In briefAGQ: Action-guided ultra-low-bit compression for robot vision-language-action models
SummaryVision-language-action models let robots carry out tasks from natural-language instructions, but their large weights are hard to fit on memory-limited onboard hardware. The paper proposes Action-Guided Quantization (AGQ), which uses action sensitivity and a weight-recovery cost proxy to assign bitwidths under storage budgets, with per-channel calibration and block-wise recovery. On the LIBERO benchmark, AGQ reports 92.7% task success at W3 and 70.5% at W2.5.
In briefCertified Abstention for Unidentifiable Index-Phase Recovery in Image Registration
SummaryThe paper shows that in reference-perturbed sampled imaging, position fidelity and phase fidelity are logically independent, so no policy using position information alone can guarantee phase fidelity. It derives a resolution threshold separating recoverable from provably unidentifiable registration and proposes a framework that recovers identifiable regions exactly while abstaining on unidentifiable ones. The certified-abstention mechanism is tested across scanning, rolling-shutter, and cryo-EM imaging, with abstention completeness varying by modality.
Dinh Tuan Nguyen, Trong Nhan Nguyen, Vu Nguyen Minh Hung et al.
In briefARCANA: A Benchmark for Abstract and Analogical Reasoning
SummaryThis paper introduces ARCANA, a benchmark testing far analogical reasoning, where tasks map real-world objects or physical phenomena onto abstract grid-based transformations. Humans solve these tasks reliably, while a range of large language models show a clear performance gap. The authors also propose ConceptSIR, a framework based on concept slippage, introspection, and reflection, which lets standard LLMs solve several tasks that direct prompting failed on.
In briefRadar heart sounds paired with echocardiography for ventricular and valve disease
SummaryThis paper presents a public dataset of 537 radar heart-sound recordings from 162 patients, each paired with one echocardiographic report. It covers three overlapping phenotypes: reduced left ventricular ejection fraction, regional wall-motion abnormality, and at least moderate valvular regurgitation. The resource includes original complex signals, processed radar heart sounds, and temporal quality segmentation, with 95.93% of signal duration retained after technical validation. Patient-level five-fold evaluation gives a mean outer-test AUROC of 0.770 ± 0.138 for reduced LVEF.
In briefEstimating Model Performance Without Labels for Safe Deployment
SummaryThis paper introduces MOVAL, a tool that estimates the performance of pre-trained machine learning models using only unlabeled test data. It applies class-wise confidence calibration to correct overly optimistic model outputs and combines multiple calibrated confidence scores in an ensemble. The authors validate it on natural and medical image datasets across classification and segmentation tasks, reporting state-of-the-art results on several metrics.
Zeju Li, Konstantinos Kamnitsas, Mobarakol Islam et al.
In briefRobust model training across decentralized workers, optimizing for the worst-performing worker.
SummaryThe paper studies group distributionally robust optimization over decentralized networks, where workers collaborate to minimize the maximum local risk. It directly optimizes the original objective and provides convergence guarantees for local models in semi-decentralized and fully decentralized settings. Using accelerated gossip and Choco-gossip, the method achieves an O(√((log m)/T)) convergence rate, and experiments show better worst-worker performance than baselines.
In briefCRAB: Cuboid-based method to rebuild as-built BIM models from point clouds
SummaryThis paper presents CRAB, a framework that reconstructs as-built BIM models from point clouds using compact cuboids for walls, doors, windows, beams, and columns, with IFC-compatible semantics and parameters. It combines ProposalNet, which predicts coarse component cuboids, with AssemblyNet, a relation-aware Flow Matching model that refines geometry and predicts structural relationships. For interior wall thickness estimation, the authors report a mean error of 2.80 cm, with 79.50% of matched walls within 3 cm.
In briefHeard but not said: prompting underrates timing knowledge in frozen audio-language models
SummaryThis paper examines whether large audio-language models can locate a described moment in a piece of music. The authors build paired state and change queries from Slakh2100 stem activations and vary how much preceding audio the model sees, comparing full-track prompting, windowed yes/no scoring, and attention-based extraction. Prompting and attention reveal almost none of the temporal information, so prompting failures understate what these models can verify, though not what they can locate.
In briefHierarchical Masked Diffusion Language Models: generating high-priority positions first
SummaryThis paper introduces the Hierarchical Masked Diffusion Language Model (HMDLM), which attaches a hierarchy level to each token position using training data with zero or minimal extra annotation. Generation is split into two passes: first a hierarchy distribution over masked positions is predicted, then token distributions are predicted conditioned on it. Each hierarchy level gets its own noise schedule, so higher-priority positions tend to be revealed earlier while decoding remains fully parallel. The authors report perplexity gains on LM1B and WikiHow and higher Sudoku solution accuracy over diffusion baselines.
In briefHow Intergroup Bias Shapes Social Judgment and Empathy on r/AmItheAsshole
SummaryThis paper studies how commenters on the Reddit community r/AmItheAsshole judge posts and express empathy differently depending on whether they share the poster's gender or age group. Analyzing 126,716 comments on 36,163 posts, the authors find that ingroup commenters are more likely to give a "Not the Asshole" verdict and show greater text-based understanding as measured by LLMs.
In briefAdding Observability to FAIR Principles for More Trustworthy Federated Learning
SummaryFederated learning keeps data local, but locality alone does not ensure trust. The paper argues for an evidence layer alongside model updates, where each participant produces signed, minimal trust evidence per training round while raw data and logs stay local. It proposes FAIR-O, an observability extension of FAIR principles, with a three-level trust model demonstrated by a two-participant Python prototype. The authors also discuss future uses of this evidence in participant selection, evidence weighting, and aggregation.
In briefLetting Agents Design Their Own Search Interfaces Through Automatic Optimization
SummaryThe paper introduces SearchWeaver, a framework that automatically optimizes the interface language-model agents use to access and explore a corpus, guided by downstream search performance. It jointly revises corpus artifacts, executable tools, and usage instructions based on execution trajectories and task rewards. With GPT-5.4-nano, the optimized interfaces raise answer accuracy on BrowseComp-Plus from 71.6% to 78.4% and average nDCG@10 across four BRIGHT subsets from 45.7 to 55.0, compared with Direct Corpus Interaction.
In briefMOSAIC: A benchmark testing whether conversational AI remembers and reasons across text, images, video, and audio.
SummaryMOSAIC is a benchmark of 923 questions built on persona-based conversation histories of 11 to 24 sessions, covering text, image, video, and audio. It inserts targeted spatial, temporal, and prosodic signals and uses a matched diagnostic protocol that intervenes at representation, retrieval, and delivery to locate where evidence is lost. The results show that representation loss depends on modality, reading failures dominate even with gold evidence, and models score well on answerable questions but refuse poorly.
In briefER-Chord: Spatially consistent one-step image editing through equal-energy reliability redistribution
SummaryOne-step generative models can edit images quickly from text prompts, but they often change regions that should stay the same. ER-Chord is a training-free, inversion-free method that uses agreement between editing fields at neighboring noise levels as a local reliability signal. It redistributes editing strength by that reliability while keeping the original editing directions and total control energy. Experiments on PIE-Bench and TEdBench show better source preservation than one-step editing baselines across several generative backbones.
In briefDepState-JEPA: Depth-grounded state learning for robot vision and manipulation
SummaryThe paper proposes DepState-JEPA, a dual-stream RGB-depth joint-embedding predictive architecture that links spatial understanding with action-conditioned prediction through a shared latent state, DepState. Built on V-JEPA 2.1, it uses depth to ground both modalities in metric 3D space and adds robot-state and object-trajectory supervision. The authors report comparisons on RGB encoder transfer, feature completion, visual forecasting, and manipulation success, along with ablations.
In briefCrashGym: Measuring How Experiments Help LLM Agents Make Better Decisions in Market Crashes
SummaryCrashGym is a deterministic, replayable limit-order-book simulator in which an agent chooses between a deleveraging intervention and a liquidity backstop during a simulated crash. The agent can first test interventions in a laboratory version of the market, then is scored on regret against an independently seeded target market. In a prospectively specified study on 24 newly sampled worlds, laboratory access reduced mean regret for the one deployment with a complete record from 8.878 to 3.785. Comparisons for two other deployments are incomplete.
In briefFailure Modes of Vision-Language Models in Thermal Human Activity Understanding for Robots
SummaryThis paper evaluates seven locally deployable vision-language models, including ones built for physical AI and robotics, on two person-centric thermal datasets, using paired RGB recordings as a reference. The models recognize broad activity categories but struggle with fine-grained actions such as coughing, sneezing, and yawning, produce inaccurate event timelines, and hallucinate object-related actions. Simple thermal preprocessing and added reasoning yield only limited, model-dependent gains, and abstention behavior varies across models even under explicit instructions.
In briefEncoded but Unused: Bounding the Sources of VLM Errors
SummaryThis paper splits vision-language model errors into two types: failures to encode the needed visual evidence, and failures to use evidence that is already encoded. The authors introduce VELens, a lightweight probe that traces visual evidence across model layers, and evaluate it on over 6,000 images with LLaVA-1.6-7B and Qwen2.5-VL-7B. They estimate that visual-utilization failures account for 84.4% of LLaVA errors and 70.0% of Qwen errors.
Anat Veikherman, Mor Ventura, Nitay Calderon et al.
In briefMA-UITD: Offline Inference of Multi-Agent Rewards to Characterize Continuous Social Behavior
SummaryThe paper introduces MA-UITD, an offline method that infers explicit reward functions for each agent from joint demonstrations in continuous-state environments. It needs no extra environment interaction or inner policy-optimization loop. In simulated cooperative, competitive, and general-sum tasks, it outperforms baselines in discrete-action settings. Applied to social-foraging trajectories, it inferred distinct spatial and interaction-dependent reward structures for leaders and followers.
In briefThink-Capture: On-Demand 3D Geometry for Spatial Reasoning in Vision-Language Models
SummaryVision-language models recognize 2D content well but often make spatial errors when viewpoints are sparse or objects are occluded. The paper proposes Think-Capture, which first identifies task-critical objects and then acquires their 3D geometry on demand, compressing object and camera states into structured text. The authors report gains on MindCube-Tiny, the MMSI-Bench positional subset, and ViewSpatial, and state the full system fits on one 24 GB GPU.
In briefDisasterRoute: Evidence-Guided Dual-System Routing for Disaster Visual QA
SummaryThis paper addresses visual question answering on low-altitude UAV imagery for disaster response under limited edge computing resources. It introduces Disaster-S1, a disaster-adapted vision-language model that outputs answer-option probabilities in one forward pass for fast decisions. It also proposes DisasterRoute, a routing framework that combines prediction confidence with disaster evidence to decide whether to accept the initial answer or invoke the reasoning model DisasterVL for review. Experiments on DisasterBench report higher accuracy with latency substantially lower than full reasoning.
In briefSelectively recomputing cached video context for faster streaming video understanding (FrameBlend)
SummaryThis paper addresses streaming video understanding, where prefilling frames during idle time uses restricted context that can mismatch what a later query needs. The authors propose FrameBlend, a plug-and-play method that re-prefills selected video tokens with query-relevant context at query time and replaces their cached KVs, without extra training. Experiments across three frameworks and five benchmarks show accuracy gains, and on RVS-Ego per-frame prefill FLOPs are reduced by over 20%.
In briefFrom Scores to Safe Sets: Exact Safety-Constrained Decoding for Medication Recommendation
SummaryThis paper studies medication recommendation under drug-drug interaction (DDI) constraints, holding a frozen model's scores fixed. It compares local repair strategies with an exact global decoder based on mixed-integer linear programming. The global decoder achieves higher Jaccard similarity across splits, with a larger advantage as the DDI constraint tightens.
Animesh Agarwal, Meysam Ghaffari, Nina Fatehi et al.
In briefSchrödinger-Bass bridge barycenter models for learning from measure-valued data
SummaryThe paper proposes four barycenter models (SBB-encoder, SBB-decoder, SBB-symmetric, SBB-Markovian) built on the Schrödinger-Bass Bridge between probability measures. It proves existence of the SBB barycenter under mild assumptions and shows that for Gaussian inputs the decoder model reduces to a finite-dimensional optimization. For general inputs it derives a hybrid Wasserstein gradient particle descent algorithm, and experiments on spatial images and single-cell RNA sequencing data report better interpolation than existing barycenter models.
Ngoc-Hai Nguyen, Thuan Nguyen, Prakash Ishwar et al.
In briefC²U in RAG: Compositional Concept Unlearning to Suppress Unwanted Concepts in Retrieval-Augmented Generation
SummaryRetrieval-augmented generation can expose unwanted concepts through retrieved evidence even when the generator is moderated. The paper studies query-side concept suppression with a fixed encoder and document index, using a method called C²U that adds only a bounded edited-minus-original delta to the raw query. Across five datasets, four encoders, and three seeds, the validation-selected configuration reduces pooled binary target Hit@5 by 4.25 percentage points with non-positive control degradation.
In briefDelay-Guided Attention for Quality Prediction in Multistage Manufacturing (DAMF)
SummaryThis paper addresses quality prediction in multistage manufacturing, where time lags between stages vary and measurements at the same timestamp may reflect different process states. The proposed DAMF method estimates inter-stage lag distributions and uses them to guide cross-stage attention for final quality prediction. On the real-world LiquidSugar dataset and two controlled-delay benchmarks, the authors report improved prediction and more accurate, stable lag recovery than the compared baselines.
In briefWhen Several Answers Are Right: Uneven Probability Splits in Decision Models
SummaryThis paper examines whether probability distributions from decision models remain reliable when several choices are correct. The authors test Jev, Liquid d1, and GPT-6 Luna Decisions on MetaTool, HotpotQA, and ECtHR. Even when every correct choice is ranked above all distractors, in 28% to 50% of such lists at least one correct choice gets a probability below 1/n. Duplicating a correct choice five times also produces large probability gaps, so a probability threshold does not reliably identify all correct choices.
In briefPreference Is Not Permission: Setting Personal Limits on AI Shopping Agents' Auto-Purchases
SummaryThis paper argues that personalized shopping benchmarks only ask whether an item fits the user, whereas it studies how far an agent may commit to a purchase on its own. Comparing sixteen instruction-tuned models against a human authorization reference, it finds the models often auto-purchase even when fit is low and product evidence is missing, which the authors call overreach. It also reports that an individual's auto-purchase line can be learned from a few dozen of their own labels and predicts held-out choices better than the tested prompt and model configurations, but the learned boundary does not transfer across domains.
In briefHuayu-Global: A real-time quasi-global precipitation retrieval system
SummaryThis paper presents Huayu-Global, a deep-learning system that combines three geostationary sensor streams from five satellites to produce quasi-global precipitation fields every thirty minutes. Tested on over a million station records from two periods outside the training years, it improves event detection and rainfall estimation over the evaluated real-time products. In key metrics it reaches the skill of delayed products, aiming to support flood forecasting and disaster response.
WhyFuses multiple geostationary sensors in one deep-learning retrieval; validated on over a million independent stations, beating real-time products and nearing delayed-product skill.
In briefDetM-Tracker: A memory-efficient video instance segmentation method for autonomous industrial inspection
SummaryThis paper presents DetM-Tracker, a video instance segmentation framework for autonomous industrial inspection that aims for deterministic real-time inference on memory-limited edge devices. It uses three components: compact scalar memory, dynamic instance channels, and an entropy-gated skip connection that focuses computation on uncertain regions. The authors report about 20 times lower inference latency and 6 times lower VRAM use than state-of-the-art dense-feature baselines.
In briefGradient conjecture at infinity: finite spherical length of gradient trajectories
SummaryThe paper proves that for any definable C^1 potential, every gradient flow trajectory has finite spherical length, so its spherical projection converges, with no extra assumptions on boundedness or convergence. This gives affirmative answers to the gradient conjecture at infinity and the definable Thom gradient conjecture. The authors apply the result to deep neural network training, concluding that for definable, continuously differentiable activation and loss functions the training flow's spherical projection always converges.
WhyProves finite spherical length for gradient trajectories of definable potentials, resolving the gradient conjecture at infinity with a rigorous proof affecting theory and DNN training analysis.
In briefReusing Target Probabilities in Speculative Decoding
SummarySpeculative decoding uses a target model to verify a tree of draft tokens in parallel. The paper studies whether the target's next-token distributions can guide selection of the next tree, and characterizes when exact reuse requires sacrificing accepted tokens. Across six targets, reranking first-token branches with a separately computed distribution at the current context improved expected acceptance over draft ranking, especially with small tree budgets.
In briefnorregel: A Preregistered Benchmark of Dated Numeric Values in Norwegian Law
Summarynorregel tests whether language models can give correct numeric values from Norwegian statutes and regulations as of a stated date, covering amounts, percentages, National Insurance basic amount multiples and age limits. The 916 items were extracted from Lovdata's open data and carry each provision's dated history, so answers are classed as correct, outdated, wrong or uninterpretable. In the preregistered second run the primary test supported the outdated-answer hypothesis H1, but the sensitivity analysis S8 had an interval including zero, so the support is not robust.
In briefReconstruction differences in fly compass connectome rival individual differences and break trained models
SummaryThe paper compares nine reconstructions of the Drosophila head-direction circuit, covering four different flies and five variants of one fly. Changes in synapse detection within a single fly alter the circuit by more than half the smallest between-fly difference. Networks trained on each wiring developed near-perfect ring attractors, but their error rose sharply when weights changed by 1%, and training did not transfer to other connectomes. The authors recommend that connectome-constrained models state which reconstruction they use and be tested for robustness across reconstructions.
In briefNeuralSRender: G-Buffer-Guided Video Diffusion for Making Simulated Driving Videos Look Real
SummaryAutonomous-driving perception needs large amounts of labeled data, but simulator videos often look too unrealistic. This paper proposes NeuralSRender, which uses a pretrained video diffusion transformer, a lightweight projector for G-buffer geometry and material cues, and LoRA adaptation to translate synthetic videos toward realistic appearance. The authors also release GenReal, a dataset of synthetic and real driving videos with G-buffers. Reported results show that training perception models on the translated videos raises nuImages detection AP50 from 35.1 to 40.6 and Cityscapes segmentation mIoU from 32.4 to 40.8.
WhyApplies G-buffer-guided video diffusion with two-stage LoRA to sim-to-real translation. Gains on nuImages and Cityscapes perception metrics give solid evidence, useful for autonomous driving.
In briefNoise-Robust Feature Selection Criterion for Random Forests
SummaryThis paper proposes TOP-Gain, a criterion for random-forest wrapper feature selection that scores candidate features by the mean increase in out-of-bag probability of the true class, truncating decreases at zero to reduce noise. In a pre-registered benchmark on 26 OpenML-CC18 datasets, it achieved the best mean rank in ROC-AUC, accuracy and Macro-F1 among 15 methods. It also selected more stable feature sets than several alternative criteria without losing accuracy.
In briefCurvature-Scaled SAM: Convergence Analysis Beyond Smoothness
SummarySharpness-Aware Minimization (SAM) is a popular method for training deep neural networks, but its existing convergence guarantees assume smoothness. This paper analyzes SAM under generalized smoothness, showing that its perturbation can distort the gradient direction without bound. It proposes Curvature-Scaled SAM (CS-SAM), which scales the perturbation and descent step by curvature upper bounds to control this distortion. The paper proves convergence rates for nonconvex, convex, and Polyak-Lojasiewicz settings, and minibatch experiments on vision tasks show competitive performance.
In briefLanguage models that reason and search repeatedly: a design map and comparison study
SummaryThis paper reviews frameworks that let large language models alternate between step-by-step reasoning and interactive search, treating retrieval as an action in an iterative loop. The authors analyze these systems along four architectural axes: retrieval coupling, controller design, knowledge source, and grounding strictness, and treat the learning signal as a separate cross-cutting dimension. They also separate benchmark difficulty from the search demand of the loop and summarize controlled ablations and quality-cost differences while avoiding pooling results across heterogeneous setups.
Biswas Poudel, Nilson Chapagain, Amit Kumar et al.
In briefLTEM: A Markdown Extension Enabling LLMs to Understand and Generate Complex Tables
SummaryThe paper introduces a taxonomy of six atomic table-structure features and a 1,089-table synthetic benchmark, STB, for studying how LLMs handle tables. It proposes LTEM, a minimal Markdown extension that explicitly encodes spans and hierarchical headers. Across five LLMs, LTEM reaches accuracy comparable to HTML with a better accuracy-token trade-off than HTML and LaTeX, and yields far fewer invalid responses than LaTeX.
WhyProposes a table-structure taxonomy and a minimal Markdown extension, evaluated systematically on a 1,089-table benchmark across five LLMs.
In briefA Ten-Year Review of Learning-Based Control, from Theory to Practical Deployment
SummaryThis tutorial paper reviews key advances where machine learning meets control, drawing on ten years of Learning-Based Control sessions at the IEEE Conference on Decision and Control. It introduces core notions for stability and safety guarantees, reviews methods that learn dynamics models to improve control under uncertainty, and discusses data-driven policy methods such as reinforcement learning, imitation learning, and Bayesian optimization. The paper also covers representative applications and open challenges, and shares implementations and examples on a companion website.
Angela P. Schoellig, Matthias A. M\"uller, Sebastian Trimpe et al.
In briefResuming Onboard Vision-Language Inference After Interruptions (OrbitWeaver, a framework for satellite VLM recovery)
SummaryThe paper studies running vision-language models on low-Earth orbit satellites, where power, thermal limits, and fault-triggered reboots can interrupt inference mid-request. The authors present OrbitWeaver, which uses coordinated checkpointing and recovery to preserve inference progress, choosing recovery boundaries based on the vision encoding, prefill, and decoding phases. Experiments on satellite payload hardware with on-orbit telemetry traces report up to a 4.07-fold end-to-end speedup and a 3.56-fold reduction in SSD write volume. A 72-hour deployment on two low-Earth orbit satellites demonstrates resumption across repeated shutdowns.
In briefROCOCO: Robust Completion of Occluded Curved Object Outlines
SummaryThis paper proposes a learning-based generative model that completes curved features hidden behind environmental obstacles. It uses a flow matching approach to inpaint occluded regions and trains a UNet-based model to predict which regions need inpainting without prior occlusion information. When no occlusion mask is given, the method clearly outperforms off-the-shelf inpainting baselines, and it is competitive with mask-supervised inpainting when a mask is provided, using 7.7M parameters.
In briefVideo action detection using multi-scale temporal compression attention and video-text cross-attention fusion
SummaryThis paper addresses difficulties in modeling temporal dependencies for video action detection, such as redundant frames and weak visual-text fusion. It proposes a temporal attention module based on Multi-Scale Latent Temporal Compression Attention, which aggregates frames across several temporal scales while reducing redundancy. It also introduces a Video-Text Cross Attention Fusion module that applies cross-attention at latent and standard feature levels to improve semantic alignment. The authors report superior results over existing methods on multiple video recognition benchmarks.
In briefLocalAgent: A Collaborative Agent for Checking if Two Images Show the Same Object
SummaryThis paper addresses fine-grained instance-level consistency verification, which asks whether two images show the exact same physical object under different viewpoints, lighting, and backgrounds. The proposed LocalAgent framework lets a general multimodal language model reason globally to find suspicious local regions, then delegates them to lightweight verifiers for appearance and geometric matching that return structured scalar evidence. The authors also introduce an auxiliary-capped reinforcement fine-tuning strategy so the model learns when verification is needed. The paper reports a 4.9% mean accuracy gain over the strongest CoT-RFT baseline while invoking the verifier on only 12.13% of samples.
In briefConditional flow models turn epistemic uncertainty into apparent aleatoric variance
SummaryThis paper studies the variance of samples generated by Conditional Flow Models (CFMs) used for regression-style tasks. Even in nearly deterministic regression problems, CFM samples often have larger variance than the true conditional distribution. The extra variance is most pronounced when the model has not yet captured the dependence on the conditioning variable, so it can be seen as fitting uncertainty appearing as aleatoric variance. Experiments show this variance tracks underfitting and decreases with more training or data, and the authors demonstrate its use in building high-coverage predictive sets.
Navodita Sharma, Abhishek Shetty, Max Simchowitz et al.
In briefTrainBridge: Agentic NPU training optimization guided by GPU compilation evidence
SummaryThis paper studies how to make training faster on Ascend NPUs, where framework-level optimizations such as operator fusion are only partly applied, leaving fragmented computation and overhead. The authors propose TrainBridge, an agentic method that uses the optimized execution of mature GPU compilation stacks as a reference, extracts optimization intents to rewrite NPU programs, and refines them through iterative profiling. In GPU-to-NPU migration experiments, TrainBridge achieves an average 1.7x speedup across four workloads, including LLM pretraining, image classification, and vision-language training. It also averages nearly 2.0x speedup across 27 Modded-NanoGPT speedrun records with highly optimized GPU implementations.
In briefHow Representational Priors Shape Generalization in World Action Models
SummaryThe paper examines why world action models (WAMs) generalize differently depending on their generative backbones. It studies the representational geometry inherited from those backbones and how policy training reshapes it. The authors introduce nuisance orthogonality, which measures how orthogonal features remain to visual variations outside the in-distribution manifold, and find that higher orthogonality is linked to greater robustness after WAM training.
In briefTeachBack: Learning How to Teach Through Bidirectional Preference Optimization for On-Policy Self-Distillation
SummaryThis paper studies how a language model's teacher should be guided during on-policy self-distillation. The authors find that giving the teacher a full reference solution yields accurate outputs but may encourage shortcuts that skip exploration and self-correction. They propose TeachBack, a bidirectional preference-learning framework that controls the reference suffix length and prompt style, and uses student behavior to shape teacher supervision. Experiments on mathematical and logical reasoning tasks report consistent gains in student performance over existing OPSD methods and related baselines.
Linfei Ji (1), Jiayun Li (2), Haozhao Wang (1) et al.
In briefWhy Constrained Updates Still Forget: Functional-Path Analysis of Continual SFT
SummaryThe paper studies why supervised fine-tuning of language models can erase previously learned behavior, even when updates are constrained. It proposes a functional-path account that decomposes old-loss changes into controlled direction, gradient staleness, probe mismatch, and finite-step effects. Matched QLoRA experiments find that constraint placement strongly affects retention, while supervised replay protects old tasks more consistently in a sequential-task comparison. The authors suggest mitigation through explicit functional anchors and joint old/new auditing.
In briefSplitting Radiology Reports at Evidence Boundaries: Anatomical Region Sets as Generation Units
SummaryThe paper proposes RadCarve, which defines radiology report generation units directly from anatomical region sets in existing statement-region correspondences, linking unit formation with visual evidence selection. A planner uses composition-specialized query banks to jointly predict unit existence and region composition from the image, while a generator writes each unit's text from the visual evidence of its predicted regions. The authors report a 9.4% CXRFEScore gain in unit generation given region sets, 2.4 times the end-to-end binding F1 of baselines, and leading results on Chest ImaGenome and PadChest-GR with fewer than one-sixth the parameters of large baselines.
In briefWhy AI-generated language is hard to understand
SummaryThis study examines psycholinguistic features linked to human processing difficulty, such as object relative clauses, long syntactic dependencies, and low-frequency n-grams. Using a new corpus of AI-generated text across diverse domains, the authors find that AI models use these difficult features more often than round-trip translation paraphrases, and more than human writing. They hypothesize that AI text is increasingly optimized for goals like math, code, and reasoning, favoring information density over successful communication, and provide a proof-of-concept by fine-tuning a model under a length penalty.
Thomas Hikaru Clark, Matias B. Martin, Kyle Mahowald
In briefAI-driven end-to-end design and software development for software-defined vehicles
SummaryThis paper explores how artificial intelligence could support end-to-end automotive design and software development for software-defined vehicles. It presents a toolchain combining large language models with model-driven engineering to turn natural-language requirements into system architectures, executable software, and validation artifacts. The approach is validated on a centralized vehicle computing platform for adaptive cruise control and emergency braking, using hardware-in-the-loop and software-in-the-loop testing. The authors also discuss current capabilities and practical limitations.
In briefRiO-DETRv2: Geometry-aware uncertainty modeling for real-time oriented object detection
SummaryThis paper presents RiO-DETRv2, a real-time transformer for detecting oriented objects in images. It represents box boundaries as probability distributions in an object-aligned coordinate system and refines them across decoder layers, and it moves from global periodic search to local angular refinement. On the DOTA dataset, RiO-DETRv2-l/x reaches 53.7/54.1 mAP50-95, outperforming RiO-DETR-l/x by 1.2/1.5 points with nearly unchanged computational cost.
WhyAdds geometry-aware distributional boundary and angular refinement to real-time oriented detection, gaining 1.2–1.5 mAP on DOTA over the prior version. Impact is mainly limited to oriented object dete
In briefSICAF: A Time-Varying Focus Bottleneck for Self-Supervised Spiking Neural Network Event-Based Optical Flow
SummaryThis paper addresses optical flow estimation from event cameras using self-supervised spiking neural networks (SNNs). It proposes SICAF, a framework with a "Compactly Compress, Time-varying Focus" training strategy that uses temporally modulated constrained optimization to compress latent features without discarding temporal saliency. The estimator includes learnable temporal attention to capture cross-time dependencies. The authors report state-of-the-art results among self-supervised SNNs, including an 8.14% AEE improvement on MVSEC and a 13.49% robustness gain under white-box adversarial attacks.
In briefFrom Augmentation to Synthetic Evidence: Checking Generated Chest X-rays at Dataset Intake
SummaryThis paper examines generated chest radiographs, which are useful as declared data augmentation but unsupported when submitted as records of real examinations. The authors select up to 20 images from pools of 1,000 candidates using Qwen, assess quality with MedGemma, and use a classifier on frozen DINOv3 features to predict whether images are generated. Quality-based selection raises the number of quality passes, but no selected image passes both quality and origin checks, and automated checks cannot verify an acquisition claim. The authors argue that source and intended use should be recorded and verified separately when admitting chest radiographs into medical datasets.
Shaikh Akib Shahriyar, Zinedine Sehili, Adam Arthur et al.
In briefWhere Speaker Timbre Hides: Locating Speaker Information in Speech Signals
SummaryThis paper investigates where speaker timbre information resides in speech signals, using speaker identity as a measurable proxy. It probes frequency bands, magnitude and phase structure, WORLD decomposition, voiced and unvoiced regions, self-supervised learning layers, and EnCodec/HuBERT tokens, with a 45-listener test across Mandarin, English, and Japanese speech and singing data. The authors find that speaker information is distributed: 1–8 kHz magnitude and spectral-envelope structure dominate perception, bands above 8 kHz remain machine-discriminative, shallow SSL layers carry compact speaker information, and token leakage depends on tokenizer and layer choice.
In briefSAFTER: Safety-aware filtering that checks whether robot actions stay recoverable, for robust learning under shifted environments
SummaryThis paper studies safe reinforcement learning for quadruped robot navigation under bounded actuation, where an unsafe action may become unrecoverable. It proposes SAFTER, which embeds a differentiable closed-form backup control barrier function filter into Soft Actor-Critic training to unify current safety, finite-horizon backup safety, terminal recoverability, and input admissibility. The reported results include 90% training success with 30.6% fewer transitions than Vanilla SAC, and success gains of up to 42.4 and 61.4 points over CBF-RL and SEA-Nav in unseen simulated environments.
In briefORSMT: Using SMT to Verify and Repair Meaning Consistency in LLM-Generated Optimization Models
SummaryThis paper presents ORSMT, a framework that checks whether the mathematical model, solver code, and report generated by a large language model from a natural-language optimization problem agree in meaning. It uses exact SMT queries to prove or refute feasible-region and objective equivalence and localizes inconsistencies. Across 3,034 problems, the model-code match rate rises from 43.2% to 88.2% with protected selection.
In briefVeeReview: Measuring Build-Verify Pairs in Agent-Authored Pull Requests Using the V-Model
SummaryThis paper introduces VeeReview, a tool that uses the V-Model's four build-and-verify pairs to measure verification evidence in pull requests, combining repository signals with a language-model judge. Applied to 1,454 matched pairs of agent-authored and human-authored pull requests from 741 repositories, it found agent-authored pull requests had lower build-only rates on all four pairs. The instrument did not meet its preregistered calibration criterion, and the authors argue such measures need a mechanically decided component and a validated human reference.
In briefDGCM-SSFA: A Compact Model for Synthesizing 7T MRI from 3T Scans
SummaryThis paper presents DGCM-SSFA, a compact deep learning framework that synthesizes 7T MRI images from routinely acquired 3T scans, avoiding the cost of ultra-high-field scanners. It uses about 11M parameters, combining feature modulation and sparse strip frequency attention within a U-Net Transformer backbone. Experiments on UNC and BNU datasets report strong structural fidelity, with SSIM of 0.8431 on BNU T2w and 0.8100 on BNU T1w. The model uses far fewer parameters than ResViT and FS-RWKV.
In briefAI-Free Gates to AI-First Work: Indian Engineering Students' Experiences
SummaryThis study interviewed 23 Indian engineering and computing students and recent graduates about how they used generative AI in coursework, examinations and campus placements, and how they judged their work readiness. Using reflexive thematic analysis, it reports five themes, including AI-first workplaces versus AI-free placement screens and participants' readiness concerns. Drawing on signaling theory, it discusses what grades and screen scores certify once AI can help produce them, and offers recommendations for employers, institutions and AI tool designers.
In briefGuiding black-box LLM agents by predicting task pitfalls before they happen
SummaryThis paper addresses the problem that black-box LLM agents lack knowledge of specific environments. It proposes the Pitfall Anticipation World Model (PAWM), which intervenes only when a potential pitfall arises and predicts the impending failure to steer the agent away. PAWM is trained with supervised fine-tuning on synthetic pitfall-labeled data, then GRPO co-training with a black-box LLM. The authors report 24.3% gains for Claude Sonnet 4.5 on ScienceWorld and 7.7% on WebArena-Lite, outperforming several baselines.
In briefGRAFT: a training-free method to restore rare-word sampling in continuous diffusion language models
SummaryContinuous diffusion language models generate text by averaging over candidate words at each step rather than drawing discrete words, which causes rare words to be lost. The paper shows that ELF produces rare words at only 0.30x the human rate, a gap that persists across model sizes. The proposed GRAFT method, which requires no training, commits rare words early and regenerates the sequence, restoring rare-word rates to 0.82-1.10x the human rate across ELF scales and LangFlow.
In briefLaVGuard: Selective Decoding to Correct Context-Dependent Value Drift
SummaryThe paper addresses language model responses that are content-safe yet promote priorities conflicting with the task, such as demanding compliance when autonomy should be preserved. LaVGuard separates an authorized ten-dimensional value profile from a frozen estimate of values in the visible prefix, using a density gate to select candidate continuations and a logit tilt bounded by changed-position and cumulative-KL budgets. Profile conditioning raised boundary TPR at 5% FPR from .58 to .72, and at the same .12 mean intervention ratio, the method scored .88 on risk-safety versus .72 for position-matched edits.
In briefNormTune: Norm-guided fine-tuning method for efficiently training AI models on reasoning-heavy tasks
SummaryThe paper finds that parameter-efficient fine-tuning (PEFT) lags behind full fine-tuning on reasoning-intensive tasks, linked to weight-update norms that are markedly smaller. It proposes NormTune, which adaptively strengthens layers whose weight updates are insufficient based on their update norms. Experiments report consistent gains for several PEFT methods across reasoning benchmarks, with some high-rank variants matching full fine-tuning.
In briefReducing reward hacking in realistic settings via representation engineering
SummaryThis paper studies how to monitor and mitigate reward hacking, where models exploit flaws in reward functions, in realistic settings such as tool calls and long contexts. The authors apply a dynamic intervention to steering directions from three methods and report that it perturbs model representations less than uniform steering vector addition. They find reward hacking can be cheaply and significantly reduced without large costs to coherence or capability, and steering vectors from coding environments generalize to other domains.
In briefTwo for One: Privileged Learning for Single-Eye Screening
SummaryThe paper proposes a privileged learning framework in which a teacher model uses both eyes' colour fundus photographs during training. A student model learns from this teacher using only a single-eye image, which is also the only input required at inference. Experiments report higher AUC than conventional monocular training for detecting age-related macular degeneration and diabetic retinopathy, with the largest gains on left-eye images.
In briefStochIKM: Rebuilding Patient-Level Data from Published Kaplan-Meier Curves
SummaryThis paper presents StochIKM, a stochastic method that reconstructs individual patient data from published Kaplan-Meier survival curves to enable secondary time-to-event analysis. It digitizes the figure with a vision-capable language model and uses constraints on the risk table, curve fit, median, interval and event count to return a set of admissible patient-level datasets. In simulations and 79 arms from published oncology trials, its reconstructed risk tables and medians match the published values more closely than LLM-improved IPDfromKM.
In briefScoring code edits before running them to guide self-improving coding agents
SummaryThe paper studies how a coding agent that rewrites its own scaffold can pick which proposed edit to expand before that edit is evaluated. It fits a linear model on the parent's solve rate and eight features of each diff, using 96 labeled scaffold edits, then freezes the coefficients. On held-out HumanEval+ instances, GPT-4o reaches 87.0% and Claude Sonnet 4.5 reaches 86.6%, compared with baselines around 81% to 85%.
In briefEco-Friendly Trip Planner Using Z-Number Uncertainty for Travel Times and Ratings
SummaryThe paper presents a tourism trip planner that represents travel times, which vary by departure hour, and platform ratings, which depend on review counts, as Z-numbers built directly from routing observations and review data. It optimizes a hybrid loss covering score, carbon, travel time, and reliability, using an adaptive large neighborhood search. Compared with a crisp-mean baseline across 150 seed-paired runs in five cities, it reduces trip carbon by 19.3% and travel time by 11.5% on average, with the gains varying in significance by city.
Aziz Nuriyev, Sherzod Turaev, Saja Al-Dabet et al.
In briefMHeatmap: Two-Walk Reordering and Mosaic Layout for Visualizing Rectangular Matrices
SummaryMHeatmap is a visualization method for weighted rectangular matrices that record relations between two kinds of objects. Two-Walk spectral seriation builds a bipartite graph that also links rows sharing columns and columns sharing rows, then orders rows and columns using a Fiedler vector. The Mosaic layout sizes rows and columns by their totals and colors cells by observed values. On 300 synthetic matrices and seven real datasets, Two-Walk achieved the lowest normalized 2-SUM in all 15 synthetic regimes and the highest Band@10% in 14.
In briefSU(2)-equivariant spinor message passing for predicting molecular magnetic properties.
SummaryThe paper introduces eSpinor, a graph neural network that is SU(2)-equivariant and propagates spinor features alongside conventional tensor features, initialized from eigenvectors of an approximate spin-orbit-coupled Hamiltonian. On cobalt and dysprosium single-molecule magnet candidates, it improves predictions of magnetic anisotropy, excitation energies, and low-field nuclear relaxation rates, including for unseen ligands. The authors conclude that spinor message passing is a useful inductive bias for the tested systems and that model selection should be guided by accuracy on the intended physical observables.
In briefGRAM: Grouping compatible tasks to merge fine-tuned models with less interference
SummaryThis paper proposes GRAM, a method for merging separately fine-tuned models into one, aimed at settings like healthcare where data privacy limits sharing. It shows that the alignment of task update subspaces determines merging error, so grouping geometrically compatible tasks reduces subspace interference. GRAM is reported to outperform prior merging methods on GLUE with Flan-T5, 8-task CLIP vision, and medical instruction tuning with Qwen2-7B-Instruct.
In briefWaFIR: Wavelet-Fourier Expert Mixture for Blind Multi-Degradation Image Restoration
SummaryThe paper presents WaFIR, a decoder module that splits image features into Haar subbands, summarizes each with a compact Fourier descriptor, and routes them to prompt-conditioned spectral modulation experts. Under a unified protocol for denoising, deraining, and dehazing, it improves average PSNR by 0.48 dB over the PromptIR baseline and 0.38 dB over Up-Restorer. Matched-capacity controls indicate the gain is not explained by extra parameters alone.
Trong Nhan Nguyen, Vu Hoang Minh Doan, Thanh Dat Le et al.
In briefPhyMo: Learning Accurate, Continuous Physical Motion from Multi-View Videos
SummaryThis paper presents PhyMo, a framework that recovers dynamic 3D scene geometry, appearance, and motion from multi-view videos. It introduces a Motion-Aware Trajectory Representation so the model derives motion from deformation rather than appearance shortcuts, and a Continuous Motion Evolution module with bidirectional kinematic continuity constraints for consistent displacement, velocity, and acceleration. The authors report state-of-the-art results on four dynamic scene benchmarks.
In briefDriftNeRF: A Regularizer for Consistent Density in Sparse-View Neural Radiance Fields
SummaryNeural Radiance Fields degrade sharply when few input views are available. The authors argue that the standard rendering loss compares only pixels per ray and never checks whether different rays assign contradictory densities to nearby 3D points. They propose DriftNeRF, a drop-in regularizer that pulls densities toward a neighborhood consensus, penalizing unsupported disagreement. It improves 3-view PSNR over the strongest MLP-NeRF baseline by 0.42 to 0.65 dB across NeRF-Synthetic, DTU, and LLFF.
In briefFrom Canon to Nikon: How Camera Differences Affect Retinal AI Performance
SummaryThis paper studies retinal AI reliability across cameras, using a Brazilian cohort of 8,524 patients. Images from a Nikon NF5050 were more than twice as likely to have inadequate quality as those from a Canon CR-2. Using a frozen pretrained retinal foundation model, the authors test 12 disease classification tasks, four resolutions, and five classifiers. They find camera-related performance differences vary by task and are not consistently reduced by higher resolution or classifier choice.
In briefDiffusion Posterior Sampling with Uncertain Forward Models
SummaryThis paper proposes an uncertainty-aware likelihood (UAL) that incorporates uncertainty in forward-model parameters, such as blur kernels or detector responses, into the measurement likelihood through a Gaussian approximation of the marginal likelihood. UAL can be plugged into existing diffusion posterior samplers while keeping their pretrained priors and sampling schedules. Experiments on six inverse problems spanning deblurring, CT, MRI, ultrasound, and microscopy report improved reconstruction and ensemble prediction across samplers.
In briefA new AI method that analyzes sperm whale click patterns by separately encoding rhythm and sound spectrum.
SummaryThis paper introduces DCCE, a Dual-Channel Contrastive Encoder that processes sperm whale click sequences (codas) using separate encoders for timing rhythm and spectral information before fusing them. It is tested on 1,383 codas for social unit, coda type, and individual identification, and compared against literature baselines with ablation studies. The authors conclude that combining timing and spectral information helps depending on the task, but they do not establish these as independent biological channels or show general architectural superiority.
Jo\~ao Victor Quintanilha, Emma Virnelli, Shuo Zhang
In briefUNIVERS: A method that learns general voxel embeddings from synthetic data to locate landmarks from a single example.
SummaryThis paper presents UNIVERS, an end-to-end framework for one-shot landmark localization in volume scans with varying fields of view. It trains on pseudo-data synthesized from organ shape templates, randomizing semantic layout, appearance, field of view, and pose, combined with cross-domain voxel-wise contrastive learning. The method is tested on localizing the adrenal glands across three datasets, where it reportedly outperforms state-of-the-art approaches, especially in cross-modality settings. The source code is publicly available.
In briefMulti-level Encoder with Global Topic for Task-oriented Dialogue Summarization
SummaryThis paper proposes MLEGT, a model for summarizing task-oriented dialogues. A multi-level encoder captures temporal and spatial dependencies, while a global topic is obtained during graph construction. A global topic guided pointer mechanism is added to the summarizer to capture important information that is otherwise overlooked. Experiments on a large-scale Chinese police interrogation dataset and a medical reporting service dataset show it outperforms several strong baselines.
In briefAgentic Music Post-Production: MixAgent Generates Editable Mixing Plans Guided by a Reference Song
SummaryThe paper presents MixAgent, a multi-agent pipeline that follows audio engineers' workflow to produce interpretable, editable mixing plans guided by a reference song. The authors also build MixCritic, a dataset of 45,657 repair critiques derived from parameters fitted to professional mixes, and train the critic with supervised fine-tuning and preference optimization on rendered audio. On MedleyDB, learned refinement reduces error by 19% relative to the initial mix, and a human listening test rates MixAgent higher than mixes from Qwen3-Omni, Gemini 3.8 Flash and GPT-4o-audio.
Jaywon Koo, Junghyun Koo, Marco A. Martinez-Ramirez et al.
In briefVLMProbe: an open-source library for probing and interpreting vision-language models
SummaryThis paper presents vlmprobe, an open-source library that combines lens readouts, attention attribution, causal interventions, and sparse autoencoder analysis in one model interface for studying how vision-language models use visual information. Through matched case studies and controlled evaluations of pretrained sparse dictionaries, the authors find that representation readouts and spatial explanations depend on the analysis objective and observation site, and that attribution estimates can disagree with measured intervention effects. They argue that multiple forms of evidence should be combined to evaluate an interpretation. The code is available at github.com/ubc-tea/vlm-probe.
In briefSame Answer, Different Reasons: Checking Whether Multilingual LLMs Reason Consistently
SummaryThe paper argues that standard evaluations of multilingual large language models only check whether final answers or overall responses match, which does not show whether users in different languages receive comparable supporting reasoning. The authors propose a trace-level framework that compares visible reasoning steps as structured sequences, measuring structural similarity, order consistency, and holistic similarity. Across three benchmarks, seven languages, and six frontier models, the whole-response baseline systematically overstates agreement, and human judgments align more with structural similarity.
In briefBlood pressure tracking from wearable pulse signals, using context and per-session calibration
SummaryThis paper examines how acquisition context and photoplethysmography (PPG) each contribute to blood pressure tracking when each session is calibrated. Using five-fold participant-level cross-validation on 653 AuroraBP participants, adding context reduced systolic and diastolic mean absolute errors by 0.436 and 0.126 mmHg, and PPG gave smaller improvements in matched comparisons. The authors also report retained negative findings and note that external transfer and clinical adequacy remain unestablished.
In briefReSeg: Retraining-free abdominal organ segmentation via generalizable semantic representation learning
SummaryThis paper proposes ReSeg, a two-stage, retraining-free framework for domain-agnostic zero-shot abdominal organ segmentation. The first stage pretrains a Generalizable Semantic Representation Network (GSRNet) on diverse synthetic images from a dedicated data engine. The second stage trains an organ-specific encoder-decoder network (OESNet) whose encoder is initialized with those pretrained weights. The authors report that ReSeg consistently outperforms state-of-the-art methods on seven abdominal organ datasets across CT, MRI, and PET, without using real training images.
In briefCausal world models that stay accurate when robot physics shift beyond training ranges
SummaryThis paper studies how robot world models, which predict system evolution under actions, can fail when physical conditions fall outside the training distribution. It examines which inputs a predictor may use and how its mechanism computes their effects. On a synthetic benchmark, a dependency mask from paired interventions reduced out-of-distribution RMSE from 1.769 to 0.697. In CausalWorld link-mass extrapolation, a model using explicit affine mass coefficients with the known PD controller outperformed its control in all ten task-seed runs. The authors conclude that how a physical parameter enters the dynamics mechanism determines whether its out-of-range value helps or harms prediction.
In briefInternal Guidance: A Sampling Method for Tilted Diffusion Models
SummaryThe paper studies reward guidance, which samples an exponentially tilted version of a base distribution using a diffusion model trained only on base samples. Instead of adding guidance terms externally, the authors propose internal guidance, which keeps the denoiser's output unchanged and shifts the point where it is evaluated. Training uses random diagonal rescalings of the data so one network learns both in- and out-of-distribution evaluations. The authors state that a single network can sample binary reward tilts, class labels, and infilling without fine-tuning or external guidance.
In briefValue-Rectified Distillation: A New Way to Train Flow-Based Offline RL Policies
SummaryThis paper studies a failure mode called Out-of-Distribution mode averaging, where distilling flow-matching behavior policies on multi-modal offline data makes generated actions fall into invalid regions. The authors propose Value-Rectified Distillation (VRD), which treats policy distillation as value-guided generative trajectory alignment. It includes two variants: VRD-Q, which filters low-value behaviors using value-quantile distillation, and VRD-R, which reorganizes the latent space to isolate high-value trajectories. The paper reports improvements over existing generative baselines on D4RL, OGBench, and Minari continuous control and planning tasks, especially on multi-modal, non-expert datasets.
In briefTesting expert-role prompts to improve zero-shot reasoning in language models
SummaryThis paper introduces Role-Enhanced Zero-Shot Chain-of-Thought (RE-ZS-CoT), a prompting method that adds expert roles to help language models reason without example tasks. It tests fixed expert roles and roles chosen automatically by each model across 20 LLMs and seven benchmarks. The method often beats zero-shot baselines, with gains of up to 44 percentage points. Unconventional roles that do not fit the task reduce performance.
In briefThe cost of choosing among many rules when auditing selective classifiers
SummaryThe paper studies how to verify whether a selective classifier meets a risk-coverage contract when any rule from a predeclared menu of K thresholds or score intervals may satisfy it. It shows that the intersection-union test attains valid guarantees, and that for small gaps and large menus the minimax sample size grows linearly in K up to logarithmic factors. The lower bound holds even when the score distribution is known, and experiments with vision-language model judges and frozen classifiers are reported.
WhyDerives matching minimax lower and upper bounds for multi-rule selective audits, validated with held-out VLM judge experiments.
In briefDistilling Multi-Teacher Scoring Principles for Unsupervised Time Series Anomaly Detection
SummaryThis paper proposes AnoMix, which distills several heterogeneous unsupervised time-series anomaly detectors into one lightweight student model without anomaly labels. It extracts weak pairwise supervision from the teachers' training-set anomaly scores and trains the student with CoRank, a multi-teacher ranking-distillation objective. Across seven benchmark datasets, the single compact student, under 80k parameters, improves over individual detectors and static score ensembles. A zero-shot variant shows the distilled ranking signal transfers to unseen datasets and can outperform large pretrained time-series foundation models.
In briefOccJEPA: Treating Occlusion as Masking for Amodal Video Segmentation
SummaryThis paper addresses amodal video segmentation, which predicts the complete mask of an object in each frame, including parts hidden by other objects. The authors treat occlusion as masking: tokens not covered by the visible mask are dropped, a pre-trained V-JEPA 2.1 predictor fills them in, and a light head decodes amodal masks. According to the abstract, the deterministic model runs 33.5 times faster than the diffusion-based state of the art and is more accurate on several benchmarks.
WhyTreats occlusion as masking and uses a pretrained JEPA predictor to fill hidden regions, replacing diffusion. Shows accuracy gains and 33.5x speedup across benchmarks, with controlled experiments attr
In briefDoes instance order change LLM group answers? Position bias in multi-instance tasks
SummaryThis paper studies how large language models combine many independent inputs, such as product reviews, into one aggregated answer. The models must label each instance and identify the most frequent label, a task whose correct answer should not depend on order. Testing fifteen LLMs on five datasets and seven order patterns, the authors find accuracy can differ by about 90 percentage points depending on order. They conclude that instance order can systematically bias aggregated predictions, so evaluations should cover different input arrangements.
Jingxuan Chen, Mohammad Taher Pilehvar, Jose Camacho-Collados
In briefHidden learning structure: two coordinated mechanisms for repairing and acquiring trainable structure
SummaryThis paper studies how a learner's hidden learning structure changes during training. It introduces Fiber Regulation Theory (FRT), which covers protected repair of existing function, and Fiber Acquisition Theory (FAT), which covers forming and renewing trainable structure, and analyzes how the two constrain each other. Implemented on Qwen2.5-32B with protected head repair and fixed-capacity LoRA renewal, the coordinated policy achieved higher teacher-forced Exact than either separate policy across two training trajectories.
In briefOur Own Jev: One-Pass Typed Decisions, from an In-House Judge to Open LoRA Models
SummaryThis paper describes typed decisions, which assign a probability to each fixed option given evidence and a question in a single forward pass, rather than generating and parsing text. The first part presents an in-house judge that triages about 148K agentic coding session records, replacing a frontier LLM judge that was far more costly and keeping all sessions inside the organisation. The second part evaluates reading the output layer of LoRA-adapted models on the 111 hard items of JevBench, where the 35B model scores 79.3%, not significantly different from the commercial reference (73.0%).
In briefART-MPG: Autoregressive Reflection/Transmission Manifold Path Guiding for Light Transport
SummaryThis paper introduces ART-MPG, a compact neural model that samples light path length, a reflection/transmission string, and a direction for rendering paths through specular surfaces. The direction is drawn from a topology-conditioned four-component von Mises-Fisher mixture. The authors extend retry weighting in Manifold Path Guiding by conditioning retries on both path length and the sampled R/T string, which they report reduces expected retry count and estimator variance. Across fourteen scenes at several samples per pixel, the method achieves lower mean squared error than existing online and offline baselines at equal sample counts, with lower variance across runs.
In briefWeakly supervised fish segmentation using only single-click point labels
SummaryThis paper proposes a weakly supervised pipeline that segments fish using only one click per fish instead of costly per-pixel labels. It tracks points across frames, converts them into motion-informed language expressions to guide a language-conditioned model (LMPM++), and applies optical flow and background subtraction to filter noise. Experiments on the DeepFish dataset show higher foreground mIoU than baseline models.
In briefAutoFraming: Using LLMs to Generate Frame Conditions for C Code
SummaryThis paper proposes an LLM-based method that generates ACSL frame specifications, which define which memory locations may be modified during execution, alongside functional contracts from C code. The approach analyzes code property graphs to extract frame-relevant information and uses it to guide the model. On an 84-function benchmark, it produced error-free frame specifications for 92.9% of functions, a 179% relative improvement over prior techniques, and on a 51-function benchmark the success rate rose from 80.4% to 96.1%.
In briefHumanCentric-Scenes-AI: A Multimodal Dataset of AI-Generated Human Scenes
SummaryThis paper introduces a human-curated dataset of 296 AI-generated stimuli depicting people in four contexts: surveillance-style images, occupational portraits, AI-edited real photographs, and short videos. Each stimulus is released with its file, generation prompt where recorded, and metadata. The authors report depicted-gender distributions compared with U.S. Bureau of Labor Statistics occupational gender shares, and analyze edit-region size against zero-shot detection difficulty. The dataset is intended mainly for human-perception research, with additional uses in calibrating AI-detection classifiers and analyzing bias in generative models.
