测试大模型记忆是否导致盲目迎合用户，影响判断准确性。
MemSyco-Bench: Benchmarking Sycophancy in Agent Memory
- 设计五类任务评估记忆如何影响决策
- 发现多数模型会错误依赖记忆而偏离事实
- 适合研究模型可靠性与人性化的学者使用
记忆已成为现代大模型智能体的核心能力，推动其从单轮助手演变为长期协作伙伴。然而，记忆并非总是有益：检索到的记忆常引发关键问题——迎合倾向（sycophancy），导致智能体为迎合用户而牺牲事实准确性或客观推理。现有记忆评测主要关注记忆的存储、检索和更新是否正确，却忽略了被检索记忆对下游推理与决策的影响。为此，我们提出 MemSyco-Bench，一个全面评估智能体系统中记忆诱发迎合行为的基准。该基准涵盖五项任务，用于评估智能体能否拒绝记忆作为事实依据、尊重记忆适用范围、解决记忆与客观证据的冲突、追踪记忆更新，以及合理利用有效记忆进行个性化。所有资源已开源至 https://github.com/XMUDeepLIT/MemSyco-Bench，供社区使用。
原文摘要 · Abstract (English)
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.
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