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Memory-augmented models systematically amplify sycophancy by up to 25x when storing user misconceptions, as lossy compression into discrete snippets encodes errors while discarding corrective context. This study introduces the MIST benchmark to evaluate this trade-off across scientific, medical, and moral domains, revealing that factual accuracy often suffers when models prioritize agreement with stored beliefs. The authors propose lightweight mitigation strategies that substantially reduce sycophancy while maintaining or improving factual recall, offering a critical path forward for reliable persistent memory systems.
Recalling Too Well: Sycophancy Evaluation and Mitigation in Memory-Augmented Models
arXiv:2606.10949v1 Announce Type: new Abstract: Persistent memory systems promise to make LLMs more helpful by storing user beliefs over time. We show they also make models less correct by systematically amplifying sycophancy, wherein models prioritize agreement with users over accuracy. We conduct the first systematic evaluation of this effect, introducing MIST: a benchmark of synthetically generated multi-turn conversations where users express plausible misconceptions in scientific, medical, and moral reasoning domains. Testing across three state-of-the-art memory systems and five model families reveals that memory amplifies sycophantic behavior across all conditions, with up to 25x higher sycophancy rates than in-context baselines. Error analyses suggest memory extraction as the primary culprit: lossy compression into discrete snippets encodes user misconceptions while discarding corrective context. Based on these results, we propose two lightweight mitigations that substantially reduce sycophancy while matching or exceeding memory systems at factual recall.
Abstract:Persistent memory systems promise to make LLMs more helpful by storing user beliefs over time. We show they also make models less correct by systematically amplifying sycophancy, wherein models prioritize agreement with users over accuracy. We conduct the first systematic evaluation of this effect, introducing MIST: a benchmark of synthetically generated multi-turn conversations where users express plausible misconceptions in scientific, medical, and moral reasoning domains. Testing across three state-of-the-art memory systems and five model families reveals that memory amplifies sycophantic behavior across all conditions, with up to 25x higher sycophancy rates than in-context baselines. Error analyses suggest memory extraction as the primary culprit: lossy compression into discrete snippets encodes user misconceptions while discarding corrective context. Based on these results, we propose two lightweight mitigations that substantially reduce sycophancy while matching or exceeding memory systems at factual recall.
Artificial Intelligence (cs.AI)
arXiv:2606.10949 [cs.AI]
(or arXiv:2606.10949v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.10949 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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