PRIME: Planning and Retrieval-Integrated Memory for Enhanced Reasoning
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917072724819968 |
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| author | Tran, Hieu Yao, Zonghai Tran, Nguyen Luong Yang, Zhichao Ouyang, Feiyun Han, Shuo Rahimi, Razieh Yu, Hong |
| author_facet | Tran, Hieu Yao, Zonghai Tran, Nguyen Luong Yang, Zhichao Ouyang, Feiyun Han, Shuo Rahimi, Razieh Yu, Hong |
| contents | Inspired by the dual-process theory of human cognition from \textit{Thinking, Fast and Slow}, we introduce \textbf{PRIME} (Planning and Retrieval-Integrated Memory for Enhanced Reasoning), a multi-agent reasoning framework that dynamically integrates \textbf{System 1} (fast, intuitive thinking) and \textbf{System 2} (slow, deliberate thinking). PRIME first employs a Quick Thinking Agent (System 1) to generate a rapid answer; if uncertainty is detected, it then triggers a structured System 2 reasoning pipeline composed of specialized agents for \textit{planning}, \textit{hypothesis generation}, \textit{retrieval}, \textit{information integration}, and \textit{decision-making}. This multi-agent design faithfully mimics human cognitive processes 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_22315 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PRIME: Planning and Retrieval-Integrated Memory for Enhanced Reasoning Tran, Hieu Yao, Zonghai Tran, Nguyen Luong Yang, Zhichao Ouyang, Feiyun Han, Shuo Rahimi, Razieh Yu, Hong Artificial Intelligence Computation and Language Inspired by the dual-process theory of human cognition from \textit{Thinking, Fast and Slow}, we introduce \textbf{PRIME} (Planning and Retrieval-Integrated Memory for Enhanced Reasoning), a multi-agent reasoning framework that dynamically integrates \textbf{System 1} (fast, intuitive thinking) and \textbf{System 2} (slow, deliberate thinking). PRIME first employs a Quick Thinking Agent (System 1) to generate a rapid answer; if uncertainty is detected, it then triggers a structured System 2 reasoning pipeline composed of specialized agents for \textit{planning}, \textit{hypothesis generation}, \textit{retrieval}, \textit{information integration}, and \textit{decision-making}. This multi-agent design faithfully mimics human cognitive processes 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. |
| title | PRIME: Planning and Retrieval-Integrated Memory for Enhanced Reasoning |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2509.22315 |