mattrl-multi-agent-reasoning

name	mattrl-multi-agent-reasoning
title	Collaborative Multi-Agent Test-Time Reinforcement Learning for Reasoning
version	0.0.2
engine	skillxiv-v0.0.2-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2601.09667
keywords	["multi-agent","reasoning","test-time-RL","collaborative-deliberation","experience-retrieval"]
description	Enables LLM-based agent teams to improve reasoning accuracy at inference time through collaborative deliberation and structured experience retrieval, achieving 3-8% accuracy gains without expensive multi-agent training.

Overview

Implement a multi-agent reasoning framework where specialist LLM agents collaborate to solve complex reasoning tasks (mathematical, medical, educational problems) without requiring resource-intensive training. The system uses test-time experience retrieval and consensus mechanisms to boost accuracy.

When to Use

• When you have complex reasoning problems requiring multiple perspectives or domain expertise
• When you need to improve accuracy without retraining models
• For domains where specialist agents can provide meaningful cross-checking (medical, scientific, mathematical reasoning)
• When you want distribution-shift robustness across problem variations

When NOT to Use

• For real-time applications requiring <100ms latency (multi-turn deliberation adds latency)
• For simple classification or single-turn tasks where multi-agent discussion adds overhead
• When agent diversity is limited (single-perspective problems)

Key Technical Components

Multi-Expert Agent Assembly

Create a team of specialist agents, each optimized for different reasoning approaches or problem domains.

# Multi-expert team configuration
agents = [
    {"name": "math_specialist", "prompt_template": "solve this math problem step-by-step"},
    {"name": "logic_analyzer", "prompt_template": "analyze the logical structure"},
    {"name": "verification_agent", "prompt_template": "verify the solution correctness"},
]

Test-Time Experience Retrieval

At inference, retrieve relevant solved problems from a memory pool and inject them into agent deliberation.

# Experience pool construction at test-time
def retrieve_test_time_experiences(problem, pool, k=3):
    """Retrieve k most similar problems from the pool"""
    similarities = [compute_similarity(problem, exp) for exp in pool]
     (pool, key= x: similarities[pool.index(x)], reverse=)[:k]