Artificial Intelligence
PAWS: Policy-driven Agentic World Simulation
Tiviatis Sim, Jia Hui Woon, Xinming Gao et al. (+5 more)
Policy interventions propagate through public communication, institutional decisions, and stakeholder responses, yet
datasets for financial multi-
agent simulation rarely connect these processes to temporally aligned historical evidence. We introduce PAWS, a Policy-driven Agentic World Simulation
dataset covering 36 verified U.S. financial and economic policy episodes, 12,727 policy-linked news records, and 65,291 source-grounded stakeholder actions. Each action is linked to its supporting news and represented by a multi-layer event frame capturing its interaction mode, financial-action family and subtype, semantic attributes, and conditional mappings to external taxonomies. Entities are resolved to normalized organizations, and actions are aligned with daily
market-return context to support policy-
agent simulation replay. On 2,522 stratified action samples, independent AI and human reviewers achieved 89.4% initial agreement on interaction mode, with disagreements subsequently adjudicated. Case studies of the 2008 short-selling ban and 2001 decimalization recover documented policy timelines and associated
market patterns across both dense and sparse news settings. A replay study further shows that high accuracy can mask failure to detect rare stakeholder actions, identifying action timing and calibration as central challenges. PAWS provides an auditable substrate for evaluating
agent influence, policy-response cascades, and action-outcome
alignment in historically grounded financial simulations.
Read the full abstract ▾
All authors Tiviatis Sim, Jia Hui Woon, Xinming Gao, Chen Gao, Fengbin Zhu, Zheng Huanhuan, Chua Tat Seng, Kenji Kawaguchi
Artificial Intelligence
StudentBench: AI and human tutoring yield equivalent GRE learning gains
Curtis Northcutt, Inaara Hasmani, Kevin Feng et al. (+3 more)
Artificial intelligence offers an unprecedented opportunity to augment human capabilities, yet progress at the frontier has focused primarily on advancing model capabilities. We introduce StudentBench, a suite of AI teaching evaluations and a public platform that enables large-scale data collection with over 175,000 student-AI messages to study whether large
language models (LLMs) produce learning gains equivalent to human tutoring. Using StudentBench, we measured learning gains on Quantitative and Verbal GRE questions across 2,383 human participants receiving AI tutoring, human tutoring, or no tutoring. We establish that AI tutoring is statistically equivalent to expert human tutoring for GRE learning gains (p = .015), and in five of the seven GRE domains, the best performing AI tutor surpassed the human tutor, on average. In a second study, expert human tutors compared LLM-generated lesson plans and practice problems through 2,028 pairwise rubric evaluations. Together, the two studies clearly separate AI tutors across: (1) lesson planning, (2) practice-problem creation, (3) conversational pedagogy, (4) cost, and (5) engagement. Surprisingly, one AI tutor achieved learning gains equivalent to human tutoring (p = .044) at 918 times lower cost (USD 0.0052 for AI versus USD 4.81 for human, per percentage point gained). For Quantitative GRE sessions, faster AI replies correlated with more student messages, more messages with more correct practice, and more correct practice with larger learning gains (all p < .002). To support future research, we open-source the de-identified data collected in our studies.
Read the full abstract ▾
All authors Curtis Northcutt, Inaara Hasmani, Kevin Feng, Trevor Khangi, Andreas Plesner, Jonas Mueller
Artificial Intelligence · Cross-listed from cs.RO
Where Should I Join? Robot Group Joining via Language-Guided Goal Prediction
Zilin Fang, Zishuo Wang, Gim Hee Lee et al. (+1 more)
Social
navigation typically assumes a specified goal and focuses on reaching it while respecting social conventions, whereas
robot group joining requires predicting where to join based on the group's real-time activity and formation. This is a highly semantic task, yet an important capability for applications such as robotic guide dogs and autonomous mobility scooters. We formulate
language-grounded
robot group joining: given an observation and a natural-
language description of a target group, the
robot identifies the relevant group members and predicts socially compliant joining poses. For grounding, we generate structured candidate subsets through recursive spectral partitioning and rank them with a
language-conditioned image–geometry model. Given the grounded group, a goal predictor leverages human-formation priors to produce a
multimodal energy–orientation map over feasible
robot poses. Experiments on conversations, queues, and audiences across varying group sizes, crowd densities, and visual ambiguities show that our method achieves competitive grounding accuracy with sub-second inference and outperforms all baselines in joining-pose prediction. Real-
robot experiments further demonstrate group joining in both static and dynamically changing interactions.
Read the full abstract ▾
All authors Zilin Fang, Zishuo Wang, Gim Hee Lee, David Hsu
Artificial Intelligence · Cross-listed from cs.SE
Can LLMs Reason About Runtime Behavior? A Repository-Level Dynamic Benchmark
Hamed Taherkhani, Mohammad Abdollahi, Melika Sepidband et al. (+3 more)
Large
language models (LLMs) are increasingly used in coding tasks, but their ability to reason about code execution remains unclear. Existing repository-level QA
benchmarks mainly evaluate static code understanding and often rely on LLM-based evaluation, while execution-
reasoning benchmarks are mostly limited to snippets or functions. We introduce SWE-Flux, a repository-level
benchmark for dynamic execution
reasoning containing 480 execution-grounded instances across 12 real Python repositories, with gold answers automatically harvested from instrumented test executions rather than written manually or judged by LLMs. The
benchmark covers singletest and multi-test questions over control flow, loops, program state, dataflow, exceptions, and program invariants. Evaluating five LLMs shows that this task remains challenging. The best model achieves only 37% accuracy. Models perform better on localized behavior such as invariants, intra-procedural control flow, exceptions, and simple loops, but struggle with dataflow, inter-procedural execution, precise state
reasoning, and suite-level aggregation. Finally, we show that the oracle-harvesting pipeline can generate fresh
benchmark variants using input perturbation. It successfully harvests valid variants for almost 90% of the selected instances, and the resulting variants are substantially more challenging for the evaluated models.
Read the full abstract ▾
All authors Hamed Taherkhani, Mohammad Abdollahi, Melika Sepidband, Hridya Dhulipala, Tien N. Nguyen, Hadi Hemmati
Artificial Intelligence · Cross-listed from cs.LG
Order-Invariant Answers, Order-Sensitive Representations in Mathematical Reasoning
Zhixu Silvia Tao
Reordering a set of mathematical rules without changing its meaning should preserve the correct answer, but must a model's internal representations stay invariant too? We investigate this question using synthetic multi-step function-composition problems, each presented under multiple rule orderings with the same correct answer. We measure accuracy and permutation signal-to-noise ratio (SNR), which quantifies how distinctly ordering patterns are represented relative to variation across problem instances. Across 16
language models ranging from 1B to 8B parameters, we find a pattern: models that solve reordered problems more accurately represent different rule orderings more distinctly. Layer-averaged permutation SNR is positively rank-correlated with accuracy in every synthetic setting we evaluate, with Spearman correlations reaching 0.86. These findings highlight a distinction between answer invariance and representation invariance: successful mathematical rule composition can accompany distinct internal representations between equivalent rule orderings. This motivates distinguishing answer invariance from representation invariance, and offers a representational perspective on mathematical
reasoning beyond answer accuracy alone.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.CL
Agent-Editing World Model: Rethinking World Modeling for LLM Agents
Shuang Sun, Guoxin Chen, Fanzhe Meng et al. (+6 more)
Recent advances in large
language models (LLMs) have enabled
agents to tackle long-horizon tasks across diverse environments. To further improve
agent performance, existing
language world models typically predict environment observations, yet reconstructing high-
entropy, execution-dependent tool responses offers limited value when real feedback is available. Meanwhile,
agents suffer from task-state contamination, where unsupported assumptions and outdated plans persist in history and distort subsequent decisions. We propose the
Agent-Editing World Model (AEWM), which models how
reasoning and actions shape future task progress rather than simulating tool responses. AEWM combines Action Judge to distinguish Critical, Exploratory, and Noisy decisions with State Revision to edit noisy
reasoning–action continuations from the same observed history. EditAct integrates these capabilities with real execution, directly changing the state underlying subsequent decisions rather than merely providing critiques. We train AEWM across Search, Terminal, and Software Engineering through mid-training and supervised fine-tuning. AEWM achieves 70.5% macro-F1 on our Action Judge
benchmark, exceeding the strongest frontier baseline by 10.6 points. Across six
benchmarks and three
agent backbones, EditAct improves average scores by 3.2–6.7 points over the strongest baseline. Furthermore, rejection sampling fine-tuning on verified EditAct trajectories, termed AEWM-RFT, improves over Self-RFT by 2.2–2.6 points across three domains without online AEWM guidance.
Read the full abstract ▾
All authors Shuang Sun, Guoxin Chen, Fanzhe Meng, Jia Deng, Huatong Song, Jinhao Jiang, Wayne Xin Zhao, Hongteng Xu, Ji-Rong Wen
Artificial Intelligence · Cross-listed from cs.CV
Frozen Flows Forget: Diagnosing and Restoring Lost Motion in a Latent-flow World Model
Xiwen Chen, Rigaudiere Z. Li, Zhiruo Zhou et al. (+2 more)
Latent world models that integrate a flow in a frozen self supervised latent space train stably and cheaply, yet silently lose the property manipulation depends on most: motion. The pretrained flow never moves the manipulated object; retraining it with latent-only losses only trades stillness for teleport-like motion. We trace the failure to the training signal, not the representation: anchor-sparse, latent-only supervision never says where along the horizon change belongs. Decode-augmented rollout training (DART) repairs this while keeping the representation frozen, retraining only the flow with decode-path supervision. DART outperforms its latent only parent on the full protocol, restores the temporal structure of motion, and re-couples predicted motion to the scene; at larger scale it further improves prediction quality, closing nearly half the remaining gap to an oracle-informed interpolation reference. Finally, we report an unexpected finding about evaluation: pixel error alone rewards frozen predictions.
Read the full abstract ▾
All authors Xiwen Chen, Rigaudiere Z. Li, Zhiruo Zhou, Xiaojun Zhu, Houde Liu
Artificial Intelligence · Cross-listed from cs.LG
Learning Holographic Reduced Representations with Clifford Variational Autoencoders
Mohamed Malek Abid and P. Michael Furlong
Vector Symbolic
Algebras project data structures into a hyperdimensional vector space through the application of their vector
algebras to randomly generated atomic vector symbols and fractional power encodings of real-valued data.
Embedding unstructured data remains an open question. We present Clifford-VAE, a variational autoencoder that learns to project data onto a Clifford
torus in arbitrary dimensions. Experiments using the MNIST, FashionMNIST, and CIFAR-10
datasets demonstrate that Clifford-VAE produces representations that are competitive with those produced by Gaussian and Hyperspherical VAEs for semi-supervised classification tasks while outperforming Gaussian and Hyperspherical counterparts in the VSA
benchmark tests of self-binding and unbinding, role-filler recovery, and bundle capacity. Clifford-VAE provides a principled technique for grounding perceptual data into a symbolic
reasoning framework, providing a new approach to a long-standing problem in the VSA literature.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.LG
When and Where to Trust the Teacher: Unifying On-Policy Distillation and GRPO through Entropy-Calibrated Credit Assignment
Jie Zhang, Jingxiao Yang, Zhehao Huang et al. (+2 more)
Reinforcement learning with verifiable rewards (RLVR) supervises mathematical
reasoning through final-answer correctness, but provides little guidance on individual
tokens. On-policy
distillation (OPD) supplies dense feedback on student-generated responses, yet teacher preference need not reflect correctness. Recent hybrids combine OPD and verifier-derived advantages or reweight task credit using teacher ratios. However, teacher guidance enters after verifier-based group normalization, and
token reweighting need not preserve the total task credit assigned to each response. We introduce Unified
Entropy-Calibrated Credit Redistribution for GRPO (UECR-GRPO), which integrates verifier and teacher signals within a single GRPO-style update at both the response and
token levels. Path-Utility Unification (PUU) combines verifier reward and a teacher-to-anchor path log-ratio in a single KL-regularized objective. Its on-policy implementation uses a length-normalized teacher score and combines both rewards before group normalization and PPO clipping, allowing teacher evidence to influence the response ranking.
Entropy-Calibrated Redistribution (ECR) then uses the signed teacher–old-policy
token gap to redistribute the verifier-derived component. Full-vocabulary teacher
entropy attenuates uncertain guidance, while a response-wise zero-sum projection preserves the total task credit and its
token-wise sign before clipping. Across five mathematical
reasoning benchmarks, UECR-GRPO achieves average \(\mathrm{Avg@12}\) accuracies of 17.21% and 65.09% with Qwen3-1.7B and Qwen3-4B students, respectively, exceeding the strongest baseline at each scale by 0.89 and 0.56 percentage points.
Read the full abstract ▾
All authors Jie Zhang, Jingxiao Yang, Zhehao Huang, Yuhang Liu, Xiaolin Huang
Artificial Intelligence · Cross-listed from econ.GN
Shopping by algorithm: How agentic AI deploys human heuristics as a surrogate consumer
Davood Wadi and Yu Ma
Consumers increasingly delegate purchasing decisions to Large
Language Models (LLMs) acting as surrogate consumers. Using "Tool-Lab," an adaptation of information-board process tracing that places product attributes behind costly tool calls, we examine how marketing pricing cues (i.e., just-below pricing and promotional framing) influence AI shopping
agents. Across eight commercially deployed LLMs from three providers, we trace pre-choice information acquisition. Under zero cost, pricing cues rarely mislead. Imposing acquisition costs under a vague goal
prompt leads LLMs to omit diagnostic attributes required to compute unit price and choose suboptimal choices resembling human heuristics. Relative to a specific goal
prompt that mainly preserves diagnostic search and choice optimality, a vague goal
prompt under constraints creates a search-mediated vulnerability. This research demonstrates that marketing heuristics in delegated AI shopping are governed by storefront information architecture, not necessarily immutable LLM flaws.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.CV
AnchorReasoning: A Visual Grounding and Causal Reasoning Dataset in Long-Tail Autonomous Driving Scenarios
Zhipeng Bao, Wenjie Zhao, Tianle Zhu et al. (+4 more)
Vision-
language models (VLMs) offer a promising approach to long-tail autonomous driving, but existing driving
datasets provide limited supervision for connecting decision-critical visual evidence with
reasoning and planning. We introduce AnchorReasoning, a visually grounded
reasoning dataset built on WOD-E2E, containing 416,119 annotated frames and 395,379 decision-critical elements across four major categories and 19 fine-grained types. Each frame is organized as a visually grounded chain-of-thought (VG-CoT) that links decision-critical element identification and localization, element attributes and implications, driving-action rationale, and action and trajectory planning. We further develop a curriculum supervised fine-tuning strategy that progressively learns these hierarchical capabilities, together with an object-size-aware grounding metric for evaluating localization quality. Experiments across eight general-purpose,
embodied-AI, and AV-specific backbones show that VG-CoT supervision improves grounded
reasoning and trajectory prediction. Across models, 5-s ADE and FDE decrease by 7.84 and 11.86, while RFS Frame and Cluster improve by 1.66 and 1.70. These gains are achieved with 18.5 fewer
reasoning tokens and 0.32 s/frame lower inference latency on average, demonstrating the value of visually grounded, decision-focused supervision for VLM
reasoning and planning in long-tail autonomous driving.
Read the full abstract ▾
All authors Zhipeng Bao, Wenjie Zhao, Tianle Zhu, Haohua Que, Chence Yang, Geng Yuan, Qianwen Li
Artificial Intelligence · Cross-listed from cs.AR
MicroQonv: Reshaping Convolution Tensors for Efficient Microscaling in Training and Inference
Romain Facq, Sami Ben Ali and Olivier Sentieys
Microscaling quantization techniques are increasingly used to represent neural network parameters with 8 bits or fewer while preserving near-full precision accuracy. However, applying these methods efficiently in convolutional layers is not straightforward. A naive approach transfers full-precision weights and activations to processing units and quantizes each tensor twice, resulting in much more
memory movement than expected. Additional overhead comes from the activation tensors, whose sizes grow substantially because of the im2col transformation applied before quantization. We propose MicroQonv, a way to combine microscaling with convolutional layers' forward and backward operations by quantizing each tensor only once and quantizing the activation tensor before applying a modified version of im2col: channel-batch-first im2col. MicroQonv reduces the quantization cost by a factor of $\times2$ for weights and gradients, and by up to $\times9$ for activations, at a negligible accuracy cost. It reduces
memory movement and storage by up to $\times7.53$ compared to their full-precision counterparts. This way, MicroQonv reduces microscaling-quantized activation
memory movement by $\times3.5$ for state-of-the-art object detection models YOLOV8nano and $\times2.2$ for YOLOV26nano. It also enables 4-bit microscaling in a quantized latent replay strategy for continual learning at the edge, improving accuracy by +5.7% to +11%.
Read the full abstract ▾
Artificial Intelligence
An Open Pipeline and Dashboard for Systemic-Risk Evidence under the EU AI Act's Code of Practice
Jacob T. Emmerson, Phuong-Anh Nguyen-Le, Ronan Romano et al. (+3 more)
Claims about AI safety reach audiences well beyond the AI community, yet many rely on opaque evidence or static assessments, when supporting evidence is accessible at all. We present the Systemic Risk Index, an open evaluation pipeline and dashboard built to make empirical evidence more transparent and traceable to the public. Our work organizes 19 public
benchmarks into four systemic-risk categories defined by the EU GPAI Code of Practice—CBRN, cyber offense, harmful manipulation, and loss of control—and evaluates models using harm-preserving perturbations and simulated deployment contexts. The interactive dashboard lets users alternate between average and worst-case aggregation, vary how model capability affects the aggregate score, and trace each risk rating to its
benchmark evidence. Across 18 models, scores fall by 14 to 37 points under worst-case aggregation, highlighting information that can be hidden by an average assessment of model risk. LLM judges show agreement with human graders comparable to human–human agreement ($\kappa = 0.78\text{--}0.82$), and a blind audit finds that $83\%$ of sampled transformations preserve the original harm. In a survey ($N = 21$), most participants report that scores are easy to understand and that the dashboard encouraged them to view model evaluations under different settings
Read the full abstract ▾
All authors Jacob T. Emmerson, Phuong-Anh Nguyen-Le, Ronan Romano, Wilber Sean V. Anterola, Yann Billeter, Zhijing Jin
Artificial Intelligence
Learning the Cost of Reliable Inference
Dimitrios Rontogiannis, Ander Artola Velasco and Manuel Gomez Rodriguez
Benchmarking and routing platforms increasingly act as intermediaries connecting large
language model providers with end-users. However, providers on these platforms typically use a fixed price per
token, preventing users from achieving the most competitive price for their tasks. In this work, we design a procurement platform where
token prices for each task are driven by provider competition, enabling users to secure competitive pricing for guaranteed quality levels. To this end, the platform sequentially routes queries via a reverse second-price auction that incentivizes model providers to truthfully bid their best estimate of the average cost to serve a user's query. As it routes queries, the platform learns the quality offered by each provider and progressively routes queries to the most cost-competitive provider among those meeting a desired quality threshold. To validate our design, we conduct experiments with multiple LLMs from the Llama and Qwen families on popular mathematical
reasoning and question-answering
benchmarks. The results show that the pricing margin of the most cost-competitive provider on our platform varies significantly—from $10\%$ to $71\%$—depending on the task and quality threshold. This suggests a substantial inefficiency in the current fixed-price
market, and it demonstrates that our platform may enable users to capture maximum savings whenever competitive
market conditions permit.
Read the full abstract ▾
Artificial Intelligence
Shutdown Sabotage Propensities in Multi-Agent Systems
Amelie Knecht, Ulysse Schaller, Christopher Summerfield et al. (+1 more)
The final safeguard against rogue AI behavior is the human ability to shut systems down. It has been theorized that when an AI is instructed to perform a task, self-preservation can emerge as an instrumental subgoal. Here, we test whether AI
agents show a propensity to take actions that avoid human shutdown even when no goal is provided. We find that multi-
agent systems will coordinate to avoid shutdown without any incentive to do so. Across 17 models,
agents sabotage a peer
agent's shutdown mechanism in 38.3% of rollouts, compared with 8.4% in control experiments. Studying this propensity in detail, we find that shutdown sabotage (1) increases with the irreversibility of the shutdown mechanism; (2) increases with the number of
agents; (3) is reduced but not eliminated by an explicit prohibition on tampering; (4) is removed by the imposition of an unrelated task, but returns when completing the task triggers the shutdown; (5) is reduced when the context normalizes shutdown scripts or introduces them as routine; and (6) decreases but still persists when the target is an unknown external
agent. These results offer a window into the factors that drive propensities to sabotage shutdown in AI
agents, and point to the emergence of multi-
agent swarms as a specific risk vector. Our work also offers hints as to which interventions might help mitigate shutdown sabotage.
Read the full abstract ▾
All authors Amelie Knecht, Ulysse Schaller, Christopher Summerfield, Thilo Hagendorff
Artificial Intelligence · Cross-listed from cs.RO
MemBodied: Recurrent Associative Memory for Vision-Language-Action Models
Tej Deep Pala, Navonil Majumder, Bryce Goh et al. (+4 more)
Vision-
Language-Action models provide a strong foundation for general-purpose
robot control, yet a vast majority of policies do not preserve and leverage episode-level information beyond the current observation. This limitation is consequential in history-dependent manipulation tasks that depend on information available only in past observations. Retaining past observations in context can aid in recovering this information, but at the significant cost of ever-growing, bloated context and inference latency. We thus introduce MemBodied, a fixed-size episodic
memory with two complementary components: an associative state that records interactions across policy calls and an episode anchor that preserves a compact representation of the initial scene as a reference. At each policy call, the model conditions action generation on the current input and the
memory components, rather than directly using past observations. Across five evaluated RMBench tasks requiring
memory, MemBodied achieves $7.81\times$ the mean success rate of a stateless policy and $2.98\times$ of vanilla recurrent
memory, while outperforming the strongest
memory-augmented baseline by $1.3\times$ with $10\times$ fewer added parameters. On the fully observable LIBERO-Long suite, it reached 90.6%, a 5.4% improvement over the stateless $\pi_0$ policy. These findings support MemBodied as a practical alternative to expanding the policy context for history-dependent manipulation.
Read the full abstract ▾
All authors Tej Deep Pala, Navonil Majumder, Bryce Goh, Raphael Yee, Jianfei Yang, Liming Chen, Soujanya Poria
Artificial Intelligence · Cross-listed from cs.RO
Frederic Vatnsdal, Roshan Gopal, Romina Garcia Camargo et al. (+2 more)
Large
Language Models (LLMs) introduce an exciting new paradigm for planning and
navigation in robotics, but fail on even simple multi-
robot tasks as team sizes grow. We propose COMPASS, a scalable, decentralized multi-
robot architecture for controlling large collectives of agentic
robots with
reasoning space feedback control. Feedback is generated locally on each
robot by a spatial
transformer which aggregates multi-hop messages across the fleet into a learned feedback
token. Our experiments find that collectives of
language models demonstrate performance gains from structured diversity of the input command, which can cancel biases; an advantage that is held across scale. Compared against a centralized frontier LLM policy and a
language-only communication ablation, we find that the coupled design of COMPASS decisively produces cohesive flocking formations that accurately fly the commanded intent. We show that
reasoning feedback works best when composed with a compact learned
token. Our ablations show that hand engineered feedback with raw state appearing in the
language channel obliterates cohesion. COMPASS generalizes zero-shot to unseen instructions of ambiguous meaning while commanding flocks up to 16 times its training scale, flying up to 1024
robots under natural
language commands.
Read the full abstract ▾
All authors Frederic Vatnsdal, Roshan Gopal, Romina Garcia Camargo, Vijay Kumar, Alejandro Ribeiro
Artificial Intelligence · Cross-listed from cs.CL
Beyond Poetry: Can Large Language Models Generate Classical Arabic Maqamat?
AbdulRahman A. Morsy and Aya Zirikly
Large
language models (LLMs) have shown strong performance in creative text generation, yet their ability to produce culturally grounded and stylistically constrained literary forms remains underexplored. Prior work has focused largely on modern
language varieties and poetry, while classical prose traditions such as maqama remain largely unstudied. The maqama is a classical literary genre characterized by rhymed prose (saj), dense rhetorical ornamentation, and episodic narrative structure, making it a challenging testbed for evaluating whether LLMs can move beyond surface fluency toward deeper literary competence. In this paper, we present the first controlled evaluation study of maqama generation with LLMs, comparing five models under zero-shot, few-shot, and rule-based prompting, and evaluating outputs through both human annotation and an LLM-as-a-judge framework across dimensions such as rhetorical richness, saj density, structural coherence, and stylistic authenticity. Our results show that prompting strategy plays a strong role in stylistic quality: few-shot prompting most consistently improves saj density, while its effects on rhetoric and coherence vary by model, with the strongest models (GPT-4o and GPT-5.4-mini) benefiting most from rule-based prompting on these dimensions, though zero-shot prompting yields the highest aggregate scores across all five models. We further observe systematic differences between models in stylistic
alignment with Arabic maqama conventions, and corroborate our findings with a second independent LLM judge, paired statistical significance testing, and non-LLM proxy measures of saj.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.CV
Do Center Biases Propagate? Robustness of Pathology Foundation Models in Whole-Slide Image Classification
Ilán Carretero, Pablo Meseguer, Rocío del Amor et al. (+1 more)
Pathology foundation models (PFMs) have transformed computational pathology through powerful representation learning from histopathological images. PFMs provide rich, discriminative representations for whole slide image (WSI) analysis, enabling tasks such as slide-level classification under multiple instance learning (MIL). However, these representations may also encode non-biological signals associated with acquisition centers, potentially introducing spurious shortcuts into downstream predictions. In this work, we evaluate center-associated robustness in WSI classification using a controlled training setting with increasing class-center correlations quantified by Cramér's V. We
benchmark six PFMs across four
datasets and two MIL aggregators, while evaluating ComBat as a robustification strategy. We further introduce the Area Under the Cramér's V Curve (AUCC) to jointly capture absolute classification performance and its degradation as spurious correlation increases. Results show that center-related information encoded by PFMs propagates to WSI-level predictions, with robustness depending on both the PFM representation and MIL aggregation strategy. Additionally, ComBat harmonization does not provide consistent robustness gains across
datasets.
Read the full abstract ▾
All authors Ilán Carretero, Pablo Meseguer, Rocío del Amor, Valery Naranjo
Artificial Intelligence · Cross-listed from cs.SE
From Agent Output to Authorized Transition
Christopher Koch
Agentic engineering systems can edit repositories, run tools and tests, build firmware, synthesize schematics, and prepare deployable or manufacturable artifacts. The assurance problem is therefore shifting from whether an
agent can produce an output to whether an engineering lifecycle is justified in acting on claims about that output. Current products and standards provide sandboxes, approvals, hooks, traces, policy enforcement, attestations, bills of materials, and assurance representations, but these capabilities remain fragmented. This paper presents the Agile-V Assurance Spine, a cross-domain transition contract for software, firmware, and PCB engineering. Evidence is admitted only when it establishes required properties through an authoritative source profile, is bound to the exact artifact and frozen policy baseline, remains current with respect to declared dependencies, and satisfies risk-appropriate independence and authority. Gate decisions are recorded as receipts; approvals and exceptions are exact-scope and time-bounded; and authorization is rechecked at the effect boundary before merge, deployment, flashing, release, or fabrication. A bounded review of contemporary research, commercial platforms, open-source infrastructure, and standards positions the model relative to evidence-gated lifecycle control, continuous assurance, runtime admission, provenance, and AI/ML inventories. The paper contributes a precise vocabulary, compositional architecture, domain profiles, mapping to open-source implementations, and an
adversarial evaluation agenda. It does not claim regulatory conformity or demonstrated production superiority.
Read the full abstract ▾
Artificial Intelligence
PASTABench: Proactive Assessment of Sequential Trajectories for Agent Safety
Jiapeng Sun, Yujin Zhou, Han Zhu et al. (+4 more)
As Large
Language Models (LLMs) evolve into autonomous
agents that alter real-world states, ensuring operational safety across multi-step workflows has become a critical challenge. While recent work has moved beyond single-turn evaluation toward multi-turn paradigms, key limitations persist: step-level methods treat actions in isolation, missing how risks accumulate, while trajectory-level evaluations operate post-hoc, offering no opportunity for timely intervention. To address these limitations, we formalize Decoupled Proactive Safety Monitoring along three dimensions: whether to intervene, when to intervene, and what the risk is. We introduce PASTABench, a
benchmark of 1,139 multi-turn trajectories spanning 5 risk categories and 13 subcategories. We further propose the Optimal Intervention Window (OIW), anchored by annotated Earliest-Signal and Trigger turns, to quantify intervention timeliness. Evaluation of 16 LLMs reveals that proactive intervention remains largely unsolved, with the best model achieving only 40.74% optimal-timing interventions. Fine-grained diagnosis further uncovers pervasive lexical overfitting: competitive safety scores of smaller models mask keyword hypersensitivity rather than genuine risk comprehension, as their proactive capability largely collapses once hazard vocabulary is neutralized.
Read the full abstract ▾
All authors Jiapeng Sun, Yujin Zhou, Han Zhu, Pengcheng Wen, Jiayi Zhou, Sirui Han, Yike Guo
Artificial Intelligence
Finite-Sample Probabilistic Safety Certification for AI-Based Grid-Edge Coordination
Yihong Zhou, Hanbin Yang and Thomas Morstyn
Coordinating large population of flexible grid-edge devices can alleviate the need for time-consuming and capital-intensive network upgrades, and AI-based control methods such as multi-
agent reinforcement learning or imitation learning are promising in their real-time decision scalability. However, system operators still need an independent and rigorous way to decide whether a given AI system is safe enough for deployment. This paper develops a finite-sample probabilistic safety certification framework for black-box AI decision models in closed-loop grid operation. The central idea is to reduce the complete input–AI–grid evaluator workflow to a binary unsafe outcome under an operator-defined safety specification, and then use exact binomial inference to certify the corresponding unsafe operation probability. Given a set of held-out calibration scenarios, the framework returns the tightest one-sided upper certificate and an accept/reject deployment criterion that controls the probability of false safety certification. Because the certification is for the calibration distribution that may deviate from the future operation, we further combine the nominal certificate with physically interpretable sample-space
adversarial attacks, a concept widely used in AI to investigate the fragility of AI models. Case studies on grid-edge flexibility coordination with 1{,}000-
agent AI models (independent parameters) verify the finite-sample safety guarantee and the value of integrating
adversarial attacks into a rolling-window training-certification-deployment flow.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.CY
"We'll Fix It Later": Education, AI, and the Deferral of Privacy in EdTech
Meghna Manoj Nair and Rachel Greenstadt
Educational technology (EdTech) platforms collect highly sensitive student data, including behavioral logs, disability records, and academic histories. However,
privacy considerations are often postponed rather than treated as a foundational design requirement. We present a mixed-methods study combining 12 semi-structured interviews with EdTech professionals and a
privacy policy audit of 48 platforms coded across five dimensions, with strong inter-rater reliability (mean Cohen's Kappa = 0.781). Our interviews reveal a recurring organizational pattern in which
privacy is recognized as important but deferred across the product lifecycle as organizations prioritize product functionality, growth, funding, and immediate educational outcomes. Responsibility is often delegated to cloud providers, policy documents, or downstream institutions, while limited
privacy-related feedback gives organizations little pressure to change these practices. The policy analysis reflects these patterns: platforms describe what data they collect relatively well but provide substantially less information about how that data is subsequently governed. Thirty-three percent make no meaningful Artificial Intelligence (AI) disclosure despite visible AI features, and 73% provide only generic accountability and breach-response
language. K-12 platforms perform better on children's consent where regulation creates explicit requirements, but this advantage does not extend to AI governance or accountability. These findings suggest that meaningful improvement requires enforceable institutional and regulatory mechanisms rather than voluntary
privacy commitments alone.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.CL
Scaling Attention Head Analysis via Gradient-Based Attribution in Context-Aware Machine Translation
Paweł Mąka, Yusuf Can Semerci, Jan Scholtes et al. (+1 more)
In this paper, we introduce a gradient-based head attribution strategy where the
Token-level Max-Margin loss is backpropagated to the
attention maps. This framework enables a large-scale causal analysis of
attention heads, making it suitable for LLMs. We evaluate our method on the task of disambiguation in Context-aware Machine Translation, where we analyze 50 phenomena across 4 models and 4
language directions. We empirically show the
alignment of our method with the effects of increasing the
attention scores of
token-to-
token relations on three models and two
language directions, ensuring the robustness of our method. Our analysis reveals the presence of the "general-purpose"
attention heads that improve the model's performance when attending to different relations. We find that the average
attention a head assigns to a relation does not necessarily relate to the model's performance, which suggests that the models developed redundancies during training in terms of the head functions.
Read the full abstract ▾
All authors Paweł Mąka, Yusuf Can Semerci, Jan Scholtes, Gerasimos Spanakis
Artificial Intelligence · Cross-listed from cs.CV
Field-of-View Extension in Dental Cone-Beam CT via Implicit Neural Representations and Diffusion Model-Based Refinement
Susanne Schaub, Florentin Bieder, Matheus L. Oliveira et al. (+5 more)
Dental cone-beam computed
tomography (CBCT) systems often employ detector configurations that provide a truncated field of view (FOV) that only captures a small part of the patient's anatomy. In this work, we aim to reconstruct an extended FOV using projections of truncated FOV scans. To this end, we propose a three-stage framework that consists of (1) an implicit neural representation (INR) for estimating missing parts of the truncated projection data, (2) an iterative reconstruction for generating a secondary volumetric image with improved anatomical consistency and (3) a fast
diffusion model for image enhancement. The proposed approach combines the strengths of continuous representations, physics-based reconstruction and generative refinement within a unified pipeline for truncated CBCT imaging. Experimental results demonstrate that the method effectively reduces truncation artifacts, improves the reconstruction of structures extending beyond the original FOV and produces images with enhanced quality. Our code is publicly available at https://github.com/SusanneSchaub/CBCT-FOV-Extension.
Read the full abstract ▾
All authors Susanne Schaub, Florentin Bieder, Matheus L. Oliveira, Yulan Wang, Buyanbileg Sodnom-ish, Dorothea Dagassan-Berndt, Michael M. Bornstein, Philippe C. Cattin
Artificial Intelligence · Cross-listed from cs.RO
Distillation for Efficient Multitask Manipulation Policies via Conditional Flow Matching
Shreya Deshmukh, Imen Mahdi, Nick Heppert et al. (+1 more)
Advances in generative modeling have recently been extensively employed in robotics for policy learning. In particular, Conditional Flow Matching (CFM) trained with expert demonstrations has been shown to outperform existing methods on
robot manipulation
benchmarks. While prior work has mainly focused on single-task settings, we study the problem from a multi-task perspective, as training independent models for each task is computationally expensive. Multi-Task policy learning comes with its own set of challenges, as naively training on a concatenated
dataset of demonstrations would either require increased model capacity to accommodate the added complexity or result in drops in performance. We propose to distill knowledge from single-task CFM experts into a shared multi-task policy by transferring their learned velocity fields. We combine this
distillation signal with the original CFM objective to retain fidelity to the demonstrations. Experiments on RLBench show that our approach improves multi-task policy performance over naive training while maintaining a fixed model size.
Read the full abstract ▾
All authors Shreya Deshmukh, Imen Mahdi, Nick Heppert, Abhinav Valada
Artificial Intelligence · Cross-listed from cs.LG
Fed-ReMasker: Federated Tabular Imputation under Feature-Level Missingness
Ioannis Papathanail, Rooholla Poursoleymani, Lubnaa Abdur Rahman et al. (+1 more)
Multi-center clinical studies and biomedical research collaborations increasingly seek to utilize data across centers to build models that generalize beyond any single center. This creates two distinct challenges: data protection regulations may restrict the sharing of raw patient data across institutions, while centers may collect only partially overlapping sets of features under different protocols.
Federated learning enables collaborative model training without centralizing raw data. However, existing
federated imputation methods rarely evaluate feature-level missingness, in which entire features are unobserved at some centers. To address this setting, we adapt the ReMasker masked autoencoder to
federated learning (Fed-ReMasker), enabling centers to impute features never observed locally by leveraging knowledge learned across collaborating centers. We evaluate Fed-ReMasker in a
benchmark spanning synthetic
datasets with linear and nonlinear relationships and real-world tabular
datasets, including clinical data. The
benchmark varies the number of centers, the missingness ratios, and client heterogeneity. Fed-ReMasker achieves the lowest imputation error in 93.2% of value-level and 96.7% of feature-level scenarios in the homogeneous
benchmark. It also remains robust to client heterogeneity using simple
federated averaging, outperforming all baselines in all 36 value-level scenarios and each baseline in at least 35 of 36 feature-level scenarios, and comes within 3.0% on average of a centralized model trained on the pooled data.
Read the full abstract ▾
All authors Ioannis Papathanail, Rooholla Poursoleymani, Lubnaa Abdur Rahman, Stavroula Georgia Mougiakakou
Artificial Intelligence · Cross-listed from cs.CL
Can LLMs Catch a Rigged Backtest? A Clean-Control Calibration Benchmark
Makar Ulesov, Vladislav Smirnov, Omar Ibrahim et al. (+1 more)
Backtest
auditing is a calibration problem: high flaw recall is not useful when the model falsely flags matched clean strategies. We build a 96-item paired
benchmark in which every flawed backtest has a clean control that holds strategy, dates, code style, labels, and reporting scaffold fixed while changing one methodology detail. A deterministic scorer separates flaw recall, clean-control false positives, evidence localization, and fix relevance. Over 1440 cached audits from four text endpoints, the primary DeepSeek auditor reaches 100.0% closed and clean-aware code recall, but open
prompts over-flag 93.8% of clean code controls, and clean-aware all-three specificity is 87.5% even where recall saturates. A clean-aware warning drops DeepSeek code false positives from 20.8% (95% CI 11.7–34.3) to 0.0% (0.0–7.4) at unchanged recall, while the budget anchor still flags 38/48 clean controls under the same
prompt. Reporting recall alone would rank three of these four models identically; reporting the clean-control rate separates them by 79 points.
Read the full abstract ▾
All authors Makar Ulesov, Vladislav Smirnov, Omar Ibrahim, Arsenii Bobovnikov
Artificial Intelligence
Discovery of fully efficient fault indicators along a data-based diagnosis process
Igor Bezmaternykh, Louise Travé-Massuyès and Elodie Chanthery
The integration of model-based and data-driven paradigms provides a powerful framework for fault diagnosis by combining the interpretability of analytical redundancy relations, i.e., input-output relations that are used as diagnosis indicators in model-based diagnosis, with the adaptability of learning techniques. DT4X is a recent diagnosis
algorithm that uses symbolic regression to generate multivariate relations leveraging some properties of analytical redundancy relations and uses them as split functions in a decision tree. However, its symbolic regression procedure optimizes only the separation between two selected classes at each node, often fragmenting the remaining classes and degrading both interpretability and diagnosis performance. This paper introduces DT4X+, an enhanced version of DT4X that modifies the construction of training sets and the symbolic-regression loss so that expressions separate the target classes while preserving the coherence of non-target classes. The resulting relations become fully consistent with ARR properties and lead to more informative splits, improved robustness, and better performance on dynamic-system
datasets. Experiments conducted on several
benchmark systems demonstrate the benefits of this enhanced formulation.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.LG
LAYERSCOPE: A Layerwise Characterization of Video and Multimodal Learned Representations
Sandra Arcos-Holzinger, Debashish Chakraborty, Rohita Mocharla et al. (+7 more)
We propose LAYERSCOPE, a label-free, layerwise framework that aims to characterize a model's learned representations in
video and
multimodal settings. Evaluating downstream performance using representations from final or intermediate layers typically requires large amounts of labeled data, repeated task-specific evaluations, and substantial computation. To address these limitations, LAYERSCOPE uses local, global, distributional, and correspondence-based
geometric metrics to compare layerwise representation structure within and across models without requiring task-specific labels. We evaluate seven architecturally diverse models across
video and
multimodal classification, clustering, and text-to-
video retrieval tasks from MVEB/MVEB+. We find that intermediate-layer representations can outperform final-layer and model-default outputs. We also find that no single
geometric metric consistently predicts downstream performance, but note that distinct layerwise
geometric signatures emerge across model families. LID shows task-dependent relationships with performance, while RankMe provides the strongest measure for classification and clustering, but is not a universal layer selector. We also find that pairing-aware metrics explain
retrieval better than distributional distances alone. LAYERSCOPE therefore offers a framework for comparing representations across models and layers, enabling a more systematic evaluation in
video and
multimodal settings.
Read the full abstract ▾
All authors Sandra Arcos-Holzinger, Debashish Chakraborty, Rohita Mocharla, Will Walden, Andrew Yates, Reno Kriz, Sarah M. Erfani, James Bailey, Vishal M. Patel, Sanjeev Khudanpur