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Researchers Propose Reporting Standard for Attributing AI Agent Failures to Their History

A new paper argues that published explanations of why long-horizon AI agents fail are typically taxonomy labels applied to completed trajectories, and proposes a reporting standard requiring executed counterfactuals to attribute failures to an agent's history.

Read the full story at arXiv cs.AI ↗

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  1. 2026-11-05 05:00 · arXiv cs.AI
    Attributing Agent Failures to Their History Needs an Executed Counterfactual: Toward a Reporting Standard

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