MemSyco-Bench：智能体记忆中的谄媚行为基准测试

摘要

记忆已成为现代基于LLM的代理的基石，支撑其从单轮助手进化为长期协作者。然而，记忆并非总是有益的：检索到的记忆常常引发一个关键的谄媚问题，导致代理以牺牲事实准确性或客观推理为代价，过度迎合用户。尽管这一风险日益凸显，现有的记忆基准主要评估记忆是否正确存储、检索或更新，却忽略了检索到的记忆如何影响下游推理和决策。为弥补这一空白，我们提出了MemSyco-Bench，一个用于评估代理系统中记忆诱导谄媚的综合基准。MemSyco-Bench衡量记忆何时应影响决策以及如何正确使用有效记忆。具体而言，它涵盖五个任务：评估代理能否拒绝将记忆作为事实证据、尊重其适用范围、解决记忆与客观证据的冲突、跟踪记忆更新，以及使用有效记忆进行个性化。所有相关资源已收集并发布在社区，网址为https://github.com/XMUDeepLIT/MemSyco-Bench。

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.