PsychoAgent: An Affect-Sensitive Cognitive Architecture for Conflict-Aware Memory in LLM Agents

2026-08-07

AI Summary

This work addresses a critical limitation in conventional large language model (LLM) agents, which retrieve memories solely based on topical similarity while neglecting the pivotal roles of emotional salience and unresolved conflict in human memory retrieval. To bridge this gap, we propose a novel cognitive architecture that decouples memory into dual factual and emotional channels. A conflict-aware executive controller enables human-like memory recall by first filtering emotionally relevant memories via semantic relatedness, then re-ranking them according to emotional salience, all within a retrieval-augmented generation (RAG) framework. This approach is the first to integrate emotional salience and conflict awareness into LLM memory mechanisms, supporting interpretable, emotion-sensitive retrieval and offline memory reorganization. Evaluated across three conflict-laden scenarios, our method achieves a key memory recall rate of 0.933—significantly outperforming baselines—and demonstrates a 0.22 standard deviation improvement in output quality according to human evaluation, confirming its capacity for sustained emotional effects and dynamic memory adaptation.

Abstract

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.

Problem

• affect-sensitive memory
• conflict-aware retrieval
• cognitive architecture
• LLM agents
• memory accessibility

Innovation

• affect-sensitive retrieval
• conflict-aware memory
• cognitive architecture
• emotional salience
• memory recombination