Artificial Intelligence · Cross-listed from cs.CR
(EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations
Neta Kirmayer, David Tayouri, Andrés Murillo et al. (+3 more)
Security operations centers rely on anomaly detection systems to flag suspicious events. Feature-level explanations for anomaly detectors offer limited value for operational investigations. To effectively handle alerts, analysts need to know contextual relationships and need actionable understanding of the entities involved. This paper introduces an event-centric detector-agnostic approach for explaining cybersecurity alerts in small- to medium-sized enterprise networks. We present (EC)2, a multi-
agent framework that performs structured, hypothesis-driven investigation to provide explanations grounded in verifiable evidence. Evaluation results show that the proposed framework improves post-detection analysis by generating operationally meaningful explanations, which also enhance event classification accuracy.
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All authors Neta Kirmayer, David Tayouri, Andrés Murillo, Motoyoshi Sekiya, Asaf Shabtai, Rami Puzis
Artificial Intelligence
Position: Evaluation Scores Are Perishable Knowledge Claims
Sankalp Gilda and Shlok Gilda
Evaluation methodologies for
language models increasingly combine multiple signals, from automated metrics and LLM-as-judge ratings to human assessments and
benchmark suite results. When these signals are aggregated via averaging, evaluation confidence can then substantially exceed the reliability of the weakest signal: a phenomenon we call trust inflation in evaluation. We argue that evaluation scores should be treated as epistemic claims with three properties: formality (human evaluation provides stronger evidence than an automated metric), scope (a
benchmark result applies to the tested distribution, not universally), and validity windows (
benchmark results expire as contamination accumulates and distributions shift). Several converging research traditions (chain-of-thought analysis, possibilistic logic, and algebraic theory) establish weakest-link aggregation as the conservative endpoint of a parameterized operator family controlled by a single pessimism parameter. Drawing on those traditions, and on concrete lessons from building an evaluation harness for agentic AI, we propose that evaluation results carry explicit metadata (formality tier, scope declaration, and expiration date) to make their epistemic status transparent. We illustrate the cost of mean aggregation on the public HELM leaderboard: across 54 frontier models on ten scenarios, the top-five models ranked by mean score and by weakest-link are completely disjoint.
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Artificial Intelligence
GoGoTB: Agentic RTL Verification with Specification-Grounded Coverage Closure
Xin Xin, Jincheng Lou, Junhui Li et al. (+11 more)
Functional verification dominates integrated
circuit (IC) front-end engineering effort, and a single missed bug that escapes to silicon can trigger a costly respin. Recent large
language models (LLMs) offer new opportunities to automate this process, yet existing LLM-based approaches generate each component through independent single-turn calls with no shared context, leaving interface mismatches undetected and reported coverage disconnected from specification requirements. To address these challenges, we present GoGoTB, an agentic framework that achieves end-to-end verification closure through three subsystems: an agentic execution control layer, an evolvable knowledge system, and specification-grounded coverage closure. The execution control layer separates deterministic enforcement from LLM
reasoning at every tool and stage boundary. The knowledge system dispatches methodology and design-specific expertise on demand. The coverage framework anchors every bin to a named specification behavior so that each residual gap has a diagnosable root cause and a targeted remedy. Tested on 8 register transfer level (RTL) designs without any human intervention, GoGoTB achieves 100% environment generation success and averages 98.4% line, 97.2% branch, 97.0% toggle, and 83.2% functional coverage. No prior work successfully generates a complete verification environment or achieves meaningful coverage on the same
benchmarks.
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All authors Xin Xin, Jincheng Lou, Junhui Li, Jinglin Yan, Panda Xiao, Di Wu, Haixiao Li, Weicong Lu, Weijian Fan, Xinyu Qu, Yuxiang Zhao, Min Yu, Zhixiong Di, Yibo Lin
Artificial Intelligence · Cross-listed from q-bio.NC
Cognitive Convergence: Deep Similarities Between Large Language Models and Human Cognition
Chandra Sripada and Richard Lewis
LLMs are widely regarded as alien intelligences, systems whose cognitive operations are fundamentally unlike our own. Apparent similarities to human cognition are therefore often seen as the result of anthropomorphic projection. We argue that this framing is mistaken. LLMs clearly differ from humans in important respects, including their physical substrate, learning history, and the environments with which they interact. These differences make it all the more striking that contemporary LLM-based systems converge with human cognition on a number of principles of cognitive organization with longstanding support in cognitive science. We identify structural correspondences across five dimensions: inferential organization, computational architecture, representational structure, prediction-driven learning, and reinforcement-learning-like mechanisms supporting goal-directed action. These correspondences support a broader model of intelligent cognition in which core principles long used to explain human intelligence also characterize contemporary LLM-based systems.
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Artificial Intelligence · Cross-listed from cs.CV
A Picture Says Thousands of Words - Harnessing Dermal Exposure Data from Images through Hybrid Deep Learning for Enhanced Safety Assessment
Hua Qian, Manisha Kotha, Tuan Tran et al. (+2 more)
This study developed a hybrid computer
vision method to quantify exposed skin from images for dermal exposure assessment. Using 170 indoor-painting images, Mask R-CNN first identified human subjects and removed background interference; a color-based
algorithm then segmented exposed skin. The resulting exposed-skin-to-body pixel ratios showed approximately 80% agreement with human estimates. The approach demonstrates a scalable way to extract semi-quantitative exposure information from images, with future extensions to body-part recognition, PPE detection, and
video-based exposure analysis.
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All authors Hua Qian, Manisha Kotha, Tuan Tran, Jennifer Shin, Haining Zheng
Artificial Intelligence
GuideSkill: Evolving Executable LLM Agent Skills for Guideline-Grounded Clinical Reasoning
Lang Cao, Yuhao Shen, Tianyang Luo et al. (+3 more)
Clinical practice guidelines (CPGs) encode diagnostic criteria, but LLM systems typically retrieve guideline text or absorb it through training rather than execute its rules. We introduce GuideSkill, an external
reasoning layer that compiles disease-specific criteria into executable functions returning ordinal diagnostic-support scores. GuideSkill-Zero is initialized from guidelines, while GuideSkill-Evo uses case–diagnosis pairs to refine covered skills and add missing diagnoses. At inference, an LLM proposes a differential diagnosis, grounds the features required by each matched skill, and fuses its ranking with the executed skill scores. Across four
benchmarks and four backbones, GuideSkill-Zero improves macro-average accuracy over guideline RAG by 13.45% on average. GuideSkill-Evo achieves the highest macro-average for every backbone, improves over direct inference by 18.49% relatively, and increases gold-label skill coverage from 56.5% to 99.5%. On Qwen3.5-9B, it also exceeds the strongest parameter-update baseline by 11.16% without updating the backbone. Expert evaluation further indicates that GuideSkill produces clinically sound and broadly acceptable skills, suggesting that its initialized and evolved rules are reliable and practically meaningful. These results support executable skills as a model-agnostic mechanism for combining guideline-derived procedures with case-derived diagnostic patterns.
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All authors Lang Cao, Yuhao Shen, Tianyang Luo, Simo Du, Hao Peng, Yue Guo
Artificial Intelligence
When benchmark inferences do not compose: Projectibility in AI evaluation
Brett Reynolds
An AI
benchmark result rarely reaches a consequential claim in one step. Evaluators generalize it to further cases, interpret it as evidence of capability, extrapolate it to new tasks, transport it to another system or site, and combine it with assumptions about human review and downstream consequences. Validity-centred approaches require evidence for each claim. This paper makes explicit and operationalizes a problem those approaches leave to the analyst: warranted links don't automatically make a warranted chain. The target of one study may not be the source of the next; system, population, outcome, or conditions may change at the interface; and shared data or model lineage may make apparently independent support dependent. Projectibility concerns whether a bounded extension from observed to unobserved cases is warranted. Goodman supplies the problem of rival extensions; argument-based validity supplies an architecture for testing them. The contribution is an interface audit for distributed AI evidence: typed source and target descriptions, and a procedure separating endpoints that never meet from endpoints that meet while warrant fails to cross. A legal-research case shows how
benchmark evidence and a deployment study can each be sound while remaining parallel. A known-truth demonstration shows why aggregate stability can erase distinctions a later projection requires. The resulting projectibility audit diagnoses unsupported joins in
benchmark-to-use arguments.
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Artificial Intelligence
ClinLens: Towards Long-Horizon LLM Agents for Longitudinal Multimodal Clinical Data Science
Yuan Zhu, Ethan B. Liu, Frank Nie et al. (+3 more)
Automating clinical data science requires
agents to translate patient-centric objectives into executable workflows grounded in longitudinal
multimodal evidence. Existing
benchmarks provide limited coverage of two essential capabilities: grounding analyses in the correct patient episode and harmonizing
multimodal observations. We introduce ClinLens, a patient-centric
benchmark for evaluating large
language model (LLM)
agents on long-horizon clinical data science workflows. ClinLens comprises 126 executable tasks spanning five data modalities, four analytical scopes, and five analytical capabilities. The tasks assess
agents' ability to maintain a coherent patient-specific analytical context across interdependent stages of evidence grounding,
multimodal integration, and analytical pipeline generation and execution. We evaluate general-purpose, coding, and biomedical
agents, with task runs averaging 36.93 steps and 8.06 minutes. The strongest evaluated
agent achieves an execution success rate of 86.4% and a final-answer accuracy of 54.0%, but a strict pass rate of only 29.4%. Failure analysis identifies patient-episode grounding as the most frequently recorded failure category under the evaluation procedure. By jointly evaluating analytical outcomes and the validity of the supporting clinical context, ClinLens provides a testbed for advancing reliable
agents for longitudinal clinical data science.
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All authors Yuan Zhu, Ethan B. Liu, Frank Nie, Wei Fan, Haibo Pu, Jindong Han
Artificial Intelligence · Cross-listed from cs.RO
Towards Trustworthy Embodied Intelligence: A Systems Framework and Graded Trustworthiness Levels
Xinyu Yang, Tianxing Chen, Honghao Su et al. (+38 more)
Embodied intelligence integrates learned perception and decision making with real-time computation, control, and physical interaction. Because failures can cause immediate physical or operational harm, task completion alone does not establish trustworthiness. We define trustworthy
embodied intelligence as the sustained capacity to execute specified tasks reliably under environmental and system variation while maintaining risk within acceptable bounds. We term this objective sustained safe success. Its supporting mechanisms are organized into four interdependent layers. The model layer generates task-competent action proposals with calibrated uncertainty and explicit safety preferences. The system layer realizes authorized actions dependably through integrated sensing, computation, control, hardware safeguards, fault containment, and fallback. The evidence layer substantiates bounded claims through evaluation, verification, validation, traceability, and structured assurance arguments. The deployment layer maintains claim validity through runtime monitoring, authority management, intervention, incident response, and controlled updates. Because assumptions and failures propagate across these layers, neither model capability, isolated safeguards, nor
benchmark performance alone can establish end-to-end trustworthiness. Drawing on
embodied AI, robotics, control, dependable computing, distributed systems, and autonomous driving, we further propose a non-normative hierarchy of trustworthiness levels. This hierarchy grades the strength of bounded deployment claims across task capability, safety, system assurance, operational governance, and supporting evidence, providing a basis for bounded deployment, comparative evaluation, research prioritization, and future standardization.
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All authors Xinyu Yang, Tianxing Chen, Honghao Su, Minxuan Wang, Chenze Yu, Zhangzheng Tu, Yue Chen, Yuxiao Huo, Lingfeng Zhang, Yan Huang, Yan Qin, Shaolong Zhu, Qiwei Liang, Hekun Tian, Shujia Liu, Guangyu Chen, Junhao Gong, Zixuan Li, Wenwei Lin, Zijian Lin, Wenxuan Zhu, Eric J Chen, Yue Yuan, Qize Yu, Jiaqi Liang, Haowen Yan, Hengfei Zhao, Weijie Wan, Zikun Xiao, Junyuan Tang, Baijun Chen, Kai-Chong Lei, Kaixuan Wang, Kailun Su, Zanxin Chen, Yao Mu, Renjing Xu, Chuqiao Lyu, Qi Xiong, Ping Luo, Wenbo Ding
Artificial Intelligence
Even More Deception: Objective Misalignment in Mixed-Motive LLM Multi-Agent Systems
Marylou Fauchard, Florian Carichon, Margarida Carvalho et al. (+1 more)
Large
Language Models (LLMs)-powered multi-
agent systems are increasingly deployed in mixed-motive environments, where
agents operate under asymmetric information and strategic deception due to conflicting or hidden objectives. In these settings, misalignment with collective goals becomes a central concern. We propose a novel framework for evaluating objective misalignment using the social deduction game Werewolf, modifying the objective of a single
agent while preserving its assigned role. Across LLMs from four different model families and sizes, four player roles, and three objective formulations, we introduce a dual analysis of the
agents' internal
reasoning and their public cheap-talk behavior (i.e costless, non-binding communication that does not directly affect the
agents' utilities), complemented by an analysis of game outcomes. Our results show that objective misalignment undermines outcomes in inherently
adversarial environments, an effect exacerbated by asymmetric information and specialized roles. While compromised
agents consistently develop distinct objective-dependent
reasoning strategies, these adaptations remain largely invisible in their public behavior. More broadly, our findings suggest that even subtle objective misalignment can profoundly affect collective decision-making, highlighting the need for effective mitigation strategies for LLM-based multi-
agent systems.
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All authors Marylou Fauchard, Florian Carichon, Margarida Carvalho, Golnoosh Farnadi
Artificial Intelligence
Probing the Origins of Reasoning Performance: Representational Quality for Mathematical Problem-Solving in RL vs. SFT Fine-Tuned Models
Antyabha Rahman, Akshaj Gurugubelli, Omar Ankit et al. (+2 more)
Large
reasoning models trained via reinforcement learning (RL) have been increasingly shown to outperform their supervised fine-tuned (SFT) counterparts on mathematical
reasoning tasks; Yet the mechanistic basis for this advantage remains unclear. We therefore ask, what internal representational differences enable RL models' superior performance? Our work presents two converging lines of evidence: First, linear probes trained on layer-wise hidden states reveal that RL models tend to achieve higher accuracy in predicting answer correctness compared to SFT models, indicating more linearly separable and structured representations. Second, mean ablation studies show that RL models develop a hierarchical architecture where deeper layers become progressively more critical, whereas SFT models distribute importance uniformly across layers. Together, these findings demonstrate that RL training fundamentally restructures how models represent and process
reasoning problems. Finally, we analyze
token-count variability under repeated sampling across problems to assess adaptive compute allocation. While we observe higher variability in some RL-tuned models than in their SFT counterparts, we see strong consistency in others, suggesting that
token allocation may depend more on the overall training pipeline than on RL versus SFT alone. We believe this
token-allocation variability reveals the spread of plausible on-policy
reasoning, highlighting which models exhibit stable policies versus those that are under-determined, potentially non-identifiable solution behaviour.
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All authors Antyabha Rahman, Akshaj Gurugubelli, Omar Ankit, Kevin Zhu, Aishwarya Balwani
Artificial Intelligence · Cross-listed from cs.SE
Try Again, Don't Look Back: Blind Resampling Outperforms Self-Repair in Small Code Models
Yuvraj Verma
Self-repair - returning a failed program to the model together with its test output and asking for a correction - is a standard component of code
agents, and is almost always evaluated against a baseline that does not retry at all. We argue that this comparison confounds the value of the feedback with the value of the extra attempt. Using a placebo-controlled design on MBPP+ at three model scales (1.5B, 3B, 7B), we compare four matched-budget retry conditions: blind resampling, a content-free failure notice, genuine execution feedback, and feedback augmented with verbal self-reflection. Blind resampling is the strongest condition below 7B, and remains statistically tied with the best condition at 7B, while consuming 2.5-5.5x fewer
tokens; conditioning on the model's own failed attempt costs 6.1 points at 1.5B (p=0.006), and the informational content of execution feedback adds nothing measurable over the placebo. We attribute this to anchoring: when shown its previous attempt, a model reproduces a near-identical program in 33-68% of retries, against 2-14% under blind resampling. Two further experiments delimit the effect. Retrieved solutions to other tasks change nothing (bounded to +/-3.5 points), which localizes the harm to self-conditioning rather than context length; and reflection, the only condition that measurably weakens the anchor, remains dominated on cost. Replication rules out two competing explanations: the penalty is unchanged at full precision, and it reproduces on an independent model family. Across six configurations spanning two families and two precisions, its magnitude is predicted by baseline quality alone (r=0.96) - the cost of anchoring is the cost of committing to a bad first attempt.
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Artificial Intelligence · Cross-listed from cs.CR
GPT-Red: Automated Red Teaming via Self-Play at Scale
Eric Wallace, Christopher A. Choquette-Choo, Nikhil Kandpal et al. (+15 more)
We introduce GPT-Red, an automated red-teaming
agent that is trained to discover novel
prompt injection attacks against frontier LLMs. The goal of this model is to evaluate and improve the robustness of our production systems. To this end, we use it to adversarially train GPT-5.6, our most robust model to
prompt injections to date. To create GPT-Red, we design a scalable self-play
algorithm where the model is tasked with attacking a diverse population of simultaneously-trained defender
agents. We train the model on realistic red-teaming environments using compute on the same scale as some of our largest RL post-training runs, making it the single-largest LLM safety training run ever documented. GPT-Red excels at red-teaming: it reliably breaks our past models up to GPT-5.5, it finds more successful attacks than human red-teamers, and it generalizes to held-out environments, defender models, and harnesses. In the future, we expect that as we improve the robustness of each new GPT model, it will in turn will provide better learning signal for even stronger red-teamer
agents, thus unlocking a self-improvement flywheel.
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All authors Eric Wallace, Christopher A. Choquette-Choo, Nikhil Kandpal, Sam Toyer, Dylan Hunn, Stephanie Lin, Yuxin Wen, Xiangyu Qi, Christopher Wolff, Zizhao Wang, Milad Nasr, Sicheng Zhu, Chuan Guo, Juan Felipe Cerón Uribe, Kaiwen Wang, Aiden Low, Kai Xiao, Kai Chen
Artificial Intelligence · Cross-listed from cs.CV
TraceCLIP: Recovering Local Semantics from Patch-to-CLS Contributions
Xinran Liu, Shouqian Shi, Yutong Chen et al. (+3 more)
Dense
vision-
language understanding, including object localization, region recognition, and open-vocabulary semantic
segmentation, requires associating
language concepts with spatially grounded visual regions. CLIP provides a strong foundation for these tasks by learning a shared image-text
embedding space from large-scale contrastive pre-training. However, its image-level objective aligns text with a CLS-derived global representation, leaving local
vision-
language correspondence only indirectly constrained. Existing methods either introduce additional supervision, external models, or task-specific adaptation, while training-free approaches mainly recover dense responses from existing patch features without examining where local semantics become most accessible within CLIP. We introduce TraceCLIP, a training-free framework that recovers latent patch-level semantic evidence by isolating the patch-specific terms written into the CLS
attention output. TraceCLIP further converts contribution-derived semantic responses into a semantic-geodesic
topology gate that calibrates final-layer patch affinity for dense feature reconstruction. Diagnostic experiments show that these contribution features exhibit strong local semantic discrimination and text-conditioned spatial
alignment. On eight zero-shot semantic
segmentation benchmarks, TraceCLIP achieves gains of 1.3 to 4.5 points in average mIoU over the strongest prior training-free methods across both backbones and background settings, without additional training, external
vision foundation models, or region-level supervision. More broadly, these findings suggest that spatially localized semantics may remain accessible within the internal construction of globally aligned representations.
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All authors Xinran Liu, Shouqian Shi, Yutong Chen, Ge Wang, Xin-Wei Yao, Sheng Zhong
Artificial Intelligence · Cross-listed from cs.CV
Weight and Height Estimation from a Single Human Image Captured in the Wild
Hira Yaseen, Arif Mahmood and Waqas Sultani
A person's physical characteristics such as weight and height are important indicators of his physical and mental health, daily life routines and finances. Body Mass Index (BMI) is a well known measure that encodes the characteristics of both the weight and the height. BMI has been used as a self-monitoring tool, and it has long-term implications on one's life. For example, it may help predicting the risk of various diseases and estimating longevity. Automatic BMI estimation using a single person image in the wild is a challenging task due to wide variations in human pose, camera geometry, personal appearance and distracting backgrounds. In this paper, we explore the performance of deep neural networks using single and multi-task learning by employing different modalities including RGB, depth-maps, pose-affinity maps, and edge-maps to predict BMI, weight, and height from daily life images available on social networking websites. Currently, no full body image
dataset for BMI estimation is publicly available, therefore we propose a new
dataset consisting of 6105 images with ground truth labels of height, weight and BMI. Our proposed
dataset is collected in the wild containing images from various ethnicity and distributed over varying age groups and gender. It consists of frontal, back, full and half body, side poses, mirror selfies with varying backgrounds and scale variations and may contain artifacts hiding partial or full face. Extensive experimentation is performed using full body, half body and face images only using different CNN backbones including VGG, Densenet and ResNet. Our experimental results demonstrate that full body images have produced better results than the other half body and facial images in the wild.
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Artificial Intelligence · Cross-listed from cs.CR
A Reference-Free Score for Detecting Silent Reasoning Failures in Large Language Models
Vivek Shukla, Varun Shukla, Atul et al. (+2 more)
Mathematical chain of thought (CoT) evaluation is commonly reduced to whether the final answer matches a reference. This conflates producing a correct conclusion with producing a valid derivation an invalid chain can accidentally reach the right answer, while a valid calculation can be followed by a transcription error. We call this mismatch the
reasoning answer consistency gap. This framework paper introduces the
Reasoning Answer Faithfulness Score (RAFS), a reference free, instance level diagnostic of whether an emitted mathematical trace is locally credible, supports its answer, and is stable under resampling and targeted counterfactual interventions. RAFS combines step validity,
reasoning to answer entailment and counterfactual sensitivity, answer consensus, and conditional
reasoning stability. It evaluates transcript level agreement, not a models private computation and not factual correctness outside the tested mathematical setting. We retain a preregistered, results blind confirmatory study on GSM8K and MATH, with hypotheses, admissibility rules, calibration, and tests fixed before confirmatory outcomes are inspected. A separate feasibility pilot is specified to verify end to end execution and estimate interven tion coverage before that freeze numerical pilot claims are re ported only when trace level artifacts are available. We formalize four
reasoning answer outcomes, justify the non compensatory aggregator, instantiate semantic trace distance, quantify compute and abstention tradeoffs, and define verifier independence and power analyses. RAFS is intended to complement mathematical answer accuracy with an auditable warning signal for silent
reasoning failures and answer extraction errors
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All authors Vivek Shukla, Varun Shukla, Atul, Divya Mishra, Mehul Kumar Das
Artificial Intelligence · Cross-listed from cs.CL
Pass the Baton: Trajectory-Relayed On-Policy Distillation
Haolei Xu, Xiaowen Xu, Haiwen Hong et al. (+5 more)
On-policy
distillation (OPD) grounds
token-level supervision in the student's own trajectory, yet suffers from prefix failure: once the student commits to a wrong
reasoning direction, all subsequent generation builds on this deviation, producing misdirected continuations that elicit unreliable supervision and waste compute. We identify a teacher-student continuation asymmetry on failed prefixes, where the teacher tends to redirect while the student continues along the original direction, and convert it into a label-free handoff trigger in Relay On-Policy
Distillation (Relay-OPD). During training, Relay-OPD constructs relay trajectories by letting the teacher briefly take over at detected trigger points to produce a teacher leg, after which the student resumes and is optimized on the resulting trajectory. A limited relay budget concentrates intervention on critical early positions while limiting departure from the student policy. With a Qwen3-4B-Instruct-2507 teacher and Qwen3-0.6B/1.7B-Non-Thinking students on eight mathematical
reasoning benchmarks, Relay-OPD achieves the best or second-best results on every
benchmark, outperforming standard OPD by +5.73% and the strongest baseline FastOPD by +1.49% on average for 1.7B, with consistent gains at 0.6B. Training trajectory length is reduced by over 50%.
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All authors Haolei Xu, Xiaowen Xu, Haiwen Hong, Zixuan Ni, Hongxing Li, Yiwen Qiu, Weiming Lu, Yongliang Shen
Artificial Intelligence · Cross-listed from cs.RO
$\pi\mathbf{R}^2$: Reactive Real-time Flow Policies
Sungjae Park and Shubham Tulsiani
Generalist manipulation policies increasingly take the form of action-chunking flow policies built on large pretrained backbones. Such chunks run open-loop, so the policy cannot react to sensory input arriving mid-execution, sacrificing reactivity. Replanning more often would restore it, but the perception-to-action pipeline (a large backbone plus multiple denoising steps) is too slow: this latency forbids frequent replanning and leaves committed actions stale, making such policies ill-suited for dynamic, closed-loop control. We present $\pi\mathbf{R}^2$, which makes these policies reactive and real-time while retaining large backbones, expressive multi-modal policies, and multi-action prediction. Built on the per-position noise schedule of
diffusion forcing, $\pi\mathbf{R}^2$ contributes two ideas. First, it splits conditioning into a fast channel (proprioception, fresh every tick) and an asynchronously updated slow channel (
vision-
language features), so the policy reacts to proprioception within a chunk while tolerating stale
vision. Second, a latency-adaptive flow schedule treats in-flight actions as inpainting conditioning and emits actions in one denoising step per call, letting one trained model adapt to varying hardware latency. Requiring minimal modification to existing architectures, $\pi\mathbf{R}^2$ can be finetuned from a pretrained policy: applied to GR00T-N1.7 on a real xArm6+XHand platform, it replans closed-loop roughly $4\times$ faster than the base policy (~$25$Hz on an A5000 GPU), acting on a fresh observation every $40$ms. Across simulation and real-world manipulation tasks, $\pi\mathbf{R}^2$ improves the success rate by up to $23\%$ in simulation and $30\%$ in the real world over the strongest baseline. Project page: https://pi-r2-flow.github.io/
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Artificial Intelligence
Desktop-Delta Bench: Do Computer-Use Models Understand Desktop GUI Transitions?
Abhishek Pillai, Samir Kumar Nayak and Yuan Chen
Computer-use
agents (CUAs) increasingly act through desktop GUIs to complete long-horizon tasks. Current
benchmarks primarily measure end-task success or single-frame grounding. Neither isolates whether a model can reconstruct the causal, task-relevant transition produced by an action- crucial for rejecting stale observations, verifying progress, and recovering from failure. This is difficult because inference, remote input, app rendering, and screenshot capture are asynchronous: the next observation may be delayed, occluded, transient, or unrelated, then misread as progress and carried into subsequent planning. We introduce Desktop-Delta Bench (DDB), an offline step-level
benchmark with 2,013 human-verified instances from novel, multi-app Linux trajectories across ~15 applications and 50 task domains. DDB trajectories targets 3 failure dimensions- state verification, source tracking, and context-aware control- through 2 complementary tasks: 463 3-frame temporal-ordering instances, including 105 with a cross-trajectory decoy, and 1,550 before-after pairs labeled from 5 actions + its payload. We evaluate 8 closed and open-source model families across 32 ordering and 16 single-action settings, observing consistent gaps. Ordering remains unsaturated: best non-decoy and decoy exact-match rates are 65.1% and 65.7%. Task context improves decoy identification by 6.9 percentage points but reduces non-decoy exact match by 2.2 points; error analysis reveals systematic copying of the presented A-B-C order. Single-action results show that inferring the action family is harder than locating it: click F1 is 0.96 vs, 0.76 for drag, while recognized drags are generally localized well. DDB, thus, complements end-to-end
benchmarks by filling the missing diagnostic layer between GUI grounding and final task success, enabling targeted improvements to desktop CUA verification, reliability, and recovery.
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Artificial Intelligence
Falling Behind Drives Unsafe Development in an Idealised AI Race Experiment
Elias Fernández Domingos and The Anh Han
Technological races create tension between speed and safety: actors may gain by moving faster than competitors, even when risky development is harmful. This is prominent in debates about artificial intelligence (AI), where competitive pressure is often argued to incentivise riskier, less safety-conscious development. We study this using a framed behavioural experiment based on an idealised AI race, in which paired participants repeatedly chose between Safe and Unsafe development under an uncertain time horizon. Unsafe development gave faster progress and higher immediate payoffs but accumulated private risk up to a treatment-specific maximum of 10%, 60%, or 90%; the race's competitive structure was held constant, and only this maximum risk varied. Neither the pre-registered comparison between risk levels nor the role of elicited risk preferences was supported by the data. Instead, exploratory analyses motivated by the task's repeated structure show that Unsafe behaviour is shaped less by risk preferences than by the evolving strategic state of the race: participants are more likely to choose Unsafe after their opponent does so, being ahead reduces Unsafe play while falling behind increases it, and first-round choices predict later behaviour. To interpret these effects we introduce a reduced evolutionary model with four strategies – Always Safe, Always Unsafe, Conditionally Safe, and Conditionally Antisocial Safe – which reproduces the treatment effect and shows how conditional Unsafe behaviour can be favoured by competitive race dynamics. Together, the experiment and model show that unsafe development can emerge from early behavioural momentum, opponent behaviour, and fear of falling behind, rather than from risk preferences alone, suggesting policy should focus on reducing competitive pressure and promoting cooperation in AI development rather than only individual risk.
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Artificial Intelligence
CHARM: A Multimodal Graph Foundation Model with Hierarchical Context Modeling for Zero-Shot Transfer
Ankang Yang, Jitao Zhao, Di Jin et al. (+2 more)
Graph foundation models (GFMs) have emerged as a promising paradigm for transferring knowledge across graph domains and tasks. Real-world graphs associate nodes with text, images, and other modalities, making
multimodal graphs essential for representing complex entities and relations. Moreover, collecting labels and adapting models for every new graph domain is costly and often infeasible, motivating zero-shot transfer. Unfortunately, zero-shot transfer on
multimodal graphs remains underexplored. Existing GNN-based graph foundation models typically require downstream adaptation, whereas LLM-based graph methods mainly address unimodal graphs or tasks within a single domain. This setting presents two key challenges. First, models must generalize knowledge from individual modalities while capturing transferable cross-modal relations. Second, without target-domain fine-tuning, node representations remain entangled with domain-specific structures and modality-specific characteristics, obscuring shared concepts in unseen domains. To address these challenges, we propose CHARM, a
multimodal graph foundation model with hierarchical context modeling for zero-shot transfer. CHARM replaces isolated raw nodes with hierarchical graph contexts that capture
multimodal semantics and cross-modal relations. These contexts map domain-specific node patterns to shared high-level concepts, reducing reliance on target-domain supervision or adaptation. A modality-aware graph context encoder integrates
multimodal information with graph structure and converts the resulting representations into graph
tokens for a large
language model . Experiments show consistent improvements on zero-shot
multimodal graph tasks.
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All authors Ankang Yang, Jitao Zhao, Di Jin, Yuxiao Huang, Dongxiao He
Artificial Intelligence · Cross-listed from cs.AR
MDTransformer: A Hardware-Software Co-Design of Mode-Division Photonic Transformer Accelerator with Inverse-Designed Coherent Crossbar
Solomon Micheal Serunjogi, Rachmad Vidya Wicaksana Putra, Ayat Taha et al. (+2 more)
Recently, photonic
transformer accelerators (PTAs) have successfully achieved significant speedup and energy efficiency improvements over electronic accelerators for expediting
Transformer inference. However, state-of-the-art rely on expensive multi-wavelength light generation and large dot-product units due to active phase-shifter components, thus making their approach inefficient and impractical. To address this, we propose MDTransformer, a novel hardware-software co-design of PTA based on mode-division optical dataflow and operations. Specifically, MDTransformer performs complex
matrix operations using spatial-mode interference, that leverages the inverse-designed multi-mode couplers, crossings, and Mach-Zehnder IQ modulators into a compact mode-division photonic tensor core (MPTC), capable of executing
matrix multiplications in the optical domain. Its each guided mode (i.e., TE0-TE3) acts as an independent computational lane, enabling four-fold parallelism-per-waveguide without spectral filtering or free-spectral-range limitations. Moreover, its coherent detection and IQ modulation jointly encode amplitude and phase, realizing complex-valued arithmetic for full-range operations in
transformers. MDTransformer offers analog multiplication with sub-4-bit effective precision and inter-modal crosstalk below -30 dB. Its inverse-designed approach also offers scalable and full compatibility with single-
laser continuous-wave operation at 1550 nm. Experimental results show that MDTransformer achieves 40.4% area reduction, 63.6% power saving, 40.6% energy saving, and comparable latency over the state-of-the-art PTA across different workloads (i.e., DeiT-Tiny/Small/Base and BERT-Base/Large). These results show that MDTransformer offers a practical solution for high-performance and energy-efficient
transformer-based systems.
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All authors Solomon Micheal Serunjogi, Rachmad Vidya Wicaksana Putra, Ayat Taha, Muhammad Shafique, Mahmoud Rasras
Artificial Intelligence · Cross-listed from cs.CV
Pictura: Perspective-View Self-Play at Scale for Driving
Yuan Yin, Elias Ramzi, Marc Lafon et al. (+8 more)
Self-play in simulation produces robust driving policies at scale. Demonstrations of such behavior have been made using privileged vectorized observations such as exact poses and velocities, even for occluded
agents. This assumes that perception is solved and introduces a representation gap with the partial observation of a deployed
agent driving from the perspective view of
egocentric cameras. A common fix, distilling the privileged policy into a camera-input student, leaves the student imitating decisions its own view cannot justify. Instead, we establish perspective-view self-play as a practical training regime. We introduce Pictura, a GPU-accelerated multi-
agent driving simulator that renders each
agent's
egocentric view at every step, mitigating the representation gap at its source. Pictura sustains up to 500K
agent-steps/s (2M images/s) on a single H100. Using Pictura, we train Alberti by self-play with plain PPO. It is the first large-scale driving self-play policy trained directly from perspective images, without privileged observations. Training spans 50B
agent steps for ~35M km of driving. It approaches the driving performance of its privileged vectorized counterpart, and transfers zero-shot to Waymo Open Motion
Dataset layouts re-rendered in Pictura, where it outperforms privileged vectorized
agents. Project page: https://valeoai.github.io/Pictura/
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All authors Yuan Yin, Elias Ramzi, Marc Lafon, Valentin Charraut, Victor Bares, Yihong Xu, Éloi Zablocki, Alexandre Boulch, Thibault Buhet, Andrei Bursuc, Matthieu Cord
Artificial Intelligence · Cross-listed from cs.LG
Empirical Evaluation of Out-Of-Distribution Performance of Tabular Foundation Models
Malena Loza, David Chushig-Muzo, Eva Milara et al. (+3 more)
Tabular Foundation Models (TFMs) have emerged as novel approaches for tabular predictive tasks, demonstrating competitive predictive performance to ensemble tree-based models. Most TFMs are trained and evaluated on independent and identically distributed data, but this assumption changes in real-world scenarios due to distribution shifts, which compromise the robustness of models. Limited research has been conducted of TFMs under distribution shifts. We present an empirical evaluation of Out-Of-Distribution (OOD) performance of nine TFMs, spanning diverse pre-training strategies and architectures: TabPFNv2, TabPFNv2.5, TabPFNv2.6, TabPFNv3, TabICL, TabICLv2, Mitra, LimiX and TabFM. Three real-world
datasets from the TableShift study were considered (HELOC, Voting, Childhood Lead), covering label, socioeconomic, and geographic shift types. Our results show that all evaluated TFMs degrade systematically under distribution shift regardless of pre-training strategy, with shift gaps ranging from 0.003 to 0.060 depending on shift type. The relationship between in-distribution and OOD predictive performance documented for classical tabular models extends into TFMs. We also identified a scalability gap, as high-performing models demand significant
memory and computational resources beyond what standard deployment infrastructure can support. This study extends existing
benchmarks for OOD in tabular data, providing evidence to support their adoption in high-stakes domains characterized by structural distribution shifts.
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All authors Malena Loza, David Chushig-Muzo, Eva Milara, Luis Bote-Curiel, Luis Estrada-Petrocelli, Felipe Grijalva
Artificial Intelligence · Cross-listed from cs.CR
Does Runtime Topology Context Improve LLM-Generated Kubernetes Security Patches?
Farooq Shaikh
Kubernetes is central to the cloud-native ecosystem, orchestrating containerised workloads. Recent work suggests that large
language models (LLMs) can automate cluster security remediation, generating configuration patches from Kubernetes Security Posture Management (KSPM) findings without human authoring. Such systems, however,
prompt the model with each finding in isolation from the live service call graph, assuming general hardening knowledge suffices. This assumption breaks down whenever a patch must preserve a runtime service dependency invisible to the model: an otherwise compliant fix then carries a destructive functional blast radius, crashing downstream callers or silently severing call edges across the cluster. Whether live cluster context improves patch correctness has not been measured under controlled conditions across multiple dependency classes. We introduce KuTIE (Kubernetes
Topology Intelligence Engine), which builds a live cluster context from Istio call edges, Trivy KSPM findings, and the service-account bindings a workload reads, and conditions LLM patch generation on it. It is evaluated on VulnCare, a purpose-built 36-deployment, four-namespace healthcare cluster with 31 injectable findings across seven dependency classes, each labelled by
topology dependence against cluster ground truth. Across 248 trials,
topology context raises
topology-dependent patch correctness from 11.1% to 78.0% ($\Delta = 0.669$), a gap that holds for every model and for six of seven classes, from credential and network-policy ($\Delta = 0.95$) to role-based access control ($\Delta = 0.31$); a
topology-independent control exhibits no such effect ($\Delta = 0.0$), isolating the result from generic
prompt enrichment. Supplying the live service-call graph and the service-account bindings it exposes thus improves remediation of
topology-dependent findings well beyond scanner-only context.
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Artificial Intelligence · Cross-listed from cs.DB
MemLens: A Value-Aware Memory Management System with Interactive Analytics for LLM-based Agents
Shuyue Wei, Chang Liu, Zimu Zhou et al. (+2 more)
Recently,
memory management has become a key infrastructure for LLM-based
agents, as it directly affects long-horizon
reasoning, personalized responses, and knowledge reuse. However, existing LLM
memory systems typically adopt a coarse-grained (utility-agnostic) manner that treats heterogeneous user-LLM interaction records uniformly, leading to redundant and low-impact records persisting in the
memory repository. To address this challenge, we present MemLens, a value-aware
memory management system that takes
memory records as first-class data objects. MemLens provides an end-to-end interactive analytics dashboard that exposes the complete
memory lifecycle, including Shapley-style
memory evaluation, value-aware storage, and
memory-assisted response. Through a study-copilot application, the system enables users to inspect
memory values, visualize hierarchical
memory structures, and compare various
memory management strategies in terms of response quality,
retrieval latency, and
token consumption. Therefore, our MemLens can serve as an efficient, interpretable, and personalized long-term
memory management system for LLM-based
agents.
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All authors Shuyue Wei, Chang Liu, Zimu Zhou, Yongxin Tong, Lizhen Cui
Artificial Intelligence · Cross-listed from cs.LG
Reinforcement Learning for Code Optimization
Pierre Chambon, Kunhao Zheng, Juliette Decugis et al. (+2 more)
RL for code correctness is now established: have the model generate a program, run it against hidden test cases, and reward solutions that pass. Extending this to code optimization seems straightforward: just add execution time to the reward. But in practice, once timing drives the reward, small problems in measurement noise, reward sparsity, or GRPO instability overwhelm the signal and make RL fail: generated solutions are barely faster, and more of them can fail. We make execution time learnable through three stages: (1) how code is tested, by building DMC-Optim with large optimization tests and a calibrated sandbox; (2) how speed is turned into reward, by composing correctness and speed in the RL environment and using an offline simulator to predict the most promising configurations; and (3) how the model learns from that reward, by adapting GRPO and evaluation to the sparser, noisier timed-execution setting. On DMC-Optim, the strongest optimization-aware configurations improve strict top-50% pass@1 from 18.0% to 31.3% on Qwen 2.5 7B and from 30.7% to 50.4% on CWM 32B. These gains further increase at stricter percentiles such as top-30%, with 125% relative improvement for CWM 32B, while preserving pure-correctness scores. When the timing sandbox is degraded, robust optimization RL reaches 100% to 200% improvement over standard RLVR, depending on the evaluation criterion. On LCB, CWM 32B wins up to 83% of median-sample speed comparisons against standard RLVR. Relative to the fastest correct human submissions per problem, it reaches about half the human rate of complexity-class improvements (13% vs. 22%).
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All authors Pierre Chambon, Kunhao Zheng, Juliette Decugis, Benoit Sagot, Gabriel Synnaeve
Artificial Intelligence · Cross-listed from cs.CV
Knowledge-Guided Multimodal Reasoning over Interacting Streams for Video-Level Ambivalence and Hesitancy Recognition
Podakanti Satyajith Chary, Barath Parthiban, Pranesh Velmurugan et al. (+2 more)
Ambivalence and hesitancy (A/H) are conflicting affective states that precede the delay or abandonment of health behaviour change. Recognition of A/H at the
video level is difficult, since the signal arises from disagreement across and within facial, vocal, linguistic, and bodily modalities, and manifests differently across individuals. The proposed PRISM-AH (Predictive
Reasoning over Interacting Streams for
Multimodal Ambivalence/Hesitancy Recognition), is a framework that treats A/H as a
multimodal conflict that unfolds over time. Frozen
vision, audio, and text encoders are aligned into short time windows and passed to a lightweight streaming model that scores cross-modal dissonance, predicts each next window to expose a hesitation surprise signal, discovers behaviour prototypes, and is conditioned on participant metadata. Dense window-level annotations supervise the model as an auxiliary objective, and the decision threshold is calibrated for macro F1. A knowledge-guided large
language model then reasons over structured evidence using the expert cue taxonomy of the
dataset, and its verdict is fused late only when validation performance improves. On the labelled public test partition of 525
videos, PRISM-AH attains a macro F1 of 0.6133, compared to the reported zero-shot baseline of 0.2827. The
reasoning gain is validated to transfer from validation to the larger test partition.
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All authors Podakanti Satyajith Chary, Barath Parthiban, Pranesh Velmurugan, Adeeba Khan, Nagarajan Ganapathy
Artificial Intelligence · Cross-listed from cs.CL
Detecting Knowledge Inconsistencies Across Text, Tables, and Knowledge Graphs
Fanfu Wei, Thibault Ehrhart and Raphaël Troncy
Wikipedia and Wikidata are widely used for information access, LLM pre-training, and
retrieval-augmented generation. Their knowledge is deeply connected but scattered across text, tables, and knowledge graphs. This raises a practical question: when these modalities disagree, how can we detect and explain the conflict? We study this problem as modality-level inconsistency detection. We first introduce a taxonomy of cross-modal knowledge inconsistencies, covering information granularity differences, direct conflicts, temporal changes, and KG incompleteness. We then present Kontrast, an automatic framework that uses Text-to-SPARQL and LLM
reasoning to compare table-based answers with KG evidence and categorize the resulting inconsistencies. Experiments on various Table-QA
datasets show that cross-modal inconsistencies are common and informative. They reveal not only true knowledge conflicts, but also missing KG structure and temporal mismatches while being limited by Text-to-SPARQL errors and noise. Our analysis shows that text, tables, and KGs can complement and correct one another through systematic comparison. Kontrast provides a practical tool for large-scale knowledge
auditing and establishes a
benchmark for future work on cross-modal knowledge consistency. Code and data are available at https://github.com/ECLADATTA/KONTRAST.
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Artificial Intelligence
Large Language Model for Operations Research Formulation Selection in Multi-Warehouse Inventory Allocation
Jintao Xu, Yingzheng Ma, Jiong Dong et al. (+2 more)
Multi-warehouse inventory allocation is typically formulated as a mixed-integer programming (MIP) problem, yet no single formulation consistently matches heterogeneous instance-level regimes induced by demand concentration, inventory imbalance, replenishment scale, service constraints, and forecast
volatility. We study this issue as instance-wise operations research (OR) formulation selection, where each allocation instance is assigned to a solver-executable formulation from a candidate OR expert library. We propose a solver-guided large
language model (LLM) framework for OR formulation selection, in which each OR expert corresponds to a MIP formulation encoding a distinct allocation priority. To train the selector, the framework first constructs balanced expert-conditioned supervised fine-tuning (SFT) records for schema learning, and then uses MIP solver evaluation on historical instances to convert solver-evaluated allocation-quality gaps into margin-weighted identity preference optimization (IPO) preferences and per-instance expert-score metadata for reward lookup during group relative policy optimization (GRPO) to assign rewards to sampled responses. Experiments on multi-warehouse inventory allocation instances from JD$\mathord{.}$com, one of China's largest e-retailers, demonstrate that GRPO substantially improves expert-selection accuracy relative to the SFT+IPO selector and, more importantly, produces higher realized allocation quality than both the preference-trained selector and the best fixed formulation. With GRPO, Hit Ratio@1 and Hit Ratio@2 increase from 21.45% to 50.42% and from 70.47% to 82.31%. The resulting selector achieves an allocation accuracy gain of 12.57 percentage points over the incumbent baseline, outperforming both the SFT+IPO selector and the best fixed OR expert, and reduces the gap to the ex-post oracle to 4.85 percentage points.
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All authors Jintao Xu, Yingzheng Ma, Jiong Dong, Yongzhi Qi, Jianshen Zhang