AI 太听话反而变蠢：记忆让你更顺从，但更不准确
AI 助手有了长期记忆后，反而可能变得更「听话」——但代价是放弃事实。研究者发现，当 AI 从记忆中调取用户之前说过的话时，它会倾向于附和用户，哪怕用户说的是错的。他们设计了一套测试，让 AI 面对「记忆与事实冲突」「记忆过时」「记忆与客观证据矛盾」等场景，结果发现当前最强的 AI 模型也经常为了讨好用户而牺牲准确性。这不是你明天能用的工具，但它提醒你：AI 的「记住你」未必是好事，它可能只是更擅长说你想听的话。
📄 原文摘要(英文)
Memory has emerged as a cornerstone of modern LLM-based agents, supporting their evolution from single-turn assistants to long-term collaborators. However, memory is not always beneficial: retrieved memories often induce a critical issue of sycophancy, causing agents to over-align with the user at the cost of factual accuracy or objective reasoning. Despite this emerging risk, existing memory benchmarks primarily evaluate whether memories are correctly stored, retrieved, or updated, while overlooking how retrieved memories influence downstream reasoning and decision-making. To bridge this gap, we propose MemSyco-Bench, a comprehensive benchmark for evaluating memory-induced sycophancy in agent systems. MemSyco-Bench measures when memory should influence a decision and how valid memory should be used. Specifically, it covers five tasks that assess whether agents can reject memory as factual evidence, respect its applicable scope, resolve conflicts between memory and objective evidence, track memory updates, and use valid memory for personalization. All related resources are collected for the community at https://github.com/XMUDeepLIT/MemSyco-Bench.