In briefEvoWorld: Out-of-Sight World Evolution in Generative Video Models
SummaryThis paper argues that generative video models render plausible visuals but lack the reasoning needed to determine how events change a world. It defines the Out-of-Sight Causal Evolution problem and proposes EvoWorld, where a vision-language model tracks world state and reasons about consequences while a segment-autoregressive renderer produces the video. The authors also release EvoWorld-Data and EvoWorld-Bench for training and evaluation, and report that EvoWorld improves event-conditioned causal evolution over recent models while preserving visual quality and consistency.
In briefTailSteer: A Method to Curb Visual Drift in Multi-turn Image Editing
SummaryThe paper observes that strong single-turn image editing does not ensure reliable multi-turn editing, since small unintended changes accumulate over repeated edits. It proposes TailSteer, a final-stage training framework that aims to reduce this drift while keeping the capabilities learned earlier by the model. Experiments on the FLUX, SenseNova, and Qwen model families report substantially less cumulative drift over 15 to 20 editing turns, with base-model performance largely preserved on benchmarks.
In briefCoupled Scene Graph Anticipation: Constrained Recall Can Mask Structural Inconsistency
SummaryThe paper proposes CoupledSGA, a model that predicts object relationships in future video frames by letting relationships sharing an entity influence each other. It adds a Structural Consistency Loss based on mutually exclusive predicates. The authors show that With-Constraint evaluation cannot capture within-group violations, and on Action Genome the loss lowers the No-Constraint contradiction rate from 45.92% to 4.07% while reducing R@20 from 42.21% to 35.18%.
Zeno Nongmaithem, Anushttha Srivastava, Anshit Sinha et al.
In briefPhone-Based VR Exergames That Turn HIIT Workouts into Games with Real-Time Pose Tracking
SummaryThis paper presents a VR exergame system that gamifies High-Intensity Interval Training with 11 predefined combo moves. A standard smartphone on a tripod captures full-body movement, which is processed in real time by a pose estimation algorithm and assessed by a backend using MCP and GPT-4o. In a within-subject study of 20 participants aged 18 to 25, VR sessions produced higher enjoyment, immersion, and likelihood to recommend than non-VR sessions. The system's evaluations agreed with a certified trainer's assessments in 87.5% of 10 sessions.
Abhiram Kothagundu, Pheelip Pritipal Raipure, Abhijit Chandrakant Patil et al.
In briefConceptARC at Scale: Testing Concept Understanding in Reasoning Models
SummaryThis paper introduces ConceptARC at Scale, a generator that creates thousands of ARC-style tasks targeting specific concepts. Using it, the authors build ConceptARC-100, a dataset of 1,600 manually reviewed tasks covering 16 concepts, ten times the size of the original benchmark. They evaluate recent reasoning models and models trained from scratch, and analyze their internal representations. The representations cluster by operations applied to objects, such as recoloring, removal, or translation, suggesting models form stable abstractions of transformation operations.
Philipp Mondorf, Adrien Deli\`ege, Solim LeGris et al.
In briefReusing output templates to simulate shell command results with small language models
SummaryThe paper introduces template gap reconstruction (TGR), a method for simulating shell command output with small language models without executing the commands. A template selector picks a reusable output template, and autoregressive models generate the varying gaps between fixed literals. Evaluated on a target environment excluded from the template bank, TGR improves output edit similarity when template coverage is high and reduces generated tokens by 26.3% to 38.3%.
In brief"Turns Matter": Improving Streaming Video Reinforcement Learning with Turn-Level Credit Assignment
SummaryThe paper addresses streaming video understanding, where models must repeatedly decide whether to respond or keep observing. It proposes TurnWise, a turn-level GRPO framework that computes group-relative advantages for each streaming decision from its remaining return. Token losses are averaged within each turn and then across turns, so response and waiting decisions receive equal weight. Experiments show the method outperforms prior RL approaches for streaming video understanding.
In briefAirlock: Limiting Fault Spread in Multi-Agent Systems via Selective Online Containment
SummaryThe paper presents Airlock, a runtime and guardrail for multi-agent systems that performs selective fault containment after an alert. Its tree-structured runtime, EdgeCitadel, narrows permissions from parent to child agents, while an append-only provenance graph records cross-agent data consumption. After an alert, Airlock traces forward to isolate tainted executions and pending actions, and backward to identify root causes. An optional optimized cut minimizes disruption to healthy work under a deadline.
In briefSurvey of dexterous robotic hands in the foundation-model era
SummaryThis survey reviews research on dexterous robotic hands from January 2024 to early November 2026, including earlier studies to show technical lineages. It organizes the literature into five connected layers: hardware, data collection and curation, cross-embodiment transfer, learning and foundation models, and evaluation and applications. The authors conclude that the value of human data depends on kinematic, visual and contact alignment rather than recording volume alone. They also argue that completing a task is not sufficient to establish physical correctness or deployment readiness.
In briefStudy of symmetry breaking at the edge of stability using central flow analysis
SummaryNeural networks have continuous symmetries that yield quantities conserved under gradient flow, such as layer balancedness in linear and ReLU networks. At the edge of stability, where iterates oscillate around a smoother trajectory described by central flow dynamics, finite-step gradient descent may not preserve these conservation laws. The paper studies how gradient flow conservation laws evolve under this central flow description, analysing a scalar two-layer model, two-layer matrix factorisation, and a two-layer ReLU network to determine which laws remain and which are broken.
In briefReasoning Over Supplied Facts in Multilingual LLMs
SummaryThis paper introduces Rel-Bloom, a multilingual diagnostic benchmark of 450 multiple-choice questions built from 30 Wikidata relations. Questions are arranged across five Bloom-inspired levels and three veracity conditions, and 43 models were tested in six languages. Accuracy drops sharply from the Apply to Analyze level in open-weight models, and Evaluate items remain difficult even for strong models. The authors conclude that greater model capability does not by itself guarantee reliable reasoning over supplied relational evidence.
Zohreh Abbasi Liasi, Batoul Hammoud, Anas Madkoor et al.
In briefEnergy-Guided Transport: A Projection-Free Method for Physics-Constrained Flow Matching
SummaryThe paper proposes Energy-Guided Transport, a training-free framework that expresses nonlinear physical constraints as a smooth potential energy landscape. Instead of interrupting ODE integration with algebraic projections, it applies a continuous dissipative force to intermediate generative states to steer them toward the physical manifold. The method is evaluated on various 1D and 2D physical systems, including the chaotic Navier-Stokes equations.
In briefBeyond the Principle-Practice Gap: Responsible AI as a Situated Ethical and Epistemic Practice
SummaryResponsible AI research has found that ethical principles are often not reflected in AI design and development, a gap usually treated as a problem of implementation, awareness, or compliance. Based on fourteen interviews with people designing and governing AI-enabled anti-trafficking systems, this paper argues that the gap arises from institutional and professional conditions that shape which issues actors recognise as ethical problems requiring action. It describes four linked moments: perception, judgement, situated action, and socio-technical materialisation. The authors conclude that responsible AI governance needs epistemic infrastructures that make the knowledge of affected communities receivable and actionable in design.
In briefALTO: A Corpus of Tech-Facilitated Gender-Based Violence in African and Levantine Languages
SummaryThis paper presents ALTO, a dataset of 3,022 examples of tech-facilitated gender-based violence in Levantine Arabic, African French, and Swahili. The examples were crowdsourced with civil society organizations in Jordan, Francophone West Africa, and Kenya, and annotated using active learning. Existing harmful content detection models show poor recall on ALTO, which improves with fine-tuning, and models often miss covert harms, with markedly higher error rates in Swahili. The annotation taxonomy, dataset, and trained models are released.
Tianwei Zhang, Ivy Njoki Nyagwencha, Zahraa Dirani et al.
In briefWhere It Breaks: Diagnosing Failures of a Vision-Language Pac-Man Agent
SummaryThis paper studies a Qwen3.5-9B-based vision-language agent that plays Pac-Man using only four movement actions, asking whether poor performance comes from reading the screen or from choosing actions over long sequences. Using separate perception checks and primitive-policy reinforcement learning on textual boards, completion on eight held-out mazes rose from 0% to 80.2% at a selected checkpoint. The authors find that accurate static screenshot reading can coexist with errors during interaction, and that further optimization can come with worse action choices.
In briefAgent-driven generative priors for synthesizing and segmenting pathology images
SummaryThis paper proposes a three-stage framework to address the scarcity of pixel-level annotations in pathology images. A pathology agent system screens image quality to build TCGA-200K, a dataset of 217,030 patches from TCGA, and a generative model trained on it is adapted to semantic segmentation with a lightweight head. The authors also introduce PathSeg-380K, with expert annotations for about 380,000 cells across 12 cell types. Experiments on multiple pathology datasets report state-of-the-art segmentation performance.
In briefPINR-ESI: A Physics-Informed Self-Supervised Neural Method for Mapping Brain Activity from EEG
SummaryThis paper proposes a method for extended-source EEG imaging that requires no paired source labels or simulation-based supervised pretraining. It uses a lead field matrix to constrain an implicit neural representation so that predicted and measured EEG agree, modeling cortical current density as a continuous field. Simulations showed the clearest advantage in identifying multiple and larger active cortical regions. Evaluations on real EEG from epilepsy patients and intracranial stimulation experiments supported the framework's feasibility for source imaging.
In briefAdds a Detector-Agnostic Subspace Signal to Improve Near-OOD Detection
SummaryThis paper proposes PLUS, a method for near-out-of-distribution (near-OOD) detection that builds a low-dimensional per-class subspace from a sparse autoencoder's most class-active neurons. Inputs are scored by their whitened squared Mahalanobis distance within the subspace of their predicted class. This signal is fused additively with existing detection scores at a single fixed global weight. Across 18 detectors and 126 detector–backbone combinations, it consistently improves near-OOD AUROC while far-OOD performance is broadly maintained or improved.
In briefLifeVal: A Benchmark for Testing AI on Personalized Consumer Tasks
SummaryThis paper introduces LifeVal, a benchmark for evaluating how well AI models handle personalized agentic consumer tasks across 148 categories, such as managing finances, moving apartments, searching for a job, and finding childcare. The authors estimate the tasks' value through a survey of over 3,800 consumers, cross-referenced with the American Time Use Survey and the Multinational Time Use Study. Task creators who met an expertise threshold built the tasks from real ChatGPT conversations, memories, reference files, and personal context, and each task passed at least three rounds of human quality control.
Jenny Yang, Elizabeth Proehl, Anjan Bharadwaj et al.
In briefSekai: Incremental Event Memory for Reasoning Video Segmentation That Tracks Changing Targets
SummaryThis paper introduces Generalized Reasoning Video Segmentation (GRVS), a task where the objects matching a query can change as new events occur in a video. The authors build Isekai, a benchmark of 60 videos and 197 queries, and propose Sekai, an online framework with an event memory reasoner and a memory-grounded decoder. Sekai reaches 50.33 J&F, improving on the strongest baseline by 3.2 points.
In briefSecureGARD: Ontology-Guided Security Framework for Tool-Using AI Agents
SummaryThis paper presents SecureGARD, a framework that governs large language model agents which call external tools, aiming to block indirect prompt-injection attacks. It maps tool calls to a shared semantic representation and uses an executable ontology to decide whether each action is ready, waiting, or forbidden, with waiting states enabling bounded replanning. The ontology is refined offline from verified training runs and frozen after held-out validation. The authors report a 0% attack success rate on AgentDojo, ASB, and AgentDyn, along with improved task utility under attack.
In briefDo unlearning algorithms learn the right features?
SummaryThis paper examines how machine unlearning, which removes the influence of deleted training data from a model, is evaluated. The authors argue that comparing only post-deletion outputs can hide differences in the features encoded by an unlearned model versus one retrained on the remaining data. They prove that models agreeing in output can be broken apart by a single gradient step on their prediction heads. They also introduce a feature recovery measure that separates missing from extraneous features, finding that many methods performing well on standard metrics fail to recover the retrained features.
Polina Dolgova, Amartya Sanyal, Sebastian U. Stich
In briefSelf-Directed Apple Tasting: Online Learning with Partial Feedback
SummaryThis paper studies the classical apple-tasting setting, a partial-feedback model where the learner sees the true label only when it predicts 1. It considers a self-directed variant in which the learner adaptively chooses the order of points to predict on. In the realizable case, the authors establish a tetrachotomy for the expected minimax number of mistakes, with rates depending on the VC dimension, the ∅-centered star number, and the self-directed dimension. In the agnostic case, the minimax regret is either $\tilde \Theta(\sqrt{n})$ or $\Theta(n)$, depending on the VC dimension.
In briefAgentTrustCI: Build-Time Tests for Java LLM Agent Policy, Grounding, and Trajectories
SummaryThis paper presents AgentTrustCI, a Java and JUnit 5 framework that checks LLM agents at build time for policy compliance, grounding of answers in tool output, and adherence to required workflows. It applies these checks to recorded traces and was evaluated on 55 synthetic cases, where the full pipeline correctly classified all injected faults and passing controls and missed no critical cases. The authors note that these results reflect coverage of the authored regression suite rather than production accuracy.
In briefCardioDrift: fully automatic heart motion tracking on a 3D atlas mesh with slice-shift correction
SummaryThis paper presents CardioDrift, a fully automatic, atlas-based probabilistic method for tracking subject-specific cardiac motion from cine cardiac MRI. It generalises Coherent Point Drift to jointly register atlas surfaces of several cardiac regions to segmentation contours across the cardiac cycle, with slice-shift correction built into the same model. Evaluated on 1301 UK Biobank subjects and the M&M dataset, it outperforms the strongest baseline on all distance and overlap metrics on both datasets.
Reza Akbari Movahed, Abuzar Rezaee, Shatha Alamri et al.
In briefFinding Where Search Agents Make Mistakes: Graph-Based Step-Level Checking
SummaryThis paper studies search-augmented reasoning systems that can reach correct answers despite errors in intermediate steps. The authors introduce GraphRail, which represents requirements, evidence, and reasoning as structured claims and compares them without a generative judge or gold answer. Across three multi-hop QA benchmarks and three reasoning frameworks, GraphRail achieves higher error recall than generative verifiers and remains stable under stochastic extraction.
In briefTiming analysis to identify dispatchers in distributed model extraction
SummaryThe paper examines how API distillation campaigns spread requests across many load-balanced accounts to evade content classifiers. It shows that request arrival times and stream completion times reveal the underlying dispatching strategy, such as round-robin counters or key-shuffling pools. Using a 20 percent seed set, the timing analysis ranks the remaining accounts of a campaign within 20 minutes with an AUC of 0.92 to 1.00.
In briefElastic Expert Parallelism for Resource-Efficient RL Post-Training
SummaryThis paper addresses response generation, the main computational bottleneck in reinforcement learning post-training of large language models, where in mixture-of-experts models finished workers remain occupied because others still need their experts. The authors present FlexFloor, which jointly adapts response scheduling and the active worker group, using historical response lengths and memory limits, and progressively releases workers while keeping expert availability. Compared with the verl baseline, it generates about 65.40% more response tokens per unit of occupied compute time and improves generation throughput by 52.74%, with little variation in rollout reward.
In briefBeyond Accuracy: What AI Assurance Must Establish in Public Services
SummaryThis paper examines how public bodies can decide whether AI-generated evidence, such as classifications and summaries, is reliable enough to use. The authors tested eight GPT-4o configurations classifying 1,200 UK Prime Minister's Questions into 21 policy topics against human labels and repeated runs. Accuracy was similar across settings, but 24.3% of labels changed between runs and agreement between configurations did not ensure correctness. The authors also analysed 37 UK and EU policy documents on testing and oversight requirements.
In briefDetecting hidden robot action errors using calibrated relational world models
SummaryRobot actions can meet shape and range checks yet still have wrong meaning, such as swapped axes, inverted signs, or reversed gripper polarity. The paper uses learned state-action-effect relations as offline contracts, calibrating alarms on clean held-out transitions and ranking likely faulty fields with a fixed inverse vocabulary. On ten LIBERO Spatial tasks with 432 source episodes, at a 2.5% clean false-positive rate, the exact conditional of a four-component joint Gaussian mixture reached 65.7% recall, versus 46.1% for ridge residuals. These are retrospective diagnostics, not deployment calibration guarantees.
In briefDynamical State-Space Diffusion for Spatiotemporal Prediction
SummaryThis paper proposes Dynamical State-Space Diffusion (DSSD) for spatiotemporal prediction. Unlike standard diffusion models that add independent noise to each frame, DSSD learns the temporal structure of the noise process itself, building a temporal kernel from damped oscillatory modes of a stable state-space model. The denoiser architecture stays unchanged. On scientific-field and robot-video datasets, DSSD outperforms diffusion baselines that share its denoiser.
In briefReproducibility and robustness of large language models in political and social science research
SummaryThis chapter examines the reproducibility problem in research using large language models, whose probabilistic outputs vary across models and versions. It proposes a distribution-based standard for replicability, reproducibility, and robustness, along with a standardized prompt script and error-structure analysis. Using three datasets, the authors show that the choice of measurement can reverse the sign of downstream regression coefficients. They also present a stacked ensemble of open-weight models that improves precision in the correction step while retaining estimate validity.
In briefPredicting human motion by progressively compensating for lost prior knowledge
SummaryThis paper addresses human motion prediction, which forecasts future human poses from observed past motion sequences. The authors propose Continual Prior Compensation (CPC), a framework that splits predictions for different time horizons into subtasks trained in multiple stages, with a learnable Prior Compensation Factor measuring prior knowledge loss. An extended version, CPC++, adds a Fine-Grained Prior Compensation Factor for each subtask. Experiments on three benchmark datasets with several state-of-the-art backbone models show the frameworks are effective and flexible.
In briefUncertainty-guided efficient communication for drones sharing sensor data
SummaryThis paper proposes UCP, a framework that makes communication efficient for UAV teams sharing perception data. It uses observational uncertainty to find sensor-limited regions and discriminative uncertainty to find ambiguous regions near the decision boundary, generating request and supply maps. Received messages are fused by U-Fusion, an uncertainty-guided cross-attention module. Experiments on three benchmarks cover 3D object detection and trajectory prediction.
In briefTestBED: An Open-Source Toolkit for Bayesian Experimental Design
SummaryThis paper presents TestBED, a general-purpose open-source toolkit for Bayesian experimental design (BED). It breaks the BED process into modular, reusable components covering tasks, objectives, training, policy architectures, and evaluation, along with scalable implementations of core primitives such as expected information gain estimators. The authors use TestBED to run a large-scale benchmark of contemporary BED methods, including controlled studies of design policy choices such as time embeddings.
In briefTrick-or-Treat: Adversarial Testing of Medical AI Using Synthetic Images
SummaryThe paper introduces Trick-or-Treat, a benchmark that pairs real chest radiographs with synthetic clinical photographs to test whether models reason correctly across image types. In mismatched pairs, three frontier models hallucinated clinically significant photograph findings 45-57.5% of the time, compared with 25-37.5% when the photographs were shown alone. The models tended to ignore the mismatch and invent findings that fit the radiograph instead.
In briefFrom Certified Pairs to a Certified Hub: Checking Information Exchange Standard Crosswalks with a Reasoner
SummaryThis paper addresses how digital twins in a national ecosystem are linked to a common foundation through mappings, and defines valid mappings as coherent and conservative when merged into one ontology. The authors test crosswalks from the Information Exchange Standard (IES) to thirteen ontologies, first pair by pair and then across whole hubs, using matching, human review, SKOS certification and the HermiT reasoner. Pairwise certification and review catch different errors, and together they make eleven of twelve overlapping pairs coherent and conservative. However, merging three building crosswalks made five Brick classes unsatisfiable, showing that pairwise certification does not compose and that the hub is the unit to certify.
In briefReal-time interactive world simulation with Cosmos for training physical AI
SummaryThis paper presents a shared framework that adapts Cosmos world foundation models for real-time interactive simulation. It streams visual observations in response to actions and scene controls, letting users and embodied agents interact with evolving virtual environments. The approach is demonstrated on camera navigation, robot manipulation, controllable video transfer, and multi-view autonomous vehicle simulation, with code and model checkpoints released.
NVIDIA: Anis Ahmad, Jon Allen, Malik Aqeel Anwar et al.
In briefLearning to track cells in 3D microscopy videos using sparse labels
SummaryThis paper presents a pipeline for tracking cells in 3D time-lapse microscopy, where only some cells are annotated, to reconstruct cell lineages. It detects cells from sparse annotations, calibrates candidate links using image features, and jointly selects lineage graph events. On 199 videos from 2 zebrafish embryos, it achieves an overall score of 0.9762 in 5-fold cross-validation.
In briefPrivate mean estimation without known covariance cannot avoid dimension dependence
SummaryThis paper studies the limits of differentially private Gaussian mean estimation when the covariance matrix is unknown. When the covariance is known, optimal sample complexity depends only on its spectrum rather than the ambient dimension. The authors prove that private estimators cannot achieve similar guarantees when the covariance is unknown. They give lower bounds showing error gaps of order √(d/n) when the sample size is below the effective dimension, and of order √d/n^{3/2} above it, even for diagonal covariances.
WhyThe authors prove dimension-dependent lower bounds for private Gaussian mean estimation with unknown covariance, contrasting the known-covariance case.
