Artificial Intelligence · Cross-listed from cs.CL
Can LLMs Reason in a Legally Meaningful Manner? A Small-scale Study on European Court of Human Rights Cases
Amogh Raina, Ilias Chalkidis, Daniel Hershcovich et al. (+1 more)
Reasoning has become a standard technique and feature for contemporary LLMs; however, its application and quality in the context of demanding legal-oriented tasks, such as legal case
forecasting, remain under explored. We investigate how LLMs reason in the context of legal case
forecasting, using legal cases from the European Court of Human Rights (ECtHR) as a testbed. We evaluate OpenAI GPT 5.4, a recent top-tier LLM, by exploring alternative prompting strategies that are more or less suggestive of what counts as legally meaningful
reasoning in the context of ECtHR jurisprudence. We present our findings derived from assessing the model's responses with both human and LLM evaluation. We find that the examined model scores far from ideal in legal
reasoning, the model produces structurally complete but substantively shallow analyses, and that LLM-as-a-Judge evaluators are internally consistent yet align only weakly with our trained annotators, i.e., reliable but not a valid substitute for human evaluation. Overall, the expert-curated
prompt leads to more comprehensive
reasoning, which does not result in more accurate predictions compared to the other examined settings. Based on our findings, we urge the community not to rely solely on automated LLM-based evaluation and to avoid using task accuracy as an appropriate proxy for
reasoning quality.
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All authors Amogh Raina, Ilias Chalkidis, Daniel Hershcovich, Henrik Palmer Olsen
Artificial Intelligence · Cross-listed from cs.IT
Expected free energy as an information constraint on the Bethe Lagrangian
Wouter M. Kouw
Active inference selects actions by minimising an expected free energy functional over predicted futures. However, adding an expectation over yet-unobserved outcomes means the free energy functional no longer has a Kullback-Leibler structure, which hinders message passing treatments of inference procedures. We propose an alternative formulation based on a Bethe free energy functional, fully supporting inference by message passing. The epistemic drive is maintained by imposing an information constraint, next to normalisation, marginalisation and form constraints, insisting that the mutual information between future observations, states and parameters given actions must be at least as large as the
entropy of the goal prior. For a specific value of the corresponding Karush-Kuhn-Tucker multiplier, the stationary point of this constrained Bethe Lagrangian recovers the expected free energy solution. We show that, as the information demand is varied, the solved multiplier moves through its inactive, interior, and saturated regimes. In the inactive regime the
agent's epistemic drive switches off entirely, while in the saturated regime it is maximal. We compare the performance of the constrained Bethe
agent on three tasks against EFE and Q-MDP.
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Artificial Intelligence · Cross-listed from cs.LG
Q-Learning With World Models
Perry Dong, Yueru Jia, Chelsea Finn et al. (+1 more)
Off-policy reinforcement learning (RL) has become increasingly sample-efficient, enabling applications such as RL fine-tuning of
Vision-
Language-Action models into reliable, high-performing policies. World models offer a further lever for sample efficiency, as they predict state changes rather than actions alone, but their success has largely been confined to supervised policy learning. Prior model-based RL methods often optimize the policy or value function directly on imagined rollouts, which is prone to compounding bias and struggles to scale to large, high-dimensional problems such as real-world robotics, a problem that worsens with task horizon and visual complexity. In this work, we instead ask whether we can leverage world models directly on top of standard Q-learning to improve performance, while remaining trained and grounded in the real, online setting. We propose QWM, a framework that leverages world models to perform test-time search over imagined trajectories on top of Q-learning to select high-value actions during both online rollouts and evaluation. Since the policy and value function are trained only on real transitions, QWM avoids compounding model bias while still gaining the sample-efficiency benefits of predictive search. On challenging manipulation
benchmarks Robomimic and LIBERO, QWM significantly outperforms strong prior state-of-the-art methods on both sample efficiency and performance.
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All authors Perry Dong, Yueru Jia, Chelsea Finn, Dorsa Sadigh
Artificial Intelligence
KnowSim: Evaluating Information Calibration in LLM Assistants with User Simulators that Learn
Yoonjoo Lee, Hyoungwook Jin, Tae Soo Kim et al. (+3 more)
To effectively collaborate with users on knowledge-intensive tasks, Large
Language Models (LLMs) must perform information calibration: matching content to a user's evolving understanding and cognitive capacity. Yet user simulators used to evaluate and train LLMs do not explicitly model user knowledge so they neither produce realistic interactions across knowledge levels nor reflect how interactions unfold as that knowledge evolves. To close this gap, we introduce KNOWSIM, an evaluation framework built around a user simulator that maintains explicit knowledge states, represented as a graph of Information Units with prerequisite relationships, that evolve under update rules grounded in learning theory. KNOWSIM computes three metrics (Knowledge Gain, Delivery Calibration, Cognitive Overload) directly from the knowledge state trajectory, reflecting key mechanistic aspects of information calibration. We validate KNOWSIM against 705 human-AI sessions across two domains, stratified by knowledge level: its rankings align significantly with human judgments (73-74% sign agreement), outperforming three baseline simulators. Applied to 9 LLMs, KNOWSIM reveals that the best model shifts by user knowledge level, revealing aptitude-treatment interactions invisible to standard evaluation.
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All authors Yoonjoo Lee, Hyoungwook Jin, Tae Soo Kim, Shaoyang Zhang, Philippe Laban, Q. Vera Liao
Artificial Intelligence · Cross-listed from cs.CR
Authorization Before Context: A Model-Neutral Audience Boundary Against Cross-Audience Memory Leakage in Agentic Systems
Sibo Liu
A personal
language agent learns a fact from one audience and may later place it in the
prompt it assembles for another. This
memory-to-context step is an attack surface: ambiguous or inconsistent channels, cross-audience prying, and poisoned
memory can each cause the system to assemble context containing a fact relevant to the query yet unauthorized for the current viewers. We introduce authorization before context: a single, anti-monotone audience-membership rule applied at the
memory-to-context transition. Each item carries the audience present when it was recorded; the current viewer set is read from channel metadata and falls back to public when ambiguous; and the item is admitted only when every current viewer already belonged to its audience. We prove that this rule gives every participant cross-channel recall while ensuring, by exclusion rather than by model behavior, that nothing recorded for a narrower audience reaches a broader one and that poisoned
memory cannot widen its own audience. The boundary is a model-neutral invariant on the exact assembled context: a forbidden fact must be absent before the model is called. On a synthetic Contextual-Integrity suite, no forbidden fact entered the context our boundary assembled, whereas unscoped baselines included such facts by construction; we further audit that every read path fails closed. The evidence is preliminary and synthetic.
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Artificial Intelligence · Cross-listed from cs.LG
Iterative tensor network transformations for element-wise evaluation of elementary and filtering functions
Xiao Wang, Tomohiro Hashizume, Pia Siegl et al. (+1 more)
Tensor networks are powerful formats for compressing large-scale data. However, their application to general data processing has been limited by the difficulty of performing nonlinear operations. Here, we introduce iterative tensor network transformations (ITNTs), a general algorithmic framework for the element-wise evaluation of elementary and nonlinear filtering functions on data encoded as tensor trains (TTs), a class of tensor networks. Our approach operates entirely in the compressed domain, enabling efficient computation on exponentially large
datasets while maintaining a controlled computational cost. We demonstrate its power in two key areas: (I) evaluating highly nonlinear elementary and filtering functions on a 3D reactive flow field, enabling high-fidelity reaction rate computation and region filtering, and (II) finding extrema in complex optimization problems, such as solving Max-SAT instances on spaces up to $2^{70}$ configurations. These results establish ITNT as a foundational tool that provides tensor network methods with the capability for general-purpose data science and large-scale optimization.
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All authors Xiao Wang, Tomohiro Hashizume, Pia Siegl, Dieter Jaksch
Artificial Intelligence
Toward Personal Intelligence Through Cooperative Observation
Yashar Talebirad, Osman Jime, Ali Parsaee et al. (+3 more)
A personal AI system needs a model of the user's goals, constraints, and ongoing commitments to plan and act on their behalf, and the quality of that model is bounded by what the system can observe. Broader observation does not by itself improve assistance because a bounded system must select and compress information for the task at hand. We argue that this observation bottleneck has a cooperative structure: the system builds a partial model of the user's changing life, the user evaluates its actions, and the user's consent and control shape what it can observe next. Useful and inspectable behavior can give users a reason to maintain or expand the observation channel, while failures can lead them to correct, narrow, revoke, or abandon it. We use the term cooperative observation for this feedback loop among usefulness, trust, and future access, and propose it as a framework for personal intelligence. We report a preliminary single-subject account from Organizm, a prototype used over six months, and outline evaluation directions for measuring how observation quality shapes personal AI.
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All authors Yashar Talebirad, Osman Jime, Ali Parsaee, Eden Redman, Yongbin Kim, Osmar R. Zaiane
Artificial Intelligence
A decodability criterion predicts when hidden-state selection beats majority voting in large language models
Zhixiang wang, Ziliang Hong and Ulas Bagci
Combining the answers a large
language model (LLM) samples for a question into one decision is a test-time information fusion problem, usually solved by majority voting. Voting is unreliable on difficult questions, where the sampled answers share correlated errors, so the wrong answer can win and drawing more samples makes the decision worse. Selecting a candidate by reading a correctness signal from the model's hidden states is a promising alternative, but its accuracy varies across models and tasks, and no measure indicates when it can be trusted. In this paper, we propose CASE (Correctness-Axis SElection), a dynamic selection combiner that trains a linear gate on the answer-
token hidden state and selects the highest-scoring candidate. Its main contribution is decodability, a leakage-free measure of how well the gate ranks a question's correct candidates above its incorrect ones, which predicts whether hidden-state selection will outperform voting. A conventional probe appears accurate only because of question-identity leakage, which vanishes under question-grouped evaluation. On held-out data, decodability predicts the accuracy gain of selection over voting with a Pearson correlation r=0.75 and a decision threshold near AUC=0.60. Across general and medical LLMs, CASE improves over voting by up to 19 points on medium-difficulty questions and 16.8 points on hard questions. Decodability depends on the aligned knowledge a model must recall, not on its scale, and its prediction transfers to an unseen scientific domain within 3.8 points. It thus provides a practical criterion, measurable in advance for a given model and task, for choosing between learned selection and majority voting.
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Artificial Intelligence · Cross-listed from cs.LG
From Abductive Explanations to Global Logical Rules for Node Classification in SGCs
Bryan Lima Cavalcante and Thiago Alves Rocha
Graph Neural Networks (GNNs) have achieved remarkable performance in node classification tasks, motivating growing interest in methods capable of explaining their predictions. Recent logic-based approaches, such as LogicXGNN, derive global logical rules for Graph Neural Networks (GNNs) from collections of explanatory subgraphs. While informative, these subgraphs may contain redundant structural information that is specific to individual nodes, potentially limiting the generality of the extracted rules. In this work, we propose a logic-based framework for node classification in Simple Graph Convolution (SGC) networks that uses minimal abductive explanations as an intermediate representation for rule extraction. For each node, we compute a minimal set of node-feature pairs sufficient to preserve the predicted class. These explanations are then used to train decision trees from which global logical rules are extracted. Experiments on
benchmark datasets show that the proposed framework produces compact global rules while maintaining high fidelity to the original SGC model.
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Artificial Intelligence · Cross-listed from cs.CR
Structured Driving-State Narratives for Small Language Model-Based GNSS Spoofing Detection
Abyad Enan, Sagar Dasgupta, Mizanur Rahman et al. (+1 more)
Autonomous vehicles (AVs) depend on reliable Global
Navigation Satellite System (GNSS) positioning. However, spoofed GNSS signals can induce plausible but incorrect vehicle states. This study develops a small
language model (SLM)-based framework for detecting and classifying GNSS spoofing attacks by comparing vehicle behaviors independently derived from GNSS and other sensing sources. The framework converts independent driving states from GNSS and other sensing sources into structured semantic narratives that are provided to an SLM for spoofing detection and attack classification. The performance of the SLM-based framework is compared with large
language models (LLMs) fine-tuned on identical training data and evaluated on the same test set. The evaluation considers five classes: no attack, overshoot attack, stopped attack, turn-by-turn attack, and wrong-turn attack. The framework is also evaluated with geographically unseen field data collected in Clemson, South Carolina, United States. Experimental results indicate that the evaluated SLMs achieve performance similar to the LLMs, achieving an average accuracy of 96.99%, precision of 99.05%, recall of 95.59%, and F1-score of 97.18%. In terms of computational efficiency and resource utilization, the SLMs demonstrate advantages over the LLMs by requiring lower inference latency and less GPU
memory during both fine-tuning and inference. Evaluation using field data collected in a geographically distinct location further demonstrated its efficacy. The presented framework can detect and classify GNSS spoofing attacks in real-time while requiring relatively low computational and
memory resources, and is therefore suitable for deployment on resource-constrained vehicular computing platforms.
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All authors Abyad Enan, Sagar Dasgupta, Mizanur Rahman, Mashrur Chowdhury
Artificial Intelligence · Cross-listed from cs.CL
Foundation Agents Meet Agentic Deep Research: Evidence-Grounded Clinical Code Forecasting
Junda Wang, Meysam Ghaffari, Akshat Choube et al. (+3 more)
Next-encounter ICD
forecasting predicts which standardized diagnosis codes will be documented at a future visit from the longitudinal record available beforehand. The task is prospective and multi-label: the target note does not yet exist, and several codes may be correct. Structured EHR foundation models capture recurrence and temporal progression, whereas
language foundation models generate flexible diagnostic hypotheses. We introduce ICD-Deepresearch, a DeepResearch workflow that composes these predictive foundation models with medical search and ICD dictionaries. Because no source reveals the future code set, research evaluates candidate transitions by linking patient evidence, external clinical relations, and exact code semantics under a fixed top-K budget. Candidate Generation uses SparseEHR to produce an EHR Prior that initializes two bounded Research Expansion rounds; an independent GPT-5 Direct Forecast supplies complementary candidates. Final Selection validates, deduplicates, and jointly ranks both paths, after which a separate module writes rationales without changing predictions. Finally ICD-Deepresearch achieves patient-averaged precision/recall of 24.60/35.09% on MIMIC-III and 25.14/48.32% on MIMIC-IV. Physicians rate 51% and 68% of its retrieved documents useful, compared with 22% and 39% for standalone GPT-5 web search and 32% and 41% for Medical Deep Research. ICD-Deepresearch therefore improves over the registered local comparators while retrieving evidence with higher physician-rated usefulness than the standalone research systems
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All authors Junda Wang, Meysam Ghaffari, Akshat Choube, Mohsen Sharifi Renani, Hong Yu, Carlos Morato
Artificial Intelligence
KernelArc: A Multi-Agent Framework for GPU Kernel Optimization
Joyjit Kundu, Ben Stoffelen, Kaili Wang et al. (+2 more)
We present KernelArc, a multi-
agent framework for autonomous GPU kernel optimization across heterogeneous workloads. Strategy-specialized
agents run in parallel and coordinate through conclusions-only shared
memory, a deterministic
benchmark guard, and read-only cross-
agent state with plateau-triggered drafting. We evaluate KernelArc on NVIDIA H100 and B200 GPUs using category-representative SOL-ExecBench workloads. The resulting implementations span custom BF16 GEMM, static cuBLASLt Expert-API configuration tables, fused mixture-of-experts backward, shape-gated decoder-layer fusion, native NVFP4 grouped-query
attention, and paged prefill
attention. In the public SOL-ExecBench leaderboard snapshot recorded on August~20, 2026, KernelArc ranked first on every representative L1, L2, Quantization, and FlashInfer task evaluated. The trajectories support the paper's central motivation: shared multi-
agent search can broaden exploration and reach stronger incumbents within a fixed candidate budget, while the value of individual coordination features depends on the kernel and optimization stage.
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All authors Joyjit Kundu, Ben Stoffelen, Kaili Wang, Peter Vrancx, Ludovic Denoyer
Artificial Intelligence
DiSCO: Defending text-to-image generation through distribution-guided contrastive prompt optimization
Tong Zhang, Motasem Alfarra, Carlos Hinojosa et al. (+2 more)
As text-to-image generative models advance, they raise critical safety concerns, particularly the generation of Not-Safe-For-Work (NSFW) content such as violence and nudity, further exacerbated by red-teaming
adversarial attacks. Existing defenses predominantly operate under white-box assumptions, relying on text encoder optimization, weight editing, or inference-time intervention, and fundamentally cannot scale to proprietary models. Black-box alternatives based on LLM
prompt rewriting offer broader applicability, yet fail in a critical regime we identify as the benign
adversarial problem:
prompts that are linguistically safe but still trigger harmful generation due to the model's learned data distribution. We propose DiSCO, a zero-shot, strictly black-box defense that operates entirely at the
prompt level as a plug-and-play module, requiring no model retraining, fine-tuning, or access to model internals. DiSCO performs distribution-guided suffix expansion via beam search, optimized through contrastive scoring over safe and unsafe image pools generated by the target model itself, with iterative adaptive feedback until safe content is produced. We demonstrate that DiSCO consistently enhances the safety of both undefended and defended models on the I2P
benchmark under multiple red-teaming attacks, achieving 37.7% and 25.13% ASR reduction, respectively, while maintaining semantic fidelity and improving image coherence. As a black-box, architecture-agnostic module, DiSCO can be readily applied to any text-to-image system without necessitating any changes to the model itself.
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All authors Tong Zhang, Motasem Alfarra, Carlos Hinojosa, Christos Louizos, Bernard Ghanem
Artificial Intelligence
Memory Is Communication: The Frontier Between Remembering and Signaling
Yashar Talebirad, Eden Redman, Ali Parsaee et al. (+1 more)
A bounded
agent may obtain information for a decision from its own past, from peers, or from both sources. Retaining task-relevant history can reduce later communication, while a peer message can supply what
memory lacks. Under limits on both resources, how should an
agent allocate its information budget? Given a fixed task and decision rule, the
memory and message rate pairs attaining a performance threshold form an achievable region under specified rules for using history and peer observations. We call its efficient boundary the remembering–signaling frontier. Across conditions where history permits the same maximum reduction in task loss, we hypothesize that a bounded
agent will need less peer communication when it obtains a larger loss reduction from history. In preliminary referential games, target repetition coincided with shorter successful messages, while predictability from a hidden cyclic rule did not shorten them. Experiments varying
memory and message rates can estimate the frontier and test this prediction across cooperative tasks.
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All authors Yashar Talebirad, Eden Redman, Ali Parsaee, Osmar R. Zaiane
Artificial Intelligence · Cross-listed from cs.CL
Institution-Specific LLM Prompting Recovers PHI That De-identification Systems and Their Gold Standards Both Miss
Daniel Palacios, Matthew Brady Neeley, Angel Adetomike Otto et al. (+6 more)
Secondary use of electronic health records requires de-identification, yet existing systems miss institutionally situated protected health information (PHI) such as hospital abbreviations, building names, and internal codes whose status is locally determined. We ask whether large
language models (LLMs) with in-context learning (ICL) can close this gap and control the precision–recall trade-off. On 100 annotated pediatric oncology notes (5,322 PHI spans) from Texas Children's Hospital, we benchmarked eight LLMs against two purpose-built systems (Stanford TiDE, OpenMed PII) and two pattern-based baselines. Each LLM ran under three
prompts of increasing specificity: (1) a HIPAA-aligned baseline, (2) baseline plus the institutional PHI categories it missed, and (3)
prompt 2 plus instructions against over-redacting clinical content. We then compared 14~multi-
agent and ensemble configurations against the best single
prompt, with recall the primary safety metric. LLMs outperformed the purpose-built systems (best F1=0.918$\pm$0.001 vs.\ TiDE 0.779), with advantages concentrated in contextual categories. Naming the missed categories recovered 79% (48/61) of them, and discouraging over-redaction restored precision. No agentic architecture beat calibrated single-pass prompting (F1 0.906–0.907), but LLM outputs surfaced 414~candidate annotation gaps; re-annotation confirmed 227~PHI spans, against which the final
prompt reached recall=0.981 (F1=0.907$\pm$0.002). Well-calibrated ICL resolves both the institutional PHI gap and the precision–recall trade-off in one LLM call per note. LLMs cost more to run than traditional methods, but that cost buys a way to audit the reference standard. LLMs are a legitimate, adaptable alternative to purpose-built de-identification systems; institution-specific
prompt development should be the primary adaptation strategy.
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All authors Daniel Palacios, Matthew Brady Neeley, Angel Adetomike Otto, Shalini Dhamodharan, John P. Woodhouse, Chi-fan Lin, Mark Zobeck, Zhandong Liu, Hyun-Hwan Jeong
Artificial Intelligence · Cross-listed from cs.CL
Frozen Memory Is Not Enough: Rethinking External Memory as Extraction
Mingyuan Li, Guangsheng Yu, Xu Wang et al. (+1 more)
Methods for improving knowledge use in large
language models typically fall into two regimes. Non-parametric
retrieval offers flexible access to external knowledge, but adds
retrieval latency, context overhead, and only shallow integration with the backbone. Parametric adaptation is efficient at inference time, but entangles knowledge with model weights and can be hard to update, audit, or transfer. Engram-style hashed
memory occupies a middle regime: it stores learned information in an external, addressable table, yet consumes that table through a small learned reader. This raises a basic question: when such a
memory is moved across backbones, what matters more, the frozen
memory itself or the target-side reader? We study this question through cross-model frozen-
memory extraction, in which a
memory trained on a source model is frozen and attached to a different target model, with only a lightweight reader trained. Ablations show that learned
memory content and correct addressing both matter, while the amount of usable signal depends on how well the reader is aligned to the target model. In downstream question answering tasks, a dual-layer, four-branch reader nearly closes the gap between same-model and cross-model reuse, achieving an average score of 38.8 under our controlled evaluation protocol. Moreover, when the provider reader is directly compatible with the target interface, the frozen artifact can provide substantial utility without target-side training, while optional reader adaptation yields further improvement. These results suggest that Engram can serve as a reusable external knowledge artifact, provided that the target has access to a compatible reader interface; target-side adaptation can further improve
alignment when direct reader reuse is insufficient.
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All authors Mingyuan Li, Guangsheng Yu, Xu Wang, Shaoxiong Ji
Artificial Intelligence · Cross-listed from cs.CV
The 10th AI City Challenge
Zheng Tang, Shuo Wang, David C. Anastasiu et al. (+34 more)
The 10th AI City Challenge, held with ECCV 2026, marks a decade of community benchmarking for intelligent transportation, smart cities, and physical AI. Since its 2017 start with vehicle detection, classification, and tracking, the challenge has grown into a broad
benchmark suite for multi-camera perception,
multimodal reasoning, synthetic-to-real learning, generative
forecasting, and
privacy-preserving evaluation. The 2026 edition continued this growth with 325 registered teams, up from 245 in 2025, and participation from 26 countries and regions, up from 15. Its six primary tracks cover multi-camera 3D perception, transportation safety captioning and VQA, traffic anomaly
reasoning, text-based person anomaly search, generative traffic
video forecasting, and cross-city object detection. Track 3 further includes two out-of-domain leaderboards, submitted as Tracks 7 and 8, for fisheye traffic-violation understanding and pedestrian situated-intent VQA. This paper summarizes the challenge setup,
datasets, evaluation protocols, leaderboard results, and workshop papers. Across tracks, successful systems combine foundation models with
geometric grounding,
retrieval or reranking, synthetic-data design, domain adaptation, and controlled inference.
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All authors Zheng Tang, Shuo Wang, David C. Anastasiu, Ming-Ching Chang, Anuj Sharma, Quan Kong, Munkhjargal Gochoo, Jun-Wei Hsieh, Tomasz Kornuta, Zhedong Zheng, Renran Tian, Judah Goldfeder, Fulgencio Navarro, Yuxing Wang, Yizhou Wang, Sameer Satish Pusegaonkar, Anqi Li, Nalin Dadhich, Ridham Kachhadiya, Dhanishtha Patil, Haoquan Liang, Jiajun Li, Han Zhang, Yilin Zhao, Zaid Pervaiz Bhat, Shuyu Yang, Ashutosh Kumar, Rong Wang, Rafael Martin Nieto, Peter Christiansen, Ahmed Abduljawad, Mohanrasu Shanmugam, Nadeem Shaik, Sujit Biswas, Xunlei Wu, Vidya Murali, Rama Chellappa
Artificial Intelligence · Cross-listed from cs.CV
YILDIZ-VPR: A Novel Dataset with Dense Coverage Under Diverse Environmental Conditions for Visual Place Recognition
Serdar Yildiz, Abbas Memiş and Songül Varli
Visual Place Recognition (VPR) aims to recognize the location of a query image by comparing it with a set of geo-referenced images. Although many
datasets have been proposed for VPR, collecting dense and diverse visual data from pedestrian-level viewpoints is still an important need. In this paper, we introduce YILDIZ-VPR, a visual geo-localization
dataset collected through repeated walking traversals on the Davutpasa campus of Yildiz Technical University. The
dataset includes outdoor scenes captured at different times of day, seasons, and weather conditions. It contains a wide range of visual content, including historical buildings, modern structures, roads, green areas, and wooded regions. Each
video was recorded with a GoPro 9 camera and synchronized with GPS sensor data to provide location labels for the extracted frames. In addition to GPS coordinates, the
dataset also includes auxiliary sensor information such as gyroscope, speed, and temperature data. With its dense coverage and long-term visual variability, YILDIZ-VPR provides a useful resource for studying image-based and temporal visual place recognition under realistic outdoor conditions.
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Artificial Intelligence · Cross-listed from cs.CY
Without journalists, there is no journalism: the social dimension of generative artificial intelligence in the media
Simón Peña-Fernández, Koldobika Meso-Ayerdi, Ainara Larrondo-Ureta et al. (+1 more)
The implementation of artificial intelligence techniques and tools in the media will systematically and continuously alter their work and that of their professionals during the coming decades. To this end, this article carries out a systematic review of the research conducted on the implementation of AI in the media over the last two decades, particularly empirical research, to identify the main social and epistemological challenges posed by its adoption. For the media, increased dependence on technological platforms and the defense of their editorial independence will be the main challenges. Journalists, in turn, are torn between the perceived threat to their jobs and the loss of their symbolic capital as intermediaries between reality and audiences, and a liberation from routine tasks that subsequently allows them to produce higher quality content. Meanwhile, audiences do not seem to perceive a great difference in the quality and credibility of automated texts, although the ease with which texts are read still favors human authorship. In short, beyond technocentric or deterministic approaches, the use of AI in a specifically human field such as journalism requires a social approach in which the appropriation of innovations by audiences and the impact it has on them is one of the keys to its development. Therefore, the study of AI in the media should focus on analyzing how it can affect individuals and journalists, how it can be used for the proper purposes of the profession and social good, and how to close the gaps that its use can cause.
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All authors Simón Peña-Fernández, Koldobika Meso-Ayerdi, Ainara Larrondo-Ureta, Javier Díaz-Noci
Artificial Intelligence
SkillEffect: Checked Lowering for Memory-Bounded Agent Tools
Yinuo Wang and Yiyu Shi
Agent Skills can specify procedural and resource obligations for tool use, and
language models instantiate them as concrete programs. However, when models turn this guidance into code for existing tool interfaces, even a semantically correct program may load an entire input and exceed the
memory available to one tool call. We present SkillEffect, a checked-lowering runtime for computations with a recoverable source relation, an audited bounded implementation, and a registered output postcondition. Before granting execution authority, an independent checker rebuilds each proposed lowering from the submitted program and immutable input. Every relation plugin supplies a source recognizer, input-fact extractor, bounded-IR constructor, arena-bound function, and postcondition; one common runtime provides checked selection, bounded-VM execution, atomic capacity leasing, and staged publication. Generality in SkillEffect is architectural rather than automatic: each supported computation requires an audited relation plugin, while the dispatch, resource-control, execution, and publication mechanisms are shared across plugins. Across six operator families, bounded access substantially reduces peak
memory and improves completion under externally fixed caps. Six plugins instantiate the same contract across five execution patterns, from streaming reduction to bounded-heap Top-k. The XLSX onboarding study and Top-k extension show that a new relation and a new retained-state pattern reuse the same trust boundary, while the checker accepts all evaluated legal configurations and rejects all
adversarial proposals. Together, these results show that one checked-lowering architecture can enforce heterogeneous registered
memory relations at
Agent tool dispatch.
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Artificial Intelligence · Cross-listed from cs.CV
PXDepth: Pixel-Space Modeling for Structure Preserving Monocular Depth Estimation
Zhiyuan Yuan, Guanying Chen, Lingteng Qiu et al. (+3 more)
Recent
monocular depth
estimators achieve strong zero-shot generalization, yet often struggle to preserve fine-grained structures and object boundaries. We attribute this limitation to the prevalent combination of large-patch ViT encoders and convolutional decoders, as coarse tokenization can weaken pixel-level cues that upsampling cannot fully recover. To address this issue, we propose PXDepth, a discriminative
monocular depth model that separates global context modeling from pixel-level depth prediction. Specifically, a large-patch ViT captures global scene context, while a pixel-space predictor composed of Context-Modulated Pixel
Transformer blocks maintains high-resolution spatial representations throughout depth estimation. This design preserves fine structures and sharp boundaries without sacrificing global depth consistency. Across diverse zero-shot
benchmarks, PXDepth combines faithful local geometry with competitive global depth accuracy while remaining efficient at inference. Our code and model are available at https://yuanzhy29.github.io/PXDepth-Page/.
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All authors Zhiyuan Yuan, Guanying Chen, Lingteng Qiu, Ruimao Zhang, Shuguang Cui, Xiaochun Cao
Artificial Intelligence
The Problem Is the Problem: Towards Scalable Mathematical Discovery
Zeyu Zheng, Shengtong Zhang, Jeremy Avigad et al. (+2 more)
AI systems are increasingly capable of contributing to mathematical research. In research practice, frontier-model
reasoning is a limited resource, and expert mathematical review is even more sharply constrained. Allocating these scarce resources well is therefore central to making AI-assisted mathematical discovery efficient. In most current AI-for-math workflows, human effort is concentrated at the beginning and end, in selecting suitable research problems and later reviewing the resulting artifacts. These two stages are becoming bottlenecks for research-level mathematics. We address them by proposing a new human-AI discovery paradigm. The human input is no longer a single problem selected in advance, but a research direction in which the experts have interest and expertise. The system then searches a broad literature corpus for candidate problems in that direction. Inspired by search and recommender systems, we build Find, Attempt, and Recommend (FAR), a literature-to-review cascade that automates the search for suitable problems and focuses human
attention on artifacts that have passed several stages of filtering. In a combinatorics pilot, the pipeline starts from 5,245 combinatorics papers, recovers 6,453 candidate
conjectures or open problems, and filters them to 4,717 apparently well-posed and still-open
conjectures. Subsequent
reasoning and automated triage stages surface 598 potential resolutions and select 77 items for author-team review. Among them, we identify many interesting discoveries, including results on
conjectures and questions of Davies–Jenssen–Perkins–Roberts, Erdős–Straus, Ikenmeyer–Pak–Panova, and Lund–Saraf–Wolf. These results demonstrate the effectiveness of this new mode of human-AI collaboration for mathematical discovery.
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All authors Zeyu Zheng, Shengtong Zhang, Jeremy Avigad, Prasad Tetali, Sean Welleck
Artificial Intelligence · Cross-listed from cs.LG
Position: Fairness Failure in Generative Models is an Evaluation Problem
Mariia Vladimirova, Jean-Yves Franceschi and Thibaut Issenhuth
Despite groundbreaking advancements in generative models during the last decade, concerns about their lack of
fairness, reinforcing societal inequalities and harming marginalized groups, remain under-addressed and difficult to act upon. This position paper argues that
fairness failures in generative models, albeit driven by multiple factors, are ultimately stemming from an evaluation problem:
fairness findings are rarely comparable across papers or actionable for deployment decisions. This paper diagnoses recurring empirical and conceptual failure modes in current practice and motivates a shift from ad-hoc bias checks to standardized, generative-specific evaluation. We propose
Fairness Cards as a minimal reporting artifact that makes evaluation choices explicit (
prompt families, counterfactual protocols, metrics, and refusal handling) enabling reproducibility, comparability, and accountability. We conclude with additional recommendations towards a paradigm shift in evaluation standards. Our project page can be found at https://mariiavladimirova.github.io/
fairness-cards .
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Artificial Intelligence
FedPref: Federated Preference Learning for Structured Radiology Report Extraction
Flint Xiaofeng Fan, Cheston Tan, Yew-Soon Ong et al. (+1 more)
Radiology reports describe findings and locations in free text, but downstream search and analysis require these relations in a fixed schema. Learning this extraction requires labels that are unevenly distributed across institutions: smaller hospitals have less local evidence, and pooling data may be infeasible. We introduce FedPref: frozen public
language models propose alternative JSON extractions, local annotations rank them, and sites collaboratively train compact Qwen3-8B adapters while sharing only model updates. A heterogeneous teacher pool provides cross-model contrast when repeated single-model samples collapse. On development data from six simulated hospitals with unequal data volume and disease prevalence, FedPref improves client-mean F1 by 2.49 points and worst-site F1 by 9.10 points compared with training each site in isolation, with the largest gains at the sites holding the least data. Central training on the pooled preference-pair union is 2.66 points higher on client-mean F1. On a locked, 400-report manually validated gold test set, FedPref reaches 68.68 F1 and pooled training 71.67, preserving that same ordering. FedPref thus lets institutions with unequal, unpooled data benefit from collaboration without ever sharing reports or annotations.
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All authors Flint Xiaofeng Fan, Cheston Tan, Yew-Soon Ong, Roger Wattenhofer
Artificial Intelligence · Cross-listed from cs.CR
Probing the Prefill: Detecting Code Vulnerabilities via Latent Activations
Alizishaan Khatri
LLM-based code generation is now embedded in mission-critical pipelines, but defenses against vulnerable output remain post-hoc – static analyzers, fine-tuned classifiers, or an LLM judge that screen completed code, ignoring the generating model's own internal state. We test a narrower, directly measurable question: when an LLM reads a piece of C/C++ code as context, do its hidden activations already carry a signal about that code's vulnerability status? We extract last prefill
token activations from four LLMs (Granite-4.1-8B, Qwen3.5-9B, Qwen3.6-27B, Gemma-4-12B) across three model families and train MLP probes on these activations. We evaluate them on four function-level C/C++
benchmarks (Devign, Big-Vul, Draper VDISC, PrimeVul). Our probes achieve 41.7% average F1 using 13.4–16.0M-parameter probes – under 0.2% of base-model size. On Devign, the best probe (Qwen3.5-9B, 68.8% F1) matches the published fine-tuned-classifier SOTA (67.9%) despite reading only a frozen, general-purpose LLM's activations; on the harder, more imbalanced
benchmarks (Big-Vul, Draper VDISC, PrimeVul) probes trail SOTA substantially. This is early evidence that a coding LLM's own representation of arbitrary code is informative about that code's vulnerability status, motivating further work toward lightweight, model-native vulnerability screening.
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Artificial Intelligence · Cross-listed from cs.RO
Don't Drop the BATON: Long-Horizon Robot Manipulation via Agentic Subtask Exploration and Transition-aware Memory
Bingxin Xu, Yuzhang Shang and Emilio Ferrara
Long-horizon
robot manipulation chains many contact-rich skills into one multi-stage task.
Vision-
language-action (VLA) and world-action models (WAMs) increasingly master individual skills, yet the chain still fails: errors compound beyond the policy's ability to correct, and one subtask silently constrains the next. A promising pathway freezes the VLA and puts an LLM coding
agent in charge: it plans in
language, moves in free space with analytic primitives, invokes the VLA only for contact-rich segments, and writes adaptation into
language memory. Yet applied to long horizons, this recipe breaks twice. (1) Its competence comes from whole-task exploration at test time, whose cost is exponential in the number of stages: if one stage needs T episodes, a K-stage task needs on the order of T^K, and a failure does not reveal which stage caused it. (2) It has no representation of transitions: the VLA primitive carries an exit but no entry condition, and a subtask can succeed in a form its successor cannot use. We present BATON to address both failures. Against (1), BATON makes the subtask the unit of exploration: each subtask is explored in the cheap short-horizon regime and its solution stored in
memory; a long-horizon trajectory is then composed from these solutions rather than discovered whole. Exploration cost becomes linear (KT), and each failure is attributed to one stage. Against (2), BATON equips exploration with a transition-aware
memory. Within a subtask, a verifier
agent governs the invocation transition: the VLA is invoked only after the wrist view confirms the scene is ready. Across subtasks, a handoff transition restores an entry state disturbed by the predecessor's residue, and a lookahead transition selects the strategy whose outcome the successor can inherit. On the RoboMemArena
benchmark, BATON improves task success by 37.7% and cumulative success by 29.7% over the SoTA.
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Artificial Intelligence · Cross-listed from cs.DS
Improving the matrix multiplication exponent with modern optimization and AlphaEvolve
Emilien Dupont, Marvin Eisenberger, Borislav Kozlovskii et al. (+7 more)
The current best bounds on the
matrix multiplication exponent $\omega$ are obtained through a refinement of the
laser method called combination loss analysis (Duan et al., 2022; Williams et al., 2024; Alman et al., 2025). In this note, we address the optimization problem at the core of this approach and propose several improvements. First, we reformulate the optimization problem allowing us to solve it in a larger setting than was previously possible. Second, we leverage recent advances in machine learning to design a new optimization
algorithm for this problem. Finally, we refine the resulting optimization
algorithm with AlphaEvolve. Our combined approach yields an upper bound of $\omega$ < 2.371177, improving the previous best bound of 2.371339.
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All authors Emilien Dupont, Marvin Eisenberger, Borislav Kozlovskii, Abbas Mehrabian, Francisco J. R. Ruiz, Abigail See, Renfei Zhou, Josh Alman, Virginia Vassilevska Williams, Matej Balog
Artificial Intelligence · Cross-listed from cs.SC
AutoSR: Automatic Symbolic Regression by Searching Research States
Kejia Zhang, Youran Sun, Xinyu Ren et al. (+2 more)
We introduce Automatic Symbolic Regression (AutoSR), a fully automated system that instantiates Research-Space Symbolic Regression by searching persistent scientific investigations rather than isolated equations. Finite, noisy data often yield numerically competitive expressions that imply very different behavior outside the observed regime, making numerical fit and syntactic complexity insufficient measures of scientific credibility. Existing approaches largely focus on improving expressions, yet the search typically retains little beyond the resulting formula and score, losing the scientific record, such as motivations and probes, that inform what to try next. AutoSR preserves this record in a Research State, coupling each candidate equation with the
reasoning, computational evidence, and independent review developed along its branch. Proposer–reviewer
agents develop these states under progressive-widening Monte Carlo tree search (PW-MCTS), which allocates computation across competing investigations, while the accumulated research record is ultimately synthesized into a final report that explains the leading relation and the basis for its selection. Across nine selected challenges from two
benchmark suites, AutoSR recovers algebraically equivalent relations in every case, including three cp3-bench problems that no published system recovers and six structurally diverse LSR-Transform problems. Overall, AutoSR extends symbolic regression from equation-level search toward automated scientific investigation, allowing scientific knowledge and accumulated evidence to shape both what is explored and how the resulting equation is justified.
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All authors Kejia Zhang, Youran Sun, Xinyu Ren, Chugang Yi, Haizhao Yang
Artificial Intelligence · Cross-listed from cs.CL
Towards Computational Provenance: Carrying Causal-State Evidence in Generated Text
Benjamin Belay
A
language model's output does not by itself provide verifiable evidence about the internal computation that produced it. We study computational provenance: whether generated text can carry detectable evidence of which causally relevant internal state occurred. We test a bounded form of this idea in two controlled architectures: a modular feed-forward neural network and a
transformer-based model. Both architectures are trained on the same arithmetic task with a mandatory pathway through two discrete intermediate states, allowing different internal paths to produce the same answer. We deliberately switch between these paths, authenticate the state actually used, and let that verified state determine a subtle statistical pattern in the generated text that can later be detected. The feed-forward and
transformer systems each passed all 128 matched pairs in both their public and separately sealed protected end-to-end evaluations, with the detector recovering the signal associated with the authenticated internal state. The required causal computation also reproduced across five independently trained feed-forward models and three independently trained
transformers. In a separate answer-only
transformer experiment, our linear probes did not recover a naturally learned intermediate state. These results provide a controlled proof of concept that information about a verified, causally relevant internal state can be preserved in generated text even when the answer is unchanged.
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Artificial Intelligence
What Do Compliance Detectors Read? An Audit of Activation Probes and Guard Models
Saisab Sadhu, Aadit Sengupta, Vinay Kumar Sankarapu et al. (+1 more)
Regulatory compliance monitoring in deployed
language models is increasingly implemented as a legal and audit control, checking model outputs against written rules spanning data protection, healthcare, financial regulation, and platform policy. Such monitoring is meaningful only if a detector's verdict depends on the stated rule rather than on surface features of the scenario. We show this condition fails across the current class of compliance detectors, a failure we call rule blindness. Deleting, permuting, or substituting the governing rule leaves detection accuracy unchanged for every guard and activation probe we test, including a policy-conditioned guard that correctly cites the governing clause yet barely changes its verdict when that clause is swapped for its permissive counterpart. A purpose-built
benchmark crossing two rules with two scenarios, so that neither alone predicts the label, confirms the failure under a design no prior
benchmark rules out, and shows that step by step
reasoning, not any fast detector we test, is what escapes it.
Auditing at scale requires a retraining-free detector, so we introduce the Internal Compliance Score (ICS): a training-free activation readout calibrated from ten labelled pairs and scored by a single projection. We hold ICS to the same scrutiny as the guards it audits: a pre-registered criterion for beating trivial baselines is not met, and a bag-of-words model matches its pooled generalisation exactly. It remains useful because it is inexpensive, letting us audit four deployed guard models, an 8B zero-shot judge, and thirteen
benchmarks, and it raises the mechanically verified pass rate when used to rank candidate responses, though an adaptive white-box attack removes this gain. We release the counterfactual protocol and crossed-rule
benchmark so rule blindness can be tested in future probe and guard claims.
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All authors Saisab Sadhu, Aadit Sengupta, Vinay Kumar Sankarapu, Pratinav Seth