Framework argues AI agent restart monitors should be judged by decision value, not accuracy
Original title: Monitor Accuracy Isn't the Answer: A Decision-Value Framework for Evaluating AI Agent Restart Monitors
A 90% accuracy monitor can swing from a 5-point loss to a 35-point gain in agent success depending on residual restart budget, an exact analytical result.
The paper shows analytically that classification accuracy metrics do not determine whether restarting an AI agent improves success: a fixed 90%-accuracy monitor can swing from a five-point loss to a 35-point gain depending on the replacement attempt's residual-budget success probability. It introduces a decision-sufficiency criterion and a finite-sample decision audit over existing traces. In a study of 194 executions on 19 SWE-bench Verified SymPy issues, an apparent 2.29-point restart gain traced to a single failed attempt, and zero of 103 live comparisons met the certification bar.
- The decision audit certifies restart-vs-continue preferences without additional model calls, or abstains.
- A second model showed a 42.29-point loss; the initial gain did not replicate on prospective task cohorts.
- The audit draws on a binary-action specialization of Blackwell's comparison of experiments.
From the original article
1 min read at huggingface.co
Classification accuracy and true-positive and false-positive rates alone do not determine whether restarting an AI agent improves success.
By Siddharth Jain, Venkat N. Vedam · huggingface.co
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