Memadapter: Counterfactual Adaptation Against Memory-induced Sycophancy
MemAdapter calibrates retrieved memories with counterfactual reasoning to reduce sycophancy in long-memory LLM agents.
Long-term memory can make LLM agents sycophantic toward users' historical beliefs even when stored memories are objectively correct. MemAdapter estimates that risk with counterfactual induction, calibrates each memory's influence through context-aware self-reflection, and grounds the final answer in evidence. Experiments on three benchmarks show more reliable use of retrieved memory, and the authors released code.
- Correct memories can still cause context-dependent sycophancy.
- Counterfactual induction estimates risk from retrieved memories.
- Reflection calibrates each memory's influence on the current task.
- Improves reliability on three benchmarks; code is public.
From the original article
1 min read at huggingface.co
Long-term memory enables LLM-based agents to retain and reuse information across tasks and sessions, supporting personalization and long-horizon interactions. However, persistent memories can also induce sycophancy, causing agents to over-align with users' historical beliefs even when they are inaccurate, outdated, or inconsistent with objective evidence.
By Ruqing Ning, Haibo Meng, Zhishang Xiang · huggingface.co
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