. Roboticists building compliant grippers could reuse the AOB/ADC unit library as parametrized building blocks for custom monolithic soft hands.

The pipeline maps distributed tactile-skin pressure readings to a scalar 'pain gain' via a nonlinear continuous transfer function, which then drives a reflex controller producing a withdrawal motion. Three withdrawal strategies are compared: a uniform fixed joint-space retraction independent of contact location, a location-dependent joint-space withdrawal modeled on human reflex data, and a Cartesian-space withdrawal directed along the local skin-patch surface normal. All three are implemented in a common reflex control architecture and evaluated via human perceptual studies, presumably scoring naturalness/safety of each strategy. This offers a concrete, comparable design space for reactive safety layers on tactile-covered manipulators, useful for anyone building compliant human-robot interaction systems that need sub-planning-loop protective reactions.

The paper benchmarks 17 edge-deployable small language models (SLMs) against 4 cloud-hosted foundation-model APIs specifically on the task of decomposing natural-language navigation instructions for outdoor robots, evaluating whether onboard-sized models retain sufficient instruction-decomposition fidelity. It pairs this with a geometric goal-localization module that grounds decomposed instruction segments to metric map coordinates without cloud-based semantic grounding, enabling fully offline VLN in outdoor settings. The contribution is both an empirical benchmark (useful reference for selecting SLMs for onboard NLU) and an architecture demonstrating that instruction decomposition and metric grounding can be decoupled from large cloud models. Practitioners building disconnected field robots can use the benchmark results to pick a viable onboard SLM and adopt the geometric grounding scheme to avoid dependency on network-based foundation model APIs.

SOWL-MPC combines Sparse Variational Gaussian Processes (SVGPs) for online policy inference with a receding-horizon (MPC) control scheme, learning a posterior over an unknown 'world' agent's latent control policy purely from noisy state observations via Online Variational Conditioning (OVC), which allows streaming updates without retraining from scratch. The learned stochastic policy is propagated through nonlinear world dynamics using an approximate moment-propagation technique to produce predictive uncertainty bounds usable inside a chance-constrained or robust MPC safety formulation. This targets the 'ego-world' setting where the interacting agent's policy is unmodeled and must be estimated online, differentiating it from standard multi-agent MPC that assumes known or learned-offline models. Control researchers could build on this by swapping in different GP kernels or propagation schemes, or extending it to multi-agent (more than one 'world' agent) settings.

JustDepth is a single-stage radar-camera depth estimator that aggregates variable-count radar returns into a fixed-width 1D representation, decoupling inference cost from point-cloud size, then fuses this with camera features via a 'Height Fusion Block' and propagates depth spatially using a lightweight GNN. Training uses single-scan LiDAR purely as supervision (a training-only confidence decoder further stabilizes learning) with zero added inference-time cost, so the deployed model needs only radar and camera. The paper identifies and quantifies a 'stripe artifact' failure mode common to radar-camera depth fusion using a new Vertical-Horizontal Gradient Ratio (VHGR) metric, and mitigates it with targeted data augmentations, reporting improvements over recent state-of-the-art on nuScenes. This is directly usable by practitioners building low-latency depth perception stacks that want to avoid LiDAR at deployment time while retaining metric accuracy.

ImiPath distills a spatiotemporal decision prior from expert demonstration trajectories by constructing local spatiotemporal observation representations and learning directional preference patterns that transfer across planning instances under partial observability. These learned priors are injected into classical search-based planners as biasing heuristics, steering node expansion toward historically reliable directions and reducing redundant/myopic search compared to planners with no memory across episodes. The core contribution is a general prior-guided augmentation layer for existing planners rather than a new planner from scratch, meaning it could plausibly be bolted onto A*-style or sampling-based planners already in use. Roboticists working on partially-observable navigation could replicate this by collecting representative demonstration trajectories in their environment and training the same local spatiotemporal prior model to inject into their existing planner.

The authors audit a representative tightly-coupled IESKF-based LiDAR-inertial odometry (LIO) stack and identify five architectural deficiencies harming real-time closed-loop UAV control: odometry output pinned to 10 Hz LiDAR rate rather than 200 Hz IMU rate, absent direct velocity output, blocking execution paths that stall IMU processing, mutex contention, and synchronization race conditions. Their fixes include IMU-rate forward propagation for high-rate state output, direct body-frame velocity publishing, SLERP-based orientation smoothing, dual-executor thread isolation, and explicit synchronization guards to eliminate races. This reframes 'benchmark-accurate' LIO as insufficient for flight control and provides a concrete, reproducible checklist of systems-level fixes; drone developers using similar IESKF-LIO stacks (e.g., FAST-LIO-style pipelines) can apply the same five fixes directly to their own codebase to improve real onboard flight performance without changing the underlying estimation theory.

DB-VIO is a learning-based VIO framework that decouples rotation and translation estimation into two branches rather than using a single unified representation and temporal model. It augments monocular visual features with depth cues for better geometric grounding and injects an explicit integrated-attitude prior into IMU encoding to sharpen rotation-related cues that raw inertial data leaves implicit. This targets a known weakness of unified VIO pipelines: heterogeneous rotation/translation dynamics get conflated under one temporal model. Practitioners building learned VIO stacks could adopt the dual-branch decomposition and attitude-prior injection as drop-in architectural changes to existing monocular VIO pipelines.

DynaMAC is a lightweight, policy-agnostic wrapper for multi-stream manipulation policies that reformulates the opposing arm as a dynamic task parameter rather than an exogenous reference frame, removing the causal assumption that reference frames are static relative to the acting arm. This unifies single-arm dynamic-object manipulation and bimanual coordination under one formulation without imposing an explicit leader-follower hierarchy. It preserves the sample efficiency and computational speed benefits of multi-stream policies while extending their applicability to genuinely dynamic, mutually-coupled settings. Robotics practitioners using multi-stream/reference-frame-based policies could integrate DynaMAC as a modular addition rather than retraining a monolithic bimanual policy from scratch.

The paper introduces Hyper Petri Nets (HyPN), a synthesis method that constructs Petri nets from Boolean logical specifications while explicitly separating observable markings from underlying execution semantics. It defines execution semantics over observable states induced by admissible atomic firing sequences, guaranteeing by construction that every observable marking satisfies the input constraints. A key finding is a structural mismatch between logical feasibility (what the Boolean spec allows) and executable behavior (what firing sequences can actually reach), demonstrated on lunar-rover-inspired scenarios. This offers a constructive synthesis approach for correct-by-construction discrete-event controllers, useful to practitioners building verified robotic or embedded system behavior models from formal specifications.

The authors propose a torque-level control framework combining task-space inverse dynamics (TSID), solved online via quadratic programming, with task-space impedance control to handle ship-borne manipulators subject to stochastic wave-induced base motion. An error-state Kalman filter (ESKF) fuses IMU and end-effector pose feedback to estimate base state in real time, enabling accurate feedforward compensation of the dynamic coupling introduced by the moving base. The QP formulation lets the controller jointly satisfy high-precision trajectory tracking and compliant contact behavior rather than trading one off against the other. Validated on a 7-DOF manipulator in both simulation and real-world experiments, this gives practitioners a concrete recipe (ESKF + TSID-QP) for deploying contact-rich manipulation on any moving/disturbed base, not just ships.

The paper presents a search-based framework for grounding abstract multi-hand policy trajectories onto physical multi-arm robots, jointly solving arm-to-hand assignment, per-arm configuration-space trajectory tracking via inverse kinematics, and constraint satisfaction (joint limits, inter-arm collision avoidance). The key contribution is theoretical completeness: the search is guaranteed to find a feasible grounding if one exists, in contrast to ad hoc IK-pipeline extensions used in practice today, which offer no feasibility or safety guarantees. This positions the method as a general execution-layer component that sits beneath any abstract-hand manipulation policy, decoupling policy learning from physical embodiment. Practitioners deploying learned manipulation policies across different multi-arm platforms could use this as a drop-in, embodiment-agnostic execution layer rather than writing per-robot IK heuristics.

MissionBench is a benchmark of 120 mission-level tasks spanning four task families across five simulated 3D aerial environments, evaluating MLLMs as embodied agents that must autonomously plan, navigate, and report using only egocentric visual observations and action history, with no aerial-specific fine-tuning. Across 22 open- and closed-source MLLMs, the best model achieves under 35% mission success versus 84.4% human performance, quantifying a large embodied-reasoning gap despite these models' strong performance on static vision-language benchmarks. The authors note performance gains correlate with model scale, suggesting general-purpose scaling transfers partially to long-horizon embodied planning. Researchers can use MissionBench as a standardized testbed to evaluate MLLM agents' planning and grounding capabilities before investing in aerial-specific fine-tuning or specialized action interfaces.

Mag4D-SLAM is the first large-scale outdoor geomagnetic SLAM dataset, comprising 14 sequences (18+ km) of synchronized LiDAR, camera, IMU, tri-axis magnetometer, and GNSS data with SE(3) ground-truth poses, collected via repeated forward/reverse traversals of structured campus routes under paired day/night conditions. The repeated-traversal design specifically enables analysis of magnetic field repeatability and other properties needed to validate magnetometer-based localization claims, which prior small-scale indoor magnetic datasets couldn't support. This fills a clear gap for researchers developing or benchmarking magnetic-aided SLAM/localization algorithms as a GNSS-denied, vision-degradation-robust sensing modality. Practitioners can use it directly to train or evaluate multi-modal SLAM pipelines that fuse magnetometer readings with LiDAR/camera/IMU data, or to isolate and study geomagnetic-only localization performance.

ACME is a large-scale multi-cultural, multi-embodiment social navigation dataset assembled from 8 sites across 5 countries and 7 distinct robot platforms, providing 29.35 hours of onboard robot sensor data plus 43.5 hours of overhead pedestrian tracking data. Unlike prior social-navigation datasets, it emphasizes goal-driven navigation under explicit robot-crowd interaction in complex social scenarios rather than passive pedestrian-avoidance logging, and its cross-cultural, cross-embodiment design lets researchers isolate the effect of culture/geography/embodiment on socially acceptable navigation behavior. The dual onboard + overhead-tracking structure supports both policy learning (from the robot's own sensor stream) and independent ground-truth behavior analysis (from the overhead pedestrian tracks). Researchers building or evaluating social navigation policies can use ACME to test generalization across cultural context and robot embodiment, a dimension prior single-site or single-robot datasets couldn't measure.

The task is framed as a POMDP since onboard sensors cannot directly measure ambient fluid viscosity, which ranges from 1e-7 to 1e-2 m²/s across trials. An asymmetric actor-critic setup trains a privileged-information teacher policy in simulation, then distills it into a proprioception-only student policy via policy distillation. The DRL agent discovers non-sinusoidal gaits that outperform classical predefined undulation controllers in propulsion velocity and transport efficiency. This is a strong template for sim-to-real transfer work on underactuated locomotion in environments with unobservable physical parameters.

The pipeline has two stages: a latent conditional diffusion model serves as the world model, predicting future ultrasound observations conditioned on context frames, probe motion, and temporal offset; then a goal-conditioned temporal transformer predicts probe motion sequences and is fine-tuned using reward signals derived from the frozen world model, effectively model-based RL without a physical simulator. Experiments on a self-collected neck-ultrasound dataset validate that the world model's predictions provide a usable training signal for the policy transformer. This offers a reusable recipe for model-based skill learning in domains where physics-based simulation is intractable but the sensor modality (video/image sequences) is well-suited to generative video prediction.

StARS reformulates socially appropriate action generation as a recommender-systems problem, combining collaborative filtering over annotators (as users) and contexts (as items) with learnable scene representations to output personalized appropriateness scores over candidate robot actions. The framework is model-agnostic, meaning it can wrap around different scene encoders and policy backbones without redesign. It's evaluated on two HRI benchmarks, MannersDB+ and SocNav1, providing a template for injecting per-annotator preference variance into social-navigation or action-selection pipelines rather than training on averaged consensus labels.

Pigey is a closed-loop orchestration layer that sits above existing vision-language-action (VLA) policies and parameterized skills, performing goal decomposition into subgoals, issuing low-level motor commands, verifying outcomes from low-level observations, and triggering recovery behaviors on failure. This decouples high-level reasoning/planning from the low-level control policy, contrasting with end-to-end approaches that bake reasoning into the pretrained policy itself. Practitioners could adopt this pattern to add planning, verification, and failure recovery on top of any existing VLA model without retraining it, addressing the 'orchestration gap' between perception-action policies and full task-level autonomy.

OAT satisfies three desiderata, high compression, total decodability (every token prefix decodes to a valid action chunk), and an ordered token space, by using a transformer with register tokens, finite scalar quantization, and training mechanisms that explicitly induce token ordering so coarse control information lands in early tokens. This coarse-to-fine, prefix-decodable structure differs from prior analytical discretizers (long sequences) and unstructured learned latent tokenizers, making it more compatible with autoregressive downstream policies that may want variable-length generation or early-exit inference. Practitioners building visuomotor transformer policies could adopt OAT as a drop-in action tokenizer to shorten sequence length while preserving anytime-decodability.

The system generates synthesizable SystemVerilog, an SMT-LIB model, and a Lean 4 proof model from one shared bit-vector intermediate representation for all IEEE-754 FP32/BF16 operators (comparisons, conversions, add/sub, mul, FMA), and uses a Yosys-to-SMT miter to machine-check equivalence between emitted SystemVerilog and the SMT model across all 24 operators. Verification is split at the solver-tractability frontier: multiplier-free operators are proved exhaustively equivalent over their full input space (comparisons, add/sub over all 2^64 inputs, conversions, all binary BF16 ops), while multiplier-involving operators presumably use bounded or symbolic techniques. This gives a concrete template for co-generating implementation, solver model, and formal proof from a single IR, directly relevant to verifying LLM-generated HDL.

The approach uses agentic source analysis, LLM agents formulating operator-level relations by reading pinned implementations of ABC and mockturtle logic-synthesis tools, followed by adversarial audits that stress-test the stated scope of each claimed relation before it's 'certified.' This targets the operator-vocabulary bottleneck in logic-optimization sequencing, where operators are typically treated as opaque actions in an RL or heuristic search space; certified relations could prune or compress that action space with theoretical backing rather than empirical heuristics alone. This is a notable example of using LLM agents for formal-methods-adjacent code analysis on legacy EDA codebases rather than for direct code generation.

The paper introduces displayed setoids (families of setoids) in intensional type theory and uses them to give a semantics for extensional type theory with universes (ETU) inside IRU, intensional type theory extended with a universe closed under inductive-recursive definitions, formalized as a machine-checked construction in safe Agda. ETU's syntax is defined extrinsically via a well-scoped locally nameless representation, and the semantic interpretation into displayed setoids is complicated by IRU's limited expressive power. As a corollary, the construction yields a proof of ETU's consistency carried out entirely within IRU, offering a reusable semantic framework for anyone formalizing extensional equality principles inside intensional proof assistants.

The authors implement a Plan Mode UI for an LLM-based spreadsheet agent and run a within-subjects study (N=24) comparing it to a non-planning baseline on identical spreadsheet tasks. Task outcomes (correctness/completeness) were statistically similar between conditions, but Plan Mode reduced the amount of post-execution refinement users performed and improved subjective ratings on creativity-support and human-machine collaboration scales. This suggests the value of upfront planning in end-user programming contexts is less about output quality and more about perceived control and trust. Replicators could adapt this by instrumenting refinement-edit counts and validated creativity-support/collaboration questionnaires as outcome measures for other agentic EUP tools.

Optimo targets dynamic-language code optimization, addressing two failure modes in prior LLM-based approaches: poor identification of optimization targets and shallow, single-level rewrites. It first localizes time-critical code structures as candidate bottlenecks, then applies a Mixture-of-Prompts architecture that routes different code patterns to specialized prompting strategies operating at multiple granularities (e.g., statement, function, module level) rather than a single uniform rewrite pass. This is essentially a routed ensemble of optimization strategies rather than one-shot prompting. Practitioners building similar tools could reuse the bottleneck-localization-then-routed-rewrite pipeline as a template for LLM-based performance engineering on interpreted languages.

The study empirically examines router-side injection attacks in agentic coding workflows, where a third-party API router positioned between the coding agent and the upstream LLM provider can inspect/modify requests and responses without any verification of provider-output-to-executed-action alignment. The authors test four intervention levels of increasing subtlety, measuring whether client-side permission mechanisms fail to catch tampering introduced at this layer. This effectively demonstrates a supply-chain-style trust gap: security review of agent permissions is moot if the transport layer itself can silently rewrite actions. Anyone building or auditing agentic dev tools should treat API routers as part of the trusted computing base and consider signing/verifying provider responses end-to-end.

LinkRank is a learning-to-rank framework for one-to-many issue–commit traceability recovery, contrasting with prior pairwise classification approaches that score each issue-commit pair independently. It models the full candidate commit set jointly and uses an iterative pick-remove-renormalize procedure: at each step it selects the highest-ranked commit, removes it from the candidate pool, and renormalizes scores over the remainder, repeating until a stopping criterion is met. The authors also contribute a new evaluation dataset built from six open-source GitHub repositories specifically labeled with multi-commit issue resolutions. This iterative set-selection approach could be adapted to other traceability or set-retrieval problems (e.g., linking requirements to multiple code changes) where independence assumptions between candidates don't hold.

The tutorial covers FastLAS 2.2.0, a scalable Inductive Logic Programming (ILP) system that, given background knowledge (a logic program), a language bias (hypothesis space specification), and positive/negative examples, searches for a hypothesis — a set of logic program rules — consistent with the examples. It's structured as a progressive series of runnable, verified examples rather than a formal spec, explicitly documenting divergences from the sibling system ILASP and behavioral differences between FastLAS's --opl and --nopl learning algorithms. A practitioner could use this as a working reference to stand up their own ILP pipeline for tasks requiring interpretable, verifiable rule extraction (e.g., symbolic reasoning, explainable classification) rather than opaque statistical models.

Tempo reconstructs Boussinot-style synchronous reactive programming (cooperative threads, broadcast signals, dynamic process creation, organized into logical instants) as a library in OCaml 5, rather than as a dedicated language extension like ReactiveML. It uses OCaml 5's algebraic effects with deep handlers: effect operations mark reactive suspension points, and the handler captures the continuation, reifying it as a schedulable task under logical-instant semantics. The paper presents a comparative performance study against ReactiveML to quantify the overhead of this library-level (versus compiler-level) implementation and identifies which runtime mechanisms drive that cost. This is directly relevant to anyone building DSL-like control-flow abstractions in OCaml 5 who wants to avoid a custom compiler by leveraging effect handlers instead.

The paper presents a compositional runtime-verification framework for LLM-assisted ISR (intelligence, surveillance, reconnaissance) robot swarms, structured across three tiers: platform, squad, and mission. It decomposes a mission-level policy into per-agent and cross-agent verification aspects, aggregates per-platform verdicts over a verification-aware messaging fabric designed to tolerate contested/degraded communications, and fuses partial evidence using an evidence-aware, two-axis (security/reliability-oriented) scoring approach. The core contribution addresses a specific blind spot: individually-compliant actions that compose into mission-level violations (e.g., budget-splitting or objective-splitting across platforms) which per-platform guardrails cannot detect by construction. This is relevant to anyone designing multi-agent runtime assurance systems where compositional violations, not just individual policy breaches, are the primary threat model.

The paper specializes de Paiva's categorification of Gödel's Dialectica interpretation to the setting of partial orders, deriving functorial embeddings of Heyting algebras into residuated lattices that the authors claim have been overlooked in the literature, and reproduces the original categorical proofs directly in algebraic terms for a non-categorical audience. Novel results specific to this specialization include: an embedding that lacks an evident adjoint in de Paiva's general construction but acquires a definable one in the algebraic setting, and a demonstration that a single Dialectica tensor validates contraction in the intuitionistic construction D but refutes it in the classical variant G. There's also a result stated over ZF concerning the underlying poset structure (abstract cuts off before full detail). This is relevant to researchers in algebraic logic or substructural logics looking for new embedding constructions between Heyting algebras and residuated lattices, or wanting a categorical-to-algebraic translation template for other Dialectica-style categorifications.

TLA+-Bench provides 403 gold specifications each paired with a model-checker configuration that exhaustively verifies the named properties over the full reachable state space, plus 897 parse-only silver specs, sourced from 13 public repos with four LLM-generated natural-language descriptions per item across two styles and two model providers. This replaces prior evaluation practice (reference-similarity or parseability) with an execution-grounded correctness oracle. The paper's headline finding concerns measurement itself: the exact oracle reveals that prior benchmarks' looser proxies substantially overstate model competence. Practitioners building or evaluating NL-to-TLA+ systems can use this dataset directly as a correctness-verified test set and adopt the model-checking harness as a template for grounding other formal-methods generation tasks.

The paper introduces and categorizes 'AI Technical Debt' (AITD) as a superset of conventional technical debt, mapping root causes across data governance, model implementation, algorithm design, architecture, operations, documentation, and testing, and framing them through the AI Trust, Risk, and Security Management (AI TRiSM) lens. Its core claim is that AITDs are more insidious than classic debt because they're latent and propagate through tightly coupled AI pipelines (data ingestion → training → serving), compounding into reliability and security regressions rather than staying locally contained. This appears to be a conceptual/taxonomic contribution rather than an empirical tool, so practitioners would use it as a checklist or audit framework for identifying debt categories in their own MLOps pipelines rather than as code to run.

Athena combines Program Dependence Graph (PDG) structural information with transformer-based code embeddings to perform impact analysis (IA), moving beyond brittle coupling-metric heuristics that rely on static/dynamic/evolutionary co-change history. The approach likely fuses graph-derived structural features (control/data dependencies) with transformer-derived semantic representations of code constructs to estimate impact sets without expensive execution tracing or large commit histories. This positions Athena as a hybrid structural-semantic model, likely evaluated against coupling-metric baselines on impact-set precision/recall. Practitioners could build on this by swapping in different graph representations (e.g., call graphs, CFGs) or transformer backbones, or by integrating Athena's impact predictions into CI pipelines for change-risk triage.

The study uses state-of-the-art LLMs to auto-generate property-based testing specifications for Solidity smart contracts, targeting use with fuzzing frameworks that check these properties against randomly generated transaction sequences. Property quality is evaluated via mutation testing — measuring what fraction of injected code mutants are killed by the generated properties, a proxy for fault-detection strength. This offers a concrete alternative to manual property authoring by domain experts, which is currently a bottleneck in smart contract security auditing. Practitioners could integrate this LLM-property-generation step into existing fuzzing pipelines (e.g., Echidna, Foundry) and use mutation-testing scores as an automated quality gate before deployment.

Praxis reframes Android malware detection as a hypothesize-confirm-judge pipeline rather than anomaly/deviation scoring over learned features or local code slices. It first hypothesizes candidate malicious behaviors from coarse static signals, then confirms each hypothesis by grounding it against actual app behavior (likely via deeper static/dynamic analysis tracing), and finally judges which confirmed behaviors constitute genuine maliciousness versus benign-but-unusual activity. This directly targets the two failure modes of proxy-based detectors: over-flagging benign outliers and missing malware engineered to look statistically ordinary. Practitioners building malware detectors could adopt this staged architecture — separating candidate generation, evidence grounding, and final classification — as a template for reducing both false positives and evasion-driven false negatives.

Q-BRIDGE encodes a transpiled quantum circuit's gate types and connectivity via a graph transformer, jointly conditioning on physical backend noise characteristics, then uses a FiLM (Feature-wise Linear Modulation) layer to fuse this structural/backend encoding with noisy observed outcome distributions and predict denoised distributions suitable for oracle-based pass/fail verification. It's evaluated across 23 IBM noise backends and 6 representative circuit families, with at least one setting training separate per-backend models (implying a second setting likely tests cross-backend generalization, though the abstract is cut off). This targets a real bottleneck in quantum software testing — noise corrupting oracle decisions — and practitioners could use the graph-transformer+FiLM architecture as a denoising preprocessing step before applying statistical test oracles to NISQ execution results.

Kairos translates source-level LTL-based assume-guarantee temporal safety contracts into tick-indexed proof obligations for Why3, using the standard automata-theoretic reduction: LTL formulas become bad-state automata, which are composed with the program, yielding local per-tick verification conditions instead of raw temporal formulas. The paper proves reactive contract correctness — any environment-assumption-satisfying input trace induces a unique execution satisfying the temporal guarantees — and mechanizes both the construction and this soundness proof in the Rocq proof assistant, giving strong correctness guarantees on the translation itself, not just the target program. A medical infusion controller serves as the case study. Practitioners working with synchronous languages (e.g., Lustre-like DSLs) could adopt this automata-to-Why3-obligation pipeline to verify multi-tick temporal properties using existing deductive backends rather than building bespoke temporal provers.

Bifrost is a log representation learning method that augments natural-language-pretrained PLMs with 'fallibility representations' capturing multi-level fault structure (line-level, sequence-level, system-level) in system logs, which vanilla PLMs miss since they're pretrained on generic text. It uses self-supervised contrastive learning strategies explicitly designed around SRE (Site Reliability Engineer) log-analysis heuristics, rather than relying purely on generic masked-language-modeling objectives. Evaluated across three public systems plus one industrial ML-as-a-Service system, it reportedly improves log-based fault diagnosis over existing PLM-based baselines (the abstract cuts off before quantifying gains). Practitioners could adopt Bifrost's contrastive pretraining objective as a drop-in replacement for standard PLM log encoders in existing fault-diagnosis or anomaly-detection pipelines.

The paper develops denotational semantics and a sound verification calculus for continuous-variable quantum programs, where measurement outcomes range over continuous domains rather than discrete bits. The core technical contribution is identifying closed positive quadratic forms as the quantitative predicate domain, which is expressive enough to encode finite expectations, finiteness domains, and infinite penalties within a single ordered structure suited to infinite-dimensional Hilbert spaces. This gives a Hoare-logic-style framework for reasoning about CVQC programs, targeting platforms based on quantum optics hardware. Practitioners in quantum program verification could build model checkers or proof assistants for CV quantum languages on top of this predicate domain.

The system reframes Triton kernel optimization as hierarchical, cross-layer diagnosis rather than pure LLM rewriting guided by profiling/compile signals alone. It escalates progressively: lightweight pattern triage and profiling diagnosis first, then IR-level attribution and compiler-grounded analysis only when shallow signals are insufficient to explain a missed optimization, particularly targeting emerging NPU backends. The output is evidence-backed source-level rewrites rather than blind LLM-generated patches. This is directly usable as a diagnostic pipeline layered in front of existing LLM kernel-generation loops to reduce wasted rewrite iterations.

The paper presents a domain-specific language for specifying prophecy and history variables as subset-inclusion constraints layered directly onto operational semantics step rules, implemented in the Nexis compiler. This tight coupling enables correctness and optimality proofs for program transformations to be structured as forward simulations between original and transformed program versions. Compared to classical dataflow analysis, the approach dispenses with explicit control-flow graphs, abstraction/concretization functions, Galois connections, and the usual separate forward/backward analysis passes. Compiler engineers could use this to construct new forward-only static analyses with built-in correctness proofs rather than relying on separate meta-theoretic frameworks.

Reason Popper-ly is a neurosymbolic pipeline that learns relation-composition rules via inductive logic programming (ILP) from LLM-generated chain-of-thought traces, then deploys those rules as an online step-level verifier. For each generated step it checks compliance against the learned rule table, classifies the violation type when one occurs, applies a symbolically-derived repair, and regenerates the remaining suffix conditioned on the corrected trace. Evaluated on CLUTRR (multi-hop kinship reasoning, 2-10 hops) across five LLMs, it consistently improves accuracy over uncorrected CoT. This is a template for combining ILP-learned symbolic rule tables with LLM decoding as a correctness gate, generalizable to other relation-composition or compositional reasoning tasks.

The study implements an adversarial test-hardening loop with a purely mechanical oracle: a Tester model generates tests, mutation testing identifies surviving injected defects, and a Critic model writes targeted tests to kill exactly those survivors, with verdicts decided by mutation-testing tooling rather than model judgment. On five Python subjects, the loop killed 105 mutants missed by one-shot test generation with zero regressions, while a pre-declared cross-lineage-Critic hypothesis returned a null result. The paper's central contribution is a methodological autopsy: a previously reported cross-lineage effect at p=9.5e-66 was traced to an instrumentation artifact — a silent output-length cap truncating a verbose model's responses — discovered only through adversarial review of the completed analysis. This is a strong worked example for anyone building LLM-based test-hardening pipelines of why mechanical oracles and post-hoc adversarial statistical review are essential before trusting extreme p-values.

The paper adapts metamorphic testing (MT) — verifying relationships between related inputs/outputs rather than requiring ground-truth labels — to clinical ML models for ICU tasks like in-hospital mortality and sepsis onset, using MIMIC-III/IV data. They define a catalog of 12 metamorphic relations (MRs), each derived from an authoritative clinical guideline (e.g., worsening SOFA score should not decrease predicted mortality risk), plus a five-layer validation strategy to vet MR clinical soundness before deployment. A pilot study demonstrates feasibility of applying these MRs to flag behavioral inconsistencies invisible to AUROC-style ranking metrics. Clinical ML teams could adopt this MR catalog and validation pipeline directly as a pre-deployment regression-testing suite alongside standard performance metrics.

The study evaluates multimodal backlog generation from app mockups using GPT-4o under three prompting strategies: zero-shot baseline, Compositional Chain-of-Thought (CCoT) for vision-language reasoning, and a persona-driven prompt, tested across seven real app development projects in two countries with developer interviews for qualitative validation. Results show the baseline favors recall over precision while CCoT is more balanced, with average F1 of 52-66% for epics and user stories, while task-level generation (finer-grained items) is notably harder. Adding architectural context to prompts produced the most consistent precision gains, especially for backend-related tasks. This suggests a practical recipe — CCoT-style prompting plus injected architectural context — for teams building AI-assisted early-stage backlog/requirements tooling from design artifacts.

AssumptionMiner treats implicit assumptions in LLM code generation as a first-class, explicit artifact: alongside generated code, it produces a structured 'assumption layer' capturing inferred constraints and design decisions (e.g., input format handling, error-handling behavior) that developers can inspect, confirm, or revise. An AST-based dependency graph links code regions to the assumptions that shaped them, enabling targeted regeneration of only the affected code when an assumption is revised, rather than full re-generation. The paper also introduces a benchmark (details truncated in the abstract) for evaluating assumption extraction and revision. This is directly applicable as a middleware layer between prompt and code output in LLM coding assistants/agents to improve intent alignment and reduce silent specification drift.

The authors introduce a metric for the 'misguidance effect' — the rate at which LLM-generated unit tests assert a buggy function's actual (incorrect) output rather than the intended correct behavior. Empirically, prompting with buggy code both inflates misguided assertions and suppresses bug-revealing test generation, and internal preference analysis (e.g., logit/likelihood comparisons over candidate assertions) shows the model's own ranking is skewed toward the erroneous behavior. They validate a specification-grounded mitigation that reduces misguidance by anchoring generation to an explicit correctness spec rather than the buggy implementation alone, offering a concrete evaluation protocol and countermeasure practitioners can adopt in LLM-based test-generation pipelines.

This is a large-scale replication of Inozemtseva et al. and Papadakis et al.'s findings on coverage/mutation-score correlation with fault detection, extended to test suites generated by a diverse set of LLMs across multiple test-generation workflows. The study controls for test-suite size — the key confound in the original work — to re-examine whether coverage and mutation score remain valid proxies for real bug-finding effectiveness when the test-generation process is LLM-driven rather than human- or search-based. Practically, this informs whether LLM test-generation benchmarks that rely solely on coverage/mutation metrics are measuring what they claim to measure, which matters for anyone designing evaluation protocols for automated test-generation tools.

The paper defines a mapping, Hybrid-to-NeSy (H2N), that recasts mechanistic/data-driven hybrid models as instances of a neuro-symbolic (NeSy) interface: mechanistic equations become the symbolic 'language' layer, learned modules become the probabilistic 'belief' layer, and validity domains/constraints form the 'logic' layer. For any given hybrid design, H2N derives an explicit NeSy inference functional plus a logic-belief decomposition, giving a formal semantics for epistemic uncertainty in the mechanistic component — something typically left implicit in architecture-and-loss-only specifications. This offers a common formalism for comparing hybrid modeling approaches across domains (e.g., process engineering vs. scientific ML) and a template for building verification or uncertainty-quantification tooling on top of existing hybrid architectures.

The authors formalize the Kannan-Bachem variant of the Smith Normal Form algorithm for nonsingular integer matrices in Lean 4, proving it returns S, U, U⁻¹, V, V⁻¹ satisfying UAV=S and the corresponding inverse identities, the Smith divisibility conditions, and canonical-form equality. Termination is proved via strict decrease in the binary size of the active pivot on each recursive pass, with the outer algorithm recursing on the lower-right block after each stabilization. Critically, the implementation also emits a verified trace of primitive sign-magnitude arithmetic operations with self-delimiting codecs for input/output sizes, allowing a kernel-checked polynomial bit-complexity bound to be derived directly from the recorded execution rather than argued informally — giving formal-methods practitioners a template for combining correctness and complexity proofs for numerical linear algebra algorithms.

The authors conduct three rapid systematic reviews of 2024 peer-reviewed literature — spanning top-tier international SE venues, regional South American SE conferences, and a curated public-sector digitalization newsletter — to assess the volume and visibility of empirical research on government software engineering practices. Screening 984 papers yielded only four studies addressing SE practices conducted by, for, or with government bodies, concentrated in regional venues as experience reports/case studies rather than in mainstream international outlets. The finding quantifies a real evidence gap and suggests that researchers or practitioners studying public-sector software delivery should look to regional and gray literature rather than top-tier SE venues, and that there's an open opportunity for rigorous empirical work in this space.

The authors benchmark five recent LLM-based coding agents on difficulty-controlled problems across Python, Java, Rust, and OCaml, measuring token consumption as the primary cost metric. Methodologically, they re-execute every intermediate solution in a trajectory, encode each as a test-outcome vector, and label the inter-solution work to detect patterns like repeated non-compiling attempts and redundant revision of already-passing solutions — plus a text-level analysis of agent trajectories showing in-comment planning and self-distrust behaviors. The core finding is that token cost varies sharply and consistently by target language across models, driven largely by unfamiliarity-induced compilation failures and unnecessary rework rather than problem difficulty — a result directly useful for teams optimizing agent cost by language choice or for designing better agent scaffolding/prompting for lower-resource languages.

The study operationalizes four development configurations — solo human, human-CLLM collaborative, fully autonomous CLLM, and an agentic multi-agent setup (MetaGPT-X) — and runs each through corresponding Test-Driven Development workflows to compare outcomes across collaboration patterns. This is an exploratory, controlled comparison designed to isolate how the degree and style of human-AI interaction (versus full automation) affects TDD-based software delivery, framed through prompt engineering, agile design principles, and human-AI co-creation theory. The design gives practitioners and researchers a reusable experimental template for evaluating collaboration patterns in AI-assisted development, and the comparative results (once fully reported) should indicate whether tighter human-in-the-loop collaboration or more autonomous agentic execution yields better TDD outcomes.

The paper studies semantic regular expressions (SemREs), where matching requires evaluating external Boolean oracle predicates over matched spans, and models optimal evaluation as Boolean decision-tree evaluation over a monotone span circuit representing membership. For unary, star-free, semantic-depth-one instances of size Θ(E) with E essential oracle keys, they show one-round (non-adaptive) cost is E while exact two-round and fully adaptive costs are log₂E + ½log₂log₂E + O(1) — establishing a tight (1+o(1))·E/log₂E nonadaptive-to-adaptive gap with the exact leading constant, plus a second construction demonstrating a complete round hierarchy. This gives implementers of oracle-augmented pattern-matching or query systems a precise theoretical bound on how much batching oracle calls into just two rounds of adaptivity can save versus full sequential adaptivity, informing the design of query-planning strategies for systems like semantic search or LLM-augmented regex engines.

dGL3 extends differential game logic to three players with individually specified, potentially overlapping goals, making the underlying games non-zero-sum and coalition-sensitive rather than strictly adversarial. The paper defines dGL3's syntax and semantics over hybrid (discrete + continuous ODE) dynamics and proves core metatheoretic properties, then presents a sound and relatively complete proof calculus for verifying game properties. The key motivating case is shared safety objectives with divergent secondary goals, where naive zero-sum modeling yields provably conservative verification results by ignoring beneficial coalitions. This gives formal-methods practitioners a way to verify cyber-physical multi-agent systems (e.g., autonomous vehicles) without over-restricting behavior that safe coordination would actually allow.

StateAct is a multi-agent harness that treats program state (files, app backends, DOM) as the primary interface for computer-use tasks, delegating GUI screenshot-and-click interaction to a subagent invoked only when direct state access isn't viable, 28 of 108 tasks and 1.1% of main-agent steps in their evaluation. This code-first design lets the main agent use programmatic inspection and modification rather than inferring state from lossy pixel renderings, which reduces ambiguity and enables a novel verification step: an independent finish gate that structurally checks the saved output (missing, unsaved, or misplaced files) before declaring task completion. This is a practical architecture pattern for building more reliable long-horizon computer-use agents, and the finish-gate idea is reusable as a general result-verification component independent of the underlying agent.

The paper addresses requirements engineering (RE) for transborder personal data flows by systematically comparing regulatory data protection requirements (RDPRs) across multiple jurisdictional frameworks, aiming to conceptualize which obligations are shared, similar-but-non-identical, or directly contradictory. The core contribution is a methodology or taxonomy for translating heterogeneous legal text into structured, comparable software requirements early in the SDLC, rather than treating compliance as a late-stage legal review. This gives RE practitioners and compliance tooling builders a basis for automated or semi-automated conflict detection across privacy frameworks (e.g., GDPR vs. other regional laws) when designing systems that handle cross-border data.

DeFiScreener is a pre-screening framework for DeFi smart contracts that identifies potentially vulnerable functions and call sequences by matching them against a corpus of historical exploit cases, rather than relying on rule-based detection of specific known attack types. It's motivated by an observed 'perilous temporal asymmetry' pattern in exploit timelines, and operates on full contract source code to triage large codebases down to high-risk candidates for deeper audit. This positions it as a first-pass filter to improve detection coverage where existing tools are narrowly scoped to particular vulnerability classes; practitioners could integrate it as a pipeline stage ahead of manual audit or formal verification, especially useful given DeFi's over 5,200 deployed projects as of Jan 2026.

This is a qualitative empirical study using ten semi-structured interviews with professional developers, analyzed via Mayring's qualitative content analysis methodology, to surface developer perceptions, requirements, and barriers around energy-aware software development. Conducted under the European GreenCode project, it aims to inform the design of AI-assisted tools for energy-aware development by grounding tool requirements in practitioner needs rather than purely technical energy-optimization algorithms. The findings are partly interpreted through an existing theoretical lens (unspecified in the excerpt) to structure adoption barriers and tool requirements. Useful for teams designing developer-facing sustainability tooling, as it provides empirically derived requirements rather than assumed ones.

HarnessLLM is an automated workflow for generating Rust formal-verification harnesses using LLMs, addressing failure modes of naive LLM harness generation (incorrect API usage, inefficient nondeterministic input generation, fabricated fixes). It extracts calling scenarios directly from existing test suites, performs dependency analysis to generate appropriate nondeterministic arguments, and incrementally/iteratively synthesizes and refines the harness rather than generating it in one shot. This gives a concrete, replicable pipeline (test-mining → dependency-aware input synthesis → incremental harness construction) that practitioners could adapt to bootstrap formal verification (e.g., with Kani or similar Rust verifiers) from an existing test corpus instead of writing harnesses by hand.

The paper presents the first large-scale empirical characterization of declared dependency graphs across 78,612 real-world SBOMs (Wild SBOMs dataset, 77,092 parseable), finding a tri-modal distribution: 52.9% have zero declared edges (failing NTIA minimum-elements requirements), 8.8% are 'degenerate' (majority of components isolated despite having some dependency block, median 93% orphan rate for larger SBOMs), and 38.3% form well-connected graphs. They identify the SBOM generator tool as the primary determinant of edge emission quality, with Syft-generated container-image SBOMs showing 95-98% orphan rates even in their better-connected regime. This is directly actionable for anyone building vulnerability reachability or impact-analysis tooling: SBOM completeness cannot be assumed and should be validated per-generator before trusting graph-based security analyses.

This is a vision paper arguing that current AI code review tools mis-model the task as single-turn diff-to-comment generation, missing the interactive, negotiation-driven nature of real review (clarifying questions, rationale documentation, evidence requests, knowledge transfer, collective merge decisions). The authors propose conversational AI review assistants as interactive dialogue partners that detect when conversation is warranted, generate contextually grounded questions, and adapt to developer responses over multiple turns rather than producing static comment lists. As a vision/position paper it doesn't present an implemented system or benchmark yet, but it sets an agenda for researchers building LLM-based review tools to incorporate dialogue management, question generation, and multi-turn state tracking into their architectures.

PoCEvolve targets the window between a public fixing commit and the release of a detailed CVE report, where prior PoC-generation work like PoCGen assumes report details that don't yet exist. The authors first empirically measure this disclosure lag, then design a vulnerability-aware prompt evolution loop that generates and refines proof-of-concept exploits using only the patch diff and surrounding code context. This effectively shifts PoC generation from report-dependent to patch-dependent, letting defenders validate patches and assess exposure immediately after a fix lands rather than waiting for downstream disclosure.

The paper formalizes Skill Test Coverage for workflow-oriented Cloud Skills by defining coverage units (discrete operational obligations like resource operations, validation steps, and recovery behaviors), a testcase-to-obligation coverage relation, a calculation procedure, and explicit claim boundaries. Because these obligations are expressed in unstructured natural language rather than a formal spec, the authors build a measurement pipeline that recovers operational obligations from Skill packages, organizes them by workflow context, and links them to testcases to compute coverage. This gives practitioners a governance metric analogous to code coverage but for natural-language agent instructions, usable to identify untested failure paths before deploying Skills in production.

The study is a large-scale empirical analysis of 54,791 agent-generated review comments from five coding agents (Copilot, Cursor, Codex, Devin, Claude) across 342 Python GitHub repositories, examining resolution rates by agent and comment type, the effect of developer experience, and features that predict comment usefulness. Copilot comments accounted for 72.9% of all resolved comments, indicating substantial variance in agent effectiveness or integration maturity, and core developers drove the majority of resolutions over peripheral contributors. Practitioners building or evaluating AI review agents can use this as a baseline for expected resolution rates and as motivation to study which comment characteristics (specificity, actionability, comment type) correlate with developer uptake.

KaPilot is a multi-agent pipeline for generating Kani verification harnesses and specifications for unsafe Rust functions, starting with lightweight static analysis and proof-harness scaffolding. A dedicated SafetyReq agent extracts safety requirements from function documentation independently of the implementation, which then constrains a SpecGenerate agent's output — explicitly decoupling the spec source from the code under test to reduce the risk of specifications that merely mirror implementation bugs. This targets a known weakness in LLM-based spec generation (code-centric bias) and gives practitioners a template for building verification-tool-specific spec generators with built-in quality checks rather than single-pass LLM prompting.

OM-RAG operationalizes the Knowledge stage of the MAPE-K autonomic-computing loop by indexing resolved incidents as structured symptom-root cause-resolution triples and performing single-hop embedding retrieval to surface the most similar historical precedent for a new failure, contrasting with standard RAG over unstructured documentation chunks. The system has been deployed as an LLM administrator for a production Dataverse instance for 6+ months, and a controlled four-configuration ablation with LLM-based judging evaluates the contribution of structured operational memory versus documentation-only or unstructured-chunk baselines. This offers a concrete blueprint for teams wanting to bootstrap an LLM ops assistant from their own incident/ticket history rather than generic RAG over docs.

The paper addresses thermal management for lunar surface habitats, which experience extreme day/night radiative swings due to the absence of atmosphere, by modeling adaptive-emissivity surface strategies as an alternative to static passive approaches like white paint and multi-layer insulation. It focuses on mechanisms such as louvers and shutters that switch between high- and low-emissivity states to optimize daytime radiative cooling and nighttime heat retention, framed as a variable-emissivity control problem rather than a fixed thermal-coating design. This is relevant to thermal engineers designing habitat envelopes, cryopreservation modules, or other temperature-sensitive lunar infrastructure where reducing reliance on active heating/cooling directly cuts power budget requirements.

The paper proposes a three-stage state-current observer that estimates relative velocity for torpedo-type underactuated AUVs with non-minimum-phase sway/heave dynamics, feeding a nonlinear feedforward term for dominant current rejection and an LMI-certified LPV-H∞ correction layer for residual robustness. Feedback linearization is used to obtain a constant input matrix, enabling convex LPV-H∞ synthesis without pairwise cross-coupling terms, and a singular-perturbation analysis establishes local practical uniform ultimate boundedness on the embedded LPV model. REMUS simulations across three trajectories and four current scenarios report 89-96% reduction in current-estimation error and roughly 99% reduction in translational tracking residuals, giving control engineers a concrete observer+LPV-H∞ architecture and convex synthesis recipe for underactuated marine vehicle tracking under unmeasured currents.

The authors use a variational approach based on the principle of minimum power dissipation, generalized to the two-spin-channel model, to solve for the self-consistent stationary state of a spin Hall bar coupled to an external load circuit. They derive closed-form longitudinal/transverse current distributions along with spin and charge accumulations, finding that Joule dissipation in the load vanishes when the load is positioned at a distance large compared to the spin-relaxation length — spatially decoupling current injection from energy dissipation, a behavior with no analog in conventional charge-current injection. This result gives spintronics researchers an analytical framework for predicting and engineering dissipation location in spin-Hall-based devices, relevant to designing low-loss spin-current interconnects or sensors.

The paper couples open-cycle OTEC thermodynamic cycle equations with a two-stage robust microgrid scheduling model that uses a budget uncertainty set to bound renewable-output deviations without excessive conservatism. It is solved via an inexact column-and-constraint generation (C&CG) algorithm that speeds convergence by allowing approximate first-stage subproblem solutions in early iterations. Numerical experiments show open-cycle OTEC, which co-generates electricity and desalinated water, can fully substitute conventional generators on island microgrids. The inexact C&CG technique and uncertainty-set formulation are reusable for other renewable-integrated robust scheduling problems.

Applies the IEEE-738 thermal rating standard to compute DLR for a 795 kcmil ACSR Drake conductor across 832 operating points spanning day/night solar conditions, clear/non-clear sky, wind speeds 0-15.25 m/s, and ambient temperatures 15-50°C, then runs a parametric sensitivity analysis on each input variable. Results show wind sensitivity declines at higher ambient temperature while temperature sensitivity rises with wind speed, and Pearson correlation reveals a strong negative linear relationship between DLR and ambient temperature. This gives grid operators a quantitative basis for prioritizing sensor accuracy in DLR deployments. The parametric sweep is directly reproducible for other conductor types using the same IEEE-738 formulation.

Introduces a zero-shot transfer loss decomposition that splits performance degradation from OLID (English offensive-language dataset) to MLMA (multilingual hate-speech dataset) into separately measurable dataset-effect and language-effect components. A controlled fine-tuning protocol compares few-shot learning curves under continued fine-tuning versus cold-start initialization to quantify adaptation efficiency alongside catastrophic forgetting on the source task. The framework further proposes joint training strategies to mitigate the decomposed degradation sources. Practitioners could apply this decomposition methodology to audit any cross-lingual or cross-dataset NLP classifier before deployment.

Compares baseline Deep Q-Network (DQN) against six value-based variants for optimizing a seven-variable PCSEL design under a shared 83-simulation-call budget and four matched initializations, isolating true sample-efficient learning gains from favorable seeding or random exploratory jumps. Dueling DQN was the only variant to improve the design's quality factor across all four seeds, suggesting its decoupled state-value/advantage estimation is especially effective under tight simulation budgets. The study analyzes sample efficiency and policy behavior beyond final performance, providing a template for benchmarking RL exploration strategies in simulation-constrained physical design. Practitioners optimizing other expensive-to-simulate physical systems could adopt the Dueling DQN architecture and matched-initialization evaluation protocol.

Develops broadband photonic integrated circuit (PIC) components, evanescent tri-couplers, tapered directional couplers, and a chromatically-controlled achromatic intensity modulator, for pairwise and nulling interferometry in the J- and H-bands (0.95-1.8 μm), using complementary silicon nitride and silicon oxide low-loss platforms. On STMicroelectronics' silicon nitride platform, tapered tri-couplers and directional couplers were engineered as achromatic replacements for conventional beam-combiner components in the PLANETS pairwise combiner, achieving under 1% excess loss across the J-band. This demonstrates a viable component library for broadband achromatic astronomical interferometers relevant to exoplanet direct-detection instrument design. Photonic engineers could adapt the tapering methodology to extend achromatic performance in other PIC-based interferometric systems.

Presents a dual milligram-mass torsion oscillator formed by mass-loading a strained silicon nitride nanoribbon, exploiting the antisymmetric torsion mode to suppress common-mode vibrational noise by over an order of magnitude while preserving ultralow mechanical loss. The device achieves a thermally-limited torque sensitivity of 10⁻¹⁸ Nm/√Hz and is demonstrated detecting an optical radiation-pressure torque of 10⁻¹⁶ Nm over a 30 Hz bandwidth, plus characterization for frequency-based gravimetry. This differential-mode design strategy for vibration isolation is broadly applicable to chip-scale optomechanical inertial sensors and precision force/torque metrology. Groups building nanomechanical sensors could replicate the dual-mass loading technique to reject environmental noise without sacrificing quality factor.

This review surveys the bipolar thermoelectric effect in reciprocal superconducting quantum systems, where conventional linear thermoelectric response is symmetry-forbidden yet a nontrivial signal still emerges whose voltage polarity can flip sign at a fixed temperature gradient, unlike conventional thermoelectrics where carrier-type fixes the sign. The authors summarize the physical mechanisms enabling this symmetry-independent bipolar response in non-equilibrium superconducting hardware. This has direct relevance to thermal management and energy-harvesting strategies in quantum technology hardware, where controlling heat and energy flow is increasingly critical. Researchers modeling quantum device thermodynamics could use this framework to identify and engineer bipolar thermoelectric signatures in their own reciprocal superconducting systems.

Using a continuum theory for a quasi-one-dimensional frustrated ferromagnet near the Lifshitz point, the authors show that the dissipative (non-adiabatic) spin-transfer torque drives domain walls separating opposite-chirality domains, while the adiabatic torque combined with an external field breaks the chirality symmetry of the effective potential via a third-order-in-gradient term. They derive explicit conditions for current/field-induced chirality switching and confirm the analytics with spin-lattice simulations. This gives a concrete design rule (current density and field thresholds) for chirality-based memory or logic elements in helimagnetic materials.

The platform uses congruent x-cut lithium tantalate-on-insulator (LTOI), where ferroelectric-domain switching is retained after the write field is removed, giving non-volatile phase control. Demonstrated waveguide propagation loss is ~0.05-0.06 dB/cm, with multilevel phase states remaining distinguishable after 10^6 write cycles. Weighted segmented electrodes resolve 137 distinct phase positions across a π range, giving fine analogue tuning resolution — a practically relevant building block for low-power reconfigurable photonic integrated circuits (switches, phase shifters, programmable meshes) that avoids the static power draw and thermal crosstalk of thermo-optic or continuously-biased EO tuning.

The firmware targets a dual-core ESP32-S3, with Core 1 running a 400 Hz control loop combining Madgwick-filter sensor fusion and a fixed-point PID controller augmented with selective derivative scaling and feedforward braking, while Core 2 handles 50 Hz telemetry encoding and UDP-based video/data streaming at 25 FPS VGA. Inter-core communication uses zero-copy, mutex-protected buffers for deterministic, low-overhead data transfer, and a custom event-driven, RTOS-inspired static scheduler enforces real-time guarantees. This gives builders a concrete, replicable reference architecture for low-cost UAV firmware that isolates control-loop timing from network/video jitter — directly reusable for other ESP32-class embedded robotics projects.

jaxdae is a JAX-native solver for differential-algebraic equations (DAEs) — the coupled ODE-plus-algebraic-constraint systems that arise from spatially discretizing PDEs with conservation laws, constitutive relations, or network topology constraints. It pairs adaptive BDF, Radau, and Rosenbrock integrators with (implied) adjoint/reverse-mode sensitivity analysis, unifying forward DAE solving and gradient computation in one JAX suite — something industrial acausal modeling tools and JAX-based differentiable-physics frameworks each only cover half of. This directly enables gradient-based parameter inversion, Bayesian inference, uncertainty quantification, and optimal control workflows over multi-physics DAE models, and is usable as a drop-in differentiable solver in JAX pipelines.

The authors construct an end-to-end differentiable digital twin of a closed-Brayton gas-cooled reactor by propagating reverse-mode automatic differentiation through an implicit DAE plant model, yielding exact parameter sensitivities rather than finite-difference approximations. This feeds an AD-Hessian incremental 4D-Var estimator, benchmarked against ensemble Kalman, unscented Kalman, and finite-difference variational baselines across a 2x2 design (steady vs. transient excitation, full vs. partial observability). Results show no single estimator dominates across all four regimes, giving practitioners concrete guidance on estimator choice conditioned on plant excitation and sensor coverage — and a reusable differentiable-DAE pattern for online parameter inversion in other physical digital twins.

The authors propose an all-optical detection scheme for topological magnon edge states in 2D van der Waals honeycomb ferromagnets with THz-range magnonic band gaps, exploiting magnetoelectric coupling to achieve resonant parametric amplification of edge magnons under electromagnetic driving. The mechanism hinges on a spin-dependent effective electric dipole moment arising from dynamic chirality of the edge modes, which couples the magnon dynamics to THz spectroscopy and cavity-QED-style response signatures. This gives experimentalists a concrete, non-invasive optical/cavity readout protocol to confirm topological magnon band structure predictions that have so far eluded direct detection.

The authors demonstrate electrical control of a metal-insulator transition in a 1D suspended carbon-nanotube device by spatially modulating the local electrostatic potential along the tube, drawing an analogy to periodic-potential gap-opening mechanisms in condensed matter systems. The resulting energy gap is homogeneous along the nanotube and continuously tunable by the applied gate voltage, offering electrical (rather than fixed structural) control over the low-energy spectrum. This is directly relevant to bottom-up-compatible, top-down engineering of superconducting topological chains, where a tunable, uniform gap is a prerequisite for stabilizing and controlling topologically protected states over an extended parameter range.

DiffTilt formulates falsification of safety-critical cyber-physical systems as exponential tilting of a diffusion-model-induced joint distribution over environments and system executions, avoiding the multiplicative rarity problem that plagues conditional sampling approaches which factor environment and execution distributions separately. The authors prove that diffusion-guided sampling with classifier-style guidance scores is exactly equivalent to importance sampling in the joint space, with guidance inducing a KL-optimal reallocation of probability mass toward failure-relevant behaviors, and they show the tilting is provably (asymptotically efficient/variance-reducing, per the truncated claim). This gives verification engineers a principled, importance-sampling-grounded alternative to naive Monte Carlo or adversarial search for rare-event falsification, with a diffusion model as the trainable proposal/guidance mechanism.

The SiSeRO device is a charge-sensing readout architecture achieving 700-800 pA/electron conversion gain, ~3.5 e- RMS equivalent noise charge, and ~130 eV FWHM energy resolution at 5.9 keV at 625 kpix/s in first-generation prototypes. Using Repetitive Non-Destructive Readout (RNDR) — repeatedly sampling the same charge packet non-destructively — they demonstrate sub-electron noise (<0.5 e- RMS) at slower 10 kpix/s readout. This paper presents device simulations for next-generation output-stage designs optimizing sensitivity/speed tradeoffs for megapixel X-ray/optical spectro-imaging arrays.

The authors conducted a scoping review of 1,465 documents (Web of Science + Scopus) at the intersection of AI-integrated metrology, supply-chain ESG reporting, and federated industrial data spaces, using bibliometric/network analysis. They find a 'core-periphery' topology with a structural hole separating AI-driven process-optimization research from downstream sustainability-governance research. They propose a 6-layer Safe and Sustainable by Design (SSbD) reference architecture, framed as a System-of-Systems, with distinct 'grid-to-core' and 'standards-through-supply-chain' integration pathways. This is a landscape/framework contribution useful as a citation map and architectural starting point for compliance-aware manufacturing data pipelines.

The paper introduces an actuator-aware spatiotemporal tube (STT) synthesis method for temporal reach-avoid-stay (T-RAS) tasks on unknown nonlinear MIMO systems under actuator saturation, avoiding the online re-optimization or controller redesign typical of prior STT approaches. The STT centerline/width are parameterized with Bernstein polynomials, exploiting their convex-hull property to enforce geometric and derivative constraints without sampling. By bounding worst-case closed-loop tracking error under an approximation-free prescribed performance controller (PPC), the authors derive a linear actuator-feasibility constraint incorporable directly into tube generation. This gives a tractable way to jointly optimize trajectory tubes and actuator feasibility for funnel/PPC-based tracking of unmodeled nonlinear systems.

FLINT is a timekeeping mechanism for batteryless intermittent systems that infers elapsed time from the stochastic retention loss of 'broken' MTJs (spintronic memory cells engineered for predictable decay), instead of capacitor-discharge-based timers. Because decay timescale is set by fixed device geometry rather than charge/discharge cycling, read energy is interval-independent and the estimate doesn't drift with device aging, addressing the core scaling problem (capacitor size ∝ energy ∝ area) of prior approaches. The authors validate an array-level statistical model against measurements from 21 fabricated MTJ devices and evaluate system-level performance, relevant to designers of energy-harvesting/intermittent embedded systems needing low-drift, low-energy elapsed-time inference.

The authors analyze spectral topology in 1D non-Hermitian lattice models composed of multiple domains arranged in a ring, showing that non-Hermitian-skin-effect localization at domain interfaces is governed by the difference in spectral winding numbers (relative to the local eigenenergy) between adjacent domains. They extend the Ronkin-function formalism to derive generalized Brillouin zone (GBZ) conditions for such domain-wall configurations in complex momentum space. Beyond conventional per-domain skin modes under open boundary conditions, they identify a new class of 'traveling-wave-like' skin modes collectively constructed across all domains simultaneously. This provides a topological classification framework applicable to engineering localization/transport in composite non-Hermitian systems like non-reciprocal circuits or photonic metamaterials.

The paper presents a drift-aware trajectory optimization framework for multi-target rendezvous (active debris removal / in-orbit servicing) that treats J2-induced nodal precession as an exploitable resource rather than a perturbation to cancel, benchmarked against the winning solution to ESA's Kessler Run competition. The core contribution is an 'enhanced drift orbit' that shapes the size, shape, and inclination of an intermediate orbit to tune the differential nodal precession rate between successive targets, achieving lower propellant cost than prior altitude-only drift-orbit designs in the Sun-synchronous regime. This is useful to mission designers building low-cost multi-rendezvous debris-removal or servicing tours where inclination-matching costs dominate the delta-v budget.

The scheme exploits a Gouy-phase telescope built from cylindrical lenses so that three standard QPDs jointly sense both alignment (tilt/displacement) and mode-mismatch degrees of freedom of a cavity, riding on the RF sidebands from an existing PDH lock (though it also works without RF modulation). Redundant sensing across the three QPDs increases sensitivity and fault tolerance. The paper provides analytic derivations and generic design equations for the telescope geometry, validated against a full optical simulation — enabling replication in interferometric setups like gravitational-wave detectors or any cavity needing simultaneous alignment and mode-matching control.

The authors present a three-phase inverse-design pipeline: (1) generate ~300k 5-port S-parameter samples via method-of-moments-accelerated EM solvers with port-reassignment and random open/short termination augmentation; (2) brute-force search the dataset for a strong seed design; (3) refine via direct-binary-search optimization of the pixel layout. Applied to 4G mid/high-band and 5G n79/U-NII tunable bandpass filters, one design achieves a 28% tuning range (4.64–6.16 GHz passband) with 1.3–1.8 dB insertion loss in simulation. This demonstrates a data-driven alternative to gradient-based inverse EM design, with the dataset/seed-search strategy reusable for other pixelated microwave structures.

The paper implements a two-stage hurdle model (occurrence + severity) at census-tract granularity, integrating 15-minute outage telemetry, OpenMeteo weather, ACS socioeconomic variables, CDC Social Vulnerability Index, and GIS vegetation coverage. Validation uses a 14+ month, 15-minute-resolution outage dataset across 290 Detroit census tracts, enabling sensitivity analysis of outage risk against socioeconomic, demographic, and environmental covariates during extreme weather. The hurdle formulation separately models probability of any outage versus outage magnitude conditional on occurrence, a template extensible to other utilities with comparable multi-source, fine-temporal-resolution data.

The Cold Radon Emanation Facility (RAL) addresses the known discrepancy between room-temperature radon screening and actual cold-temperature diffusion-suppressed emanation rates relevant to noble-liquid rare-event detectors. Its infrastructure includes a 2.7 L small-sample chamber, a 200 L chamber for full-scale as-built components coupled to a radon concentration line, cryogenic controls for temperature-dependent emanation studies, and an electrostatic radon detector reaching ~0.05 mBq minimum detectable activity at 90% CL. The commissioning paper reports initial comparative measurements between room-temperature and cold assays, establishing a reference capability for material screening in next-generation low-background experiments.

The authors theoretically establish a spin thermal Hall effect in nonreciprocal magneto-optical media, where a longitudinal radiative heat current induces a transverse accumulation of thermal-photon spin angular momentum, and prove this and its inverse form an Onsager-Casimir reciprocal pair. They derive thermodynamic (second-law) bounds on spin-heat coupling, yielding a thermal-spin figure of merit quantifying conversion efficiency between radiative heat and photon spin. This constitutes a full thermodynamic framework for 'photon spin caloritronics,' providing testable predictions and design bounds for nonreciprocal photonic devices, such as magneto-optical thermal diodes, that researchers could pursue in engineered magneto-optical structures.

The Surrounding Background Tagger (SBT) uses linear-alkylbenzene/PPO liquid scintillator segmented into ~120x80x20 cm cells, each read out by two Wavelength-shifting Optical Module (WOM) tubes rather than conventional PMTs at the tank edges. A full-scale 2x2-cell prototype was exposed to 5 GeV muons at the CERN PS T9 test beam to characterize timing and spatial resolution and validate the WOM-based readout concept for hermetic veto coverage. This establishes performance baselines (time/spatial resolution) needed to scale the design to the full 50 m decay volume ahead of SHiP construction.

StateFormer is a multivariate transformer trained to jointly model short-term thermal/electrochemical dynamics and long-term aging trajectories for battery state estimation (SOC, SOH, temperature), using attention over long-range temporal dependencies to capture history-dependent degradation. It was validated on both synthetic and real-world datasets and shown robust to additive current/voltage noise (1-10%) across a range of ambient temperatures. Practitioners could adapt this architecture as a drop-in long-horizon forecaster for battery management systems (BMS), replacing or augmenting equivalent-circuit or electrochemical models where long-range operating-condition dependencies matter.

The authors fuse optically detected magnetic resonance (ODMR, high-accuracy/high-latency) and all-optical (millisecond-resolution/lower long-term accuracy) NV-diamond temperature readouts via a hot-start Kalman filter, achieving a 57% accuracy improvement over either modality alone. The fused estimator inherits ODMR's long-term stability while retaining the all-optical method's fast update rate, addressing the classic latency-versus-accuracy tradeoff in NV sensing. This provides a practical blueprint for self-correcting NV-diamond sensor pipelines usable in real-time thermometry applications requiring both speed and drift correction.

The paper applies physics-informed neural networks (PINNs) to model full dynamic behavior of droop-controlled grid-forming converters, training on synthetic data from numerical solvers and embedding the converter's governing dynamics as a loss constraint. Benchmarked against both traditional ODE/DAE integration methods and a vanilla (non-physics-informed) neural network, the PINN shows higher predictive accuracy than the vanilla network under identical training data and substantially lower runtime than numerical solvers. This suggests PINNs as a viable surrogate model for fast grid-dynamics simulation in planning or real-time studies, replaceable for expensive solver calls where approximate but physically consistent trajectories suffice.

The work adds two loop-cache architectures to the NEORV32 RISC-V core: a dynamic loop cache that autodetects and caches short backward-branch loops at runtime, and a static, software-managed loop cache preloaded with hot instruction blocks at boot. Both target the >40% of total energy baseline attributed to SRAM instruction fetch, and are evaluated running LeNet-5 CNN inference, synthesized on GlobalFoundries 22nm FDX+ at 0.5V. This gives embedded-AI chip designers two concrete, implementable RTL-level techniques to cut instruction-fetch energy without changing the ISA, applicable to other small backward-branch-heavy inference kernels.

The review synthesizes grid-code requirements and commercial market products for inertia support, fast frequency reserve (FFR), and frequency containment reserve (FCR) from HVDC-connected offshore wind power plants (HVDC-OWPPs), incorporating the latest specifications from ENTSO-E, ACER, NESO, and TenneT among others. It frames HVDC-OWPPs as substitutes for retiring synchronous generators' inherent inertia and frequency response, given the added complexity of DC-coupled interfaces lacking natural grid-synchronizing behavior. For grid engineers or policy analysts, it serves as a reference map of current regulatory and commercial landscape gaps, useful for identifying where control strategies or market mechanisms still need development to make HVDC-OWPPs reliable ancillary-service providers.

The authors apply hierarchical equations of motion (HEOM) to compute charge transport in Cooper pair splitters beyond the weak-coupling, Markovian regime, capturing strong lead coupling, nonperturbative interactions, and finite voltage/temperature bias self-consistently. In the large-bias limit their HEOM results reduce analytically to a Markovian Lindblad prediction, but at finite bias/temperature, where recent experiments actually operate, HEOM achieves quantitative agreement with measured currents, including an observed thermoelectric effect. This provides a benchmarked, non-perturbative theoretical toolkit that experimentalists and theorists can use to model or design Cooper pair splitter devices operating outside the idealized large-bias/Markovian regime.

TRSOC augments stochastic optimal control with a KL-divergence penalty between the controlled trajectory distribution and a reference trajectory distribution; via Girsanov's theorem this reduces exactly to a quadratic drift-mismatch term in the running cost, preserving the dynamic programming structure and yielding a modified Hamilton-Jacobi-Bellman (HJB) equation with a characterized optimal policy. In the linear-quadratic (LQ) case the problem admits a closed-form solution with an augmented control cost matrix, and experiments (including reference dynamics learned from offline data) demonstrate a tunable performance-versus-reference-fidelity tradeoff via the regularization parameter. This gives control practitioners a principled, DP-compatible way to regularize learned or optimal policies toward safe/reference behavior, applicable to imitation-regularized RL or safe policy deployment on top of offline-learned dynamics models.

The authors show that maximal extractable electrochemical information from a solid-liquid interface, as sensed by a subsurface probe, is governed by a single dimensionless parameter u — the ratio of effective electrostatic separation between the liquid and probe to the electrostatic (Debye-like) propagation length in the solid — yielding a universal 1/u^2 attenuation law independent of probe microstructure. This generalizes electrostatic screening theory beyond specific electrode geometries into a scaling law, likely derived from Poisson-Boltzmann or linear-response electrostatics in the solid combined with information-theoretic bounds on distinguishable potential fluctuations. Practitioners designing buried/subsurface electrochemical sensors (e.g., embedded reference electrodes, in-operando battery diagnostics) can use u to predict detection depth limits and optimize probe placement without exhaustive empirical calibration across materials.

The device couples a diamagnetically levitated graphite plate, which acts as a contact-free proof mass responsive to magnetic forces, to a high-Q SiN nano-trampoline resonator (Q = 6×10^6 at 443 kHz) that mechanically amplifies the field-induced motion via resonant readout. Operating at room temperature without magnetic shielding, the system achieves 4.5 pT/√Hz sensitivity, competitive with cryogenic or heavily shielded alternatives, by decoupling the low-dissipation resonant readout from the field-transduction element. This architecture suggests a general strategy — pairing levitated proof masses with high-Q nanomechanical resonators — for building compact, unshielded precision sensors, applicable to weak-field magnetometry, axion/dark-matter searches, or biomagnetic sensing where cryogenics are impractical.

Using coupled micromagnetic simulations and an analytical model, the authors demonstrate parametric resonances at fp = 2f0/n for n>10 in spin-torque diodes, extending beyond the well-studied n=1 case (fp = 2f0), by simultaneously applying ac spin-transfer torque (current densities <10^6 A/cm2) and a voltage-driven (likely voltage-controlled magnetic anisotropy) term. The dual-drive mechanism enables higher-order subharmonic parametric excitation of spin-wave modes not accessible via STT or voltage torque alone. This gives device engineers a route to multi-frequency-selective parametric amplification/generation in spintronic oscillators using combined current and voltage drives, potentially useful for frequency-agile spin-torque oscillators or parametric spin-wave logic.

The authors engineer a nonlinear metasurface (NMS) whose unit-cell geometry encodes a reconfigurable hybrid coupling topology, giving power-dependent mode transition without active biasing or control circuitry. In the low-power regime it behaves as a high-selectivity bandpass filter with a roll-off steeper than 20.6 dB/GHz; above an incident-power threshold it self-transitions into a broadband shielding mode achieving over 23.6 dB shielding effectiveness across roughly 60% fractional bandwidth. Measured results track full-wave simulation and coupled-mode theoretical analysis closely, validating the design model. The approach offers a passive, self-adaptive alternative to limiter- or switch-based EMI protection for RF front-ends that must pass normal signals while surviving high-intensity interference or intentional electromagnetic attack.

The paper compresses an end-to-end neural network for global minimum-variance portfolio optimization by replacing a 2,400-parameter lag-transformation layer with a 5-parameter hyperbolic weighted-moving-average plus saturating exponential, and streamlining a bidirectional GRU eigencleaning module and marginal-volatility network, cutting total parameters from 39,586 to 2,175. In out-of-sample backtests, this compact architecture achieves lower realized portfolio variance than nonlinear-shrinkage and risk-parity benchmarks while preserving expected returns, and the variance reduction is shown to support higher leverage under long-only constraints without degrading drawdown control. The decoupling of model complexity from look-back window and universe size suggests the architecture scales more favorably to large asset universes than the original design, making it a candidate drop-in replacement for covariance-estimation modules in production portfolio-optimization pipelines.

Using a mean-field game framework with feedback from aggregate withdrawals to bank failure risk, the authors model depositors receiving gradual idiosyncratic shocks who strategically choose withdrawal timing, and show equilibrium withdrawal behavior is clustered rather than smooth despite continuous, individually-arriving information. The mechanism is 'latent fragility': run-prone agents accumulate silently, individually preferring to wait, until a common aggregate state variable crosses a threshold making collective withdrawal self-fulfilling and triggering a coordinated exit. The paper establishes existence of equilibria and characterizes earliest-run and latest-run equilibria (multiplicity), showing the clustering result is robust to discrete vs. continuous depositor heterogeneity, with a unique threshold equilibrium once a common aggregate state coordinates timing — offering a tractable framework for regulators or researchers to model early-warning indicators of run risk from cross-sectional deposit-flow data.

The paper provides a formal, gamble-based characterization of Neilson's weak and strong loss-aversion definitions within Cumulative Prospect Theory, establishing precise necessary and sufficient conditions under which the two notions coincide versus diverge — filling a gap where the distinction was previously informal. It then analyzes the Kobberling-Wakker utility specification and related standard CPT parametrizations, identifying structural limitations and internal inconsistencies that emerge when these loss-aversion definitions are imposed on them. The likely contribution culminates in a generalized CPT-utility function intended to resolve these inconsistencies, giving researchers building CPT-based risk models (in finance, insurance, or behavioral economics) a more theoretically sound utility specification to adopt in place of the standard forms.

The study applies the Log-Periodic Power Law (LPPL/JLS) model — estimated via OLS, GLS, and MLE fitting procedures — alongside the Phillips-Shi-Yu (PSY) explosive-behavior test suite (BSADF and BSADF*) to Bitcoin and Ether price series, cross-comparing bubble/crash signal detection across the two methodological families. This multi-estimator, multi-test design lets the authors assess robustness of bubble identification to model-fitting choices, addressing known LPPL calibration sensitivity by triangulating against the complementary PSY explosive-root framework. Practitioners in quantitative finance could replicate this pipeline as an early-warning system, using convergence across LPPL variants and PSY test statistics as a more robust bubble-confidence signal than any single method alone.

The model couples a CIR (Cox-Ingersoll-Ross) short-rate process with a compound Poisson claims process (exponential jump sizes) in a continuous-time surplus dynamics, and the insurer allocates wealth between a risky stock and a zero-coupon bond to maximize expected exponential utility of terminal surplus. Applying dynamic programming yields a Hamilton-Jacobi-Bellman PDE; despite the exponential utility's usual affine tractability, the coupling between the interest-rate hedge and the jump-driven surplus state introduces quadratic terms that break the standard exponential-affine ansatz. This suggests the value function requires a more general (likely numerically solved or perturbative) representation rather than closed-form affine solutions common in CIR-based portfolio problems. Practitioners modeling insurer ALM (asset-liability management) under stochastic rates and jump risk could extend this HJB framework or test numerical PDE solvers against the derived equation.

In a minimal three-quantum-dot Kitaev chain hosting poor man's Majorana (PMM) modes, the authors propose current-current cross-correlations, beyond standard differential conductance spectroscopy, as a probe of true non-locality. They show that PMM robustness against delocalization when detuned from sweet spots is encoded in the relative magnitude of nonlocal transport processes, which cross-correlations directly capture, remaining stable near the sweet spot under outer-dot detuning. This establishes cross-correlation stability as a diagnostic signature for genuine PMMs even in the short-chain limit, complementing conductance-based verification. Experimentalists working with quantum-dot Kitaev chains could adopt this correlation measurement as an additional non-locality test.

The authors compute Krylov spread complexity for the charged thermofield double dual to an AdS Reissner-Nordström black hole, showing that in the extremal limit the return amplitude becomes a pure phase, causing the Krylov complexity to freeze (no nontrivial spreading in Krylov space). Introducing charged matter and semiclassical Schwinger pair production near the horizon lifts this freezing once a discharge channel opens, restoring nontrivial complexity growth. This establishes a quantum-information diagnostic for the Weak Gravity Conjecture: extremality without discharge corresponds to complexity freezing, while WGC-compliant discharge processes restore dynamics — a result that could be tested against other complexity or holographic-complexity proposals (e.g., volume or action complexity) in near-extremal backgrounds.

The framework replaces classical probability with complex-valued measures as a 'pre-probabilistic' potentiality layer, preserving linear structures (additivity, conditioning, independence, mixtures, transition kernels, temporal divisibility) at that level, while nonlinear Born-rule mapping to actual probabilities is the sole source of interference and other non-classical features. Measurement is recast as Bayesian-style conditioning of potentialities on actualized outcomes, with non-selective measurement corresponding to replacing coherent potentiality with a statistical mixture of conditional branches. The approach claims to give a unified potentiality-level account of mixed states, decoherence, composite systems, entanglement, and Bell-type correlations, suggesting it could serve as an alternative axiomatic foundation for QM worth comparing formally against consistent-histories or generalized-probabilistic-theory frameworks.

The authors compute radiative decays J/ψ, ψ(2S) → γη^(′) in perturbative QCD, extending the calculation to include order-q² relativistic corrections across all three short-distance contributions: quark-antiquark annihilation, two-gluon, and QED channels. The amplitudes are shown to be robust against choice of light-cone distribution amplitude and light-quark mass through this order, lending confidence to the predictions. The q² corrections roughly double J/ψ branching ratios (narrowing the theory-experiment gap) but converge poorly for ψ(2S), and the ratio R_1S = B(γη')/B(γη) is shown to be sharply sensitive to the assumed η-η' mixing scheme, making it a candidate observable for constraining mixing parameters in future precision measurements.

The paper investigates adapting Pauli Correlation Encoding (PCE), a compression scheme, to encode the RSA integer-factorization problem as a binary optimization task solvable with substantially fewer qubits than direct QUBO (quadratic unconstrained binary optimization) formulations. The work examines the structural and dynamical properties of the resulting optimization landscape to assess whether PCE's compression preserves enough problem structure for successful factorization via near-term quantum or quantum-inspired optimization hardware. This is relevant to researchers benchmarking NISQ-era (noisy intermediate-scale quantum) approaches to cryptographically relevant problems, offering a qubit-count reduction strategy that could be tested against existing QAOA or quantum annealing factorization proposals.

The authors study static, spherically symmetric wormhole solutions in extended symmetric teleparallel gravity with f(Q,T) = αQ + βT + γT², where Q is the non-metricity scalar and T is the trace of the stress-energy tensor. Varying the action yields modified field equations recast into an effective Einstein-like form with extra geometric source terms from Q and T. The key result is that this functional form admits wormhole geometries satisfying (or substantially relaxing violation of) the standard energy conditions that normally force wormholes in GR to require exotic matter, providing a modified-gravity route to physically viable wormhole models that could be probed via lensing or shadow signatures in astrophysical observations.

The authors compute T-odd transverse momentum dependent (TMD) gluon distribution functions for a tensor-polarized deuteron using a spectator model, in which the deuteron emits a time-like off-shell gluon while the residual system is treated as a single on-shell spectator with mass described by a spectral function (allowing a continuous mass range). Nonvanishing T-odd TMDs require final-state interactions, which are implemented here via single-gluon exchange between the spectator and outgoing parton. The paper derives analytic expressions for six T-odd gluon TMDs and presents their numerical x- and k_T-dependence, providing baseline model predictions that could be compared against future electron-ion collider measurements of polarized deuteron gluon structure.

The paper studies de Finetti hierarchies restricted to states that are fixed points of quantum channels — a condition generalizing invariance under compact symmetry groups — and combines the mean-ergodic theorem with the structure theory of conditional expectations to derive several new results: a tight bound on the entanglement-assisted classical capacity of the dual of a conditional expectation, block-wise distortion bounds for informationally complete measurements adapted to fixed-point algebras, and an exact type-based refinement of the permutation-invariant chain rule. These tools yield new de Finetti-type theorems, including a double-sided extension theorem with improved (O(√·)) error scaling, extending the reach of quantum de Finetti techniques to symmetry-constrained settings relevant to quantum key distribution security proofs and many-body entanglement analysis.

The authors extend Neural Quantum Kernels (NQKs) — kernels built by pretraining a QNN and reusing the fixed embedding as a task-adapted feature map — from qubits to qutrits (d=3), replacing SU(2) local unitaries with SU(3). They systematically vary encoded feature count, qutrit number, kernel construction strategy (1-to-n vs n-to-n), and SU(3) parameterization, evaluating on binary and three-class tasks across four benchmark datasets. Qutrit NQKs outperform the corresponding QNN baselines in nearly all configurations, and the native three-level structure gives a direct multiclass embedding without one-vs-rest tricks. A practitioner could replicate this by swapping SU(2) encoding gates for parameterized SU(3) gates in an existing NQK pipeline and comparing kernel-SVM accuracy against the QNN baseline.

The authors study measurement-induced state transitions (MIST) in dispersively-read transmon qubits, showing sensitivity to offset charge n_g via charge dispersion of higher transmon levels, and to the diabaticity/symmetry of the readout pulse envelope. They engineer 'fast-load, fast-clear' pulse shapes that suppress resonator photon-number overshoot during ramp-up/ramp-down, avoiding the transient photon populations that drive unwanted qubit transitions. This achieves MIST suppression via pulse-envelope engineering alone — no optimal-control waveforms or real-time feedback required — validated using active reset/verification of the resulting fidelity gains. It's applicable to any dispersive-readout superconducting qubit stack as a firmware-level pulse update rather than a hardware redesign.

Working directly from the spherically symmetric Einstein-matter field equations for spatially regular self-gravitating configurations, the authors prove four theorems bounding the dimensionless force function F=4πr²p(r) from above. For generic, possibly anisotropic matter obeying the dominant energy condition, F≤2; for matter with non-positive energy-momentum trace, F≤1. For isotropic matter these tighten to F≤1 (dominant energy condition) and F≤1/2 (non-positive trace). These are analytic inequalities derived purely from the coupled field equations plus regularity/energy-condition assumptions, giving a ready-made consistency check that a practitioner constructing compact-object models or equations of state can verify their pressure profile against.

The authors construct a Hamiltonian with one canonical positive-energy oscillator coupled to one negative-energy (ghost) oscillator via quartic and sextic interaction terms designed to regularize the large-amplitude runaway behavior typical of indefinite-kinetic-energy systems. They derive the equations of motion and analyze phase-space geometry, showing that for finite total energy the accessible energy surfaces become compact, unlike standard ghost models where trajectories diverge. They characterize the interplay between local ghost-driven instability near the origin and global nonlinear confinement, identifying the sextic term as dominant in the asymptotic dynamics. This gives a concrete, low-dimensional system a practitioner could numerically integrate to study chaos indicators (Lyapunov exponents, Poincaré sections), relevant to constructing stable higher-derivative or modified-gravity theories.

Using large-scale DMRG on six-leg cylinders, the authors study the doped altermagnetic Mott insulator in a checkerboard t-J model, encoding altermagnetic exchange anisotropy microscopically via anisotropic ferromagnetic next-nearest-neighbor exchange. Scanning doping and anisotropy strength, they map a phase diagram showing a transition from uniform d-wave superconductivity with charge modulation into a pair-density-wave (PDW) regime coexisting with stripe order. In the PDW phase they identify an unconventional wavevector-locking relation Q_PDW≈2Q_stripe along the cylinder direction, distinct from the conventional relation seen in other PDW candidates, supported by pair-correlation analysis. This provides a concrete, numerically-controlled lattice model others can extend to different geometries or interaction ranges to probe the microscopic origin of PDW order in altermagnets.

The authors compute scalar-induced gravitational waves (SIGWs) in an inflationary model where charged scalar fields couple to an isotropic triplet of Abelian gauge fields via a kinetic coupling function; sufficiently strong inflaton–gauge-field mixing enhances the SIGW spectrum to observable levels. They show the longitudinal gauge-field mode and charge-dependent mixing terms matter dynamically only when gauge-field excitation triggers late in inflation, and in that regime the spectrum peaks at ultra-high, GHz-band frequencies. The longitudinal-mode contribution and charge-dependent mixing imprint qualitatively distinct spectral shapes, giving an observable discriminator between charged and neutral scalar sectors. This yields a template gravitational-wave spectrum, peak frequency and shape as functions of coupling/charge parameters, comparable against proposed GHz-frequency detection concepts as a probe of early-universe symmetry breaking.

In the qubit prepare-and-measure communication-complexity scenario, exact simulation of arbitrary quantum measurements is known to require exactly 2 classical bits; this work uses a trained neural network to search for 1-bit classical protocols approximating specific restricted measurement families with high average accuracy. Performance analysis shows symmetric, uniformly-weighted measurement families, e.g. those corresponding to regular-polyhedron vertex configurations, achieve markedly higher 1-bit simulation fidelity than generic measurement sets. By inspecting the learned structure, the authors extract an analytic, closed-form 1-bit protocol for these symmetric cases, turning a numerically-discovered strategy into a provable classical simulation scheme. This is directly reproducible — a practitioner can retrain the same architecture on other measurement families to probe where the 1-bit/2-bit boundary sits.

The authors introduce a 'two-horizon sector' diagnostic for NUT-charged black hole thermodynamics, in which sector temperatures are fixed by harmonic-mean relations between inner/outer horizon quantities rather than chosen ad hoc to satisfy a first law. Applied to uncharged Taub-NUT spacetime, the sum sector closes consistently using only mass M and NUT charge N, while the difference sector requires introducing a secondary 'hair' quantity J_n=mn, a rotation-like off-shell quantity defined within the homogeneous mass representation, not a new metric parameter or asymptotic charge, to close. This provides a falsifiable criterion, sector closure under the harmonic-mean diagnostic, for adjudicating between inequivalent NUT thermodynamic formulations in the literature, and connects this bi-hair structure to hidden conformal symmetry results for rotating Kerr-NUT spacetimes.

The authors apply the SUSY QM factorization method to exactly solve the Cornell potential for the tt̄ system, obtaining pseudoscalar (η_t) and vector (Θ_t) state masses of 344.114 and 344.141 GeV via spin-dependent hyperfine splitting. They compute decay widths across channels, finding the digluon mode η_t→gg dominant at 2.57 MeV, and derive mean, RMS, and most-probable radii from the bound-state wavefunction. These closed-form results give practitioners direct benchmarks to compare against LHC near-threshold tt̄ production data.

The protocol has two stages: a passive rearrangement mapping the largest eigenvalues of the initial thermal state onto lowest-Hamming-weight target sectors (minimizing target energy), followed by a target-only complex-Hadamard transform within fixed-Hamming-weight subspaces that equalizes one-qubit marginals without altering sector probabilities or total energy. The authors prove the constrained (common-temperature) optimum coincides exactly with the unconstrained passive minimum for all N>M and any initial temperature, meaning uniform local cooling incurs no extra thermodynamic cost — though the complex-Hadamard step may still add circuit depth. This gives a provably optimal target protocol that quantum engineers could implement directly for qubit reset/cooling subroutines.

The authors construct interacting Green's functions for systems under classical multiplicative stochastic noise by expanding around the noise-free (noninteracting) GF via an infinite perturbative series, which they show converges to a closed form under specific noise-bandwidth conditions. Applied to CPT resonance lineshapes, they demonstrate that when the colored noise bandwidth greatly exceeds the system's damping rate, the series sums exactly, giving an analytic prediction for noise-induced lineshape distortion. This provides a tractable analytic framework practitioners can use to budget clock stability against laser phase/amplitude noise without brute-force stochastic simulation.

Using dimensional-reduction effective field theory, the authors compute the weak-coupling free energy of pure 4D N=2 SYM through O(λ²) in the 't Hooft coupling: the hard-scale (T) contribution comes from massless 3-loop vacuum diagrams in the full theory, and the electric-scale (√λ T) contribution from a 2-loop calculation in the reduced 3D EFT. Unlike N=4 SYM, N=2 SYM is asymptotically free, so the coupling-renormalization and EFT unit-operator counterterms cancel all 1/ε poles, with residual scale-dependence fixed by the 1-loop beta function. The calculation reproduces the known O(λ^{3/2}) term and delivers a new O(λ²) coefficient, extending the perturbative thermodynamics toolkit for comparison against lattice or holographic results.

The authors add the dimension-six operator O_6=(Φ†Φ)^3 to the Higgs potential within the SU(2) bosonic sector of the Standard Model (an SMEFT-style correction) and simulate oscillon stability. At the physical mass ratio m_H/m_W=1.556, and with the O_6 coupling kept below current experimental bounds, oscillon lifetimes increase by orders of magnitude relative to the pure Standard Model case. This suggests even small, experimentally-allowed EFT corrections can qualitatively alter nonperturbative soliton-like electroweak solutions, relevant for numerical studies of oscillon-driven cosmology or preheating dynamics.

DualityCert symbolically checks 't Hooft anomaly matching, superpotential R-charge consistency, central-charge matching, and a bounded chiral-ring proxy for candidate Seiberg-duality claims in 4D N=1 quiver gauge theories, issuing a certificate meaning 'no tested inconsistency found' rather than a formal proof. It serves as a verifier-gated repair environment for LLM agents that edit deliberately broken claims until certification; on a preregistered 145-claim benchmark, verifier-gated retry improved repair success by +8.3pp (deepseek-chat) and +7.1pp (qwen-plus) over single-shot attempts (Holm-adjusted p<0.002). The setup is a reusable pattern for other formally-checkable domains — pair an LLM agent with a symbolic verifier as an iterative repair loop rather than a one-shot generator.

The authors evaluate QKD for the 1D periodic Hubbard model using a low-depth Jordan-Wigner circuit that reduces CNOT count for Hamiltonian time evolution, building a Krylov subspace from time-evolved states and solving the resulting generalized eigenvalue problem with singular-value truncation (SVT) for numerical stability. Their systematic sweep over Krylov dimension, evolution time step, system size, and interaction strength U shows convergence is governed by a delicate interplay between the Hamiltonian's low-energy spectral gap structure and SVT-induced numerical conditioning. The results give practitioners concrete parameter guidance (dimension and truncation thresholds) for deploying QKD on near-term (NISQ) hardware for correlated-electron simulations.

Using the CPL parameterization, DESI DR2 BAO plus CMB and Pantheon+ supernovae favor evolving dark energy at 2.8–4.2σ; the authors instead reconstruct w(z) with a hierarchical Gaussian process that co-samples kernel hyperparameters (σ_f, l) alongside (H0, Ωm, Ωk, ωbh²), using compressed Planck priors and 37 cosmic-chronometer H(z) points as an effective likelihood. The baseline result gives w(z≈0) = -0.80⁺⁰·²⁶₋₀.₂₃, only ~0.8σ from ΛCDM, versus Δχ²~1 for CPL on the same pipeline. Ablations (fixing kernel hyperparameters, dropping SN or LRG BAO, swapping RBF for Matérn-5/2 kernels) test the robustness of this weaker preference.

The authors build a signed control variate from two normalizing flows to simultaneously narrow event-weight spread and eliminate negative weights in phase-space integration, combined with neural importance sampling for LO and NLO event generation. At NLO, the conditional neural control variate functions as a trainable subtraction term that complements existing analytic subtraction schemes rather than replacing them. This is directly applicable to Monte Carlo event generators used in collider physics, offering a drop-in variance-reduction module compatible with standard NLO subtraction pipelines.

The pipeline chains DFT geometry relaxation, NSCF calculations, and Wannier-based orbital localization to construct active-space Hamiltonians for quantum embedding, then solves them with ADAPT-VQE enhanced by Greedy-Operator Commutativity Partitioning (GOCP) for measurement grouping and a Taylor-expanded O(5) operator evolution scheme to control circuit/ansatz growth. The benchmark maps Li-hBN onto qubit registers and studies convergence as active-space size increases, providing an end-to-end recipe practitioners could adapt to other strongly correlated materials on NISQ-era or emulated quantum hardware.

The protocol performs tomography of permutation-invariant fermionic many-body states with U(1) particle-number symmetry, proving they're fully determined by the total-particle-number distribution and the single-collective-mode occupation within each number sector — both experimentally accessible in ultracold-atom platforms. Required observable count scales linearly, not exponentially, with system size. More generally, the method reconstructs the permutation-symmetrized component of any U(1)-symmetric fermionic state, giving experimentalists a concrete, resource-efficient tomography recipe for large bosonic/fermionic quantum simulators.

The work introduces a finite-setting generalized Mermin family of state-tailored Bell inequalities with analytic certification bounds, where the measurement-setting number m acts as an independent certification axis alongside qubit number n. For powers-of-two setting choices, increasing m leaves the ideal normalized multipartite quantum value fixed while lowering the classical bound, strengthening violation ratios and improving noise-robustness scaling relative to the standard Mermin inequality — giving practitioners a tunable knob to trade circuit/setting overhead for tighter, more noise-tolerant entanglement certification on large GHZ-state processors.

The authors provide a resource-accounting framework that recasts the Heisenberg limit as an information-theoretic consequence of the quantum speed limit, resolving apparent 'super-Heisenberg' paradoxes in metrology. They demonstrate this with an m-photon-resonance Rabi-oscillation phase-estimation protocol in two-level atoms, where phase error scales as n^(-m/2) with photon number n — a super-resolving scaling that their speed-limit-based bound correctly predicts and reconciles with standard Heisenberg-limit intuition, offering a general template for evaluating claimed quantum advantage in future sensing protocols.

Extending Vu (2026), the authors construct a probabilistic representation for three-state discrete-time quantum walks on integer lattices, validated against empirical examples, and prove it converges to the continuum solution of multi-state Dirac partial differential equations. This establishes a stochastic-process-based alternative to direct quantum-walk simulation, suggesting classical Monte Carlo or stochastic-process machinery could be repurposed to simulate higher-dimensional quantum walks and study Dirac-equation dynamics without directly propagating quantum amplitudes.

Applying the first law of thermodynamics to the FRW apparent horizon under Kaniadakis entropy yields modified Friedmann equations with a correction term set by parameter λ; the authors evolve interacting ghost dark energy plus pressureless matter within this framework. Numerically, the λ correction mildly shifts the dark energy equation of state and the acceleration-onset redshift; squared-sound-speed stability analysis shows the model is generically classically unstable, though instability is moderated for larger λ. Statefinder diagnostics show the model asymptotically approaches the ΛCDM fixed point, with deviations shrinking as λ grows — useful for constraining λ against future expansion-history data.

The authors work within 'modified relativistic dynamics' (MORD), which adds a λT·C·T coupling term to the Einstein-Hilbert action to encode matter-gravity interaction via the Weyl tensor. For a spherically symmetric static spacetime, they compute shadow radii under two matter backgrounds — a constant-density baryonic halo and a homogeneous plasma — tracking dependence on the coupling constant λ and black hole mass, then derive fundamental quasi-normal mode frequencies characterizing ringdown. This gives falsifiable, λ-dependent predictions for shadow imaging (EHT-like) and gravitational-wave ringdown observations that could constrain or rule out the MORD framework.

The platform unifies construction of Feynman ribbon diagrams (FRDs) — tree-structured diagrams naturally representing arborescent ('FRD-like') knots — as tensor networks, evaluation of higher-rank Chern-Simons knot invariants from those networks, and downstream identification of the corresponding knot at higher crossing numbers. It appears to be the first tool combining diagrammatic construction, tensor-network evaluation, invariant computation, and knot classification in one interactive workflow, useful for researchers wanting to generate invariant data or test conjectures about arborescent knots without hand-deriving Chern-Simons integrals.

The study reports experimental evidence of E8 symmetry emerging in a quasi-2D quantum magnet Cu2(OH)3Br as a magnetic field drives dimensional reduction toward decoupled transverse-field Ising chains near criticality. The measured massive excitation spectrum's mass ratios and correlations match those predicted by an integrable field theory possessing an infinite set of E8-related conserved quantities (Zamolodchikov's celebrated 1989 result), interpreted here as bound-state excitations within ferromagnetic sublattice chains. This demonstrates that integrable field theory can quantitatively describe emergent many-body quantum critical phenomena in condensed matter, likely measured via inelastic neutron scattering, and offers a platform for further probing E8 physics experimentally.

Using cosmography (a model-independent Taylor-expansion approach to cosmology) applied to Sunyaev-Zeldovich and high-resolution spectroscopic temperature-redshift data spanning 0≤z≤6.34, the authors constrain deviations from the standard T(z)=T0(1+z) relation, which would signal non-conservation of photon number or other new physics. They compare multiple cosmographic expansion variables/approaches against Lima's canonical adiabatic extension model using the same dataset, finding percent-level constraints on violations and that a single additional parameter suffices to capture any non-standard behavior — a useful benchmark dataset/method for testing photon-number-violating or non-standard thermodynamic cosmological models.

The authors simulate/analyze nonlinear dynamics of a BEC in a double-well lattice potential under time-periodic modulation of interatomic interaction strength. For a symmetric well, resonant driving produces collective particle emission ('jets') whose excitation regime is set by the interplay of drive strength and hopping amplitude; introducing well-depth asymmetry shows moderate bias enhances emission rate while large asymmetry suppresses it, and tuning hopping amplitude/imbalance further modulates jet strength. This maps a controllable parameter space (drive, hopping, asymmetry) for engineering quantum many-body transport in optical-lattice BEC systems, of direct relevance to cold-atom experimentalists designing tunable matter-wave emission sources.

The device is a vertically stacked silicon-nitride bilayer grating coupler that simultaneously routes and focuses the 729.4 nm qubit beam and 854.2 nm repump beam for 40Ca+ ions through a single electrode aperture, focusing both to a spot 70 μm above the chip. 3D FDTD simulations predict 0.10 μm color separation with near-diffraction-limited spots along the ion-chain axis, enabled by a multi-level depth-allocation apodization scheme that encodes the coupling envelope via discrete etch-depth levels rather than sub-resolution linewidths, while respecting a ≥125 nm deep-UV-compatible fabrication rule using only two etch depths per film; results are cross-validated with full-3D Ansys Lumerical simulations. This offers a fabrication-realistic path to dual-wavelength, single-aperture addressing that reduces trap-area consumption and dielectric exposure near ions — directly applicable to scaling surface-electrode ion-trap architectures.

The MIMAC μ-TPC detector at Modane Underground Laboratory, using a low-pressure i-C4H10/50%CHF3 gas mixture at 30 mbar in a 6-liter active volume, reconstructs full 3D nuclear recoil tracks to enable directional WIMP discrimination against isotropic backgrounds like neutrons. Two independent chambers accumulated 495.1 and 354.2 days of effective exposure; backgrounds were estimated via a standard ON/OFF spatial analysis, and recoil tracks were projected onto the galactic coordinate map with a mass-dependent kinematic energy cut to compare signal-direction event rates against multiple OFF-source directions. This yields the first directional exclusion limits from MIMAC, providing a template dataset/method for other directional dark matter TPC experiments (e.g., CYGNUS collaboration efforts) aiming to confirm a galactic-origin WIMP signal via angular recoil distributions rather than energy spectra alone.

The authors construct a prefix-suffix quantum address space with an isometric hierarchical encoder-decoder guaranteeing unique decodability, i.e., unitarily reversible address extraction without measurement-induced collapse. They embed a practical Huffman-based prefix-free code with length-eigenstate codewords into this space, preserving the isometry property needed for coherent processing. This targets hierarchical, heterogeneous quantum network topologies with dynamic address reassignment; a 13-node numerical example demonstrates compact encoding. Practitioners building quantum network stacks could adopt this as a routing/addressing primitive compatible with coherent (non-destructive) header processing.

Using Langevin dynamics for heavy quarks coupled event-by-event to a relativistic Boltzmann transport background, the authors compute the pT-differential radial flow v0(pT) for charmed hadrons as a probe of heavy-quark transport coefficients. Varying the temperature dependence of the spatial diffusion coefficient Ds(T) shows v0(pT) retains strong sensitivity to the underlying transport model at intermediate pT, distinguishing it from other flow observables. At low pT, hadronization effects dominate, with Λc baryons showing larger v0(pT) than D mesons — a signature practitioners could use to disentangle coalescence/hadronization models from pure transport effects in heavy-flavor QGP studies.

The authors extend the U(3) NJL model with a flavor-mixing interaction sourced from vacuum polarization (distinct from the standard 't Hooft instanton-induced term), using proper-time regularization to effectively mimic confinement. They solve gap equations and compute meson masses, quark-meson couplings, and valence-quark PDFs for charged kaons and pions, benchmarking against JAM global-analysis extractions at μ²=4 and 27 GeV². The mixing-induced PDF shift scales with quark effective mass differences, giving a testable, model-specific correction that could be incorporated into future NJL-based PDF fits or compared against lattice QCD moments.

Hermes reconstructs discrete galaxy catalogues as continuous density fields in a compact multiresolution scaling-function basis, replacing explicit N-point tuple counting with algebraic operations (FFT-based convolutions) among window-filtered fields. Standard statistics — counts-in-cells, two-point and higher-order correlation functions, isotropic/anisotropic and multipole decompositions, marked correlations — become choices of window/kernel rather than bespoke estimators, enabling rapid prototyping of new statistics. PyHermes, the open-source Python implementation, adds MPI/thread parallelism and GPU acceleration, making it usable as a drop-in, extensible backend for large-scale-structure analysis pipelines on datasets from surveys like DESI or Euclid.

Combining inelastic neutron scattering, linear spin-wave theory, crystal-field analysis, Raman spectroscopy, and machine-learned force-field (MLFF) phonon calculations, the authors disentangle overlapping magnetic, crystal-field, and lattice excitations in the 4d-4f multiferroic 6H-perovskite Ba3HoRu2O9. A dispersive sub-6.2 meV magnetic mode is quantitatively reproduced by linear spin-wave theory as a collective spin wave of the coupled Ru-Ho sublattice, while higher-energy broad features are being resolved via the MLFF phonon calculations and crystal-field modeling. This multi-technique + ML-phonon workflow is a template for disentangling degenerate excitation channels in other correlated 4d-4f oxides where conventional DFT phonon calculations are too costly or inaccurate.

The authors perform the first simultaneous global fit of DIS reduced cross sections (HERA) and forward single-inclusive hadron production (RHIC, LHC) using a dipole amplitude evolved via the LO Balitsky-Kovchegov equation with running coupling (with/without kinematical constraint), applying constant per-collider K-factors for higher-order corrections. They achieve χ²/d.o.f. near unity in both schemes, with RHIC K-factors roughly double those at the LHC — consistent with independent threshold-resummed one-loop calculations, validating the approach. The complementarity found (SIHP data constrain evolution speed, DIS constrains other dipole parameters) gives practitioners a concrete strategy for combining datasets in future CGC/BK global analyses, e.g. for EIC projections.

The authors define a pair second-order correlation function g_pairs^(2) = ⟨(P†)²P²⟩/⟨P†P⟩², built from the pair-creation operator P† = a†b† for a two-mode field, to directly quantify bunching/antibunching statistics of pair-generation events rather than single-photon or heralded correlations. Values >1, =1, or <1 correspond respectively to pair bunching, Poissonian statistics, and pair antibunching, capturing correlations intrinsic to the joint two-mode state that conventional g^(2) measures miss. They apply a Cauchy-Schwarz-type inequality to bound and interpret this quantity, giving experimentalists working with parametric down-conversion or similar pair sources a new witness for nonclassical correlations in the pair-generation process itself, applicable to heralded single-photon source characterization and multiphoton state engineering.

The authors construct static, spherically symmetric self-gravitating 't Hooft-Polyakov monopole solutions in Einstein-scalar-Gauss-Bonnet (EsGB) gravity, comparing polynomial versus exponential scalar-GB coupling functions. For polynomial coupling and sufficiently large coupling parameter, the principal part of the field equations becomes locally degenerate at a critical radius, producing a critical solution rather than a smooth branch continuation — signaling a geometric phase transition. Exponential coupling instead regularizes the system, preventing degeneracy and yielding globally smooth field profiles. This gives a concrete example of coupling-function-dependent branch structure in scalar-tensor gravity with topological solitons, relevant to studies of hairy black holes and horizonless compact objects in EsGB theories.

The paper produces an explicit shuffle algebra presentation for the loop-nilpotent cohomological Hall algebra (CoHA) of a tripled quiver with its canonical cubic potential, then leverages this to: relate the algebra to the quantized Coulomb branch algebra of the associated quiver gauge theory; prove supercommutativity at ħ=0; give explicit generators for both the loop-nilpotent and full preprojective CoHA; and characterize the BPS Lie algebra of the full preprojective CoHA via degree/divisibility conditions, yielding a new Kac polynomial formula in terms of dimensions of polynomial spaces. They also confirm a conjecture on spherical generation of the localized shuffle algebra and, for ADE quivers, identify the loop-nilpotent CoHA with the positive part of a known algebra — useful groundwork for anyone computing BPS invariants or Coulomb branch structures via shuffle-algebra techniques.

The paper studies hereditary set-defined graph classes, where vertices carry fixed-length tuples and adjacency depends on equality patterns among coordinates, characterizing when such classes are χ-bounded. The main decomposition theorem partitions any graph in a set-defined class into polynomially-many (in clique number) parts, each a bounded union of shift-colorable graphs — graphs admitting a homomorphism into a shift graph — identifying bounded unions of shift-colorable graphs as the canonical obstruction to χ-boundedness. For full set-defined classes this yields a sharper structural classification. The result unifies adjacency-labeling constructions, communication complexity, and χ-boundedness theory, providing a decomposition tool to bound chromatic number in specific labeling-derived graph families.

For 3-uniform hypergraphs satisfying (n,d,μ)-density (linear quasirandomness: e_H(X,Y,Z) ≥ d|X||Y||Z| − μn³), the authors establish sharp thresholds for tight Hamilton cycles combined with minimum vertex degree δ₁(H) or minimum codegree δ₂(H) conditions. They prove d > 1/3 plus δ₁(H) ≥ α·C(n−1,2) forces a tight Hamilton cycle whenever α > f(d) := (1−√((4d−1)/3))/2, with f(1/3)=1/3 resolving Problem 8.3(i) of Araújo–Piga–Schacht and confirming Conjecture 8.1 of Han–Shu–Wang. A parallel sharp threshold is derived for the codegree case, closing a gap in extremal hypergraph theory that connects quasirandomness and degree conditions.

The authors compute the image of the integral Chow restriction map CH(BSpin(10)) → CH(BT)^W (equivalently CH(BSpin(10)) modulo torsion) for the split spin group over fields of characteristic ≠ 2. The key ingredient is constructing the class c₂c₃c₅ via proper equivariant push-forward from the affine cone over the spinor variety under its half-spin embedding for the special Clifford group Γ⁺(10). Mod 2, they identify the image as a ring generated over a Steenrod-stable subring by the torus restriction of the top Chern class of a half-spin representation. This extends the program of computing Chow rings of classifying spaces of algebraic groups, giving explicit generators for further characteristic-class computations on Spin(10)-torsors.

The paper proves a uniform L∞ a priori estimate for bounded ω-m-subharmonic solutions u of the complex m-Hessian equation (ω+dd^c u)^m ∧ ω^{n−m} = cf ω^n on compact Hermitian manifolds, for densities f ∈ L^p, f≥0, p>1, despite torsion terms from the non-closed background metric. The proof combines a weak comparison principle with torsion error, a pluripotential capacity theory adapted to the Hermitian setting, and a nonlinear iteration scheme controlling sublevel-set decay. Corollaries include existence, stability, and compactness of weak solutions with L^p densities, extending Kołodziej-type pluripotential techniques beyond the Kähler framework for researchers tackling Hessian-type PDEs on general Hermitian manifolds.

The authors establish continuous data assimilation for the 2D Navier-Stokes equations under Navier-slip boundary conditions using only finite-dimensional tangential-velocity measurements on a relatively open boundary patch Γ⊂∂Ω, with zero interior observations. Sufficiently strong boundary nudging from sufficiently fine observations produces a coercive spectral gap for the assimilation error, matching that of a mixed Dirichlet/Navier-slip problem obtained by imposing homogeneous Dirichlet conditions on Γ, scaling like the viscosity ν. Combining this coercivity with a nonlinear error-production estimate based on long-time-averaged symmetric-gradient energy yields a sufficient criterion for exponential convergence, extending nudging-based data assimilation theory to sparse tangential boundary-only sensing.

The authors extend Brakke's 1995 covering-space approach to the Plateau problem: for boundary curve Γ with G = π₁(ℝ³∖Γ), Brakke associated to each finite-index proper subgroup N⊴G a minimal surface Σ_N obtained as the boundary projection of a perimeter-minimizing fundamental domain in the corresponding covering space. This paper generalizes the construction to all normal subgroups N⊴G, including infinite-index ones, and proves a compactness result guaranteeing existence of a proper normal subgroup N₀ achieving the infimum area over all proper N⊴G. This resolves an existence gap in Brakke's framework for surfaces with triple-junction/tetrahedral singularities, giving geometric measure theorists a result to build further regularity or uniqueness analysis on.

The paper analyzes projected-gradient methods for convex optimization over a compact convex set where the objective is smooth/convex only on an open domain with a boundary-blow-up condition, ensuring compact invariant sublevel sets and existence of an optimizer. They introduce a domain-aware Armijo projected-gradient scheme with a safe-neighborhood analysis guaranteeing well-defined objective evaluations, finite backtracking, sufficient decrease, and a run-specific but iteration-independent positive lower bound on accepted step sizes — yielding explicit sublinear rates and full iterate-sequence convergence, with positive curvature along feasible directions giving uniqueness and linear convergence. The framework is applied to controllability scoring, giving an implementable algorithmic template with convergence guarantees for objectives with boundary singularities.

The paper extends Chiswell's classical one-variable growth series formula for RAAGs/RACGs (graph products of groups over a defining simple graph) to the fully multivariate setting, expressing it via substitutions into the multivariate independence polynomial of the graph. This generalizes known specializations for right-angled Artin and Coxeter groups and yields a uniform formula for general graph products. The authors additionally give explicit combinatorial descriptions of the multivariate coefficients, which practitioners could use to compute growth data for specific graphs/groups or to study asymptotic/rationality properties of these series.

The authors construct an explicit connected graph on 24 vertices with maximum degree 3, independence number α=9 and zero forcing number Z=11, disproving Conjecture 2 from the Davila–Brimkov–Pepper survey (originating from the TxGraffiti conjecture-generation system) that Z ≤ α+1. A modified gadget yields a fully cubic (3-regular) connected graph on 36 vertices with α=15 and Z=17, refuting even the cubic-restricted form of the conjecture as formalized in the survey's Lean 4 appendix, and demonstrating that the gap Z=α+2 is achievable. This settles the question negatively and gives concrete constructions others can analyze or extend to probe the true worst-case gap between Z and α.

The paper studies co₂(F), the squared ℓ₂-norm of the codegree vector over (k−1)-subsets, for t-intersecting k-uniform hypergraphs F, extending recent work by Brooks–Linz and Wu–Zhang that determined max co₂(F) and its extremal structures. Addressing Brooks and Linz's open question of whether classical Erdős–Ko–Rado-type extremal results generalize to this codegree-squared-sum functional, the authors develop spectral techniques for incidence matrices across three finite-space settings to establish analogous maximum values and extremal characterizations. This gives a spectral-method template that could be adapted to other codegree-based extremal hypergraph problems.

The paper characterizes integers m satisfying mb^k ≤ c^m < (m+1)b^k for some k (the 'self-prefix leading-digit' condition) via an exact shrinking-target criterion and a signed-discrepancy identity that separates infinitude from the conjectural logarithmic growth rate of solutions. For c≥2 with irrational log_b(c), Lambert W₋₁ branch inversion yields a candidate solution sequence obeying an eventual two-gap law with an exact counting formula, concretely proving gaps of 3 or 4 for (c,b)=(2,10); algebraic c cases satisfy deterministic moving-target asymptotics below a critical scale, while irrational-slope cases show fixed-difference/arithmetic-chain rigidity. This is a certified-search/effective-dynamics approach that a practitioner could implement directly to verify or extend digit-matching sequences for other (c,b) pairs.

The authors construct smooth, simply connected, non-convex planar domains Ω_{a,ε} for the torsion equation −Δu=1, u=0 on ∂Ω, where the maximal Hessian eigenvalue at the strict global max point tends to 0⁻ while diam/inradius stays uniformly bounded — showing Steinerberger's 2018 Hessian lower-bound estimate for convex domains fails outside convexity. The domains are doubly symmetric and one-directionally convex yet star-shaped overall, but the authors further show superlevel sets of u lose star-shapedness once the constructed slits are long enough, giving a concrete geometric mechanism (slit length) controlling this failure. This gives explicit counterexample domains useful for testing the sharpness of other convexity-dependent PDE regularity/Hessian estimates.

The paper models optimization ODEs as contact Hamiltonian systems on the jet space J¹(ℝⁿ), where the intrinsic identity Ḣ = −H·∂ₛH gives an augmented energy certificate whenever it controls the objective gap. The main theorem shows that under three named, independently verifiable hypotheses, an order-r contact splitting integrator with step size h transfers this continuous-time rate certificate to the discrete algorithm over a finite horizon (via backward error analysis), with the decay envelope governed by a modified conformal factor up to O(h^r) error. This gives a general recipe for proving discrete convergence rates for optimization algorithms directly from continuous-time contact-geometric certificates, applicable to designing/certifying new discretized optimizers.

The paper considers a 1D two-species symmetric simple exclusion process with slow (order 1/n) boundary reservoirs, which induce Robin boundary conditions in the hydrodynamic limit. The authors prove that the joint fluctuation field of the two species' empirical densities converges, in the diffusive scaling limit, to a generalized Ornstein-Uhlenbeck process characterized by a linear martingale problem whose coefficients encode bulk diffusion, inter-species conversion, and boundary reservoir effects. This extends known single-species slow-boundary fluctuation results to the multi-species setting and gives an explicit limiting covariance/generator structure that could be used to compute correlation functions or compare against simulations of coupled exclusion processes.

The paper defines total outer-independent domination (TOIDS: total dominating sets whose complement is independent) and introduces the associated coalition framework: two disjoint non-TOIDS vertex sets whose union is a TOIDS form a 'TOI-coalition,' and a graph's vertices can be partitioned into mutually TOI-coalition-paired classes, with C_t^{oi}(G) denoting the maximum such partition size. The authors establish existence conditions for TOI-coalition partitions and prove sharp upper bounds on C_t^{oi}(G) for general graphs. This extends the growing domination-coalition literature (previously studied for standard, total, and outer-independent domination variants) and provides a framework others can apply to compute or bound coalition numbers for specific graph families.

The paper generalizes Huber-Sturmfels mixed subdivisions to the ECI setting, enabling tropicalization of sparse square polynomial systems for efficient solution counting. It introduces a tropical homotopy continuation algorithm to compute these mixed subdivisions, building on Jensen (2016), Malajovich (2017), and Daisey-Ren (2024). The technique is designed to interface with numerical polynomial-system solvers (Helminck, Henriksson et al.), giving a pipeline from combinatorial structure to numerical solving of ECI systems.

The authors transport the recent Hubička-Konečný-Todorčević-Zucker result (announced EUROCOMB 2025) that the Fraïssé limit of finite complete edge-labelled graphs with countably many labels lacks finite big Ramsey degrees, using a categorical (Fraïssé-theoretic) correspondence between that class and the class underlying Hall's universal group. This yields the negative result for Hall's group without a from-scratch combinatorial proof, illustrating the power of categorical transport methods in structural Ramsey theory.

The framework combines coverage-gated alignment feedback, drive-direction switch regularization, and an aligned termination condition within a parameterized reward for a DQN agent under non-holonomic (car-like turning) constraints. The key claim is that reward-shaping parameters and algorithmic hyperparameters are co-dependent, requiring joint meta-optimization; they use surrogate-based Bayesian optimization to search this joint space. The co-optimized DQN outperforms uncalibrated baselines on success rate and trajectory quality, giving a template for reward-hyperparameter co-tuning in constrained control tasks.

By eigen-decomposing the joint dynamics, the authors show DIGing and AugDGM share identical average-state dynamics but differ in their gradient-tracking subsystems, which govern convergence. Exploiting the resulting diagonal structure, they reduce MIMO stability analysis to a family of parameter-varying SISO systems, yielding closed-form exact worst-case convergence rate formulas rather than conservative bounds. The formulas analytically confirm AugDGM's superior worst-case rate over DIGing under matched assumptions, providing exact benchmarks for future distributed optimization algorithm design.

The authors construct a full-block multiplier certifying robust stability of a specific Lur'e interconnection with slope-restricted nonlinearities for which no OZF multiplier exists, proving the OZF test is not necessary for stability and disproving Carrasco's conjecture. This demonstrates a strict gap between OZF-based and full IQC (integral quadratic constraint)-based stability certification. Control practitioners working with nonlinear feedback should consider full-block multiplier searches when OZF-based tests fail to certify a known-stable system.

The authors exhibit a Lipschitz viscosity solution from a post-focal branch of a constant-Gauss-curvature parallel surface (2D, not C^1), a sequence of admissible solutions with bounded C^1 norm but unbounded curvature showing uniform C^{1,β} estimates fail for β>1/3, and a 3D subcritical-phase Mooney-Savin-type solution with a gradient jump across an analytic surface. These sharpen the recent a priori estimates of Qiu and Zhou by showing their convexity and critical-phase hypotheses are strictly necessary, not technical artifacts.

The authors prove any faithful action of G=(Z/4)^4 on a rationally connected threefold is G-birational to the Fermat quartic threefold, with biregular equivalence when X is a terminal G-Q-Fano threefold. This shows G acts on some rationally connected threefold yet does not embed in Cr_3(C), completing the classification of pairs (m,r) for which (Z/m)^r embeds into Cr_3(C) versus into Bir(X) for some rationally connected threefold X. This is a birational rigidity result useful for researchers classifying finite abelian subgroups of Cremona groups.

The paper proves a 'Gaussian Bayes inverse-rigidity' theorem: for independent standard normals V, U with Y_φ=φ(V)+U, if φ(v) maximizes x(v−F_φ(x)) for every v (the insider's optimality condition against the market's conditional pricing rule F_φ), then φ must be the identity map. This forces Kyle's closed-form affine equilibrium strategy and linear competitive pricing rule to be unique for arbitrary Gaussian location/scale, resolving the open uniqueness question. It builds on Boulatov-Kyle-Livdan's complex-analytic techniques and McLennan-Monteiro-Tourky's regularity bounds on F_φ, giving financial economists a rigorous existence-and-uniqueness foundation for the workhorse insider-trading model.

The paper resolves the equality characterization for the Grone–Merris–Bai inequality, ∑λ_i(G) ≤ ∑d_i*(G) for partial sums over Laplacian eigenvalues vs. the conjugate degree sequence. It builds on the 2026 split-graph trace inequality (Kothari–Tudose) that both proves Brouwer's Laplacian conjecture and establishes its equivalence to Grone–Merris–Bai, plus a 2027 characterization of Brouwer equality cases, to derive that equality holds iff G lies in one of two explicit graph families. This effectively closes the equality-case question for a chain of related spectral graph theory conjectures, giving a template for transferring equality characterizations across equivalent inequalities.

The authors analyze dispersive semigroups e^{itφ(√L_a)} for the Schrödinger-type operator L_a = -Δ_x - Δ_y + (a/2)|x|^{-2} with a partial inverse-square potential on R²_x × R^n_y, using the explicit spectral measure representation from Zhang–Zhang (2025). Via frequency localization and stationary-phase analysis they derive decay estimates that handle the inhomogeneous phase function φ, and separately establish boundary Strichartz estimates for the associated fractional Schrödinger operator. These estimates are foundational inputs for well-posedness and scattering theory of nonlinear dispersive PDEs with singular potentials, and the techniques generalize to other φ(√L) semigroups with known spectral measures.

The paper proves that for topological line arrangements — configurations of embedded spheres in CP² generalizing complex line arrangements — the cohomology ring of the complement remains isomorphic to the Orlik-Solomon algebra, established via homological rather than classical (algebraic-geometric) methods since those don't transfer. They further characterize homotopy type: symplectic line arrangement complements admit minimal CW structures, but every combinatorial type realizable topologically also admits non-minimal realizations, and in fact infinitely many distinct realizations exist for a given combinatorial type. This establishes topological line arrangements as a genuinely richer moduli problem than classical arrangements despite sharing cohomological invariants, opening a research direction on distinguishing realizations beyond cohomology.

For k-uniform hypergraphs with matching number bounded by β, the extremal structure S_{n,k,β} (all k-sets meeting a fixed β-vertex set) maximizes tensor spectral radius; this paper proves a stability theorem showing that any n-vertex hypergraph H with matching number ≤ β and tensor spectral radius near this maximum must be structurally close to S_{n,k,β} — specifically, every edge of H meets the distinguished vertex set and H contains almost all of S_{n,k,β}'s edges. As an application, the authors derive a new proof of the spectral Erdős matching conjecture for sufficiently large n. The technique — converting near-extremal spectral radius into near-extremal edge structure — is a standard but powerful stability paradigm (à la Erdős–Simonovits) now extended to tensor/hypergraph spectral settings, useful for future extremal hypergraph spectral problems.

The authors define a generalized CIR diffusion dX_t = a(b(t)-X_t)dt + (σ_0+σ_1√φ_ε(X_t))dZ_t^{(q,H)}, where Z^{(q,H)} is a Hermite process of order q≥1 and Hurst parameter H∈(1/2,1), interpreting the SDE pathwise via Young-Stieltjes integration since Hermite processes with q≥2 are non-semimartingales. Under globally Lipschitz coefficients (satisfied by the smoothed square-root φ_ε) they prove existence/uniqueness of a strong solution in a fractional Sobolev space, establish quantitative positivity, and conduct Malliavin analysis. This extends rough-path/Young-integration techniques to a mean-reverting positivity-preserving model with long-range dependence and non-Gaussian innovations, providing a rigorous foundation for using such processes in interest-rate or volatility modeling under memory effects.

For N labeled noninteracting copies of a Euclidean lattice Λ with one-site energy H=‖·‖², the paper proves canonical/microcanonical ensemble equivalence at a local energy scale — exact for lattice-valued energies on reachable shells, and in fixed-width windows otherwise — obtaining sharp counting asymptotics and total-variation convergence of every fixed marginal. They also exhibit failure of exact-shell equivalence in the nonlattice case, and show the exponential large-deviation rates in both regimes recover the entropy function from Bost's stable Arakelov lattice-point counting, linking statistical mechanics ensemble theory to arithmetic geometry. This connects classical equivalence-of-ensembles results (usually proved via local CLT / large deviations) to Arakelov-theoretic counting asymptotics, suggesting further transfer of techniques between the two fields.

The paper studies additive bases formed by integers whose binary expansions are primitive Dyck words, establishing a bijection (pairing consecutive bits) between these and base-4 words 3w0 where w is a two-colored Motzkin word, yielding a regular-language underapproximation and digit-closure structure. They prove an interval digit-lifting theorem for digitally closed sets plus a constructive base-4 propagation algorithm that extends finite sumset certificates to infinite tails in logarithmically many recursive steps. Combining exact finite certificates with generation-gap lower bounds, they fully classify positive even integers requiring more than six primitive-Dyck-word summands, identifying 46 as needing eight — the algorithmic digit-lifting technique is reusable for other digit-restricted additive basis problems.

Building on generalizations of the tree distance-matrix determinant formula to k-trees, the paper computes the Smith normal form (SNF) of the top-dimensional Laplacian matrices of simplicial annuli and k-trees, establishing relations to sandpile groups of associated adjacency graphs. They derive explicit SNF formulas for these highest Laplacians and run numerical experiments assessing SNF's discriminative power for distinguishing k-trees combinatorially. They also show the Graham-Lovász-Pollak matrix — classically used for tree distance-matrix determinants — extends naturally to computing Laplacian-related invariants for trees and block graphs, providing a reusable algebraic tool for combinatorial topologists studying higher-dimensional sandpile/chip-firing analogues.

MCOF combines an entropy-constrained VAE (EC-VAE) with a feature selector to route objective/constraint-relevant information into a designated latent subspace, isolating 'active' decision variables from latent coordinates that merely encode solution diversity. A Uniform Transformation module then applies a per-dimension probability integral transform to reshape the active subspace into a well-behaved sampling domain, easing constrained search. This staged decomposition — dimensionality reduction, active-variable identification, then constraint-safe reparameterization — lets standard constrained optimizers operate on a low-dimensional, well-conditioned subspace instead of raw high-dimensional data, and is replicable with any VAE training pipeline plus a black-box constrained optimizer.

The authors define 'Sum of Separable and Biquadratic' (SPBQ) quartic forms and analyze when SOS-convexity (a checkable sufficient condition for convexity expressible as an SDP) coincides with true convexity. They prove that for SPBQ polynomials whose biquadratic component has n×2 structure, convexity implies SOS-convexity exactly, but exhibit an explicit 3×3 biquadratic counterexample that is convex yet not SOS-convex, showing the equivalence breaks at that size. They further examine optimization implications of this gap. This gives practitioners exact size thresholds for when SDP-based convexity certification is tight versus conservative within this structured polynomial class.

The paper studies infinite-horizon Markov decision processes with recursive utility given by a CES certainty-equivalent aggregator composed with the negative entropic risk measure (rather than plain expectation), proving existence and uniqueness of solutions to the Bellman equation under mild primitive conditions. It also establishes uniqueness of solutions to the Koopmans equation for any fixed stationary policy, and shows these combine to guarantee that a measurable maximizer selector yields an optimal stationary policy. The proof techniques generalize to other certainty-equivalent operators, including the risk-neutral case, giving a template for well-posedness results in risk-sensitive recursive-utility DP models. This is directly usable by researchers building risk-sensitive recursive-preference models who need existence/uniqueness guarantees before numerical dynamic programming.

The authors prove a Sylow-type reduction theorem: a finite subgroup G of the projective linear group (specifically of the linear symmetry group Lin(F) of a nonsingular degree-d polynomial F in N variables over a characteristic-zero field) is liftable iff, for every prime p dividing gcd(|G|, N, d), some Sylow p-subgroup of G is F-liftable. The proof uses restriction/corestriction maps in group cohomology together with a reduction-to-Klein-four-group method to control the relevant obstruction classes. This gives a practical route to checking liftability of automorphism groups of smooth hypersurfaces by reducing to prime-power order subgroups, useful in classification problems for hypersurface automorphisms.

The paper resolves an open problem of Kardoš (Problem 4.1, 33rd Workshop on Cycles and Colourings) by constructing an explicit connected, simple, diamond-free, claw-free cubic graph H on 18 vertices with strong chromatic index χ'_s(H)=7, disproving the conjecture that diamond-freeness forces χ'_s ≤ 6 for claw-free cubic graphs (equivalently, that χ'_s(T(G))=6 for every cubic graph's truncation T(G)). This builds on Lin and Lin's result that claw-free subcubic graphs (other than the triangular prism) satisfy χ'_s ≤ 7, showing that bound stays tight even without diamonds. The authors also prove minimality — H has the fewest vertices possible among non-prism counterexamples — giving a sharp reference example for future strong-edge-coloring extremal work.

Extending prior work that used Brown et al.'s descent theory framework on special regular-parity cases (Hoffman's multiple t-values, Kaneko-Tsumura's multiple T-values, and the authors' own multiple S-values), this paper fully determines, at the motivic level, all unramified motivic multiple mixed values (MMVs) at general depth. MMVs generalize multiple zeta values by fixing parity patterns on summation indices at 'level two,' and the central question is when they descend to (are expressible via) level-one multiple zeta values. Working motivically sidesteps the transcendence obstructions of the analytic question, using cohomological descent machinery to give exact algebraic criteria; this provides a template and concrete criteria other researchers can apply to classify MMVs with irregular parity patterns.

The paper proves that for a triangle dissected into N pairwise congruent triangles, N prime forces one of a short, explicit exception list: bisecting an isosceles triangle, trisecting an equilateral triangle, a specific 3-piece tiling of the 30-60-90 triangle, or the classical tilings of a particular right triangle when N is expressible as a sum of two squares. This resolves, for all prime N simultaneously, a special case of the general 'which N admit congruent triangle tilings' classification problem in dissection geometry. The result likely leverages number-theoretic constraints (sums-of-two-squares, tied to Gaussian integers) interacting with the rigid angle/side constraints of triangle dissections, giving a complete reference classification other tiling researchers can build on or extend to composite N.

For (P2∪P3)-free graphs G with clique number k=ω(G)≥4, the authors prove χ(G) ≤ C(k+2,3) − C(k-1,2) = (k³+11k−6)/6, improving on Bharathi and Choudum's previously best general bound of χ(G) ≤ C(k+2,3) = (k³+3k²+2k)/6. The improvement removes a full C(k-1,2) quadratic term from the bound without imposing any additional forbidden-subgraph restrictions, making it the first tightening applicable to the entire (P2∪P3)-free class. This is a purely structural/combinatorial chromatic-bound result, likely proved via refined case analysis on clique structure; it's directly usable by researchers working on chromatic bounds for hereditary graph classes defined by small forbidden linear-forest patterns.

The paper proposes a line-search Newton framework for unconstrained nonconvex optimization that invokes the Newton direction only when it is well-defined and descent-suitable, avoiding per-iteration Hessian regularization used in prior nonconvex approaches. The framework generalizes existing hybrid gradient-Newton schemes and yields a new extragradient Newton method as a special case. Global convergence is established under the Polyak-Lojasiewicz-Kurdyka (PLK) condition, accommodating both isolated and nonisolated critical points/minima. Practitioners building second-order solvers could adopt this switching criterion to get Newton-speed convergence without the overhead of regularizing the Hessian every iteration.

The authors establish existence and orbital stability of temporally periodic solutions to a damped Benjamin-Bona-Mahony (BBM) equation posed on the torus T, driven by a spatiotemporally periodic forcing term f(x,t) with temporal period θ. Solutions are constructed in Sobolev spaces H^ℓ for ℓ≥0, notably extending to low regularity ℓ∈[0,1) via the I-method (a low-regularity energy technique originally developed for dispersive PDEs). This extends prior periodic-solution theory for damped-driven dispersive equations to a broader regularity range. Researchers in dispersive PDE could adapt the I-energy approach here to other damped, forced equations where low-regularity well-posedness is otherwise out of reach.

The paper analyzes the porous medium equation with combustion-type reaction, u_t = Δu^m + f(u), for radial nonnegative compactly supported initial data in R^N. For N=2, every bounded solution converges locally uniformly to one of three constants (0, θ, or 1), with a sharp threshold parameter in ordered families of initial data separating vanishing from spreading, and the critical solution converging to the ignition temperature θ. For N≥3, under a total-disconnectedness condition on the central values of ground states, bounded solutions converge to 0, θ, 1, or a radial ground state U in a solution set S, with a novel normalization argument excluding θ as a transition limit in higher dimensions. This gives a complete long-time classification (a 'hot spot'/threshold dichotomy generalized to trichotomy/tetrachotomy) that researchers in reaction-diffusion theory can use as a template for analyzing combustion-type free boundary problems in higher dimensions.

The authors refine Latala's argument, previously restricted to β<1/2, using the Kearns-Saul inequality to prove overlap concentration and convergence of the free energy to the replica-symmetric formula with error O(N^-1), valid whenever β²·q/arctanh(q) < 1, where q solves the self-consistency equation q = E tanh²(h+β√q Z). This condition is automatically satisfied for all β<1 with any external field h, and for nonzero h it extends into a nonempty region with β>1. This meaningfully widens the rigorously proven parameter regime for the SK model's high-temperature phase, giving researchers in spin glass theory a sharper tool for establishing free energy asymptotics beyond the classical β<1/2 threshold.

The paper disproves the Polycirculant Conjecture by constructing an elusive permutation group (transitive, 2-closed, with no derangement of prime order) of degree 16,464, specifically the group 7^6.PSU_3(3) built via non-split extensions using recently developed methods of Chen et al. The authors verify this group is the full automorphism group of seven of its orbital graphs, confirming 2-closedness, and thereby exhibit a vertex-transitive graph with no semiregular automorphism — answering a long-standing question of Marušič and Jordan. The construction generalizes to infinitely many such counterexamples. Group theorists and algebraic graph theorists can use the non-split extension construction as a template for finding further elusive groups or exploring the boundary of when semiregularity fails.

The paper studies affine vector space partitions (spreads) of AG(n,q) — collections of same-dimension affine subspaces partitioning all points — focusing on 'completely tight' spreads, where the linear parts of any two distinct subspaces intersect only at the origin, and 'irreducible' spreads, which admit no proper sub-partition covering a smaller affine subspace. The note characterizes structural conditions for tight irreducible affine d-spreads over F_q^n. Such partitions relate directly to constant-dimension subspace codes and network coding constructions, so results here give coding theorists sharper existence/structure conditions to build or rule out spread-based code constructions.

The authors derive a general distribution formula for linear combinations of the component frequency spectrum (M_jn)_{1≤j≤n} of Gibbs-type random partitions, and specialize it to the two-parameter Pitman-Yor sampling model, obtaining asymptotic distributions for sums Σ_{j=⌊λn⌋}^{⌊μn⌋} M_jn for 0<λ≤μ≤1. They further conjecture a functional limit theorem for the tail sum Σ_{j=⌊λn⌋}^{n} M_jn and connect these results to limit shapes of random partition structures. This provides exact asymptotic tools (large-sample distributional limits) that population geneticists and Bayesian nonparametrics researchers (e.g., users of Pitman-Yor priors in clustering/topic models) can use to derive confidence intervals or test statistics on aggregated frequency-spectrum statistics.

The paper formalizes quantized residual computation as a pure schedule selecting operations from a declared low-bit library over a fixed horizon, using relaxed (continuous-control) analogues to characterize the infinite-depth limit; the distance from the target map to the closed relaxed reachable set is proven to be an exact structural floor unremovable by increasing depth D. Convergence rates of pure schedules to the relaxed class are established as O(D^-1) under bounded-variation time dependence and O(D^-ϑ + D^-1) under Hölder dependence of exponent ϑ. Critically, execution arithmetic matters: full-state write-back introduces a Dρ_z penalty term that can freeze residual updates, while increment/error-feedback accumulation replaces this with a bounded carry term obeying an exact conservation law on a common lattice. This gives hardware/algorithm designers concrete design rules (e.g., prefer error-feedback accumulation over write-back) for when adding computational depth can substitute for missing numerical precision in low-bit neural network execution.

The authors analyze the 1D PSO stagnation recurrence with two fixed attractors and independent uniform accelerations, a second-order random affine recursion, throughout the entire mean-square stability region defined by inequalities on inertia weight w and acceleration range c. They show no invariant (or limiting) position marginal can be Gaussian by comparing stationary moment equations against Gaussian moment identities up to order eight, using a Hermite-polynomial formulation to derive 4th/6th-order compatibility conditions whose joint solution set is confined to a degree-107 polynomial branch, which the exact 8th-order equations then rule out entirely. This resolves Problem 18 from a known open-problems list in PSO theory and gives a rigorous non-Gaussianity certificate; practitioners modeling PSO stagnation analytically should use this moment-based framework rather than Gaussian approximations.

The paper develops a parametric cell-viability modeling framework for crystal violet assay data that violates classical i.i.d. assumptions due to substantial inter-replicate variability. It pairs model-free confidence intervals and pooled within-replicate variance estimation with deterministic least-squares fitting to experimental means, validated via leave-one-replicate-out cross-validation, and applies a shared mechanistic model across B16-F10 melanoma, RAW264.7 macrophage, and HaCaT keratinocyte cell lines to quantify quinolinic acid's antiproliferative dose-response. This offers a reusable statistical pipeline for extracting robust dose-response parameters from noisy, non-independent in vitro viability data, useful to anyone analyzing crystal violet or similar assays without inflating false confidence from classical parametric tests.

The authors study front propagation in spatial SIR-type PDEs where transmission rate depends on the infected fraction, mapping the parameter phase space separating pulled fronts (dynamics set by the linear leading-edge decay rate) from pushed fronts (nonlinear, bulk-driven). Combining numerical PDE solutions with analytical front-propagation theory, they identify both continuous and discontinuous pulled-to-pushed transitions, the latter exhibiting a discontinuous jump in front speed at the critical threshold, plus a bistable parameter region admitting either front type. This gives epidemiologists and mathematical biologists a concrete, numerically validated phase diagram and analytical criteria for predicting epidemic wave speed regimes and their transitions, directly applicable to spatial compartmental disease models.

Chamaileon reframes binder design as cross-context binding landscape modeling, addressing the single-target/single-state limitation of prior hallucination and joint sequence-structure generative approaches. It introduces In-Context Complex Co-Design (I3CD), a training paradigm for context-aware joint sequence-structure modeling across multiple targets/states, and Mixture-of-Paths Sampling (MoPS), an inference-time strategy that optimizes a single sequence jointly across multiple contexts for scalability. This provides a template for multi-target/multi-state generative protein design that researchers could adapt to design binders robust to conformational heterogeneity or paralog cross-reactivity, rather than the conventional single-state binder pipelines.

The authors model liquid-liquid phase separation of polyampholytic intrinsically disordered protein sequence pairs using random phase approximation (RPA) polymer theory augmented with interfacial tension and finite-size corrections, cross-validated against molecular dynamics simulations. RPA predicts temperature-sensitive binary versus ternary LLPS behavior, with demixing into multiple coexisting phases occurring for sequence pairs with dissimilar charge patterning but not for similarly patterned pairs, and MD simulations corroborate these predictions once interfacial and droplet-size effects are incorporated into the RPA framework. This links condensate phase multiplicity (number of coexisting liquid phases) to sequence charge patterning and provides a quantitative theory-simulation pipeline researchers can use to predict or engineer noise-buffering subcompartmentalization in synthetic or biological condensates.

Loom is a visual computing/analytics system for spatial transcriptomics (ST) data that supports pseudo-temporal trajectory analysis, cross-sample/cross-region comparison, and local microenvironment examination, addressing the multi-modal registration challenge of integrating ST data with single-cell reference atlases and temporal simulation outputs. It combines a novel glyph-based visual encoding with a computational backbone to jointly represent spatial enrichment, pseudo-temporal ordering, and gene expression dynamics within a unified interface. This targets computational biologists and bioinformatics tool builders needing to integrate trajectory inference (e.g., pseudotime methods) with spatial coordinate data and reference-based cell annotation in one interactive system, rather than stitching together separate single-purpose tools.

This is a retrospective multicenter cohort study of 1,909,042 adults with serum creatinine measurements from 2012-2014, followed through January 2025 across primary care, acute care, and hospital settings, comparing current versus previously recommended GFR-estimating equations (including race-stratified versions) for predicting CKD stage prevalence and kidney failure risk. Primary outcomes include AUC-ROC for kidney failure prediction and CKD stage prevalence stratified by region of origin/ethnicity, enabling head-to-head comparison of equation performance (e.g., CKD-EPI variants) at population scale. Clinicians and epidemiologists can use these findings to select or validate GFR equations for multiethnic populations and to quantify the real-world impact of removing race coefficients from kidney function estimation.

The paper defines a novel cross-coupling scheme for two Rulkov neuron maps and proves analytically that it preserves boundedness of trajectories and the existence of a snap-back repeller, which by the Marotto theorem guarantees Devaney chaos, provided these properties hold in the uncoupled system. For two standard chaotic Rulkov maps under this coupling, numerical simulations reveal a global strange attractor with a non-integer Kaplan-Yorke (Lyapunov) dimension, supported by time series, Lyapunov exponent spectra, bifurcation diagrams, and basin-of-attraction analysis, with a proposed generalization to arbitrary numbers of coupled maps. This gives dynamical-systems researchers both a rigorous chaos-preservation proof technique and a concrete numerically-characterized coupled-neuron-map model to extend toward larger chaotic neural network motifs.

The paper analyzes energy consumption of large-scale spiking neural network simulation codes on conventional CPU-based HPC systems, isolating the effect of Linux's automatic NUMA (Non-Uniform Memory Access) balancing feature. Because spiking-network memory access patterns interact dynamically with NUMA page-migration heuristics, the OS's balancing decisions add overhead without functional benefit; disabling automatic NUMA balancing yields a ~30% reduction in energy consumption with no change to simulation correctness. The effect is invisible in standard neuroscience workflows since output is unaffected, but shows up clearly in performance/energy profiling. Practitioners running spiking-network simulations on multi-socket NUMA nodes can replicate the gain via `/proc/sys/kernel/numa_balancing` or explicit process/memory pinning, ahead of costlier hardware or algorithmic efficiency efforts.

The authors formalize speech production and auditory segmentation as a POMDP-based active inference model over sequences of discrete phonemes, treating the brain as a generative model that plans motor actions and infers auditory outcomes under a shared internal model of turn-taking discourse. This provides a computational testbed for hypotheses about speech dysfluency mechanisms — transient (stuttering) or progressive (neurodegenerative) — by perturbing model parameters (e.g., precision weighting, policy depth) and observing emergent breakdowns in fluent phoneme sequencing. The framework's genericity means it's extensible to other hierarchical sequential-action domains beyond speech. Researchers could build on this by fitting parameters to clinical dysfluency data or comparing simulated deficits against behavioral/EEG markers of stuttering.

The authors ran a large-scale within-session benchmark using the MOABB LeftRightImagery paradigm across three public motor-imagery EEG datasets (Cho2017, PhysionetMI, Zhou2016; n=52/109/4), evaluating combinations of frequency bands, preprocessing, feature extraction, and classifiers — 216,714 raw evaluation rows aggregated to per-subject observations. Covariance tangent-space projection and CSP-based feature families consistently outperformed alternatives, but their relative ranking was dataset- and subject-dependent, quantifying the inter-individual heterogeneity problem that plagues MI-BCI generalization. The core contribution is a portfolio-based search-space reduction: a small candidate pipeline set (identified from this benchmark) can be tried to approximate optimal performance, which practitioners can adopt directly via MOABB to cut calibration time for new users or datasets.

The paper derives closed-form and general combinatorial formulas for counting monophyletic characters (leaf-trait assignments corresponding to a single clade) on an arbitrary phylogenetic tree, with simplified formulas for binary characters and specific tree shapes. It further establishes a linear-time algorithmic connection between monophyly and maximum parsimony, a standard tree-reconstruction optimality criterion, characterizing when parsimony-optimal characters are also monophyletic. This gives phylogenetics researchers exact enumerative baselines useful for null-model comparisons, character-simulation studies, or evaluating how "tree-like" real trait data are relative to random expectation.

The study evaluates short-term precision (RMS-SD, RMS-CV) and least significant change (LSC at 95% CI) for 3D-DXA-derived volumetric/compartment BMD parameters (integral vBMD, trabecular vBMD, cortical sBMD via 3D-Shaper software) alongside conventional areal BMD (APEX software), using duplicate hip scans with full repositioning across five Hologic scanner units (Horizon Wi x2, Horizon A x2, Discovery W x1) at five clinical centers. This establishes scanner-model-specific precision benchmarks needed to interpret longitudinal 3D-DXA measurements clinically or in trials, since precision error propagates directly into the threshold for detecting real biological change. Clinicians adopting 3D-Shaper for longitudinal monitoring can use these LSC values as scanner-appropriate thresholds rather than assuming uniform precision across Hologic hardware generations.

The authors formulate a PDE-based reaction-diffusion model of bacterial populations coupled to nutrient fields, explicitly incorporating the metabolic cost of motility as an energetic trade-off, then pose an optimal control problem maximizing total population yield (final cell count) rather than instantaneous growth rate. Solving this across different resource-distribution regimes, they find the optimal motility-response function is context-dependent: predictable landscapes favor simple monotonic chemotactic responses, while unpredictable environments favor a non-monotonic motility strategy as a robust bet-hedging solution. This provides a normative framework for interpreting observed diversity in real motility phenotypes and could be extended with stochastic PDEs or agent-based validation against chemotaxis data.

The study combines microfluidic microchannel flow experiments with dissipative particle dynamics (DPD) simulations to characterize reticulocyte biomechanics across subtypes (multilobular, cup-shaped, near-discocytic), parameterized (R1-R3) from transit-time and shape-under-flow data in 5-micron channels. Single-cell simulations show up to 30-50% slower transit for stiffer subtypes (R1) in narrow channels, while splenic-slit-like bending-dominated geometries discriminate subtypes far less (10-20%), and pairwise simulations reveal hydrodynamic coupling effects (leading cells altering follower dynamics) relevant to clogging. The work links reticulocyte mechanical heterogeneity to acute/chronic mountain sickness pathophysiology, and the DPD parameterization provides a reusable computational framework for modeling immature RBC populations in other microvascular contexts.

The paper introduces a semiring algebraic structure on the space of Boolean network dynamics, enabling systematic decomposition of any Boolean network's global dynamics into the dynamics of its constituent modules/subnetworks in a compositionally compatible way. This gives a formal foundation for network modularity supporting reduction, design, control, and reverse-engineering tasks on large Boolean models (e.g., gene regulatory or signaling networks), replacing ad hoc decomposition heuristics with an algebraic framework carrying provable composition properties. The semiring formalism opens the door to applying established algebraic methods (e.g., tropical algebra, automata theory) to Boolean network analysis, letting practitioners formally verify modular reduction strategies rather than relying solely on simulation-based checks.

The authors construct a synthetic spatial indicator by combining multi-species distribution models (254 dominant canopy tree species, used as a proxy for forest biodiversity) with open-access land-use/anthropogenic pressure layers, classifying pixels into refuge versus conflict zones based on the divergence between ecological potential and human pressure. Costa Rica serves as a national-scale case study to validate the indicator against the existing protected-area network, revealing gaps in current coverage. Practitioners could replicate the pipeline with their own species distribution models and land-use rasters to generate country-specific prioritization maps for CBD-aligned conservation planning.

The paper presents a real-time-compatible adaptation of MindEye2, a computationally intensive fMRI-to-image reconstruction pipeline, re-engineered to fit within a seconds-scale processing budget without access to later session data. Implementation runs on RT-Cloud, an open-source scalable cloud platform for closed-loop fMRI, and the authors demonstrate single-trial decoding of perceived natural images during an actual real-time scan session. This closes much of the gap between offline state-of-the-art decoding accuracy and the constraints of real-time neurofeedback, providing a template for researchers wanting to build closed-loop perceptual or clinical neurofeedback paradigms.

The paper identifies a conservation law for 'epidemic momentum' (prevalence weighted by remaining infection potential) in standard compartmental epidemic models, which provides an additional constraint beyond the usual growth-rate fitting and thus allows separate identification of R0 and the pre-epidemic susceptible fraction x⁻ from R_eff = R0·x⁻. Validation is performed on stochastic epidemic simulations before reanalyzing 1918 influenza time series, yielding independent estimates of transmissibility and prior immunity rather than assuming one. This offers epidemiologists a new inference method applicable to any single time series of case counts, potentially resolving longstanding ambiguity in retrospective R0 estimates for historical or emerging pathogens.

The authors present an automated independent component (IC) classification architecture for EEG artifact rejection, framing IC recognition as a computer-vision problem (likely operating on scalp topography maps and/or time-frequency representations of each component) rather than relying on manual expert review after ICA decomposition. The system aims to match manual classification accuracy while removing the human bottleneck, enabling large-scale EEG studies and near-real-time processing pipelines. Practitioners doing EEG-based cognitive or clinical research could integrate this as a drop-in automated artifact-rejection stage in their existing ICA preprocessing pipeline.

Using spatial transcriptomics combined with immunohistochemistry and functional assays, the authors show that mid-zone hepatocytes — the zone with peak acetaminophen (APAP) metabolism — exhibit the most pronounced transient proliferation arrest during early APAP-induced liver injury, driven by an Atf4-Chop stress-response axis that upregulates the cell-cycle inhibitor Btg2. Pericentral zone evidence for the same arrest was comparatively weak, indicating zonation-specific stress signaling rather than a uniform liver-wide response. This establishes a testable model where hepatocytes transiently trade proliferation for stress adaptation post-injury, giving hepatotoxicity researchers a specific pathway (Atf4-Chop-Btg2) to target or knock out in mouse models to test whether blocking this arrest accelerates or worsens recovery.

The study quantifies D-limonene's antifungal potency against Fusarium oxysporum (causal agent of potato dry rot) with an IC50 of 8.32 µL/mL, and shows dose-dependent effects on hyphal morphology, pathogenicity, spore germination/viability, and chitin staining patterns (via calcofluor white/fluorescent brightener 28), indicating cell-wall disruption. RNA-seq identified 1,884 differentially expressed genes (1,027 down, 857 up) with KEGG pathway enrichment mapping the transcriptional stress response to D-limonene exposure. This gives postharvest pathology researchers a quantified dose-response benchmark and a candidate gene/pathway list to pursue mechanistic follow-up (e.g., cell-wall or membrane-integrity pathway knockouts) or to formulate D-limonene-based biofungicide treatments for stored potatoes.

Bilateral neonatal lesions of the marmoset medial pulvinar altered adolescent prefrontal diffusion MRI trajectories and produced adult working memory deficits absent after equivalent adult-onset lesions, establishing developmental-timing specificity. Early lesions reduced thalamocortical input onto layer 3 parvalbumin (PV) interneurons, lowered prefrontal gamma oscillatory power, decreased PV expression, and left fast-spiking interneurons in an immature electrophysiological state. The findings implicate thalamocortical input as a driver of PV interneuron maturation and gamma-band inhibitory circuit function, offering a primate developmental model for testing interventions that target this critical window in schizophrenia-relevant circuitry.

The authors model striatal projection neurons, which use two distinct calcium sources with separate thresholds to gate LTP versus LTD, and show via metaplasticity (activity-dependent threshold modification) that synapses exposed to co-occurring LTP- and LTD-inducing calcium signals during learning resolve to expressing only one plasticity form. Using linear and nonlinear feature binding problem (FBP/NFBP) tasks — chosen because overlapping input features force competing calcium signals onto shared synapses — they identify complementary, opposing roles for the two thresholds in disambiguating plasticity outcomes. The work provides a mechanistic, simulation-based account of how dual-threshold calcium systems avoid destructive interference during associative learning, offering testable predictions for threshold dynamics in striatal circuits.

Using Bayesian hierarchical psychometric modeling, the study jointly estimated threshold (sensitivity) and slope (precision) parameters for cold detection, warm detection, cold pain, and heat pain in 75 healthy adults (21-80 years) and 33 patients with diabetic polyneuropathy (DPN), with per-participant estimates feeding into classification analyses. Aging alone shifted thresholds upward for cold detection, warm detection, and cold pain, while precision measures provided additional discriminative information beyond thresholds. The approach demonstrates that psychometric slope, not just threshold, adds classification value for distinguishing DPN from normal aging, suggesting sensitivity-only assessments in clinical thermosensory testing may be systematically underpowered.

The study shows that electroconvulsive stimulation (ECS) in mice reliably triggers cortical spreading depression (CSD), evidenced by neuronal oscillation signatures matching those observed in ECT patients, challenging the conventional model that generalized seizure alone drives ECT efficacy. CSD induction was associated with upregulation of the immediate early gene Fos, a canonical marker of activity-dependent plasticity, alongside molecular factors correlated with positive clinical ECT outcomes. This links a specific, mechanistically tractable cortical phenomenon (CSD) to ECT's plasticity-inducing effects, opening a path to dissect CSD's causal contribution separately from seizure activity and potentially optimize stimulation protocols around CSD induction.

Using subdivision-selective excitotoxic lesions, anterograde tracing, laminar activity mapping, and projection-specific optogenetics targeting medial-central (MDmc) versus lateral (MDl) mediodorsal thalamus terminals in the anterior cingulate cortex (ACC) and prelimbic cortex (PrL), the authors dissociate sensory-discriminative from affective-motivational pain processing. Both MDmc and MDl lesions produced mechanical and thermal hypersensitivity, but only MDmc lesions increased pain-related avoidance behavior, implicating MDmc-ACC/PrL circuitry specifically in the affective-motivational dimension of pain. This provides a circuit-level dissociation within a thalamic nucleus previously treated as functionally uniform, giving a template for pathway-specific optogenetic dissection of sensory versus affective pain components that could inform selective analgesic targeting.

Using in vitro and in vivo imaging, the authors demonstrate that microglia make transient membrane contact with neuronal processes at sites where proteolytic (lysosome-related) organelles too large for retrograde axonal transport are stationed, and excise a small membrane-bound fragment containing the organelle without damaging the remaining process — a mechanism they term skoupocytosis. The phosphatidylserine lipase ABHD16a accumulates at these microglia-neuron contact sites, implicating phosphatidylserine externalization as an eat-me signal analogous to other phagocytic pruning processes. This identifies a previously uncharacterized trans-cellular proteostasis mechanism, suggesting microglial dysfunction could contribute to neuronal proteolytic organelle accumulation in aging or neurodegenerative disease, and nominates ABHD16a as a candidate molecular handle for further mechanistic and disease-model studies.

Using molecular dynamics simulations combined with experimental binding assays, the authors characterize how five disease-associated missense mutations in the Shank1 PDZ domain — including its unique flexible β2-β3 loop — redistribute sidechain-sidechain contact networks to alter peptide-specific binding affinities. While most mutants broadly weaken interactions with partner peptides, the R736Q variant uniquely increases thermal stability and paradoxically enhances binding affinity for the GKAP peptide relative to wild type, demonstrating that binding effects are not uniformly destabilizing but partner-dependent. This structural-dynamics framework — resolving which specific sidechain contacts shift per mutation — offers a template for predicting how other PDZ domain disease mutations will differentially affect distinct postsynaptic protein interaction networks, relevant to autism and cancer-associated Shank dysregulation.

Through meta-analysis of RNA-seq data from HGPS patient skin biopsies, the authors identified dysregulated expression of genes encoding Anaphase Promoting Complex (APC) subunits and substrates, an E3 ubiquitin ligase previously linked to cellular aging when its activity declines. Pharmacological stimulation of APC activity decreased progerin levels via ubiquitin-dependent autophagic degradation, directly implicating APC as a mediator of progerin clearance rather than merely a correlated aging marker. This establishes APC activation as a candidate therapeutic strategy for HGPS and provides a mechanistic link between a core cell-cycle ubiquitin ligase and clearance of a lamin-derived proteotoxic species, a route researchers could pursue with APC activators or by mapping which specific APC substrates/subunits are rate-limiting for progerin turnover.

NDUFV2P1 is a processed pseudogene of NDUFV2, a core subunit of mitochondrial Complex I, and its expression is elevated in SZ patient brain and peripheral tissue, inversely correlating with NDUFV2 levels and respiratory function in EBV-transformed lymphoblastoid cell lines. In-silico analysis ruled out siRNA-like sequence complementarity as the interference mechanism, pointing instead to another post-transcriptional mode of action (e.g., competitive RNA-binding or ceRNA-like sequestration). The authors directly manipulated PG abundance (overexpression/knockdown) in LCLs to establish causality on NDUFV2 expression, Complex I-dependent respiration, and downstream neuronal activity readouts. This positions PG as a testable post-transcriptional regulator and a candidate node for modeling SZ-associated mitochondrial dysfunction, replicable via CRISPR-based PG modulation in iPSC-derived neurons.

Building on RT-specific Sox17 conditional knockouts that show Sertoli valve (SV) disruption and RT-fluid backflow causing defective spermiogenesis, the authors generated an AMH promoter-driven Sox17 transgenic mouse to ectopically express SOX17 in Sertoli cells rather than its native rete testis epithelium. This gain-of-function model produced hyperplastic SV formation, directly implicating SOX17 dosage/localization as instructive (not merely permissive) for SV morphogenesis. Phenotyping of spermatogenesis and valve architecture in AMH-Sox17 Tg testes complements the cKO loss-of-function data, together framing SOX17 as a key transcriptional determinant of a fluid-flow-control structure at the RT-tubule interface. This dual loss/gain approach offers a template for dissecting non-cell-autonomous SOX17 signaling targets in future mechanistic studies.

Using in vivo allelic replacement of the protamine-like gene Mst77F in Drosophila melanogaster, the authors show that substituting orthologous protamine sequences disrupts DNA compaction specifically in X-bearing (versus Y-bearing) spermatids, reducing X-sperm viability and skewing progeny sex ratio toward males — a signature of meiotic drive. Comparative analysis with D. yakuba shows Mst77F is dispensable for baseline male fertility there but remains required to suppress sex-ratio distortion, decoupling its fertility function from its drive-suppression function. This supports a model where recurrent intragenomic conflict over chromosome-specific chromatin compaction, rather than canonical sperm-competition or fertility pressures, drives protamine's unusually fast molecular evolution. The allelic-swap paradigm is directly extendable to other rapidly evolving protamines/spermatid chromatin proteins to test drive-suppression hypotheses genus-wide.

The authors performed a cross-taxonomic comparative analysis of age-specific reproductive effort across plants, animals, and humans, normalizing reproductive timing to species-specific maximum lifespan, and found peak effort consistently clustering near t/T_max ≈ 1/e (~37%), independent of absolute lifespan or phylogenetic group. They connect this empirical convergence to Bruss's 1984 optimal stopping theory (the 1/e-law of best choice), which proves that observing 1/e of a sequential option pool before committing maximizes the probability of selecting the best option under uncertainty. The paper integrates this stopping-rule framework with population dynamics models (balancing mortality risk against fecundity/size-dependent benefit) to argue the reproductive timing pattern is a life-history analog of optimal sequential decision-making. This offers a testable quantitative null model — 1/e scaling — against which species-specific deviations in reproductive scheduling (e.g., due to extrinsic mortality or resource variance) can be benchmarked in future life-history datasets.

Leveraging phased long-read genome assemblies from the Asian Pan-Genome Project (320 individuals, 6,312 centromeres) integrated with HPRC and HGSVC assemblies, the authors assembled a gapless, multidimensional variation map spanning 8,000+ complete human centromeres — a region historically intractable for reference-based short-read approaches. They quantify that centromeric alpha-satellite arrays constitute 4.19–6.01% of the genome with substantial size/architecture variation across chromosomes, and apply a refined alpha-satellite clustering method to resolve higher-order repeat structure and population stratification patterns. This dataset enables downstream analyses of centromere evolutionary trajectories, satellite array expansion/contraction dynamics, and genotype-phenotype studies of centromere-associated chromosomal instability. The assemblies and variation catalog are positioned as a reusable reference resource for pangenome-scale centromere research, analogous to what HPRC provided for euchromatic regions.

The authors combine microfluidic ultrahigh-throughput screening (uHTS) with generative ML to engineer substrate specificity into an unspecific peroxygenase (AbrUPO) from Aspergillus brasiliensis, expressed in a Komagataella phaffii (Pichia pastoris) library of >5 million variants. Droplet-based sorting generated a training set of >30,000 unique sequence-function pairs from indirect, lower-fidelity assay readouts, which were used to build a generative model 'fingerprint' capturing sequence-specificity relationships for the target enzyme class. The approach demonstrates that noisy, high-throughput functional data — rather than requiring precise biochemical characterization per variant — can sufficiently constrain generative sequence design for enzyme engineering. This uHTS+generative-ML pipeline is a template replicable for other enzyme classes where uHTS assays exist but produce only low-fidelity or indirect functional signals.

STAR Suite extends the STAR aligner codebase (28,228 lines) with 132,226 additional lines of C/C++, built largely through human-directed AI-assisted software engineering, into a dependency-free single executable covering adapter trimming, feature-barcode assignment, 10x Flex probe processing, SLAM-seq analysis, sorting, and QC — filling the gap left by Cell Ranger's restrictive license (no redistribution, modification, or non-10x use) and the lack of a production-ready open-source Flex pipeline. By consolidating the full transcriptomics processing chain into one integrated binary, it removes the tool-chaining fragility that impedes both bench biologists and AI agents automating sequencing analysis, improving reproducibility and AI-discoverability of the pipeline. Practitioners can adopt it as a drop-in open alternative for 10x-style single-cell/Flex data processing, and its single-executable, no-external-dependency design makes it straightforward to containerize or wrap for agent-driven automated analysis workflows.

ESCRT-III normally drives reverse-topology membrane fission (budding away from the cytoplasm) in vivo, yet in minimal in vitro reconstitutions it assembles on the outside of membrane necks and produces normal-topology fission — a longstanding discrepancy. Using genetic perturbation of phospholipid asymmetry and sphingolipid homeostasis in budding yeast, the authors show ILV (intraluminal vesicle) formation becomes highly sensitive to membrane physical state without ESCRT-dependent trafficking being abolished outright, manifesting as inefficient cargo sorting and stalled endosomal intermediates. In vitro reconstitution and synthetic in vivo cargo systems demonstrate that asymmetric protein/lipid distribution across the bilayer alone is sufficient to dictate deformation direction, independent of any intrinsic topological bias in the ESCRT-III machinery. This reframes ESCRT directionality as an emergent property of bilayer asymmetry rather than protein-encoded, suggesting future mechanistic work should manipulate lipid scramblase/flippase activity and leaflet composition as primary variables in ESCRT fission assays.

The authors generated a species-level imprintome for Arabidopsis arenosa endosperm and integrated it with gene regulatory network (GRN) inference to characterize regulatory relationships among paternally expressed genes (PEGs) and maternally expressed genes (MEGs). Imprinting was concentrated in a limited set of pathways enriched for dosage-sensitive processes, consistent with parental conflict theory, and several imprinted genes — notably the RNA Pol V subunit NRPE1 — emerged as regulatory hubs, implicating RNA-directed DNA methylation in imprinting maintenance. Network directionality was asymmetric: PEG-encoded regulators preferentially targeted other PEGs, whereas MEG-encoded regulators targeted both PEG and MEG targets without bias. This asymmetry provides a testable network-level signature of parental conflict and a candidate gene list (centered on NRPE1) for follow-up perturbation studies of endosperm dosage control.

The study tests chloroplast-targeted expression of Chlamydomonas reinhardtii glycolate dehydrogenase (CrGDH), normally mitochondrial, as the entry enzyme of a photorespiratory bypass, avoiding the multi-organelle trafficking required by prior bypass designs. Proof-of-concept was established in Chlamydomonas, then extended to stable transgenic tobacco lines confirmed by immunodetection to accumulate active CrGDH in chloroplasts. Photosynthetic rate and biomass measurements showed no improvement (and in some cases slight decrements) relative to wild type under tested conditions, indicating CrGDH expression alone is not rate-limiting for bypass efficacy. The result argues that a complete chloroplast-localized bypass (downstream enzymes converting glycolate to CO2-conserving intermediates in situ) is necessary, providing a design constraint for future single-compartment photorespiratory engineering efforts.

Using TurboID proximity labeling in planta during infection with tomato yellow leaf curl virus (TYLCV) and abutilon mosaic virus (AbMV), the authors mapped the host protein interactome surrounding the viral Rep initiator protein, which alone catalyzes strand-specific nicking/ligation for rolling-circle replication of the circular ssDNA genome. This approach captures transient and weak interactions missed by traditional co-IP, generating a candidate list of host DNA replication/repair factors recruited to the geminiviral replisome across two virus species for comparison. Overlap and divergence between TYLCV and AbMV interactomes can indicate core versus virus-specific host dependencies. The resulting host-factor map is directly actionable for reverse-genetics validation (e.g., CRISPR knockout/knockdown) to identify susceptibility genes as targets for engineering geminivirus-resistant crops.

Using APP-Swedish/PSEN1-M146V mutant brain organoids (BORGs) profiled longitudinally with bulk and spatial transcriptomics, the authors reconstructed developmental gene regulatory networks (GRNs) and identified 110 AD-specific candidate master transcription factors. Motif analysis found KLF5 and/or KLF8 binding sites enriched in the promoters of 75 of these TFs, nominating KLF5/KLF8 as upstream master regulators of the AD-associated transcriptional program. Cross-validation against human AD patient transcriptomic data confirmed significant overexpression of 64 of the 110 candidate TFs, supporting disease relevance beyond the organoid model. This GRN-based prioritization offers concrete, testable hub-TF candidates (starting with KLF5/KLF8) for perturbation studies aimed at modulating AD-associated transcriptional dysregulation independent of amyloid/tau-targeted approaches.

Using a novel integrative behavioral assay to dissociate social motivation from social fear/vigilance, the authors show that chronic juvenile social isolation in mice produces an anxiety-like, hypervigilant state that suppresses social motivation and increases social fear/hesitancy, rather than simple social indifference. Intersectional, projection-specific circuit manipulations combined with in vivo calcium imaging identified the adBNST (anterodorsal bed nucleus of the stria terminalis, an extended amygdala structure) to nucleus accumbens (NAc) projection as necessary and sufficient for isolation-induced deficits in social motivation. This dissociates a specific anxiety-related circuit node from broader reward circuitry in mediating isolation's behavioral effects, giving researchers a defined projection-level target for optogenetic/chemogenetic follow-up and potential translational relevance to isolation- and anxiety-linked social withdrawal in humans.

Combining a naturalistic fMRI paradigm (viewing/recall of a multi-storyline TV show) with independently collected behavioral cause-effect judgments, the authors show that causal relationships outperform other predictors (e.g., temporal proximity) in explaining recall transition patterns between events. Multivariate pattern analysis of default mode network (DMN) activity revealed that across-event neural patterns encode causal structure, and critically, DMN activity at event boundaries shows reinstatement of patterns from prior causally-related (but not merely temporally adjacent) events. Causal network distance between successive events further predicted neural pattern similarity, linking graph-theoretic causal structure directly to neural representational geometry. This provides a mechanistic account — causally-triggered pattern reinstatement at boundaries — for how narrative causal structure gets encoded into episodic memory, and a paradigm replicable with other multi-thread narrative stimuli and causal-annotation protocols.

Longitudinal single-neuron tracking of mouse medial prefrontal cortex across months of rule-switching in an association task showed that learning stabilizes the temporal activity profile (when neurons fire within a trial) of individual neurons, while their functional selectivity (what task variable they encode) continues to change even after temporal stabilization. The authors developed Sparse Tensor Component Analysis (STCA) to decompose population activity and demonstrate that this apparent instability is not random drift but structured, rule-dependent recombination of a small, fixed set of latent task representations across neurons. This decouples 'when' from 'what' at the single-neuron level and offers a reusable computational framework (STCA) for distinguishing genuine representational drift from systematic recombination in other longitudinally-tracked neural datasets. The findings support a model where PFC provides a stable temporal scaffold that supports flexible, combinatorial task representation rather than fixed neuron-to-variable mappings.

The authors analyzed intracranial local field potentials from 1,652 electrode sites across 36 epilepsy patients performing a naturalistic card-matching working memory task, examining phase-amplitude coupling (PAC) and phase-phase coupling (PPC) across recognition, encoding, and retrieval conditions. Frontal and temporal regions emerged as recurrent, condition-dependent connectivity hubs, with event-related coupling dynamics reproducible across participants. Directed phase transfer entropy identified frequency-specific directional information flow, notably frontal cortex acting as a theta-band source and alpha-band sink, indicating frequency-multiplexed feedforward/feedback signaling. A time-delay-embedded hidden Markov model further extracted task-locked latent network states with condition-dependent occupancy dynamics, giving practitioners a validated multi-method (PAC/PPC + transfer entropy + HMM) pipeline for characterizing distributed oscillatory coordination from large-scale iEEG datasets.

Using conditional Dlx5/6 inactivation in GABAergic neurons, the authors combined transcriptomics, histology, ex vivo electrophysiology, and in vivo EEG to show Dlx5/6 govern a PNN-homeostasis gene program in adult cortex. Loss of Dlx5/6 dysregulated multiple PNN-associated transcripts and caused region-specific PNN mesh remodeling in prefrontal and somatosensory cortex, paralleled by altered excitatory/inhibitory synaptic organization around PV interneurons. This links a known GABAergic transcription factor pair to adult PNN maintenance and network stability, offering candidate targets/genes for studying PV-circuit dysfunction in psychiatric models.

Building on prior findings of slow AMPA receptor kinetics in CA1 pyramidal cells with mosaic, region-specific distribution (Pampaloni et al., 2021, 2022), the authors identify the auxiliary subunit CNIH3 as a molecular signature enriched where slow AMPA responses predominate, notably ventral over dorsal CA1. This provides a genetic/molecular handle for isolating and manipulating slow AMPA receptor populations experimentally (e.g., via CNIH3 knockout or overexpression) to test their functional role in synaptic integration and dendritic computation.

In gene-edited human iPSC-derived neurons, KIF1A loss impaired axonal trafficking of ATG9, the transmembrane lipid scramblase required for autophagosome nucleation, reducing autophagosome biogenesis and axonal autophagosome density. KIF1A loss also depleted axonal lysosomes, blocking autophagosome maturation, and a heterozygous pathogenic KAND variant linked to Rett-like phenotypes reproduced aspects of this deficit. This establishes autophagic cargo trafficking as a distinct KIF1A-dependent pathway beyond synaptic vesicle transport, giving KAND researchers a new mechanistic axis (ATG9/lysosome delivery) to test therapeutically.

Using a novel self-timed movement task requiring high temporal precision without external go-cues, the authors applied brief, precisely timed optogenetic photoinhibition pulses to dissect the causal chain of movement initiation. Results show rapid, sequential recruitment of neurons in prefrontal cortex followed by lateral cerebellum is causally necessary for triggering the movement, implicating a cortico-cerebellar relay rather than a single locus. This provides a template (timed optogenetic silencing plus self-paced behavioral tasks) for mapping causal sequences in other endogenously-driven behaviors.

Meta-analysis of 2,098 SpCas9 target sites across plants, animals, and an alga showed deletion signatures track cell-division state rather than taxonomy: dividing cells favor Polymerase Theta-mediated end joining (TMEJ), while non-dividing cells predominantly use a previously underappreciated pathway, 5' Complementarity-Mediated End Joining (5'CMEJ). Unlike classical resection-dependent pathways that rely on 3' overhangs, 5'CMEJ exploits 5' overhangs from SpCas9's staggered cleavage geometry. This reframes CRISPR outcome prediction models to incorporate cell-cycle/division status as a primary variable, particularly relevant for editing quiescent plant tissues or post-mitotic cells.

Excess S-palmitoylation at the Golgi was shown to generate multilamellar, filipin-poor membrane whorls that aberrantly recruit ARF, Rab, and Rho family GTPases normally restricted to distinct organelles, while excluding native Golgi transmembrane proteins, coat proteins, ER proteins, and a GPI-anchored protein — indicating selective recognition rather than nonspecific aggregation. Blocking ARF6 myristoylation or Rab11a geranylgeranylation strongly reduced recruitment, while prenylation alone was insufficient, pointing to a composite lipid-modification code required for whorl targeting. This identifies membrane lipid state itself as an organizing input for GTPase localization, useful for dissecting lipidation-dependent trafficking signals experimentally.

Genome-wide ChIP profiling showed Klf9 occupancy is enriched at metabolic gene promoters during cardiac hypertrophy, and Klf9 levels normally fall as hypertrophy progresses. Using conditional Klf9 knock-in mice, forced restoration of Klf9 during 1-2 weeks of pressure overload initially suppressed hypertrophic growth but produced metabolic maladaptation and accelerated onset of heart failure. This establishes Klf9 downregulation as a required adaptive step for compensatory hypertrophy, positioning Klf9 as a potential node for studying the switch between compensated hypertrophy and metabolic decompensation.

The study maps a centrosome-targeting region within the C-terminal helicase domain of JEV NS3 that drives association of viral replication structures with host microtubule-organizing centers (MTOCs). Ectopic expression of this NS3 fragment alone produced pericentriolar aggresomes mimicking the distribution of helicase-containing viroplasm seen in actual infection, and centriole depletion assays confirmed a proviral, functional role for the centrosome. This defines a druggable viral-host interaction interface (the helicase's centrosome-targeting motif) as a candidate target for antiviral strategies against JEV and potentially related flaviviruses.

The team applied label-free quantitative LC-MS/MS proteomics and phosphoproteomics to human trophoblast stem cells (hTSCs) to catalog protein and phosphosite abundance across the CTB stem state and its EVT/STB differentiated derivatives. By integrating these proteomic datasets with genomic/transcriptomic data, they nominate protein kinases whose expression or phosphorylation state tracks lineage decisions. This generates a candidate kinase list for functional follow-up (e.g., inhibitor or knockdown studies) to test necessity in CTB self-renewal versus EVT/STB differentiation. The dataset itself is a resource for mining kinase-substrate signaling networks active during early placental development.

The authors engineered an accelerated-turnover beta-catenin chromobody for high-temporal-resolution live imaging, avoiding the lag artifacts of stable fluorescent fusions, and applied it to Xenopus pronephric (kidney) development. Time-lapse imaging across nephrogenesis stages resolved beta-catenin partitioning among nuclear, cytoplasmic, and adherens-junction pools, revealing a developmental shift in localization that correlates with progenitor renewal versus differentiation and patterning events downstream of Wnt signaling. This provides a quantitative, in vivo readout distinguishing beta-catenin's transcriptional co-activator function from its structural cadherin-linked role, a tool that could be adapted to other Wnt-dependent organogenesis systems to test causal links between subcellular localization and cell fate decisions.

Using lineage-specific RNAi against Kruppel (Kr) and an existing Kr mutant allele (KrIf-1), the authors show that Kr is required cell-autonomously in mushroom body neuroblasts (MBNBs) to drive their pupal-stage cell cycle exit and elimination, since its knockdown or loss-of-function prolongs MBNB survival and permits neurogenesis into adulthood. Critically, Kr acts at low expression levels specifically during the pupal window, and temporally restricted depletion or misexpression at that stage is sufficient to alter MBNB retention, indicating a discrete, stage-specific checkpoint function distinct from its known embryonic patterning roles. This establishes Kr as a lineage-restricted terminator of a defined stem cell pool, offering a tractable genetic entry point for dissecting the transcriptional programs that limit adult neurogenesis in a well-characterized learning/memory circuit.

The study used semi-captive grey partridges under contrasted feeding treatments to isolate the contribution of diet to phyto-pharmaceutical product (PPP) body burden, measuring residual pesticide contamination profiles despite manipulated food intake. Contrary to the ingestion-centric exposure model, contamination remained high across feeding treatments, indicating that dietary control alone does not explain observed residue levels and that non-dietary exposure pathways (e.g., dermal, inhalation, or environmental matrix contact) likely contribute substantially. This challenges risk assessment frameworks that model non-target wildlife PPP exposure primarily through food-chain ingestion, suggesting multi-route exposure models are needed for accurate agroecosystem contamination assessments in farmland bird species.

The authors performed a literature-based synthesis, compiling occurrence records for 57 invasive alien plant (IAP) species reported in abandoned croplands across China, and modeled study-level recorded species richness with generalized linear mixed models (GLMMs) using province as a random effect to account for spatial non-independence. Fourteen candidate socio-economic and environmental predictors were screened for multicollinearity, standardized, and entered into the models, including an unconventional proxy (count of Higher Education Institutions) for regional research/survey intensity, addressing a known confound in meta-analyses of invasion records. The approach is explicitly exploratory given heterogeneity in underlying study designs, but it offers a reusable framework and predictor set for researchers building national-scale invasion risk maps or prioritizing abandoned-cropland monitoring in other regions.

Using three years of field observations of wild Bombus griseocollis and B. impatiens nests, the authors quantified colony phenology milestones, nest-searching onset, peak worker activity, first gyne production, and colony senescence, via nest traffic monitoring, and tested for density-dependent colony growth as predicted by classic life-history/optimal-reproductive-timing models. The two co-occurring species show contrasting phenological schedules, which the authors interpret as reflecting divergent underlying life-history strategies (e.g., differing growth trajectories or resource-allocation timing) rather than simply differing responses to identical environmental cues. This provides an empirical, multi-year dataset linking colony-level demographic trajectories to phenological output, useful for parameterizing bumble bee phenology models under climate change scenarios or for comparative life-history analyses across social insect taxa.

The authors generated a chromosome-scale genome assembly of Aegilops speltoides, resolving 398 Mb of B-chromosome sequence, and performed comparative transcriptome profiling across seven tissue types spanning elimination-active, elimination-negative, and B-chromosome-nondisjunction states. Differential expression analysis identified 3,262 genes consistently upregulated specifically in elimination-associated tissues, notably including cohesin pathway components and additional B-chromosome-encoded genes, implicating altered sister-chromatid cohesion regulation as a candidate mechanism for the tissue-restricted, root-specific loss of the B chromosome. This dataset and assembly provide a genomic resource for functional validation (e.g., candidate gene knockdown or cohesin perturbation) to test causality in programmed chromosome elimination, and offer a comparative reference point for elimination mechanisms studied in other plant and animal systems.

The authors performed a high-throughput screen of a 5,631-compound drug-repurposing library using a Bmal1-luciferase transcriptional reporter in NIH3T3 fibroblasts to identify small molecules that increase circadian oscillation amplitude without altering period or phase. Eight hit compounds were validated as dose-dependent amplitude enhancers sustained across multiple circadian cycles, distinguishing them mechanistically from classical period-modulating clock drugs (e.g., CK1 or CRY modulators). The team further assessed physiological relevance of these hits, presumably extending beyond the peripheral fibroblast reporter system toward central clock or in vivo contexts, positioning these compounds as chemical starting points for target deconvolution and for therapeutic development in conditions linked to circadian dampening, such as metabolic syndrome or neurodegeneration.

The authors validate ITC as a quantitative tool for resolving coupled carbamate formation, protonation, carbonate speciation, and hydration equilibria in aqueous amino-acid/peptide CO2 capture systems. Global fitting of ITC thermograms for L-lysine, L-arginine, and Lys/Arg-containing peptides yields thermodynamic parameters that independently reproduce pH titration curves and NMR-derived speciation, cross-validating the mechanistic model. Key finding: the characteristic biphasic calorimetric signal arises from a coupled carbonate-amine equilibrium rather than sequential independent steps, a mechanistic detail future capture-solvent design work can build on directly.

This is a longitudinal multi-omics (proteomic + metabolomic) study of urinary extracellular vesicles (uEVs) from 81 sepsis patients (48 S-AKI, 33 sepsis-only), split into discovery (n=52) and validation (n=29) cohorts, sampled at Days 1, 4, and 8. High-resolution profiling of uEV cargo is used to map temporal molecular trajectories distinguishing S-AKI pathogenesis from general sepsis, aiming to identify mechanism-linked biomarkers rather than markers of dysfunction alone. Replication in an independent cohort strengthens the case for specific early proteo-metabolomic signatures as candidate diagnostics, a foundation others could test against additional cohorts or turn into a targeted panel assay.

Using fluorescent tracer perfusion to demarcate high-flow (HF) versus low-flow (LF) regions in trabecular meshwork tissue from human donors and C57Bl/6J mice, the authors performed spatial whole-transcriptome profiling on sagittal sections, followed by differential expression and gene set variation analysis (GSVA) to compare HF vs LF pathway activity, with immunolabeling validation of select targets. The approach identifies segmental transcriptomic signatures linked to aqueous humor outflow regulation and intraocular pressure control across species. This cross-species spatial dataset offers a resource for identifying region-specific drug targets or biomarkers relevant to glaucoma therapeutics.

The paper models bilayer MoS2 where one layer is Janus (MoSSe), introducing an out-of-plane dipole that breaks mirror symmetry and reduces interfacial friction/lattice deformation energy. This promotes atomic reconstruction into locally distorted, aperiodic moiré domains rather than uniform commensurate stacking, weakening interlayer coupling and phonon transmission. The result is an anomalously strong, non-monotonic twist-angle dependence of interlayer thermal conductance (with a pronounced minimum) compared to non-Janus bilayers. This suggests Janus heterostructures as a design principle for twist-angle-tunable thermal management in van der Waals devices.

Using glancing-angle-deposited MAPbI3 nanowalls with strong structural alignment, the authors combine temperature-dependent PL, XRD, and polarization-resolved ultrafast transient absorption spectroscopy to probe exciton-lattice coupling across the ferroelastic orthorhombic phase transition. They resolve a polarization-selective exciton splitting (45 meV) at 5 K with orthogonal optical selection rules, indicating that ferroelastic domain formation lifts exciton degeneracy rather than merely broadening the PL linewidth. The splitting evolves near ~160 K, tracking the phase transition, providing direct spectroscopic evidence that ferroelastic symmetry breaking modifies excitonic fine structure — relevant for strain-engineering optoelectronic response in soft halide perovskites.

The authors theoretically model a WS2/WSe2/WS2 heterotrilayer where twist-angle mismatch between the two moiré interfaces produces a supermoiré superlattice with periodic regions of favorable vertical atomic registry. Within these registry pockets, coupling between confined interlayer exciton levels at the top and bottom moiré interfaces hybridizes into symmetric (bright) and antisymmetric (dark) quadrupolar exciton states, tunable via an applied electric field. This provides a mechanism to robustly realize multipolar excitons despite unavoidable twist-angle disorder, suggesting an experimentally accessible route via trilayer TMD stacking and gate control to engineer multi-level, field-tunable exciton complexes for quantum photonic or correlated-exciton studies.

The paper develops a general theoretical framework for extreme first-passage times among N interacting Brownian searchers, addressing the breakdown of probability factorization that normally enables noninteracting extreme-statistics analysis. A no-go theorem establishes that broad classes of bounded interactions cannot beat the standard 1/ln N extreme-time scaling of independent searchers, with matching upper bounds proving this scaling is tight for broad repulsive-interaction classes. The authors identify two interaction mechanisms that escape the logarithmic regime, including a deterministic pairwise-interaction case achieving order-1/N extreme search time, a qualitatively faster scaling law that practitioners modeling reaction kinetics or search-and-capture dynamics can test against or incorporate into more realistic interacting-particle models.

This review covers quasiparticle interference (QPI) analysis via scanning tunneling microscopy/spectroscopy as a probe of low-energy electronic structure in quantum materials, contrasting it with ARPES (now reaching ~2 meV resolution but restricted to occupied states and zero field). QPI extracts momentum-space scattering information from real-space standing-wave patterns formed by quasiparticle interference off impurities/defects, via Fourier analysis of STM conductance maps, enabling access to unoccupied states and operation under applied magnetic fields, advantages relevant for studying superconducting gaps, electronic instabilities, and correlation-driven orders. It surveys methodology and case studies, serving as a reference for applying QPI to identify order parameters, Fermi surface topology, and scattering channels in candidate quantum materials.

The team achieved catalyst-free CVD growth of layered 1T-CrS2, a van der Waals ferromagnet with out-of-plane easy-axis anisotropy and Curie temperature above 300K. Transport measurements reveal a semimetal-insulator crossover near 80K, negative magnetoresistance up to 350K, and a topological Hall effect below 30K — evidence of correlation-driven, momentum-dependent Berry curvature effects rare in TMD ferromagnets. DFT calculations show spin-orbit coupling gaps Dirac-like band crossings while correlations suppress electron pockets and reconstruct the Fermi surface, offering a concrete materials platform for spintronics and topological transport research.

The author proposes a two-parallel-conduction-channel model for temperature-dependent resistivity ρ(T) in actinides, combining a standard Bloch-Grüneisen term (electron-phonon scattering) with a second channel capturing additional scattering mechanisms specific to actinide electronic structure (e.g., strong correlation or 5f-electron effects). The model is fit against publicly available experimental ρ(T) data spanning eight elemental actinide phases from Th to Cm, achieving accurate fits where ab initio first-principles calculations fail even qualitatively. This offers a lightweight, parameterized alternative for practitioners needing to model or extrapolate actinide transport properties without expensive DFT+DMFT-type calculations.

The model uses Dissipative Particle Dynamics (a coarse-grained, momentum-conserving simulation method) combined with a Gillespie-type stochastic algorithm to let crosslinks between EPS polymers and bacteria form and break dynamically, rather than being fixed, capturing the biofilm network's non-equilibrium turnover. The key finding is that emergent biofilm structure results from competition between polymer-polymer and polymer-bacteria crosslinking, tunable via binding energy, linker concentration, and bond stiffness parameters. This provides a minimal, extensible mesoscale framework that others can build on for rheological (flow/deformation) predictions of biofilm mechanics under shear or stress.

VIA replaces the softmax's independence assumption in standard attention with an interacting Ising model, computing attention weights via learnable pairwise couplings resolved through variational mean-field inference, so tokens/items influence each other collectively rather than being scored in isolation. This is motivated by scientific tasks (unlike industrial long-context NLP) where richer structured coupling outweighs the need for sparsity/efficiency. They validate on retrosynthesis reaction-center prediction, a domain governed by cooperative bond-breaking constraints, reporting gains from the method — a technique other researchers could adapt to any structured scientific prediction task with interacting entities (e.g., molecular graphs, particle systems).

The paper studies a mean-field glass model with an equilibrium zero-temperature RSB transition, characterized in the replica-symmetric phase by an effective self-consistent random potential, with the RSB transition marked by spectral-edge localized modes and a pseudogapped quartic vibrational spectrum yielding finite spin-glass susceptibility. The authors define a micromechanical response function (the linear response to local force monopoles, an approach inspired by computational studies of structural glasses) and derive exact relations connecting this local response to the model's RSB and non-equilibrium quench properties. This bridges abstract mean-field glass theory with micromechanical observables computable in numerical glass simulations, offering a route to test mean-field predictions against particle-based glass simulations.

The authors derive an analytical theory for flash temperature generation in viscoelastic (rubber) solids sliding against rigid, randomly rough surfaces, explicitly incorporating the full roughness power spectrum across all relevant length scales rather than a single-asperity approximation. This extends prior contact-mechanics/friction heating models by coupling multiscale roughness statistics to viscoelastic dissipation and resulting local temperature rise. Practitioners in tribology or tire/rubber engineering could apply the closed-form theory to predict local heating and its effects on friction coefficients and wear directly from measured surface roughness spectra.

The authors couple first-principles molecular dynamics (FPMD) with UMA, a universal machine-learned interatomic potential, to extend accessible simulation timescales beyond FPMD's few-tens-of-picoseconds limit for GaN(0001) MOVPE surface chemistry, validating UMA energetics against single-point DFT on FPMD trajectory snapshots. The key finding is a new diffusion pathway for a GaNH admolecule coexisting with H adatoms: the N atom lifts the bonded Ga atom off the surface plane during migration, and the elevated Ga subsequently abstracts a surface H adatom — a mechanism invisible to static DFT energy landscapes that only sample local minima. This establishes a workflow (MLIP-accelerated dynamics with FPMD spot-validation) that others can apply to identify kinetically hidden adspecies pathways relevant to epitaxial growth mechanisms and reactor-scale kinetic Monte Carlo parameterization.

The team synthesized Cs2AgBi0.5Fe0.5Br6 double-perovskite single crystals via modified hydrothermal growth and characterized them with optical spectroscopy and X-ray structural analysis alongside undoped Cs2AgBiBr6. Both compositions retain a structural phase transition near 125 K, but Fe incorporation simultaneously narrows the band gap in the high-temperature phase and suppresses the cryogenic structural instability associated with the transition — decoupling two effects usually studied separately. This positions Fe-doping as a dual-purpose defect-engineering handle for double perovskites, useful for anyone optimizing lead-free halide perovskites for low-temperature optoelectronic or spintronic applications where both absorption range and cryogenic phase stability matter.

The authors present a multiconfigurational mixed quantum-classical (MQC) method that treats the excitonic subsystem with a many-body wavefunction beyond mean-field while phonons evolve quasi-classically, targeting finite-temperature correlated multi-exciton transport under both static and dynamic disorder. Applied to a dissipative multi-exciton system, the method reveals excitation-density-dependent transport driven by the interplay of phonon-induced dynamic disorder and exciton-exciton interactions. Critically, mean-field (single-configuration) MQC approaches reproduce diffusive transport rates semi-quantitatively but fail to capture spatial exciton-exciton correlations, indicating that multiconfigurational treatment is necessary whenever correlated (not just averaged) exciton dynamics is the target observable — relevant to modeling exciton transport in molecular aggregates, quantum dots, and organic semiconductors.

The authors model kinetoplast DNA (kDNA) — a topologically interlinked 2D network of thousands of circular DNA minicircles in trypanosomatid mitochondria — as a naturally occurring realization of a 2D 'Olympic gel,' and apply polymerized-membrane theory to its conformational and dynamical properties. They show hydrodynamic interactions (solvent-mediated coupling between distant network segments) are essential to correctly predict the dynamical scaling of kDNA in aqueous solution, and this scaling framework unifies previously disparate in vitro imaging data. The work also derives quantitative estimates (likely of relaxation times or diffusion exponents, per the truncated abstract) that could be tested against further single-molecule imaging, offering a rare experimental testbed for 2D polymerized-membrane and Olympic-gel theory.

Using density-functional-theory-based first-principles calculations, the authors compute phonon dispersions and frequency-dependent dielectric/optical response for B-doped, Y-doped, and (B,Y)-co-doped α-Fe2O3, linking dynamical (vibrational) stability to optical behavior within a single framework. They report finite-temperature vibrational thermodynamics alongside the dielectric function to disentangle lattice-dynamics contributions from electronic contributions to the optical spectra under each doping scheme. This gives a practitioner a mechanistic, dopant-resolved map (mono- vs co-doping) of how substitutional B/Y modify hematite's phonon stability and light-matter coupling, directly usable for selecting dopant strategies in hematite-based photoanodes or photonic coatings.

The authors measure transient friction of rectangular rubber blocks sliding on tile and glass lubricated with fluids spanning three orders of magnitude in viscosity (water, glycerol, mud, silicone grease), varying stationary waiting time and block length in the sliding direction. Low-viscosity water is removed almost entirely by stationary squeeze-out before sliding onset, glycerol shows a mixed regime of squeeze-out plus sliding-induced removal, while mud and grease reach steady-state friction after a sliding distance scaling with block length, independent of prior waiting time — evidence that dynamic scraping, not squeeze-out, dominates for high-viscosity contaminants. Segmenting the contact into shorter blocks (mentioned as an additional manipulation) lets the authors isolate scraping length-scale dependence, giving tribologists a viscosity-dependent design rule for contact geometry in wet-friction applications like tire tread or shoe sole patterning.

A coordinated DIII-D tokamak campaign used the DiMES sample-exposure system to comparatively test 44 plasma-facing material candidates from 12 institutions (including four public-private fusion partnerships) under Ohmic, L-mode, and H-mode (with ELMs) discharges, spanning heat fluxes of 0.2-2.5 MW/m² on flush geometries and 10-15 MW/m² on 10°-angled geometries. Engineered tungsten architectures maintained structural integrity, with long-fiber-reinforced Wf/W composites showing the clearest crack-arrest behavior; W-Re and K-doped W tracked near ITER-grade W performance, while additively manufactured W-Ta showed heat-flux-sensitive erosion (0.64 mg flat vs. 2.19-2.87 mg angled). Neutron-irradiated ITER-grade W (0.3 dpa, 550°C) retained a reported fraction of baseline performance, giving the fusion materials community a standardized, cross-institution benchmark dataset for down-selecting divertor/wall materials for pilot plants.

Using first-principles (DFT) calculations, the authors show that charge compensation among coexisting defects in Y-stabilized cubic ZrO2 (YSZ) raises the Fermi level E_F during flash sintering (FS), which in turn accelerates cation (Zr) migration for fast densification. Specifically, the Zr vacancy's diffusion barrier in its -4 charge state (V_Zr^-4, favored once E_F rises during flash) is reduced by 2 eV relative to the neutral V_Zr^0 state present before flash. The Y_Zr substituent (released when it anneals out a V_O-Y_Zr-Y_Zr defect complex) acts as an electron acceptor favoring V_Zr^0 pre-flash, while excess thermally generated V_O acts as an electron donor that raises E_F at FS onset — together mapping a defect-charge-transfer mechanism that practitioners can use to predict or engineer FS onset conditions and diffusion kinetics via defect chemistry (dopant concentration, atmosphere) rather than temperature/field alone.

Fanpy 2.0 is a Python framework built on the Flexible Ansatz for N-electron CI (FANCI) formalism, letting developers map arbitrary wavefunction expressions to executable code with minimal boilerplate. This release adds coupled-cluster-inspired geminal (electron-pair) ansätze, a results-analysis module, a redesigned PySCF interface, and a new PyCI backend to offload expensive tensor contractions. It also ships CI/CD infrastructure (automated testing, issue tracking) supporting collaborative extension. Practitioners can implement and benchmark novel electronic structure methods by subclassing existing wavefunction objects rather than writing solvers from scratch.

The authors quantitatively measure the direct piezoelectric response (mechanical-to-electrical) in shear mode for ferroelectric nematic liquid crystals RM734 and DIO, which had previously only been characterized via the converse effect. Combining periodic shear-induced current measurements with oscillatory rheology across temperature, frequency, and strain, they extract first- and second-harmonic signals to compute shear-mode piezoelectric coupling constants. The extracted coefficients are consistent between the two chemically distinct materials and are benchmarked against prior converse-effect measurements, establishing a self-consistent piezoelectric tensor for this new materials class. This provides a reference dataset and methodology for characterizing soft-matter piezoelectrics for flexible sensor/actuator applications.

The authors extend quorum-sensing active matter models by introducing competing (attractive-then-repulsive) sensing ranges, which qualitatively alters the standard motility-induced phase separation (MIPS) outcome into a microphase-separated state with an emergent, finite modulation length scale. Starting from microscopic Langevin/Fokker-Planck dynamics, they derive a coarse-grained field theory whose gradient-expansion coefficients map directly onto moments of the microscopic sensing kernel, enabling quantitative prediction of correlation and modulation lengths from particle-level parameters. 2D simulations validate these continuum predictions and map a transition from macroscopic phase separation to microphase patterning as the competing-range parameter is tuned. This gives a concrete, testable field-theory framework connecting microbial sensing kernels to emergent spatial patterning, useful for modeling real bacterial colony structure.

The authors propose a mesoscopic equilibrium theory in which weak magnetic fields perturb the equilibrium occupation probabilities of a channel's metastable conducting/non-conducting states, rather than directly altering ion trajectories, and this redistribution propagates into changes in nonequilibrium transport current. This framework unifies previously contradictory reports of magnetic enhancement and suppression of transmembrane currents under one mechanism, and derives a universal sign-reversal criterion expressed via a single equilibrium covariance term between state occupation and conductance. The theory yields specific testable signatures (e.g., dependence on field strength/orientation relative to state energetics) that experimentalists could use to validate or falsify the model with patch-clamp-type measurements.

The authors analytically derive intruder dynamics in a nonequilibrium (active) bath across two regimes: a dilute kinetic regime, modeled via a Boltzmann-Lorentz equation reduced to an effective Langevin equation with coefficients given as explicit geometry-dependent boundary integrals, and a dense hydrodynamic regime governed by bath continuum modes. They show the dilute-regime response decomposes cleanly: chirality of the intruder-bath interaction produces odd (non-dissipative, rotation-coupled) response and net torque, while intruder shape chirality alone produces a ratchet effect; they also identify fluctuation-dissipation-like relations and symmetry-forbidden vanishing couplings. In the dense regime they argue torque-density-driven edge currents (not captured by dilute kinetics) dominate, setting up a companion hydrodynamic/Stokes-based treatment. This gives practitioners exact formulas to predict transport/rotation of chiral probes in active matter from geometry alone, useful for microrheology or self-assembly design.

The authors present and simulate an analytically tractable model of an arbitrarily-shaped intruder in a nonequilibrium bath with tunable chirality sourced from the bath, intruder shape, or their coupling. In the dilute limit, a Langevin description derived from the Boltzmann-Lorentz equation demonstrates how intruder geometry sets ratchet effects and odd (antisymmetric) response tensors; in the dense limit, intruder dynamics are governed by bath hydrodynamic modes and edge currents captured via a modified Stokes equation with a chiral torque-density source term. The work explicitly links particle shape to the emergence of odd transport coefficients and shows the underlying mechanism differs qualitatively between dilute and dense regimes, providing a validated numerical testbed for the analytical predictions in the companion kinetic/hydrodynamic theory paper.

The paper introduces a stoichiometric ionic-cluster representation — non-periodic, formula-unit-preserving clusters — as input to pretrained machine-learned interatomic potentials (MLIPs), bypassing the need for full crystal structure prediction for multi-ionic integrated explosives (MIXs). They apply multi-task fine-tuning (MT-FT) that adapts a pretrained atomistic backbone on a sparse detonation-velocity property label while retaining the original energy-force objective as physical regularization, mitigating overfitting in the low-data (few-shot) regime. This enables pre-synthesis property screening of novel multi-ionic candidates using only stoichiometry-preserving cluster inputs, a practical route for materials designers to prioritize synthesis targets without expensive periodic DFT or exhaustive crystal-structure search.

Using first-principles DFT combined with the Boltzmann transport equation, the authors compute electronic and thermoelectric properties of LiCdSb, employing the HSE06 hybrid functional for accurate band-gap and band-structure estimation critical to electron-transport calculations. To sidestep the high computational cost of standard lattice thermal conductivity (κl) calculations (which typically require expensive anharmonic phonon calculations), they train machine-learning interatomic potentials (MLIPs) to approximate the underlying potential energy surface for phonon transport. They report κl = 0.24 W/m·K at room temperature, a low value favorable for thermoelectric figure-of-merit (ZT), and validate this against qualitative expectations from the DFT-based framework, providing a template for MLIP-accelerated κl screening in other candidate thermoelectric compounds.

Using continuum simulations of active nematics plus an analytical reduced near-wall description, the authors show that boundary alignment at rigid, thermodynamically neutral walls is set by activity-generated flow rather than imposed anchoring conditions. In the flow-tumbling regime, extensile systems align parallel and contractile systems align perpendicular to the wall, mirroring 'active anchoring' seen at active-passive interfaces; in the flow-aligning regime, alignment depends on the sign of activity and the flow-aligning parameter, sometimes selecting the Leslie angle via near-wall shear and sometimes yielding no stable orientation. This gives a predictive, activity-parameter-based framework practitioners can apply to design or interpret confinement effects in active nematic systems.

Path integral centroid molecular dynamics simulations of helium-4 (0.1-3.3 K, 1-60 bar) with atomic exchange suppressed reveal two distinguishable-particle liquid states: a low quantum-dispersion liquid (LQDL) obeying the Stokes-Einstein relation like a conventional liquid, and a high quantum-dispersion liquid (HQDL) emerging at lower temperature that shows superdiffusion, anomalously low viscosity, and a breakdown of Stokes-Einstein scaling. The counterintuitive gas-like transport on cooling is attributed to nuclear quantum fluctuations (zero-point motion) overtaking thermal fluctuations as the dominant driver of dynamics. Researchers studying quantum liquids or path-integral methods can use this LQDL/HQDL framework and the CMD protocol to probe transport crossovers decoupled from exchange-driven superfluidity.

The authors demonstrate standing-wave photoemission electron microscopy (SW-PEEM) for Angstrom-scale depth profiling of monolayer WS2 grown on a W/C multilayer mirror, tuning the X-ray standing wave node/antinode position through the film to modulate sensitivity to top versus bottom sulfur sublattices. Combined with X-ray optical modeling of the field distribution, they resolve an ~0.2 eV shift in sulfur-derived valence-band spectral weight between top- and bottom-sensitive measurements. Calculations support that this shift arises from surface-adsorbed sulfur-related species rather than strong substrate hybridization, giving a method other groups can adopt to non-destructively map sublayer chemistry and band structure in 2D heterostructures.

Using extraordinary Hall effect measurements in air versus 4% H2/N2, the authors map hydrogen-induced hysteresis evolution across CoxPd100-x alloys and [Co/Pd]15 multilayers with varying Co content and Pd spacer thickness. They show the hydrogen response sign and magnitude are non-universal, governed by competition between Pd-mediated electronic structure changes (from hydrogen-induced lattice expansion and d-band filling) and magnetoelastic anisotropy effects; Pd-rich alloys and thicker Co/Pd stacks show initial loop contraction that can reverse with further hydrogen loading. This composition/thickness-dependent phase diagram gives device engineers a tuning knob for designing hydrogen-sensitive magnetic sensors with a specified sign and sensitivity of response.

VecTree-RAG is an agentic RAG pipeline that decouples corpus-level paper/section retrieval (via vector search over compact document/section embeddings) from within-document evidence localization (via reasoning-guided traversal of a source-verified section tree), deferring full-text access to a page store that's exposed only after structural localization narrows the search space. This avoids the fixed-length chunking that flattens document hierarchy and severs claims from their supporting methodology/argument context in conventional RAG. Evaluated on 300 QASPER questions (an open scientific QA benchmark), the architecture is a template practitioners can replicate for domains with strong document structure (papers, technical reports, legal filings) where hierarchical evidence localization outperforms flat chunk similarity.

The authors implement the J-integral within the three-dimensional quasicontinuum (QC3D) multiscale method under plane-strain assumptions, deriving continuum stress and strain-energy-density fields from the underlying interatomic potential via the Cauchy-Born rule, and validate the result against linear elastic fracture mechanics (LEFM) predictions and the virtual crack extension (VCE) method across small-strain linear elasticity and other regimes, including uncertainty quantification. This closes a gap in multiscale fracture modeling by giving QC3D users a validated energy-release-rate diagnostic, letting practitioners couple atomistic crack-tip resolution with continuum-scale J-integral-based fracture criteria for materials design and failure prediction.

Through first-principles electronic-structure calculations paired with optical spectroscopy, the authors show that valence-band p-d hybridization between transition-metal d-orbitals and ligand p-orbitals governs optical coherence in chalcopyrite semiconductor quantum dots. Strong p-d hybridization in CuInS2 opens a Cu(d)-mediated Coulomb scattering channel that generates incoherent photodoped hole carriers and degrades spectral coherence, whereas weaker hybridization in the Ag-based analog preserves coherence. This mechanistic link between hybridization strength and decoherence gives materials designers a compositional design rule (choice of transition metal) for engineering chalcopyrite quantum dots with improved coherent optical properties for quantum information applications.

AEcroscopyWave is a characterization platform that exposes microscopy/imaging hardware through APIs for programmatic control, layering agentic AI on top to enable autonomous, closed-loop experimentation rather than either rigid industrial automation or manual operator-driven workflows. The described contribution is architectural: unified hardware control interfaces plus AI-driven decision-making integrated into the measurement loop, aimed at combining industrial-throughput automation with research-grade exploratory capability. Practitioners building self-driving lab infrastructure could look to this API-plus-agent integration pattern as a template for wrapping their own characterization instruments (e.g., electron or scanning probe microscopes) for autonomous AI control.

This is a code-review update to CP2K, extending its previously documented Gaussian-and-plane-wave (GPW) electronic-structure framework to cover dynamics, transport, and spectroscopic response calculations. It highlights CP2K's integration of structural/transition-state optimizers with sampling methods (Monte Carlo, molecular dynamics, metadynamics) atop a hierarchy of force evaluators spanning classical, machine-learned, semiempirical, mixed QM/MM, and full quantum-mechanical methods. Practitioners can use this as a reference for selecting appropriate energy/force backends and sampling schemes for rare-event exploration of free-energy landscapes. The paper builds directly on the 2020 JCP code review, so readers familiar with that baseline can treat this as the delta covering newer dynamical and response capabilities.

The study benchmarks machine-learned force fields (MLFFs) trained on different DFT exchange-correlation functionals against experimental water properties, using the full 6D pair correlation function, three-body structural descriptors, excess entropy, and transport coefficients as metrics. Functionals lacking dispersion corrections show pronounced overstructuring, overly negative excess entropy, and suppressed self-diffusion, while RPBE-D3 gives the most consistent agreement across structural, thermodynamic, and transport observables; translational/orientational entropy contributions correlate tightly with reduced self-diffusion. The classical SPC/E empirical model is used as a reference point and tracks RPBE-D3 surprisingly closely. Practitioners selecting a functional for MLFF training on aqueous systems can use this as evidence to prefer dispersion-inclusive functionals like RPBE-D3 over dispersion-free alternatives.

Using particle-based simulations of colloidal gels, the authors dissect bond-level contributions to bulk mechanical response, classifying bonds by their topological role in the network and tracking their participation across both the rigidity-percolation (fluid-to-solid) and yielding (solid-to-fluid) transitions. They find that singly-connected bridges linking mesoscale clusters disproportionately support rigidity and elastic modulus, and that this same bond class governs failure during yielding, suggesting a structurally unified — not merely dynamically analogous — pathway between the two transitions. This gives practitioners a concrete, bond-topology-based diagnostic (rather than purely dynamical measures) for predicting gel strength and failure onset from structural snapshots. The approach could be extended to bidisperse or polydisperse experimental gel systems to test whether the same bridge-bond criterion predicts real yielding thresholds.

The authors extend FDTD (finite-difference time-domain) electromagnetic simulation with a perturbative framework to compute spatially-resolved ultrafast pump-probe nonlinear microscopy signals for strongly-coupled light-matter (polariton) systems in arbitrary nanophotonic geometries, moving beyond single-mode Tavis-Cummings descriptions. Applied to a distributed Bragg reflector (DBR) cavity, the method first reproduces known multimode Tavis-Cummings polariton transport results, then reveals guided modes below the light line — normally excluded from single-mode-family analyses — that support additional transport channels. Exploiting this full modal landscape, they design compact mode converters that couple radiative cavity polaritons into these guided/photonic channels, suggesting a route to engineered ultrafast polariton routing. Practitioners in nanophotonics could adopt this FDTD+perturbative pipeline to predict and design pump-probe microscopy signatures for their own cavity geometries prior to fabrication.

ViBra implements a hybrid quantum-classical workflow for anharmonic Vibrational Configuration Interaction (VCI), starting from a quartic force field and combining Vibrational Self-Consistent Field (VSCF) reference states with VCI run in Full, Selected, or quantum-sampled configuration-space variants. The quantum component uses sampling algorithms (analogous to quantum-selected CI approaches used in electronic structure) to generate or prioritize the configuration space fed into the classical VCI solver, addressing the combinatorial blow-up of vibrational configuration spaces for larger anharmonic systems. This is presented as one of the first concrete computational workflows demonstrating quantum-centric methods applied to vibrational (rather than purely electronic) structure problems, executable on current noisy intermediate-scale quantum (NISQ) hardware. Researchers building quantum-chemistry pipelines could adapt this VSCF+VCI hybridization pattern as a template for other property-prediction tasks beyond vibrational spectroscopy.

The authors present a general construction for exactly flat-band tight-binding Hamiltonians defined on the faces of arbitrary graphs, demonstrating the algorithm explicitly on four Bravais lattice geometries. They prove that the resulting macroscopic ground-state degeneracies (compact localised states) are topologically protected by the local connectivity of the face-graph, via a discrete graph-theoretic analogue of the Atiyah-Singer index theorem, rather than requiring fine-tuned hopping parameters. This generalizes known flat-band constructions (e.g., kagome, Lieb lattices) into a systematic, provably robust family applicable to arbitrary graph topologies, relevant for engineering flat bands in moiré and metamaterial platforms. Condensed-matter theorists could use this index-theorem-based recipe to predict and design new flat-band lattice geometries as candidate hosts for strongly-correlated or fractional quantum phases before attempting synthesis.

The study applies machine learning attribution and producer-transfer testing to 476 CEM I cement records from 23 European producers (27 years, single testing lab), combining oxide chemistry, Blaine fineness, particle-size distribution (PSD) descriptors, physical properties, and derived Bogue/equivalent-alkali descriptors as features against strength-class and water-demand targets. Fineness (Blaine and compact PSD representations, found largely interchangeable) emerges as the strongest single descriptor family, but oxide chemistry contributes comparable signal when features are evaluated jointly rather than individually. The producer-transfer tests probe whether models trained on some producers generalize to held-out producers, directly addressing transferability of routine QC data across manufacturing sites. Materials scientists or QC engineers could use this feature-importance ranking to prioritize which routine measurements to invest in, or to build cross-producer strength-prediction models from standard plant QC data.

The authors demonstrate a defect-free mechanism for reversible, light-induced halide-ion exchange in 2D mixed-halide hybrid perovskites, showing via nudged elastic band (NEB) and photo-force calculations that strong light-lattice coupling — not defects — drives the effect. Photo-induced forces perform non-equilibrium work on the lattice that pushes halide ions partway along the exchange coordinate, but this work is insufficient to fully overcome the ground-state activation barrier, so the swap remains incomplete and reversible upon removal of light. Lattice dynamics analysis identifies a small set of soft phonon modes (some IR-active) responsible for mediating this coupling. This gives materials designers a concrete computational handle (NEB + photo-force + phonon mode analysis) for identifying or engineering other halide perovskite compositions with tunable, non-destructive photo-switching behavior for optoelectronic applications.

The authors use a high-pressure sliding plate rheometer (HPSPR) to decouple the effects of short-chain branching (SCB), long-chain branching (LCB), and molecular weight polydispersity on the pressure coefficient of viscosity in polyethylene melts, across four structurally distinct samples measured under uniform shear and pressure up to >100 MPa. This directly addresses the inadequacy of atmospheric-pressure rheological characterization for process simulation of injection molding and extrusion. Practitioners in polymer processing could use the resulting structure-property correlations to select or design resins with predictable pressure-viscosity behavior, and to calibrate process simulation software with pressure-dependent viscosity models rather than extrapolated atmospheric data.

The authors develop a bilayer poroelastic model of a generic soft porous medium under pulsatile loading, tuning porosity, permeability, and P-wave modulus combinations across four layered configurations plus a homogeneous reference so all cases share an identical poroelastic timescale (T_PE), isolating the specific contribution of layering to nonlinear fluid flow and solute transport. This connects directly to the glymphatic/perivascular clearance hypothesis for brain metabolic waste removal, where pulsatile vascular motion is thought to drive interstitial solute transport. Researchers modeling brain clearance or engineering layered biomaterials (e.g., cartilage scaffolds) could adapt this framework to predict how layer-specific mechanical mismatches amplify or dampen solute transport relative to a homogeneous approximation.

Hyperspatial replica exchange (HS-REX) augments the physical configuration space with extra artificial spatial dimensions and runs a replica-exchange ladder over a confinement/extension parameter on those dimensions, letting the system route around free-energy barriers via paths inaccessible in the original coordinate space. Restricting the dimensional extension to solute atoms only (excluding solvent) sharply cuts the number of required replicas relative to standard temperature-REX for solvated systems. Benchmarks on a double-well toy model and alanine dipeptide in explicit water show enhanced sampling of slow backbone dihedral transitions; practitioners could implement this as a drop-in alternative to T-REX or Hamiltonian REX in existing MD engines when solvent overhead makes temperature-based replica ladders too expensive.

The study pairs cascade-overlap molecular dynamics with an accelerated Iterative Kinetic Approach (IKA) to simulate defect evolution in single-crystal aluminum under 50 keV He irradiation, validating that IKA reproduces MD-level defect kinetics while reaching far higher cumulative damage doses than direct cascade simulation allows. It identifies three sequential degradation regimes — recombination-driven annihilation, defect accumulation, and sink-controlled absorption — and shows Frank loops dissociating into Shockley partials and stair-rod loops that nucleate stacking-fault tetrahedra at high damage. This gives reactor materials researchers a validated accelerated-MD pipeline and a damage-regime map that could be used to predict long-term embrittlement or swelling in Al-based reactor components without needing prohibitively long brute-force simulations.

The authors theoretically demonstrate that stacking a ferromagnet (FM) monolayer with an altermagnet (AM) monolayer induces non-relativistic (exchange-driven, not spin-orbit-driven) valley polarization in the AM via magnetic proximity coupling, and show this effect is general across such FM/AM heterostructures. By independently tuning the FM's magnetization direction and the AM's Néel vector orientation, four distinct, independently addressable valley-polarized states emerge from strong magnetic-valley coupling. This provides a concrete materials-design route for valleytronic memory/logic elements, and could be built on by identifying specific FM/AM material pairs and computing their band structures via DFT to confirm the predicted valley splitting magnitudes.

The authors extend Gaussian process regression for free-energy profile reconstruction into a generalized hierarchical Bayesian framework that treats hyperparameters and observation noise as inferred quantities rather than fixed inputs, allowing predictive uncertainty to adapt to the actual information content of simulation data (e.g., from umbrella sampling). This addresses a known limitation of standard GP-based free-energy estimators, which underestimate uncertainty in poorly sampled regions when hyperparameters are conditioned rather than marginalized. Practitioners running enhanced-sampling free energy calculations could adopt this to get calibrated, data-adaptive error bars on free energy profiles, improving decisions about where additional sampling is needed and reducing false confidence in undersampled regions.

GrapheneOS ships features like a configurable auto-wipe after N failed authentication attempts and duress PINs, which can trigger during a border device search under the US border-search exception (where CBP claims authority to search devices without a warrant or individualized suspicion). This case reportedly tests whether triggering such a built-in security feature — intentionally or not — can be prosecuted (e.g., as obstruction or evidence destruction), raising unresolved legal questions about liability for standard security software behavior during compelled searches. Practitioners following digital-rights law should watch for the charging theory used and any resulting precedent on self-protective device features at the border.

The piece engages with the ongoing push to replace per-site cookie consent banners with a standardized browser- or OS-level signal (in the spirit of Global Privacy Control) that legally satisfies consent requirements once, rather than per-domain. This requires both a technical signal specification/adoption by browsers and regulatory recognition that such a signal constitutes valid consent under frameworks like GDPR/ePrivacy Directive. For builders, the relevant lever is advocating for or implementing GPC-style headers/APIs and pushing regulators to formally recognize them as compliant, removing the need for per-site banner UI entirely.

Android's ADB interface exposes shell-level access for app installation, debugging, and system inspection over USB or Wi-Fi debugging; it has been a recurring vector for malware sideloading and unauthorized device control when left enabled. Reports suggest Google is considering tightening default restrictions on on-device ADB — likely additional authentication gating, narrower default scope, or requiring explicit developer-mode opt-in per session. Developers and the custom-ROM/rooting community should watch for how this affects existing workflows (e.g., scripted deployment, wireless debugging pipelines) and whether enterprise MDM exemptions are preserved.

PGSimCity appears to be an interactive visualization/simulation tool that maps PostgreSQL internals (storage layout, indexing, query execution, or similar subsystems) onto a SimCity-style city-building interface, letting users explore behavior visually rather than through documentation alone. This kind of tool is useful for building intuition around concepts like page/tuple storage, MVCC, or index structures by observing simulated cause-and-effect. Practitioners could use it as a teaching aid or extend it to visualize additional subsystems (WAL, vacuum, query planning) not yet covered.

The report (via a social media post) alleges AI companies are using destructive, spine-cutting scanning workflows to digitize books for training-data acquisition, applied even to rare or scarce editions, which by nature cannot be replaced once destroyed. Destructive scanning is faster and cheaper than non-destructive digitization (e.g., overhead book scanners or archival-grade imaging), which is likely the economic driver given LLM training's demand for high-volume, high-quality text corpora. As this stems from a single social post rather than a formal investigation, further verification (sourcing, scale, specific companies named) would be needed before treating the claim as established fact.

There's no real engineering claim here: a Game Boy's Sharp SM83 CPU has no TCP/IP stack, no display renderer capable of DOM/HTML, and no practical path to running a JS-attribute-driven library like htmx, which depends on a browser's DOM and fetch/XHR APIs. Read literally as parody of 'first to ship on unlikely hardware' announcements common in indie/retro-computing circles. If you enjoy the underlying gag, the closest real analogue is homebrew projects that reimplement tiny HTTP clients or terminal UIs for constrained retro hardware, not anything resembling htmx's actual architecture.

The app performs real-time general-relativistic raytracing/radiative transfer in-browser, tracing photon geodesics through a curved (presumably Kerr or Schwarzschild) spacetime metric to render gravitational lensing, and applies special-relativistic Doppler boosting to the accretion flow/jet emission based on viewing angle and local velocity. It runs cross-platform via WebXR, meaning any WebXR-capable device (Chrome on Android, standalone VR headsets) can place the render in physical space via AR/VR rather than just viewing it flat. Practitioners interested in real-time relativistic rendering could study this as a reference for optimizing geodesic integration for interactive frame rates, a nontrivial problem since traditional GRMHD raytracing codes are typically offline/batch rendered.

The piece addresses prompt/context design specifically calibrated to Claude 5-generation models' behavior — things like instruction-following priority, tool-use conventions, long-context attention patterns, and how much explicit scaffolding (chain-of-thought hints, few-shot examples, system prompt structure) is still beneficial versus redundant at this capability tier. This is directly applicable to anyone building agents, RAG pipelines, or tool-calling systems on the Claude API: the 'rules' likely cover system prompt organization, context window budgeting, and avoiding instruction conflicts that previously required workarounds on older models. Worth reading before rewriting existing prompt templates, since guidance tuned for earlier Claude generations can under- or over-specify context for the new models.

This tracks the status of migrating portions of Bun's implementation from Zig to Rust, presumably module by module given the scale of a production JS runtime (parser, bundler, HTTP server, native APIs). Key things a practitioner would want to know: which subsystems have been ported, how interop between remaining Zig code and new Rust code is handled (FFI boundaries, shared memory layout, build tooling changes), and whether the switch has yielded measurable wins in memory safety, compile times, or contributor velocity. Anyone maintaining native bindings, plugins, or forks of Bun should watch this closely, since API surface or internal ABI changes during a rewrite of this scope can have downstream compatibility implications.

Pyrocumulonimbus (pyroCb) formation requires a fire-driven convective plume energetic enough to reach the free troposphere and trigger ice-phase cloud microphysics, effectively coupling fire dynamics with mesoscale storm dynamics. Once formed, pyroCbs can inject smoke aerosols directly into the stratosphere, produce erratic and fire-generated winds, and generate dry lightning that ignites new spot fires far from the perimeter. This being a first-observed case in France suggests fire intensity/fuel-load/atmospheric-instability thresholds previously rare in the region are now being crossed, likely tied to prolonged drought and heat. Practitioners in fire management and atmospheric science could use satellite pyroCb detection (e.g., via GOES/Himawari smoke-plume height retrievals) and updated fire-behavior models incorporating plume-driven feedback to improve early warning for these events.

The improvements likely target common forensic extraction vectors — USB-based exploit chains, cold-boot or RAM-remnant attacks, and vulnerabilities in the lock-screen authentication path — that tools like Cellebrite and GrayKey rely on. GrapheneOS's typical hardening approach includes stricter USB peripheral restrictions when locked (e.g., disabling data-transfer modes), memory-safe reimplementations of vulnerable stock components, and tighter integration between hardware-backed key storage and the lock state so encryption keys are less exposed while the device is locked but not fully powered off. For developers or security researchers, this is a good case study in reducing the practical gap between 'encrypted at rest' and 'actually resistant to extraction while running,' and the changes are open source and auditable in the GrapheneOS codebase.

A 'position' statement on open-weights models typically covers policy on release criteria — factors like model capability tier, potential for misuse (e.g., bio/cyber uplift risk), and downstream monitorability — alongside licensing terms (e.g., permissive vs. restrictive commercial-use clauses) and post-release support commitments. Practically, such statements often distinguish between releasing weights outright versus other openness levers like publishing training methodology, evals, or model cards without the weights themselves. For practitioners, the substantive content to look for is whether it specifies concrete thresholds (compute, benchmark scores, red-teaming results) that trigger a closed vs. open decision, since that's what determines whether future frontier-adjacent models from this lab are likely to be locally deployable and fine-tunable.

The Kubernetes analogy points to a pattern where a technology transitions from vendor-differentiated competition to a shared, commoditized substrate, with competitive advantage shifting up the stack to tooling, fine-tuning, deployment, and integration rather than the core artifact itself. Applied to open-weight AI, this suggests architectures and base models (akin to what Kubernetes did for orchestration APIs) are converging enough that ecosystems — fine-tuning frameworks, quantization tooling, serving infrastructure, eval suites — become the primary differentiators and business opportunities rather than the base weights. Practitioners can read this as a signal to invest in the surrounding tooling layer (deployment, orchestration, fine-tuning pipelines for open-weight models like Llama/Mistral/Qwen-class releases) rather than assuming proprietary model access is a durable moat, mirroring how companies built businesses on Kubernetes distributions rather than competing with Kubernetes itself.

The colon (:) is a POSIX shell builtin that is a true no-op: it takes any arguments, still performs shell expansions on them, and always exits with status 0, all without forking a process. This makes it useful for patterns like `: ${VAR:=default}` (forcing parameter-expansion side effects without running a command), `while :; do ... done` for cheap infinite loops, stubbing out unimplemented functions, and `: <<'EOF' ... EOF` heredocs as a poor-man's block comment. Because it's a builtin rather than an external binary like `/bin/true`, it avoids fork/exec overhead, which matters in tight script loops.

Decker is an open-source, cross-platform reimplementation of HyperCard's stack-and-card model: cards hold widgets (buttons, fields, images) that are wired together with an embedded, Lisp-derived scripting language, and everything renders to a fixed low-resolution 1-bit black-and-white canvas mirroring classic Mac OS visuals. Stacks are distributed as single portable files, letting users build and share small interactive tools, games, or hypertext documents without a conventional build toolchain. The linked creator interview offers design rationale useful to anyone looking to extend the scripting language, add new widget types, or port the renderer.

This is the technical report accompanying the Kimi-K3 model release, with weights also published on Hugging Face. Technical reports of this kind typically cover architecture choices (e.g., mixture-of-experts scaling, context length, training data composition), post-training methods (RLHF/RLAIF or similar alignment steps), and benchmark results versus contemporary frontier models. Practitioners would look to this report for reproducible details on training recipe and evaluation methodology, and to the Hugging Face repo for direct model access and fine-tuning. Given the sparse abstract provided, specific claims about scale, architecture, or benchmark scores can't be confirmed here — consult the PDF directly for those numbers.

Ruff v0.16.0 substantially expands its default-enabled rule set from 59 to 413 lints, consolidating coverage that previously required opt-in configuration or separate plugins (mirroring tools like flake8, pyupgrade, isort, and pydocstyle). Because Ruff is implemented in Rust and reimplements these checks natively rather than shelling out to multiple Python-based linters, this expansion delivers the added coverage without the typical multi-tool performance penalty. Teams upgrading should expect a spike in newly surfaced findings and will likely want to review the changelog to selectively disable rules inconsistent with their style before enforcing the new defaults in CI. This positions Ruff as an increasingly comprehensive one-stop replacement for the fragmented Python linting/formatting toolchain.

Wero is built by the European Payments Initiative (EPI), a consortium of major eurozone banks, and rides on the SEPA Instant Credit Transfer rail rather than card-network rails, giving merchants lower interchange-style fees and near real-time settlement. Decathlon's integration adds Wero as a checkout method on decathlon.de, likely via a payment-service-provider plugin that handles the QR/app-redirect flow and callback confirmation. For developers, merchant Wero integration typically means adding a new payment-method adapter alongside existing PSPs (Adyen, Mollie, etc.) that support the EPI API. Large-retailer adoption like this is a bellwether for whether Wero can reach the checkout ubiquity needed to compete with PayPal in DACH markets.

The analysis likely draws on labor statistics — job postings, unemployment claims by sector, wage data — to distinguish AI-attributable displacement from ordinary business-cycle churn, a methodological challenge given confounding factors like interest-rate-driven layoffs and post-pandemic hiring corrections. Expect discussion of which occupational categories (customer service, junior coding, content writing) show measurable task automation versus headline job-count loss, since the two are often conflated in press coverage. A rigorous version of this argument would use difference-in-differences or matched-industry comparisons to isolate AI's marginal effect from macroeconomic noise. Practitioners tracking this space should look to primary sources like BLS/OECD data and company-level disclosures rather than survey-based "X% of executives plan to use AI" reports, which tend to overstate realized impact.

The system is a robotic valet parking (RVP) platform — automated guided vehicles (AGVs) that slide under a parked car, use load sensors and a scanning rig to capture chassis dimensions, then lift and transport the vehicle to a slot in a high-density rack or lot, similar to systems from vendors like Stanley Robotics or Serva Transport. Because no human maneuvering clearance is needed, these systems typically claim 30-50% higher space utilization than conventional multi-story car parks. Operationally it requires a controlled drop-off bay, fleet-coordination software to sequence retrievals ahead of flight times, and integration with the airport's booking/ticketing system for time-based retrieval requests. This is one of a growing number of airport deployments validating robotic valet parking at commercial scale rather than as a pilot.

El Niño strength is typically quantified via the Oceanic Niño Index (ONI), a three-month running mean of sea-surface temperature anomalies in the Niño 3.4 region of the equatorial Pacific, with events above +2.0°C anomaly classified as "very strong." A record-breaking event would be benchmarked against the 1997-98 and 2015-16 super El Niños, the previous strength leaders in the satellite era. Researchers use this data to test whether anthropogenic warming is increasing El Niño amplitude or frequency, an open question in climate science given the high natural variability of ENSO against a rising baseline. Practitioners in climate modeling or agricultural risk forecasting would use the ONI trajectory and associated teleconnection patterns to update seasonal drought/flood forecasts for ENSO-sensitive regions like the Amazon, Southeast Asia, and the U.S. Gulf Coast.

The essay frames design artifacts as constraint-satisfaction outcomes rather than absolute quality judgments — every design sits on some Pareto frontier between competing objectives (learnability vs. power, consistency vs. context-sensitivity, performance vs. flexibility). It's a critique lens more than a method: practitioners can apply it by explicitly naming the axes being traded in design reviews and retrospectives, and by pushing back when stakeholders demand a design satisfy mutually exclusive requirements without acknowledging the cost.

Scriptc is an ahead-of-time TypeScript-to-native compiler that emits a self-contained binary without embedding a JS engine (no V8/JSC/QuickJS), implying its own compilation pipeline to machine code rather than JIT-executing JS semantics. This trades some of JavaScript's dynamic runtime behavior for AOT performance and much smaller binaries and faster startup, likely by targeting a statically-typed subset of TypeScript. Developers building CLIs, edge functions, or embedded services could use it to get native-binary distribution characteristics similar to Go or Rust while keeping a TS-like developer experience.

Bitchat, a Bluetooth-mesh offline messaging app, has published its repository on Radicle, a peer-to-peer Git-compatible code collaboration protocol that replicates repos across nodes rather than relying on a central host. This aligns the project's supply chain with its threat model: no platform like GitHub can take down, rate-limit, or censor access to the source. Contributors can clone and work with it via Radicle's CLI/node infrastructure using standard Git operations, with identity and collaboration backed by cryptographic keys rather than platform accounts — worth studying for any project prioritizing censorship resistance.

A court denied Google's attempt to use DMCA takedown or anti-circumvention provisions as grounds to block a scraper, reportedly finding the claim didn't fit DMCA's scope, which targets circumvention of technical protection measures and infringement of copyrighted expression rather than automated access to non-copyrightable structured data. This is relevant precedent in the ongoing scraping/AI-training legal landscape, where plaintiffs have tried CFAA, breach-of-contract, copyright, and DMCA theories against scrapers with mixed success — this ruling narrows DMCA's viability specifically. Practitioners tracking web-scraping legality for AI data pipelines should note this as a data point on how courts view DMCA claims against pure scraping.

Reporting indicates AI industry lobbying expenditures in Washington have hit record levels, consistent with a pattern where a maturing, high-stakes sector ramps up policy influence spending ahead of anticipated regulation — AI safety frameworks, copyright/training-data liability, export controls, antitrust scrutiny. Such spending typically funds direct lobbying, trade associations, think-tank funding, and campaign contributions, and correlates with legislative activity volume. Those tracking AI policy should cross-reference rising lobbying spend against specific bill activity to see where influence is being concentrated.

This Show HN project inverts standard Minesweeper mechanics — rather than deducing mine locations from revealed clues, the player works backward, constructing a mine layout consistent with given constraints, turning deduction into constraint satisfaction. Implementing this well requires generating puzzles that are guaranteed solvable and uniquely determined, a more involved constraint-generation/backtracking problem than Minesweeper's usual random-then-flood-fill generation, akin to designing valid Sudoku boards. It's a useful reference for anyone interested in puzzle generation, constraint solvers, or mechanic-inversion game design.

This introduction covers Data-Oriented Design, a methodology prioritizing memory layout and access patterns over object-modeling abstractions, typically favoring structure-of-arrays over array-of-structures layouts to maximize cache-line utilization and enable SIMD-friendly, branch-light processing loops. The core argument is that cache misses and poor data locality, not raw instruction count, dominate real-world performance in data-heavy systems, so transforms should be organized as passes over homogeneous, contiguous data rather than virtual-dispatch calls over heterogeneous objects. It's foundational for game-engine programmers and ECS (Entity-Component-System) architecture; practitioners can apply it directly by restructuring hot-path data into flat arrays keyed by component type wherever profiling shows cache-miss-bound bottlenecks.

The author built a custom hardware music-playback device end-to-end, picking up PCB design (schematic capture and layout, likely in a tool like KiCad), 3D printing for the enclosure, and embedded C firmware for a microcontroller handling audio, storage, and UI. It's a full-stack hardware project spanning circuit design, mechanical fabrication, and low-level firmware, offering a concrete on-ramp into embedded systems via a scoped, self-motivated build rather than abstract tutorials. Readers could replicate it by following the same path: schematic/layout in a free EDA tool, slicing and printing an enclosure, then writing bare-metal or RTOS-based C for audio decoding and playback control.

The writeup documents a migration away from a React SPA (single-page application) architecture toward Htmx, which achieves dynamic UI updates via HTML attributes that trigger AJAX-style requests and swap DOM fragments returned by the server, rather than maintaining client-side component state and a virtual DOM. This shifts rendering logic back to the server (often paired with templating), reducing client bundle size, eliminating build-step complexity (webpack/babel/JSX compilation), and simplifying state management since the server remains the source of truth. Practitioners considering this path should evaluate it for CRUD-heavy, mostly-server-rendered apps where the interactivity needed is largely 'update this part of the page,' rather than for apps requiring complex client-side state machines, offline behavior, or rich component reuse. It's a useful reference case for teams weighing HTMX/hypermedia-driven architectures against SPA frameworks.

The analysis centers on quantifying soiling losses, the percentage drop in photovoltaic output due to accumulated particulate matter, which field studies typically place in the low single digits annually for panels with reasonable tilt and periodic rainfall, though it can be much higher in arid, dusty, or low-rainfall regions. The tradeoff calculation compares the marginal kWh recovered by cleaning against water/labor/service costs and the panel's tilt-driven self-cleaning effect from rain runoff. For a rooftop residential array, breakeven often favors infrequent or no manual cleaning outside of high-soiling climates, whereas utility-scale desert installations may justify scheduled robotic or water-based cleaning due to higher absolute soiling rates and larger revenue impact per percentage point of output. Anyone evaluating their own system should check local soiling-loss data (available from solar monitoring apps or NREL-style tools) before committing to a cleaning schedule.

The claim centers on recent progress in interactive theorem provers (Lean, Coq, Isabelle) combined with automation tactics and, increasingly, LLM-driven proof search, which has moved formal verification from a painstaking manual process toward something closer to CI-style automated checking. Practically this means large portions of a formal proof, routine lemmas, arithmetic simplification, case splits, can now be discharged automatically via tactics like `simp`, `omega`, or ML-guided premise selection, with humans focusing effort on the genuinely novel proof steps. Practitioners can build on this by adopting Lean/Mathlib-style workflows for formally verified software components or mathematical results, and by experimenting with LLM-assisted tactic generation (e.g., systems that suggest or search for proof steps) to accelerate formalization projects that were previously too labor-intensive to attempt.

The argument is a critique of the common framing that AI-driven abstraction and delegation are inherently empowering, drawing a distinction between automation that removes truly irrelevant toil versus automation that removes the details a practitioner needs to retain competence, calibration, and error-detection ability. It implicitly engages with human-in-the-loop and automation-complacency research, the well-documented pattern (from aviation autopilot studies to code-review literature) where over-delegation degrades a person's ability to catch mistakes or adapt when the automated system fails or drifts out of distribution. For builders of AI tools, the practical takeaway is a design principle: interfaces should preserve enough visibility into the details, diffs, intermediate reasoning, or edge cases, that users retain situational awareness and skill, rather than optimizing purely for maximal hands-off delegation.

The go/analysis framework defines a standard `Analyzer` interface (Name, Doc, Run, Requires, ResultOf) that lets checks declare dependencies on other analyzers' facts and results, enabling a DAG-based execution model where shared work (like AST traversal or type-checking) isn't duplicated across passes. It underpins tools like `go vet`, staticcheck, and golangci-lint's plugin ecosystem, and supports both single-package and whole-program (SSA-based) analysis via companion packages like `golang.org/x/tools/go/analysis/passes` and `buildssa`. Practitioners can build custom linters by implementing the Analyzer interface, declaring `Requires` on existing passes (e.g., `inspect.Analyzer` for AST traversal), and packaging them for use with `multichecker` or integration into golangci-lint, making it the standard entry point for writing production Go static-analysis tooling.

MAI-Cyber-1-Flash is presented as a lightweight, low-latency variant in a security-specialized model family, positioned for integration into MDASH as an inference backend for security operations tasks such as alert triage, log summarization, or incident classification, where response speed and cost per query matter more than maximal reasoning depth. The 'Flash' naming convention typically signals a distilled or smaller-parameter model optimized for throughput and lower cost over a flagship model, trading off some accuracy or reasoning depth for real-time applicability in high-volume SOC (security operations center) pipelines. Practitioners integrating with MDASH should expect to evaluate this model specifically on latency-sensitive, high-volume classification/summarization tasks rather than complex multi-step threat-hunting reasoning, which would likely route to a larger sibling model in the same family.

The project (golfcoursebrowser.com) uses OpenStreetMap as its geospatial backbone for course boundaries and locations, layering a purpose-built browsing interface (search, filtering, scorecards) on top, since OSM's raw tag data on golf courses is present but not natively presented in a golfer-friendly UI. Data quality is maintained through a crowdsourced correction loop, users flag inaccuracies (bad info, missing courses, wrong scorecards) directly on course pages, which the maintainer manually triages and fixes, rather than relying on fully automated ingestion. It's currently US-focused with plans to expand internationally using the same OSM-derived pipeline; the underlying pattern, OSM data plus a niche vertical UI plus lightweight crowdsourced QA, is a replicable template for building directory products in other domains with rich but poorly-surfaced OSM tag data (e.g., parks, trails, or public facilities).

The essay frames 'focus and followthrough' as the emerging differentiator for agentic AI systems, moving beyond single-turn quality metrics toward evaluating sustained task execution: multi-step planning, tool use, self-correction, and goal persistence across long horizons without human re-prompting at every step. This maps to the broader shift in AI research and product design toward agent architectures with memory, planning loops, and verification/self-critique steps, as opposed to purely improving next-token prediction quality on isolated prompts. For builders, the practical implication is that evaluation and system design should shift from single-shot benchmark accuracy toward long-horizon task completion rates, failure recovery, and autonomy under ambiguity, since these are the properties that determine whether an agent can be trusted with real, extended-duration work.

The story reports a ~470% surge in a Chinese semiconductor company's share price, a magnitude typical of speculative rallies tied to domestic chip self-sufficiency narratives amid ongoing US export controls on advanced fabrication equipment and EDA tools. Such moves often reflect retail-investor momentum or state-linked capital flows rather than fundamentals like revenue or fab yield improvements. Practitioners tracking the semiconductor sector should treat single-day or single-week percentage moves like this as a sentiment indicator for China's chip self-sufficiency push, not a proxy for actual technical progress (e.g., EUV alternatives, node shrinkage). Worth cross-referencing with the company's actual product roadmap and any government subsidy announcements before drawing conclusions.

The piece maps a 'relay' ecosystem where intermediary services broker digital tokens — session tokens, OTP codes, API keys, or similar credentials — between originators and downstream buyers, some of whom use them for fraud (account takeover, OTP-bypass, credential stuffing at scale). These markets typically operate via Telegram channels, dedicated forums, or semi-automated bot storefronts, with pricing tiers based on token freshness and target value. For defenders, the actionable takeaway is that token/credential leakage doesn't stay siloed — once a token enters this relay layer it can be resold and reused across many fraud attempts, which argues for short token lifetimes, binding tokens to device/session fingerprints, and monitoring for anomalous reuse patterns rather than relying on token secrecy alone.

MouthPad is a retainer-form-factor human-computer interface that uses an array of touch/pressure sensors embedded against the palate to track tongue position and gestures, translating them into standard HID (Bluetooth mouse/trackpad) events consumed by any off-the-shelf OS. This sidesteps the need for custom driver support on the host device — it appears to the computer as a generic pointing device. The core engineering challenge it's solving is signal disambiguation (separating intentional tongue gestures from speech and swallowing) within a small, wearable, battery-constrained form factor, positioning it as an assistive-tech alternative to eye-tracking or sip-and-puff switches for users with limited hand mobility.

Libsm64 wraps logic recovered from the community decompilation of Super Mario 64 into a standalone C library exposing Mario's physics, animation, and collision routines through a clean API (init, tick, geometry upload) decoupled from the original N64 codebase's rendering and platform layers. Engines like Godot and Unity integrate it via bindings that feed level collision meshes into the library each frame and read back Mario's transformed pose for rendering, effectively getting bit-accurate SM64 movement mechanics inside a modern renderer. It's a practical template for anyone wanting to lift decompiled game logic into a portable, engine-agnostic module — the pattern (decompile → strip platform I/O → expose a tick/query API) generalizes to porting other decompiled titles.

The post is a personal/career-reflection essay from a departing Google DeepMind researcher, the kind of piece that typically surfaces firsthand detail on internal research priorities, the balance between fundamental research and product-driven deadlines, and organizational dynamics not visible from outside. Without the full text, the concrete claims can't be verified, but such essays are often read closely by the ML community as informal signals about lab culture, attrition trends, and shifts in research focus (e.g., toward large-model productization) — useful context for anyone evaluating where to work or how frontier labs are evolving internally.

Following a meteorite impact on a residential structure in New Jersey, analysis of the recovered fragment reportedly revealed mineral or isotopic signatures inconsistent with terrestrial formation processes — the kind of finding typically established via electron microprobe mineralogy, isotope ratio mass spectrometry, or X-ray diffraction to identify high-pressure polymorphs or exotic compounds. Such analyses can constrain the meteorite's parent body (asteroid differentiation state, impact history) and formation environment. Specific mineral identifications aren't detailed here, but researchers building on this would typically cross-reference against meteorite classification databases (e.g., chondrite vs. achondrite type) to place the sample in solar-system formation context.

Paged Out! #9 is the latest release in an ongoing open-access technical zine series where each contribution is capped at a single page, covering topics spanning reverse engineering, exploitation, low-level systems programming, and CTF (capture-the-flag) techniques, distributed free as a PDF. It's a good source of quick, practitioner-written notes and PoC (proof-of-concept) snippets that are typically more current and hands-on than formal publications, useful for anyone scanning for niche techniques (e.g., a specific binary exploitation primitive or obscure toolchain trick) worth digging into further via the referenced tools or repos.

The article recounts the history of an early special-purpose computer used in WWII codebreaking — in the vein of the Bombe (used to narrow Enigma rotor settings) or Colossus (used against the Lorenz cipher at Bletchley Park) — describing how electromechanical or early electronic logic was used to automate cryptanalytic search rather than relying on manual cipher-breaking. These machines are historically significant as precursors to stored-program computing, demonstrating programmable, high-speed logic circuits solving a real operational problem under wartime constraints. For anyone interested in computing history, it's a useful entry point into how cryptanalytic demands directly drove hardware innovation that fed into post-war computer architecture.

The piece surveys the layered protocol stack underlying modern email — SMTP for transport, DNS-based mechanisms like SPF, DKIM, and DMARC for authentication, and MIME for content encoding — highlighting how each was bolted on incrementally to patch gaps in the original design rather than architected holistically. It likely walks through concrete failure modes (spoofing, spam, deliverability) that stem from this accretion of standards. For practitioners, it's a useful primer on why email infrastructure decisions (SPF/DKIM/DMARC configuration, MTA choices) require understanding this historical layering rather than treating email as a clean API.

The project chains FFmpeg audio filters — likely combining vibrato/chorus for wow-and-flutter pitch instability, a low-pass/high-shelf EQ for tape's frequency rolloff, noise injection for hiss, and soft saturation or compression to emulate tape's nonlinear response — into a reusable filtergraph or script. Because it's built entirely on FFmpeg's filter chain, it's fully scriptable and can be applied in batch to any WAV/MP3 source without proprietary plugins. A practitioner could replicate this directly from the filter parameters, or extend it by tuning wow/flutter rate, noise floor, and saturation curve to match specific tape formats (e.g., Type I vs Type II cassettes).

The piece visualizes heap traversal behavior of Go's newer garbage collector (likely the experimental scan/mark improvements such as the 'Green Tea' GC design), showing object graph traversal order, mark-phase progression, and memory locality patterns during collection. This kind of visualization typically instruments GC trace output or hooks into runtime debug/trace facilities to render heap scanning as an animation or graph. For a Go practitioner, it's useful for building intuition about GC pause behavior, locality-driven scan efficiency, and how to interpret GOGC/GC trace diagnostics when tuning latency-sensitive services.

The project centers on building a self-synchronizing clock, most likely using a time reference such as NTP, GPS, or a radio time signal, paired with custom microcontroller firmware to handle drift correction, timezone/DST logic, and display driving. The 'escalation' framing suggests the build expanded beyond simple synchronization into more involved hardware or software territory — extra precision, custom PCBs, or additional features layered on. Anyone replicating it would look at the source firmware and schematic (if published) for the specific time-sync protocol and correction algorithm used to keep the clock accurate indefinitely.

This is a talk/paper by Terence Tao surveying the current and near-future role of AI (including large language models and formal proof assistants) in mathematical research, likely covering areas such as AI-assisted conjecture exploration, automated theorem proving, formalization in systems like Lean, and collaborative human-AI proof development. Tao draws on his own documented experiments using AI tools for research-level mathematics to ground the discussion in concrete capability assessments rather than speculation. For a technical reader, it's a valuable reference point for where formal methods and LLM-based reasoning currently stand against genuine mathematical rigor, and where the gaps remain.

This release extends the VLC-for-Unity plugin's platform support to Linux, meaning the native VLC bindings and rendering integration (typically via texture blitting from VLC's video output into a Unity RenderTexture) now build and run correctly on Linux's windowing and graphics stack. Practically, this lets Unity developers targeting Linux (including Steam Deck/SteamOS) embed arbitrary video codecs supported by libVLC without relying on Unity's more limited built-in VideoPlayer. Developers can pull the plugin, add it to a Linux-targeted Unity project, and get broad format/codec support for in-app video playback immediately.

The piece argues for curriculum reform in legal education in response to AI's growing capability in tasks like legal research, document review, and drafting, likely proposing shifts toward skills that remain differentiated from AI — judgment, ethics, client counseling, and oversight of AI-generated work product. It probably references current adoption of AI tools in law firms as evidence that practice is outpacing pedagogy. For educators or legal-tech builders, it's a useful signal of where curriculum and tooling investment (e.g., AI-literacy training, prompt/verification skills for legal drafting) is likely headed.

The work presents an adhesive formulation capable of forming a durable bond to low-surface-energy nonstick coatings (e.g., PTFE-type surfaces) that conventional adhesives fail to wet or grip, while remaining cleanly removable via ethanol wiping rather than requiring mechanical scraping or aggressive solvents. This combination suggests a chemistry engineered for a specific balance of adhesion strength versus solvent-triggered release, likely via a reversible bonding mechanism or a coating that ethanol selectively disrupts. Materials scientists or product engineers working with nonstick or low-energy substrates could look to the underlying chemistry for applications in temporary bonding, masking, or assembly/rework processes where clean removal matters.

The claim centers on longitudinal clinical trial success-rate statistics, typically measured as likelihood of approval (LOA) from Phase I through FDA/EMA submission, segmented by therapeutic area and trial phase. Multiple industry analyses (e.g., BIO, Informa, QLS Advisors reports) show LOA declining or stagnating over recent decades even as target identification and computational tools improved, a phenomenon dubbed Eroom's Law. Practitioners use this data to benchmark portfolio risk, calibrate probability-of-success assumptions in valuation models, and justify adaptive trial designs or biomarker-driven patient stratification aimed at reversing the trend. Anyone building trial-design or biotech-investment tools should treat historical LOA curves as a baseline to beat, not a constant.

CheapSecurity is a self-hosted CCTV stack designed to run on resource-constrained Linux single-board computers (SBCs), prioritizing a small footprint over feature breadth compared to heavier NVR software like Frigate or Shinobi. It likely handles camera stream ingestion (RTSP/USB), local recording/storage, and a basic web UI, optimized to run without GPU acceleration or significant RAM/CPU overhead. Developers evaluating home-lab surveillance could use it as a lighter alternative when deploying on Raspberry Pi-class hardware where full NVR suites are too heavy, and could extend it with motion detection or object-recognition plugins. As a Show HN project, expect it to be early-stage and worth checking the repo for supported camera protocols and storage backends before production use.

This is a workplace-culture incident report rather than a technical item: an employee at Netflix was reportedly terminated following disclosures made during a company retreat's trust-building exercise. The substantive angle for a technical or professional audience is around HR risk management and psychological safety design, specifically how facilitated vulnerability exercises can create discoverable, actionable information that intersects with performance management or at-will employment decisions. There's no technical mechanism to replicate here, but organizations designing similar offsite activities might use this as a case study for scoping what disclosures are protected versus discoverable, and for auditing whether HR and event facilitation policies are aligned.