This paper studies a focused question: whether a small amount of targeted supervision can improve a tool agent’s choice of immediate recovery actions under controlled failures, and introduces Mini Recovery Tuning, a deterministic benchmark and training protocol for evaluating this question.
An unsuccessful LLM agent rollout contains more information than its final reward: the observations available to the agent, the actions it chose, and the environment's responses. Reusing this experience for learning requires identifying a decision to revise and testing a concrete alternative. We introduce the Agent Err...
Kun-Lun Zhu, Xu-Yan Ye, Yi-Bo Li et al.· 1 citation
Tool-using agents are usually scored on whether they finish a task while the tools work. Deployments are less forgiving: services time out, endpoints disappear, parameter names change, and results come back well formed but wrong. Prior work has shown that language models over-trust tool outputs that fail silently; we a...
KC-Bench is introduced, a controlled multi-turn benchmark for measuring model-level behavior across world-knowledge conflicts, input inconsistencies, and multi-source temporal conflicts, and provides a reproducible diagnostic for developing conflict-aware reasoning and execution safeguards.
Yaxing Lyu, Sheng-Jie Zhou, B. Toh et al.· 2 citations
ToolRobustBench provides a deterministic and cascade-aware benchmark for diagnosing robustness beyond clean tool-calling accuracy, where a tool-calling agent is an LLM system that selects a tool, supplies structured arguments, and interprets its returned feedback.
This paper proposes FRESH, a Failure-aware Retrieval framework over Experience-Structured Heterogeneous graphs, which transforms historical successes and failures into structured external experience for tool-using agents and consistently improves task success and tool-use reliability over no-memory agents and represent...
Jia-Xing Li, Lei Song, Rui Dong et al.· 0 citations
Tool-using agents repeatedly carry observations whose useful content can be much smaller than their original payload. We study agent-controlled forgetting: the acting model selects previously observed tool results, replaces each with a short note at its original position, and retains the exact original in a recoverable...
J. Franke· 0 citations
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