arXiv AI
Jun 11
Skill-Augmented AI Agents for Medical Research Analysis: An Exploratory Multi-Model Human Evaluation in an NSCLC Transcriptomic Biomarker Task
arXiv:2606. 11830v1 Announce Type: new Abstract: Background.
AI agents
Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
arXiv AI
Jun 11
Signed Compression Progress on a Sealed Audit is Goodhart-Resistant
arXiv:2606. 11417v1 Announce Type: cross Abstract: Compression progress is a long-standing proposal for intrinsic motivation: reward an agent when its world model becomes better at predicting or compressing experience.
arXiv AI
Jun 11
Are Frontier LLMs Ready for Cybersecurity? Evidence for Vertical Foundation Models from Dual-Mode Vulnerability Benchmarks
arXiv:2605. 23243v2 Announce Type: replace-cross Abstract: We evaluate whether frontier LLMs are ready for cybersecurity through a dual-mode benchmark: white-box function-level vulnerability detection (VulnLLM-R, across C/Java/Python) and black-box web application security testing (five production-style applications with 118 ground-truth vulnerabilities across 20+ CWE families, which we will open-source).
arXiv AI
Jun 11
MPC-Patch-Bench: Security-Aware LLM Code Patch for Multi-Party Computation
arXiv:2606. 11416v1 Announce Type: cross Abstract: Repository-level benchmarks for evaluating Large Language Model (LLM) code repair on Secure Multi-Party Computation (MPC) software do not yet exist, and directly transplanting general-purpose benchmarks such as SWE-bench fails on three structural fronts: (i) MPC repositories are dominated by generic Python infrastructure rather than cryptographic logic; (ii) high-value MPC fixes lack the standardized tests rigid extraction pipelines require; and (iii) standard fail-to-pass evaluation is insufficient for code that must also be cryptographically safe.
arXiv Machine Learning
Jun 11
TimeRouter: Efficient and Adaptive Routing of Time-Series Foundation Models
arXiv:2606. 11625v1 Announce Type: new Abstract: Time-series foundation models (TSFMs) are increasingly explored as predictive experts within emerging agentic time-series systems.
arXiv Machine Learning
Jun 11
Self-Supervised Multisensory Pretraining for Contact-Rich Robot Reinforcement Learning
arXiv:2511. 14427v4 Announce Type: replace-cross Abstract: Effective contact-rich manipulation requires robots to synergistically leverage vision, force, and proprioception.
arXiv Machine Learning
Jun 11
FronTalk: Benchmarking Front-End Development as Conversational Code Generation with Multi-Modal Feedback
arXiv:2601. 04203v2 Announce Type: replace-cross Abstract: We present FronTalk, a benchmark for front-end code generation that pioneers the study of a unique interaction dynamic: conversational code generation with multi-modal feedback.
arXiv AI
Jun 11
MARIC: Multi-Agent Reasoning for Image Classification
arXiv:2509. 14860v2 Announce Type: replace-cross Abstract: Image classification has traditionally relied on parameter-intensive model training, requiring large-scale annotated datasets and extensive fine tuning to achieve competitive performance.
arXiv AI
Jun 11
When Generic Prompt Improvements Hurt: Evaluation-Driven Iteration for LLM Applications
arXiv:2601. 22025v2 Announce Type: replace-cross Abstract: Evaluating Large Language Model (LLM) applications differs from conventional software testing because outputs are probabilistic, semantically variable, and sensitive to prompt and model changes.
arXiv AI
Jun 11
Exploration Structure in LLM Agents for Multi-File Change Localization
arXiv:2606. 11976v1 Announce Type: cross Abstract: Software engineering tools increasingly rely on LLM based agents to localize files to change to resolve a software issue.
arXiv AI
Jun 11
Can AI Agents Synthesize Scientific Conclusions?
arXiv:2606. 11337v1 Announce Type: new Abstract: Scientific AI agents increasingly retrieve evidence, reason across sources, and synthesize conclusions used in consequential decisions.
arXiv AI
Jun 11
A Survey of Reasoning and Agentic Systems in Time Series with Large Language Models
arXiv:2509. 11575v3 Announce Type: replace Abstract: Time series reasoning treats time as a first-class axis and incorporates intermediate evidence directly into the answer.
arXiv AI
Jun 11
When Does Deep RL Beat Calibrated Baselines? A Benchmark Study on Adaptive Resource Control
arXiv:2605. 26418v2 Announce Type: replace-cross Abstract: A properly calibrated rule-based autoscaler can beat every one of six mainstream deep reinforcement learning (DRL) algorithms on cost across every workload we test - so when, if ever, does DRL actually help?
arXiv AI
Jun 11
MoCA-Agent: A Market-of-Claims Code Agent for Financial and Numerical Reasoning
arXiv:2606. 11537v1 Announce Type: new Abstract: Financial and tabular question answering requires more than fluent reasoning: answers must be grounded in the exact facts, formulas, units, signs, and scales that support them.
arXiv AI
Jun 11
Precomputing Multi-Agent Path Replanning Using Temporal Flexibility
arXiv:2601. 04884v3 Announce Type: replace Abstract: Executing a multi-agent plan can be challenging when an agent is delayed, because this typically creates conflicts with other agents.
arXiv AI
Jun 11
Architecture-Aware Reinforcement Learning Makes Sliding-Window Attention Competitive in Math Reasoning
arXiv:2606. 11634v1 Announce Type: new Abstract: The rapid progress of reasoning and agentic large language models (LLMs) has increased the demand for long-context inference, but self-attention (SA) scales quadratically with context length.
arXiv AI
Jun 11
A Five-Plane Reference Architecture for Runtime Governance of Production AI Agents
arXiv:2606. 12320v1 Announce Type: new Abstract: Enterprise security was built to govern data boundaries: the protected surface was data at rest and in transit, and the controls -- access control, data-loss prevention, perimeter inspection -- governed crossings of that boundary.
arXiv AI
Jun 11
Improving Generalization and Data Efficiency with Diffusion in Offline Multi-agent RL
arXiv:2307. 01472v2 Announce Type: replace Abstract: We present a novel Diffusion Offline Multi-agent Model (DOM2) for offline Multi-Agent Reinforcement Learning (MARL).
arXiv AI
Jun 11
StatefulDiscovery: Evidence-Calibrated Claim Formation in Open-Ended Scientific Discovery
arXiv:2606. 11851v1 Announce Type: new Abstract: Open-ended scientific discovery asks agents to move beyond executing analyses for predefined questions.
arXiv AI
Jun 11
MedCTA: A Benchmark for Clinical Tool Agents
arXiv:2606. 11702v1 Announce Type: cross Abstract: To make clinically grounded decisions, medical AI agents are expected to go beyond simple recognition and be capable of tool retrieval, evidence acquisition, and integration.