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

paperactiveprovisionaldoomed-from-the-start-early-abort-of-llm-agent-episodes-via-a-recall-controlled-probe-cascade-0d1f0ac0·1 events·first seen 15h ago

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

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Llama 3.2 TextCraft Qwen 2.5-7B

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6arXiv · cs.AI·15h ago·source ↗

Early abort cascade for LLM agents predicts episode failure from hidden activations, saving ~47% inference compute

A new arXiv paper introduces a method to detect doomed LLM agent episodes early by probing internal hidden-state activations, rather than waiting for observable failure. The approach uses a cascade of calibrated per-round gates with recall budgets, guaranteeing that eventually-successful episodes survive at a user-specified rate. On TextCraft with Qwen-2.5-7B and Llama-3.2-3B, the cascade saves 37–47% of inference compute at a 90% recall target, outperforming behavior-only baselines by roughly 2x. The work provides both a practical deployment mechanism and theoretical guidance on sample complexity for certifying high recall targets.

Inference Economics Agent and Tool Ecosystem Llama 3.2 Doomed from the Start: Early Abort of LLM Agent Episodes via a Recall-Controlled Probe Cascade TextCraft +1 more