Tri-Agent Framework Enhances LLM Stability & Explainability Through Recursive Knowledge Synthesis
Published:Jan 15, 2026 05:00
•1 min read
•ArXiv NLP
Analysis
This research is significant because it tackles the critical challenge of ensuring stability and explainability in increasingly complex multi-LLM systems. The use of a tri-agent architecture and recursive interaction offers a promising approach to improve the reliability of LLM outputs, especially when dealing with public-access deployments. The application of fixed-point theory to model the system's behavior adds a layer of theoretical rigor.
Key Takeaways
- •A tri-agent framework (semantic generation, consistency check, transparency audit) is used to enhance multi-LLM system reliability.
- •Recursive Knowledge Synthesis (RKS) is achieved through iterative interaction of the three agents.
- •Empirical evaluation shows high convergence rates and strong transparency scores in public-access LLM deployments.
Reference
“Approximately 89% of trials converged, supporting the theoretical prediction that transparency auditing acts as a contraction operator within the composite validation mapping.”