Grounding Natural Language for Multi-agent Decision-Making with Multi-agentic LLMs (2508.07466v1)

Abstract: Language is a ubiquitous tool that is foundational to reasoning and collaboration, ranging from everyday interactions to sophisticated problem-solving tasks. The establishment of a common language can serve as a powerful asset in ensuring clear communication and understanding amongst agents, facilitating desired coordination and strategies. In this work, we extend the capabilities of LLMs by integrating them with advancements in multi-agent decision-making algorithms. We propose a systematic framework for the design of multi-agentic LLMs, focusing on key integration practices. These include advanced prompt engineering techniques, the development of effective memory architectures, multi-modal information processing, and alignment strategies through fine-tuning algorithms. We evaluate these design choices through extensive ablation studies on classic game settings with significant underlying social dilemmas and game-theoretic considerations.