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 i
ReTrace is a framework that evaluates recovery behavior in computer-use agents by formalizing transition-consistent recovery and demonstrating that autonomous model-based recovery improves long-horizon reliability.
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. Yet, existing remedies remain inadequate: environment resets are infeasible in proprietary settings, action replay is prohibitively expensive over long horizons, and API-based rollback is limited to web environments, leaving model-based recovery largely unexplored. To address this gap, we present ReTrace, a scalable evaluation framework for…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
Lost in Recovery: Demystifying Recovery Behavior of Computer-Use Agents via Goal-Oriented Action Replay was first seen on Hugging Face · Daily papers on Nov 3, 2026. Since then it has appeared in 11 checks on 2 different days. Its best position was #8 of 30, reached on Nov 3, 11:28 UTC. It dropped off the list after Nov 4, 03:28 UTC.
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Not at the moment. It was last on the list on Nov 4, 03:28 UTC, after 2 days of trending.
#8 on Hugging Face · Daily papers, on Nov 3, 11:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Nov 3, 2026 and has been on the list on 2 days (11 checks) since.