arXiv AI
Jul 24
AREX: Towards a Recursively Self-Improving Agent for Deep Research
arXiv:2607. 21461v1 Announce Type: new Abstract: Deep research requires agents to find answers that jointly satisfy multiple constraints.
AI agents
Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
arXiv AI
Jul 24
VoLN: Vision-Only Long-Horizon Navigation---Paradigm, Benchmark, and Method
arXiv:2607. 21400v1 Announce Type: cross Abstract: Vision-and-Language Navigation (VLN) enables embodied agents to follow natural-language instructions.
arXiv AI
Jul 24
Explainable Belief Harmonization under Dynamic Epistemic Partitions
arXiv:2607. 21210v1 Announce Type: cross Abstract: Existing approaches to multi-agent belief combination have established mature foundations for combining uncertain beliefs under common assumptions: consensus methods use iterative averaging, logic-based methods resolve conflicting knowledge bases, and epistemic logic analyzes agents' information states.
arXiv Machine Learning
Jul 24
Hankel and Toeplitz Rank-1 Decomposition of Arbitrary Matrices with Applications to Signal Direction-of-Arrival Estimation
arXiv:2604. 26787v2 Announce Type: replace Abstract: We consider the problems of computing the optimal rank-1 Hankel and Toeplitz-structured approximation of arbitrary matrices under L2 and L1-norm error.
arXiv AI
Jul 24
Verifier-First Evaluation of Agentic LLMs for Infrastructure-as-Code Generation
arXiv:2607. 20478v1 Announce Type: cross Abstract: Infrastructure-as-Code (IaC) generation from natural language requires satisfying provider schemas, dependency planning, and organizational policy constraints, not merely producing syntactically plausible configurations.
arXiv AI
Jul 24
Agentic coding without the cloud: evaluating open-weight large language models on longitudinal data preparation tasks
arXiv:2607. 21482v1 Announce Type: new Abstract: Large language models (LLMs) and agents are now widely used tools in code development, with data typically sent to third-party cloud-based models.
arXiv AI
Jul 24
WebCoach: Self-Evolving Web Agents with Cross-Session Memory Guidance
arXiv:2511. 12997v2 Announce Type: replace Abstract: Multimodal LLM-powered agents have recently demonstrated impressive capabilities in web navigation, enabling agents to complete complex browsing tasks across diverse domains.
arXiv Machine Learning
Jul 24
Exploiting Exogenous Structure for Sample-Efficient Reinforcement Learning
arXiv:2409. 14557v4 Announce Type: replace-cross Abstract: We study a structured class of Markov Decision Processes, known as Exo-MDPs, in which the state space is partitioned into exogenous and endogenous components.
arXiv AI
Jul 24
Self-Evolving Recommendation System: End-To-End Autonomous Model Optimization With LLM Agents
arXiv:2602. 10226v2 Announce Type: replace-cross Abstract: Optimizing large-scale machine learning systems, such as recommendation models for global video platforms, requires navigating a massive hyperparameter search space and, more critically, designing sophisticated optimizers, architectures, and reward functions to capture nuanced user behaviors.
arXiv AI
Jul 24
IssueTrojanBench: Benchmarking AI Coding Agents Against Malicious Issue Requests
arXiv:2607. 20759v1 Announce Type: cross Abstract: AI coding agents powered by LLMs are increasingly integrated into real-world software development, where they generate, edit, and execute code with autonomous access to local files and tools.
arXiv AI
Jul 24
TOUR: A Trajectory-Level Unlearning Benchmark for Offline Reinforcement Learning
arXiv:2607. 21111v1 Announce Type: cross Abstract: Offline Reinforcement Learning (RL) agents are trained on fixed behavioral trajectories, which makes trajectory-level deletion important when selected data must be removed after training.
arXiv AI
Jul 24
PersonaTrail: Benchmarking Personalized Web Agents through Browsing Trails
arXiv:2607. 20482v1 Announce Type: new Abstract: Recent advances in large language models have enabled web agents to autonomously execute complex tasks.
arXiv AI
Jul 24
Is Deep Research Reliable? Misleading Knowledge Induces False Conclusions
arXiv:2607. 20891v1 Announce Type: new Abstract: Deep Research agents extend LLM-based assistants into long-horizon workflows involving planning, retrieval, evidence synthesis, and report generation, yet their reliability in open information environments remains underexplored.
arXiv AI
Jul 24
Autonomous disproofs of the sum-product conjecture over $\mathbb R$ with GPT-5.5 Pro
arXiv:2607. 20525v1 Announce Type: new Abstract: OpenAI's recent disproof of the Erd\H{o}s unit distance conjecture marked a milestone for AI in mathematics.
arXiv AI
Jul 24
Regulating autonomous and agentic AI
arXiv:2607. 21345v1 Announce Type: new Abstract: Regulating activities where regulatees use autonomous and agentic AI is challenging.
arXiv AI
Jul 24
Evaluating and Guarding Citation Faithfulness in Agentic Scientific Synthesis
arXiv:2607. 20527v1 Announce Type: new Abstract: Agentic LLM systems such as OpenScholar and PaperQA2 read the scientific literature and return cited answers, and both they and their benchmarks already check whether those citations hold, with a fixed attribution model or human graders.
arXiv AI
Jul 24
SciExplore: Evaluating Autonomous Agents from Scientific Navigation to Information Integration
arXiv:2607. 20926v1 Announce Type: new Abstract: Scientific research involves complex information-seeking and reasoning workflows across heterogeneous sources.
arXiv Machine Learning
Jul 24
End-to-End Learning of Safe Optimal Feedback Control in High Dimensions with Control Barrier Function Layers
arXiv:2607. 20674v1 Announce Type: new Abstract: We consider the problem of learning high-dimensional semi-global feedback controllers under hard safety constraints enforced by control barrier functions (CBFs).
arXiv AI
Jul 24
Frontier Financial Judgement: Can agents tell what might move a stock?
arXiv:2607. 20645v1 Announce Type: cross Abstract: We introduce Frontier Financial Judgement, a challenging new benchmark developed in collaboration with professional equity analysts to assess agents' ability to replicate expert human judgements.
arXiv AI
Jul 24
Toward cryptographically verifiable authorization for autonomous AI agents: A security hypothesis, preliminary formal model, and proof-of-concept implementation
arXiv:2607. 21325v1 Announce Type: cross Abstract: Autonomous AI agents increasingly execute actions, invoke tools, and operate on protected resources with limited human oversight.