Critic-Guided Heterogeneous Multi-Agent Reasoning for Reliable Mathematical Problem Solving

2026-04-26 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, extended

Summary

A novel critic-based heterogeneous multi-agent system significantly enhances the reliability of mathematical reasoning in Large Language Models (LLMs). This framework, employing a llama-3.1-8b-instant generator and a specialized validator, incorporates an adaptive learning system where a critic assesses intermediate reasoning and guides solution regeneration. Experiments on the entire 1,319-example GSM8K benchmark demonstrate up to a 13% accuracy improvement over single-shot and non-critic models, achieving a peak accuracy of 93.56%. This performance surpasses the RDoLT framework's 90.98% with ChatGPT-4o by 2.58%. Ablation studies confirm that the primary performance gains stem from the critic-based feedback loop, not merely increasing validator model size. The approach also suggests that heterogeneity and critique reduce the reliance on larger models, enabling smaller validators (8B, 20B) to perform comparably to larger ones (70B, 120B).

Key takeaway

For Machine Learning Engineers building reliable LLM-based reasoning systems, you should integrate a critic-guided, heterogeneous multi-agent architecture. This approach, proven to boost mathematical problem-solving accuracy by up to 13% on GSM8K, allows smaller models to achieve high performance by iteratively correcting errors. Focus on adaptive feedback loops and agent diversity rather than solely scaling model size to enhance robustness and interpretability.

Key insights

Critic-guided, heterogeneous multi-agent LLM systems significantly improve mathematical reasoning accuracy by iteratively correcting errors.

Principles

• Intermediate critique prevents error propagation.
• Heterogeneous agents offer complementary reasoning.
• Adaptive feedback loops enhance solution quality.

Method

The system uses a generator-validator framework. The generator (llama-3.1-8b-instant) proposes solutions. If validation fails, the validator provides a critique, guiding the generator to regenerate a new solution iteratively.

In practice

• Implement a critic-based feedback loop for LLM reasoning.
• Combine diverse LLM agents for complex problem-solving.
• Prioritize iterative error correction over larger models.

Topics

• Critic-Guided Reasoning
• Multi-Agent Systems
• Large Language Models
• Mathematical Problem Solving
• GSM8K Benchmark
• Error Correction

Best for: Research Scientist, AI Engineer, AI Scientist, Machine Learning Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.