Doomed from the Start: Early Abort of LLM Agent Episodes via a Recall-Controlled Probe Cascade
Kai Ruan, Zihe Huang, Ziqi Zhou, Qianshan Wei, Xuan Wang, Hao Sun
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Key claim
Early failure prediction saves significant inference compute.
In plain English
Large language model agents often commit to paths that lead to failure, wasting compute resources before the failure is evident. Current methods struggle to predict these failures early, relying mainly on observable behavior. This paper introduces a new approach that uses internal representations to anticipate failures and implement an abort mechanism, saving significant compute resources. Builders might find this useful for improving efficiency in deploying LLMs for complex tasks.
Introduces a novel method for early failure prediction in LLM agents.
Demonstrates solid empirical results across multiple models and recall targets.
Deep reliability assessment
The methodology supports early failure prediction and inference-time early stopping for LLM agents using hidden-state probes, but the claims of savings and recall control are contingent on the specific configurations and may not generalize without further validation.
Reproducibility
No open source code or dataset is provided yet, but the paper mentions that the code will be released soon.
Key figure
Figure 1 illustrates a recall-controlled abort cascade where a linear probe reads the agent's hidden state at each round, aborting the episode if the failure score exceeds a calibrated threshold.