arXiv:2608. 14380v1 Announce Type: new Abstract: Many real-world tasks require LLM agents to interact with their environments over long execution horizons.
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arXiv:2609.18304v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly tackle long-horizon tasks through multi-step environment interaction, yet a single erroneous action can...
arXiv:2606. 07682v1 Announce Type: cross Abstract: AI agents are increasingly expected to complete long-horizon workflows that require sustained progress over hours, millions of tokens, and complex environments.
ParaRecover is a new process-level benchmark designed to evaluate error localization and recovery in multi-turn parallel tool-use agents. It contains 10,626 instances across two difficulty levels, built on a taxonomy of 14 error types that cover planning dependencies, tool selection, and argument matching. The benchmark introduces the SDE rubric, which assesses structural integrity, diagnostic reasoning, and evolutionary strategy during agent execution, and demonstrates that it can guide improvements in agents’ reflective recovery capabilities.
arXiv:2608. 09524v1 Announce Type: cross Abstract: Incident response planning is critical for restoring compromised software systems after cyberattacks.
arXiv:2608. 05013v1 Announce Type: cross Abstract: LLM agents are increasingly applied to open-ended everyday requests that span work, study, and life.
arXiv:2607. 13705v1 Announce Type: new Abstract: As Large Language Models (LLMs) evolve into autonomous agents, the need for unified evaluation infrastructure becomes critical.