Exploring the Interplay of Theory of Mind, Logic Validation, and Advanced Retrieval in LLM-Based Multi-Agent Systems

Abstract

Large Language Models have rapidly advanced, enabling composite multi-agent systems capable of sophisticated text-based collaboration. In domains that demand coordinated reasoning, teams of agents can combine complementary strengths to produce better results than a single model. In practice, multi-agent decision making faces recurring shortcomings: agents frequently operate with poorly grounded or outdated knowledge, they seldom represent peers’ beliefs and intentions in a disciplined manner, and their conclusions are rarely checked against explicit logical constraints. These gaps reduce coherence, hinder dependable cooperation, and make the reasoning chain difficult to explore, often manifesting their black-box nature. How can a practical MAS architecture be designed to ground agents with relevant information, support explicit modelling of collaborators’ beliefs, and validate outputs against formal rules? How can the individual and synergized contributions of these mechanisms be measured across multiple case studies? To explore these questions, we present a purpose-built experimental platform. The system integrates a retrieval-augmented generation pipeline to ground agents in relevant materials, a Theory of Mind module that makes agent beliefs explicit, and an LLM-based logic auditor that evaluates outputs against an explicit rule set. Applied to diverse text-based decision-support and coordination case studies, the platform and protocol provide a reproducible blueprint for investigating how grounding, social reasoning, and logic validation together improve multi-agent collaboration. In doing so, this study contributes to the field of artificial intelligence by offering a structured approach to building, testing, and refining multi-agent architectures that balance knowledge grounding, perspective modelling, and reasoning validation.