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

Source: [arXiv](https://arxiv.org/abs/2608.07438v1)  
Feed7 permalink: https://feed7.dev/p/2608-07438v1-117tf7f  
Published: 2026-08-07T17:22:29.000Z  
Trust: Needs Review (needs_review)

## Why Included

PsychoAgent separates factual and affective memory, then reranks relevant memories by salience. It retrieved more conflict-critical context, but output-quality differences were not significant.

## Source Summary

PsychoAgent filters affective memories by semantic relevance, reranks them by salience, and combines them with factual memory through an executive controller. In **three conflict scenarios**, retrieval scored **0.933**, versus 0.500 and 0.667 for two baselines.

## Practical Implication

For memory systems where importance is not captured by similarity alone, test a second ranking signal after relevance filtering. Keeping factual and affective stores separate also makes the retrieval policy easier to inspect.

## Agent-Ready Context

PsychoAgent filters affective memories by semantic relevance, reranks them by salience, and combines them with factual memory through an executive controller. In **three conflict scenarios**, retrieval scored **0.933**, versus 0.500 and 0.667 for two baselines.

For memory systems where importance is not captured by similarity alone, test a second ranking signal after relevance filtering. Keeping factual and affective stores separate also makes the retrieval policy easier to inspect.

Five blinded raters assessed **27 outputs**. The full system led by **+0.22 SD** after within-rater standardization, but corrected pairwise differences were not statistically significant, and retrieval incurred a small semantic-similarity cost.

## Connected Context

Feed7 judgment across 409 accumulated Signals:

This adds affective salience as an inspectable second-stage ranking signal after semantic relevance and supports separating memory types before an executive controller combines them. It extends value-aware retrieval beyond generic utility scores, but the small, statistically inconclusive output study narrows the claim to a promising architecture and retrieval result rather than demonstrated behavioral reliability.

- [MemLens: A Value-Aware Memory Management System with Interactive Analytics for LLM-based Agents](https://feed7.dev/p/2607-25992v1-0q2kxfl) — MemLens makes memory value observable at record level; PsychoAgent provides a more specific implementation consequence by applying salience only after relevance filtering and keeping affective and factual stores separate.
- [Citation Needed: Provenance for LLM-Built Knowledge Graphs — Daniel Chalef, Zep AI](https://feed7.dev/p/citation-needed-provenance-for-llm-built-knowledge-graphs-daniel-chalef-1iob5t8) — Separate memory stores and reranking improve policy inspectability, but provenance remains necessary to explain which source memories produced a combined response and to support correction or deletion.
- [Wearing the Agent: From Group Chats to Glasses — Sai Krishna Rallabandi](https://feed7.dev/p/wearing-the-agent-from-group-chats-to-glasses-sai-krishna-rallabandi-102hk49) — Affective salience could increase retrieval priority without establishing disclosure permission; the shared-agent candidate therefore adds a governance prerequisite of per-user privacy, audience filtering, and silence policies.
- [A Taxonomy of Cognitive Capability Gaps in Generative and Agentic AI](https://feed7.dev/p/2608-02553v1-12y8joy) — PsychoAgent is a concrete memory and control architecture within the taxonomy’s broader durable-state and adaptation gaps, while its limited evaluation confirms that addressing one gap does not establish overall agent reliability.

## Context Map

- Layer: agent
- Domains: research
- Topics: agent-memory, retrieval, agent-reliability

## Uncertainty

- Five blinded raters assessed **27 outputs**. The full system led by **+0.22 SD** after within-rater standardization, but corrected pairwise differences were not statistically significant, and retrieval incurred a small semantic-similarity cost.

## Agent Instruction

Use this item as source-backed context. Do not invent claims beyond the linked source. If this item conflicts with another source, call out the conflict.