Aug 7, 2026 · cs.AIJ/K move · Enter open · S save
Mohammad Amanlou, Parham Abed Azad, Farbod Davoodi, Mostafa Masumi+2
School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran. · Department of Computer Engineering, Sharif University of Technology, Tehran, Iran. · Missouri University of Science and Technology, Rolla, MO, USA. · Tehran Institute for Advanced Studies, Khatam University, Tehran, Iran.
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.