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JevAgentHarness delegates tool choices using fitted confidence thresholds

huggingface.co by Kunlun Zhu et al. Editor score 8/10

The benchmark finds that isolated decision accuracy did not predict agent success and that untuned delegation reduced success by 19 points; the harness fits thresholds on the agent’s own trajectories. This is a useful warning and design pattern for replacing expensive LLM calls.

What

JevAgentHarness delegates some agent tool choices to a decision model instead of calling a large LLM at every step.

Method

It fits a confidence threshold on the agent’s own trajectories and lets the LLM decide when confidence falls below that threshold.

Why it matters

This thresholded delegation targets lower latency and cost while limiting the success loss that uncalibrated delegation can cause.

Read on huggingface.co → Code · 3 stars

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