Facilitating Collective Reasoning Intelligence through Multi Agent Reinforcement Learning for Consensus Driven Logic Synthesis in Large Language Model Systems - Dennis Hawthorne; Gavin Nolan; Jason Reeves (Computational Intelligence Systems, 2026)
The evolution of large language models (LLMs) has transitioned from individual generative agents toward integrated multi-agent ecosystems capable of complex problem-solving. This paper explores the architectural and systemic challenges of facilitating collective reasoning intelligence within these environments. By leveraging multi-agent reinforcement learning (MARL), we propose a framework for consensus-driven logic synthesis that harmonizes divergent reasoning paths generated by heterogeneous agents. The study emphasizes the shift from simple majority voting or heuristic selection to a sophisticated logic synthesis approach where agents negotiate and refine internal rationales to achieve systemic convergence. We analyze the structural trade-offs involved in deploying such systems, including the tension between computational latency and reasoning depth, the governance of decentralized intelligence, and the implications for socio-technical infrastructure. Furthermore, the paper addresses critical dimensions of robustness, fairness, and sustainability in large-scale deployments. By examining the interplay between reinforcement learning signals and collective logic, we provide a comprehensive roadmap for developing resilient AI infrastructures that prioritize logical consistency and ethical governance. The findings suggest that collective reasoning, when mediated through MARL-based consensus protocols, significantly enhances the reliability of complex decision-making processes in financial, legal, and engineering domains.