Doomed from the Start: Early Abort of LLM Agent Episodes via a Recall-Controlled Probe Cascade

• URL: http://arxiv.org/abs/2607.06503v2
• Date: Thu, 16 Jul 2026 16:47:09 GMT
• Title: Doomed from the Start: Early Abort of LLM Agent Episodes via a Recall-Controlled Probe Cascade
• Abstract summary: Large language model (LLM) agents often waste inference compute by continuing multi-step trajectories that are already doomed to fail.<n>We study early failure prediction and inference-time early stopping for LLM agents using hidden-state probes.<n>We turn this signal into a recall-controlled abort cascade for reducing LLM agent inference costs.
• Score: 14.63102636181275
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language model (LLM) agents often waste inference compute by continuing multi-step trajectories that are already doomed to fail. We study early failure prediction and inference-time early stopping for LLM agents using hidden-state probes. Lightweight linear probes on internal activations predict eventual task failure from the first interaction round, substantially earlier than agent-monitoring methods based only on observable behavior. We turn this signal into a recall-controlled abort cascade for reducing LLM agent inference costs. The cascade applies a distribution-free calibrated failure detector at each early interaction round and jointly optimizes per-round recall budgets. This design ensures that eventually successful episodes survive all early-stopping gates at a user-specified global recall rate. After selection, the cascade is frozen and certified on independent data, providing an exact post-selection recall guarantee. We evaluate the method on TextCraft and WebShop with Qwen-2.5-7B, Llama-3.2-3B, and Qwen3-1.7B. The proposed LLM agent early-stopping cascade outperforms the best single-gate baseline in every model-environment pair, saving 1.5-8.8 times more compute at a 90% recall target. Achieved recall remains within one standard deviation of its target in all 24 configurations. The strongest settings reduce generated tokens by 60.2% on TextCraft and 54.9% on WebShop at 90% recall, while retaining savings of 45.0% and 41.5% at 95% recall. Behavior-only monitoring is consistently weaker, and adding behavioral features to hidden-state probes provides no further gain. We also characterize the sample complexity required to certify high-recall early-stopping policies. The code will be released soon.

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