ReInBench separates agent errors from recovery

Inspect the intermediate step

The benchmark contains 1,259 traces with 1,403 errors and 725 recovery cases. ReCoF uses intermediate information to predict whether recovery will occur. The distinction matters when a plausible final answer hides an earlier mistake.

ReInBench and ReCoF ↗

A useful evaluation question

Consider an agent that opens the wrong flight-search page, recognizes the error and returns to the correct booking task. A recovery label asks what changed after the mistake. A completed booking alone would not reveal that sequence. This is an illustrative explanation, not an executed test.

ReCoF project ↗

Limits of the result

The authors report a relative Macro-F1 improvement for recovery prediction. That aggregate classification measure does not tell an operator that any particular action is safe to continue, or that recovery will generalize to a new application.

ReInBench and ReCoF ↗ReCoF project ↗

What you can do

1. Read the source and its evaluation conditions before applying this approach. Open reference ↗

Where the evidence stops

Paper and project read November 8. Error annotations, predictor training and evaluation were not independently reproduced.

Sources read. Primary text checked November 8, 2026 UTC. No third-party software, model inference or complete workflow executed.

Sources & further reading