PRIME: Planning and Retrieval-Integrated Memory for Enhanced Reasoning
Hieu Tran, Zonghai Yao, Nguyen Luong Tran, Zhichao Yang, Feiyun Ouyang, Shuo Han, Razieh Rahimi, Hong Yu
Abstract
Inspired by the dual-process theory of human cognition from Thinking, Fast and Slow, we introduce PRIME (Planning and Retrieval-Integrated Memory for Enhanced Reasoning), a multi-agent reasoning framework that dynamically integrates System 1 (fast, intuitive thinking) and System 2 (slow, deliberate thinking). PRIME first employs a Quick Thinking Agent to generate a rapid answer; if uncertainty is detected, it then triggers a structured System 2 reasoning pipeline composed of specialized agents for planning, hypothesis generation, retrieval, information integration, and decision-making. This multi-agent design mimics human cognitive processes faithfully and enhances both efficiency and accuracy. Experimental results with LLaMA 3 models demonstrate that PRIME enables open-source LLMs to perform competitively with state-of-the-art closed-source models like GPT-4 and GPT-4o on benchmarks requiring multi-hop and knowledge-grounded reasoning. This research establishes PRIME as a scalable solution for improving LLMs in domains requiring complex, knowledge-intensive reasoning.
BibTeX
@inproceedings{aaai2026_primeplanningand,
title = {PRIME: Planning and Retrieval-Integrated Memory for Enhanced Reasoning},
author = {Hieu Tran and Zonghai Yao and Nguyen Luong Tran and Zhichao Yang and Feiyun Ouyang and Shuo Han and Razieh Rahimi and Hong Yu},
booktitle = {AAAI 2026},
year = {2026}
}