PRISM: A Principled Framework for Multi-Agent Reasoning via Gain Decomposition

2026-02-09

AI Summary

This work addresses the lack of a systematic understanding of the mechanisms that enhance collaborative reasoning performance in multi-agent systems and the difficulty in identifying the key factors that make them superior to single-agent approaches. The authors propose a unified theoretical framework that, for the first time, decomposes multi-agent reasoning gains into three orthogonal dimensions: exploration, information, and aggregation. Building upon this decomposition, they introduce the PRISM framework, which jointly optimizes these dimensions through role-based diversity generation, execution-feedback-driven cross-evaluation of evidence, and an iterative synthesis mechanism with closed-loop verification. Empirical evaluations demonstrate state-of-the-art performance across tasks including mathematical reasoning, code generation, and function calling, while significantly improving computational efficiency. This study thus provides both actionable theoretical insights and a practical paradigm for designing effective multi-agent systems.

Technology Category

• Multiagent Systems: Multiagent Learning
• Intelligent Robots: Multi-Robot Systems
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Multi-agent collaboration has emerged as a promising paradigm for enhancing reasoning capabilities of Large Language Models (LLMs). However, existing approaches remain largely heuristic, lacking principled guidance on what drives performance gains and how to systematically optimize multi-agent reasoning. Specifically, it remains unclear why multi-agent collaboration outperforms single-agent reasoning and which design choices contribute most to these gains, making it difficult to build better systems. We address this gap by introducing a unified theoretical framework that decomposes multi-agent reasoning gains into three conceptually independent dimensions: Exploration for diverse solution coverage, Information for high-fidelity feedback, and Aggregation for principled consensus. Through this lens, existing methods can be understood as special cases that optimize only subsets of these dimensions. Building upon this decomposition, a novel framework called PRISM (Propose-Review-Integrate Synthesis for Multi-agent Reasoning) is proposed, which jointly maximizes all three dimensions through role-based diversity, execution-grounded feedback with evidence-based cross-evaluation, and iterative synthesis with closed-loop validation. Extensive experiments across mathematical reasoning, code generation, and function calling benchmarks demonstrate that PRISM achieves state-of-the-art performance with superior compute-efficiency compared to methods optimizing partial dimensions. The theoretical framework provides actionable design principles for future multi-agent reasoning systems.

Problem

• multi-agent reasoning
• performance gains
• design principles
• large language models
• collaboration

Innovation

• Multi-agent reasoning
• Gain decomposition
• PRISM framework
• Role-based diversity
• Closed-loop validation