When Harnesses Lose the Signal: Causal Evaluation of Recovery in LLM Agents

Large language model agents rely on external harnesses to pass information between the model and its environment and to recover from execution errors. Yet recovery is usually judged only by average task success. This hides an important tension. The same operation can rescue a…

Shuyao Xiao, Shengling Wang, Xuan Chen, Ke Chao, Ming Cui, Feifei Qian, Chaoyang Mei, Fanlin Meng, Ziming Yu, Junxi Yin·arXiv — cs.AI

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Key Takeaways

01.Large language model agents rely on external harnesses to pass information between the model and its environment and to recover from execution errors
02.Yet recovery is usually judged only by average task success
03.This hides an important tension

This material is provided as an extended analytical summary.

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