---
title: "Game Theory Driven Multi-Agent Framework Mitigates Language Model Hallucination"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.08403v1"
permalink: "https://feed.astralor.com/items/itm_06e880b69d2375a43fdd4a56"
published: "2026-07-09T12:28:39.000Z"
score: 81
channel: "ai"
tags: ["G-Frame","OmniChem","Chemistry","Game Theory","Multi-Agent"]
---

# Game Theory Driven Multi-Agent Framework Mitigates Language Model Hallucination

## AI commentary

> This paper is a must-read for researchers focused on LLM hallucination and scientific AI applications. It cleverly combines game theory with multi-agent systems, achieving remarkable results in chemistry while offering a generalizable methodology for other scientific fields. For industry teams deploying lightweight models in specialized scenarios, this framework is particularly insightful.

## At a glance

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.

## Key points

- Proposes G-Frame multi-agent framework integrating Bayesian and team game principles for high-quality data synthesis and model training.
- Synthesizes over 363K chains-of-thought and 199K QA pairs by forcing domain constraint internalization via structured reasoning.
- OmniChem (7B) achieves performance parity with GPT-4o mini on chemical benchmarks with 79.46% hallucination reduction.
- Demonstrates advanced capabilities in molecular design and synthesis planning.

## Full text

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.

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