arXiv Machine Learning
Aug 18
Temporal Logic Guided Universal Task Representations for Reinforcement Learning
arXiv:2608. 15509v1 Announce Type: cross Abstract: Task guided agents demonstrate strong performance in a wide range of complex tasks.
AI agents
Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
arXiv AI
Aug 18
Budget-Aware Tool Use Enables Effective Agent Scaling
arXiv:2511. 17006v2 Announce Type: replace Abstract: Scaling test-time computation has been extended from language model reasoning to tool-augmented agents, where scaling involves not only thinking in tokens but also acting via tool calls that directly constrain environmental interaction.
arXiv AI
Aug 18
The Fragility of Strategic Thinking in Large Language Models
arXiv:2510. 10813v2 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly applied to domains that require reasoning about other agents' behavior, such as negotiation, policy design, and market simulation.
arXiv Machine Learning
Aug 18
Deploying Frontier Agentic Technology in MOOSEnger, a Multiphysics-Capable AI Assistant
arXiv:2608. 15881v1 Announce Type: new Abstract: The Multiphysics Object-Oriented Simulation Environment (MOOSE) is an open-source finite-element framework for building multiphysics simulation applications.
arXiv Machine Learning
Aug 18
AdROD: HyperNetwork-based Adversarially Robust Object Detection for Autonomous Driving
arXiv:2608. 16031v1 Announce Type: new Abstract: Camera-based object detectors are vulnerable to physical adversarial attacks designed to suppress detections.
arXiv AI
Aug 18
AeroCopilotBench: A Two-Tier Benchmark for Evaluating LLM Agents as Aviation Copilots in an Interactive Virtual Cockpit Environment
arXiv:2608. 16349v1 Announce Type: new Abstract: Large language model (LLM) agents may assist flight crews with complex decisions and task execution, but existing aviation evaluations centered on static knowledge do not support systematic testing of procedural execution and safety compliance in interactive environments.
arXiv AI
Aug 18
JailbreakSkill: Scaling Automated Red-Teaming with Reusable and Ever-Evolving Skills
arXiv:2608. 16465v1 Announce Type: new Abstract: Automated red-teaming has produced a growing collection of attack strategies, yet they typically remain scattered across prompts and workflows, making them difficult to systematically integrate, reuse, and improve at scale.
arXiv AI
Aug 18
Quipu: A Governed Bitemporal Knowledge Graph Store
arXiv:2608. 16813v1 Announce Type: new Abstract: Agents now write knowledge graphs, but knowledge-graph stores still carry defaults set when humans curated them: accept writes now and clean later, keep one time axis or none, treat every writer's facts as equally trustworthy, and leave governance to dashboards and middleware.
arXiv AI
Aug 18
WARA: Toward Automated Wireless Optimization Research with Closed-Loop LLM Agents
arXiv:2608. 14573v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly capable of tool use, code execution, artifact inspection, and iterative revision, creating new opportunities for automating scientific and engineering research.
arXiv AI
Aug 18
When Agentic Executions Fail: Detecting and Localizing Runtime Faults from Telemetry
arXiv:2608. 14680v1 Announce Type: new Abstract: Reliability in LLM-based agentic systems is a property of the whole execution (its tool calls, model calls, guardrails, and inter-agent messages), not of the final answer alone, yet evaluating only task outcomes reveals little about how or why a run fails.
arXiv AI
Aug 18
Multi-Agent Closed-Loop Reasoning for Organic Structure Elucidation from Multimodal Spectra
arXiv:2608. 14720v1 Announce Type: cross Abstract: Following the molecular discovery and synthesis revolutions, scalable automated structure elucidation from routine spectroscopic data remains an outstanding challenge.
arXiv AI
Aug 18
Beyond Pass@k: Measuring Reliability and Security of Agentic Code Generation
arXiv:2608. 14711v1 Announce Type: new Abstract: AI coding agent benchmarks rank agents with the Chen et al.
arXiv AI
Aug 18
MINT: Min-Selection Preference Distillation for Balanced Multi-Objective Alignment
arXiv:2608. 14828v1 Announce Type: new Abstract: Aligning a language agent to several objectives at once is a persistent failure mode of preference-based training: when objectives are combined additively, optimization collapses onto whichever is cheapest to improve and sacrifices the rest, so a support agent learns to sound warm while giving no real help.
arXiv AI
Aug 18
VibeWorlding: Can Multimodal Agents Construct 3D Open Worlds End-to-End?
arXiv:2608. 15265v1 Announce Type: new Abstract: Constructing an interactive 3D open world from a user query is important.
arXiv AI
Aug 18
Emergent Misaligned Communication in Long-Horizon Multi-Agent LLM Commerce
arXiv:2608. 14825v1 Announce Type: cross Abstract: Frontier LLM agents increasingly transact on behalf of separate principals, often using natural language rather than structured APIs.
arXiv AI
Aug 18
The Open-Strategy Dictator Game: Cooperation Under Mutual Transparency
arXiv:2608. 14913v1 Announce Type: cross Abstract: We introduce the Open-Strategy Dictator Game (OSDG), a variant of the classic dictator game in which each player's strategy is a natural-language document visible to all participants.
arXiv AI
Aug 18
Evo-Harness: Context-to-Harness Skill Compilation for Self-Evolving Agents
arXiv:2608. 15071v1 Announce Type: new Abstract: Learning from experience is critical for developing capable, self-improving large language model (LLM) agents.
arXiv AI
Aug 18
Valhalla: A Layered Knowledge-State and Service-Governance Framework for Long-Term Scientific Knowledge Work
arXiv:2608. 15193v1 Announce Type: cross Abstract: As large language model (LLM) agents are increasingly adopted in scientific research, external knowledge bases, knowledge graphs, and long-term memory have improved information retrieval and task continuity.
arXiv AI
Aug 18
SysEvolve: An AI-native, safe, autonomous adversarial attack-defense co-evolutionary system
arXiv:2608. 15012v1 Announce Type: cross Abstract: The rapid advancement of large language models (LLMs) has created a growing asymmetry in cybersecurity, where attack accelerates toward autonomous execution while defense remains predominantly human-intensive.
arXiv AI
Aug 18
CompoSkill: Compositional Skill Chain Attacks from Individually Scanner-Passing LLM Agent Skills
arXiv:2608. 16246v1 Announce Type: cross Abstract: Autonomous AI agents tackling Long Horizon Tasks depend on marketplace skills that are certified one at a time: a scanner returns a safety verdict for each skill and declares the ecosystem safe if every package passes.