Game Theory Driven Multi-Agent Framework Mitigates Language Model Hallucination
For lightweight LLMs in rule-based scientific domains, this framework provides a scalable method to reduce hallucinations and improve reasoning without large models.
G-Frame, a multi-agent framework using Bayesian and team game principles, synthesizes specialized data to train a 7B model (OmniChem) that matches GPT-4o mini on chemistry benchmarks while reducing hallucinations by 79.46% compared to its base model.
The application of lightweight Large Language Models in rule-based scientific domains remains severely limited by their tendency to mimic linguistic patterns rather than reproduce axiomatic reasoning, causing frequent hallucinations. Here, we show that G-Frame, an adaptive multi-agent framework integrating Bayesian and team game principles, establishes an automated closed-loop for high-quality data synthesis and model training. By forcing the internalization of domain constraints through structured reasoning, we synthesized a specialized corpus of 363,045 chains-of-thought and 199,589 question-answer pairs. The resulting 7B model OmniChem achieves performance parity with GPT 4o mini on custom benchmarks and ChemBench while exhibiting a 79.46% reduction in hallucinations relative to its base architecture. We further demonstrate the advanced capabilities of OmniChem in molecular design and synthesis planning. This work establishes a scalable paradigm utilizing adaptive multi-agents to overcome inherent reasoning deficiencies, offering a feasible pathway for accelerating knowledge discovery in specialized scientific fields.