| 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.
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.
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]
