MAR:Multi-Agent Reflexion Improves Reasoning Abilities in LLMs
📰 ArXiv cs.AI
Improve LLM reasoning with multi-agent reflexion, a technique that helps LLMs learn from mistakes and avoid degeneration of thought
Action Steps
- Implement a multi-agent architecture with multiple personas to facilitate debation and reflection
- Train LLMs with multi-agent reflexion to improve their reasoning abilities
- Evaluate the performance of LLMs with multi-agent reflexion on various reasoning tasks
- Compare the results with traditional single-agent reflection methods
- Refine the multi-agent reflexion technique based on the evaluation results
Who Needs to Know This
AI researchers and engineers working on LLMs can benefit from this technique to improve the reasoning abilities of their models, and it can be applied in various applications such as natural language processing and decision-making systems
Key Insight
💡 Multi-agent reflexion can help LLMs avoid degeneration of thought and improve their reasoning abilities by facilitating debation and reflection among multiple personas
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🤖 Improve LLM reasoning with multi-agent reflexion! 📈
Key Takeaways
Improve LLM reasoning with multi-agent reflexion, a technique that helps LLMs learn from mistakes and avoid degeneration of thought
Full Article
Title: MAR:Multi-Agent Reflexion Improves Reasoning Abilities in LLMs
Abstract:
arXiv:2512.20845v2 Announce Type: replace Abstract: LLMs have shown the capacity to improve their performance on reasoning tasks through reflecting on their mistakes, and acting with these reflections in mind. However, continual reflections of the same LLM onto itself exhibit degeneration of thought, where the LLM continues to repeat the same errors again and again even with the knowledge that its wrong. To address this problem, we instead introduce multi-agent with multi-persona debators as the
Abstract:
arXiv:2512.20845v2 Announce Type: replace Abstract: LLMs have shown the capacity to improve their performance on reasoning tasks through reflecting on their mistakes, and acting with these reflections in mind. However, continual reflections of the same LLM onto itself exhibit degeneration of thought, where the LLM continues to repeat the same errors again and again even with the knowledge that its wrong. To address this problem, we instead introduce multi-agent with multi-persona debators as the
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