An Interpretable Memory Decision Controller for LLM Agents Based on Three-Signal Complementarity: Decoupling Confidence and Consistency
A zero-parameter Memory Decision Layer cuts LLM hallucination from conflicting retrieved memories by about 56 percent.
The Memory Decision Layer is a zero-parameter controller placed between retrieval and generation that decides whether an LLM agent should trust retrieved memories. Its encoder combines relevance, reliability, and task risk, separates confidence from consistency, and can abstain. On mainstream large language models, it cuts hallucination under conflicting memories by about 56.04 percent in general settings and approaches zero hallucination in high-risk settings. Decisions use only geometric operations and add about 0.14 ms, roughly 50 times faster than the preceding embedding retrieval.
- Conflicting retrieved memories can raise RAG hallucination above a memory-free baseline.
- MDL fuses relevance, reliability, and task risk with no trained parameters.
- Hallucination falls about 56 percent generally and near zero in high-risk cases.
- Each decision adds about 0.14 ms, far faster than an LLM self-check.
From the original article
1 min read at arxiv.orgMemory systems for large language models have focused predominantly on efficient retrieval, whereas the decision of whether retrieved memories should be trusted has received comparatively little attention.
By Yiming Zhang · arxiv.org
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