这篇论文给LLM智能体加了情感记忆模块，在冲突场景下找关键记忆比普通RAG强不少，做Agent记忆方向的朋友可以看看。
PsychoAgent是一个面向LLM智能体的认知架构，将事实记忆与情感记忆分离，并通过冲突感知执行控制器整合。在三个受控冲突场景中，完整架构检索冲突关键记忆的得分达0.933，优于语义情感基线的0.500和单记忆RAG的0.667，仅付出少量语义相似度代价。5名盲评者评估了27个输出，经评分者内标准化后，完整架构的平均分最高（+0.22 SD），但校正后的两两差异未达显著。三天的示例轨迹展示了持续情感、离线记忆重组和选择性记忆加权。研究支持情感敏感检索作为模拟人类冲突效应的可检查机制。
PsychoAgent: An Affect-Sensitive Cognitive Architecture for Conflict-Aware Memory in LLM Agents
Human-like cognition does not select past experience by topical similarity alone: affective significance and unresolved conflict also shape what becomes accessible. We present PsychoAgent, a cognitive architecture for LLM agents that separates factual and affective memory and integrates both through a conflict-aware executive controller. Affective memories are first filtered by semantic relevance and then re-ranked by salience, preserving topical fit while allowing emotionally important traces to enter the prompt. Across three controlled conflict scenarios, the full architecture retrieved more conflict-critical memories than semantic-affective and single-memory RAG baselines (0.933 vs. 0.500 and 0.667), with a small semantic-similarity cost. Five blinded raters evaluated 27 outputs. After within-rater standardization, the full architecture had the highest overall mean (+0.22 SD), but corrected pairwise differences were not significant. A three-day illustrative trace further shows persistent affect, offline memory recombination, and selective memory reweighting. The findings support affect-sensitive retrieval as an inspectable mechanism for modeling human-like conflict effects in LLM agents.