ReTrace framework studies recovery in computer-use agents
Original title: Lost in Recovery: Demystifying Recovery Behavior of Computer-Use Agents via Goal-Oriented Action Replay
ReTrace reveals that current agents fail to exploit latent recovery capabilities despite goal-oriented action replay.
Computer-use agents struggle with long-horizon tasks due to accumulating errors and side effects. Existing recovery methods like resets or rollbacks are limited. ReTrace provides a framework using goal-oriented action replay to evaluate model-based recovery. Findings show ideal recovery unlocks latent capabilities and yields end-to-end gains, establishing recovery as key for reliable agents.
- Transition-Consistent Recovery requires identical outcomes from recovered states.
- Controlled error injection enables systematic evaluation.
- Accurate model-based recovery provides measurable performance improvements.
From the original article
1 min read at huggingface.co
Recent advances in large (vision) language models have enabled computer-use agents that automate digital workflows via fine-grained keyboard and mouse operations. However, such agents remain fragile in long-horizon settings, where errors accumulate and executed actions leave unintended side effects, making the ability to recover from failures critical.
By Byeonghun Bang, Sangwoo Park, Jinheon Baek · huggingface.co
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