Inside VAKRA: Reasoning, Tool Use, and Failure Modes of Agents

OpenEnv in Practice: Evaluating Tool-Using Agents in Real-World Environments

Beyond Reactivity: Measuring Proactive Problem Solving in LLM Agents

arXiv:2510. 19771v4 Announce Type: replace Abstract: LLM-based agents are increasingly moving towards proactivity: rather than awaiting instruction, they exercise agency to anticipate user needs and solve them autonomously.

Sentry: Learning to Recover from LLM Agent Failures at Test Time

Sentry is a failure‑management layer for large language model agents that learns from failures at test time. It retrieves relevant lessons from an external playbook when a failure occurs, verifies recovery without task rewards, and stores new lessons only if recovery succeeds, keeping the playbook out of the agent’s context. Across multiple benchmarks, Sentry outperforms both runtime‑intervention and context‑evolution baselines, and its lessons transfer to unseen tasks.

AgentDynEx: Nudging the Mechanics and Dynamics of Multi-Agent Simulations

arXiv:2504. 09662v4 Announce Type: replace-cross Abstract: Multi-agent large language model simulations have the potential to model complex human behaviors and interactions.

AI Agents Are Here. What Now?

Computer-Using Agent