Tri-Agent Framework Achieves Stable Recursive Knowledge Synthesis in Multi-LLM Systems

This paper introduces a tri-agent cross-validation framework designed to analyze stability and explainability in multi-model large language systems. The architecture integrates three distinct LLMs, each responsible for semantic generation, analytical consistency checking, and transparency auditing. This coordinated approach induces Recursive Knowledge Synthesis (RKS), where intermediate representations undergo continuous refinement through mutually constraining transformations, a process that cannot be replicated by a single model. The study's core contribution lies in its structured tri-agent framework, the formal RKS model grounded in fixed-point theory, and the empirical evaluation of inter-model stability under realistic, non-API public-access conditions.

The system's performance was evaluated across 47 controlled trials using publicly accessible LLM deployments. The results indicate a mean Reflex Reliability Score (RRS) of 0.78+-0.06, with a Transparency Score (TS) maintained at >= 0.8 in approximately 68% of the trials. Notably, around 89% of the trials demonstrated convergence, supporting the theoretical prediction that transparency auditing functions as a contraction operator within the composite validation mapping. These findings offer initial empirical evidence that a safety-preserving, human-supervised multi-LLM architecture can achieve stable recursive knowledge synthesis in real-world, publicly deployed environments.

However, the study also acknowledges limitations. The framework's complexity and reliance on multiple LLMs could present challenges for practical implementation and scalability. The observed convergence rate of 89% suggests that the system is not invariably stable, and the requirement for human supervision could potentially limit its autonomy and overall efficiency. Further research is needed to address these limitations and explore the potential of this approach in more complex and dynamic environments.

Transparency and ethical considerations are crucial in the development of multi-LLM systems. The tri-agent framework incorporates transparency auditing as a core component, aiming to enhance the explainability and trustworthiness of AI outputs. By promoting transparency and accountability, this research contributes to the responsible development and deployment of AI technologies. As AI systems become increasingly integrated into various aspects of society, ensuring their transparency and reliability is paramount for building trust and fostering public acceptance.