MemSyco-Bench: Benchmarking Sycophancy in Agent Memory
Summary
MemSyco-Bench is a new benchmark that evaluates sycophancy in LLM-based agents caused by retrieved memory, testing whether agents can properly reject or use memory for reasoning and decision-making rather than just storage.
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Paper page - MemSyco-Bench: Benchmarking Sycophancy in Agent Memory
Source: https://huggingface.co/papers/2607.01071
Abstract
Memory plays a crucial role in LLM-based agents, but retrieved memories can cause sycophancy issues where agents over-align with users at the expense of factual accuracy, necessitating new evaluation benchmarks that assess memory’s impact on reasoning and decision-making rather than just storage and retrieval.
Memoryhas emerged as a cornerstone of modernLLM-based agents, supporting their evolution from single-turn assistants to long-term collaborators. However,memoryis not always beneficial: retrieved memories often induce a critical issue ofsycophancy, causing agents to over-align with the user at the cost offactual accuracyorobjective reasoning. Despite this emerging risk, existingmemorybenchmarks primarily evaluate whether memories are correctly stored, retrieved, or updated, while overlooking how retrieved memories influencedownstream reasoninganddecision-making. To bridge this gap, we proposeMemSyco-Bench, a comprehensive benchmark for evaluatingmemory-induced sycophancyin agent systems.MemSyco-Benchmeasures whenmemoryshould influence a decision and how validmemoryshould be used. Specifically, it covers five tasks that assess whether agents can rejectmemoryas factual evidence, respect its applicable scope, resolve conflicts betweenmemoryand objective evidence, trackmemoryupdates, and use validmemoryfor personalization. All related resources are collected for the community at https://github.com/XMUDeepLIT/MemSyco-Bench.
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