When History Fails to Become Experience: Action Calibration in Language Agents
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Computer Science > Computation and Language
arXiv:2610.02769 (cs)
[Submitted on 2 Oct 2026]
Title:When History Fails to Become Experience: Action Calibration in Language Agents
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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 causes only a modest decline in task success. We therefore hypothesize that agents do not reliably connect past actions with their outcomes when deciding how to proceed. To test this hypothesis, we explicitly label each returned observation as the outcome of the preceding action. This simple annotation improves task success and reduces next-action repetition without introducing new environmental information. Building on this insight, we introduce a learned calibrator that explicitly reassesses past actions and selectively records experience to guide subsequent decisions, improving task success beyond outcome labeling alone.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.02769 [cs.CL] |
| (or arXiv:2610.02769v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.02769
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled When History Fails to Become Experience: Action Calibration in Language Agents, by Jingyu Liu and 4 other authors
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