Multi-Agent Model for Complex Reasoning
Published:Dec 31, 2025 04:10
•1 min read
•ArXiv
Analysis
This paper addresses the limitations of single large language models in complex reasoning by proposing a multi-agent conversational model. The model's architecture, incorporating generation, verification, and integration agents, along with self-game mechanisms and retrieval enhancement, is a significant contribution. The focus on factual consistency and logical coherence, coupled with the use of a composite reward function and improved training strategy, suggests a robust approach to improving reasoning accuracy and consistency in complex tasks. The experimental results, showing substantial improvements on benchmark datasets, further validate the model's effectiveness.
Key Takeaways
- •Proposes a multi-agent conversational model for complex reasoning.
- •Employs a three-level role division architecture (generation, verification, integration).
- •Introduces a self-game mechanism and retrieval enhancement.
- •Utilizes a composite reward function and improved training strategy.
- •Achieves significant improvements in multi-hop reasoning accuracy and consistency on benchmark datasets.
Reference
“The model improves multi-hop reasoning accuracy by 16.8 percent on HotpotQA, 14.3 percent on 2WikiMultihopQA, and 19.2 percent on MeetingBank, while improving consistency by 21.5 percent.”