arXiv:2606.05704v1 Announce Type: cross Abstract: Recent Large Language Models (LLMs) have shown impressive reasoning abilities; but they are still susceptible to hallucinations, intermediate reasoning mistakes, and unreliable reasoning results in complex mathematical reasoning problems. In this study, we introduce a critic-based heterogeneous multi-agent approach to improve the dependability of mathematical reasoning. This framework incorporates several LLM agents of different specialties and employs a critic-driven adaptive learning system to assess and guide the reasoning process based on i
The rapid advancement and deployment of LLMs have highlighted their current limitations in complex reasoning and reliability, spurring immediate research into addressing these fundamental issues.
Improving LLM dependability in complex tasks, especially mathematical reasoning, is critical for their broader adoption in sensitive applications and for building truly autonomous AI agents.
The focus is shifting from raw LLM output generation to architecting more robust, verifiable, and explainable reasoning processes through multi-agent collaboration and critical evaluation.
- · AI researchers
- · LLM developers
- · Mathematical software industry
- · Academia
- · Undifferentiated LLM providers
- · Users relying solely on single-model LLMs for complex tasks
More reliable AI systems for scientific discovery and engineering will emerge, reducing the risk of 'hallucinations' in critical applications.
This improved reliability could accelerate the integration of AI agents into complex problem-solving domains, augmenting human experts significantly.
Enhanced AI reasoning capabilities might lead to breakthroughs in fundamental scientific research, solving problems previously intractable for humans or less reliable AI.
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Read at arXiv cs.LG