FirsthandTech
arXiv — cs.AI preprintsInternational5 October 2026

When History Fails to Become Experience: Action Calibration in Language Agents

This is an official announcement record

Firsthand records what arXiv — cs.AI preprints announced and links to the original. The wording below is theirs, not ours.

arXiv:2610.02769v1 Announce Type: cross Abstract: Language agents should draw on prior attempts and environmental feedback to improve subsequent decisions within the same task. However, providing additional interaction history can sometimes reduce task success, suggesting that agents do not consistently use this information effectively. To investigate this limitation, we examine how agents use history. We find that history improves task completion overall, yet much of this benefit persists even when past actions are shuffled. Disrupting the correspondence between actions and observations cause
— arXiv — cs.AI preprints

More from arXiv — cs.AI preprints

This content is for informational purposes only and is not professional advice. Specifications, prices, plan tiers, and features change frequently and may differ from what is shown here; verify current details on the manufacturer's or company's official page before purchasing. Ratings are based on analysis of published documentation, not independent lab testing.