Socialized Learning and Emergent Behaviors in Multi-Agent Systems based on Multimodal Large Language Models

TL;DR

This paper presents M-S2L, a framework that combines multimodal perception and social learning to enable AI agents to develop emergent social behaviors and improve collaborative problem-solving in complex tasks.

Contribution

It introduces a novel multimodal socialized learning framework that integrates vision, text, and social learning pathways with reinforcement learning for multi-agent collaboration.

Findings

M-S2L agents outperform baselines in task completion and efficiency.

Emergence of communication protocols combining visual pointers and text.

Agents demonstrate shared awareness, role specialization, and adaptive problem-solving.

Abstract

This search introduces the Multimodal Socialized Learning Framework (M-S2L), designed to foster emergent social intelligence in AI agents by integrating Multimodal Large Language Models (M-LLMs) with social learning mechanisms. The framework equips agents with multimodal perception (vision and text) and structured action capabilities, enabling physical manipulation and grounded multimodal communication (e.g., text with visual pointers). M-S2L combines direct reinforcement learning with two novel social learning pathways: multimodal observational learning and communication-driven learning from feedback, augmented by an episodic memory system for long-term social context. We evaluate M-S2L in a Collaborative Assembly Environment (CAE), where agent teams must construct complex devices from ambiguous blueprints under informational asymmetry. Across tasks of increasing complexity, M-S2L agents consistently outperform Text-Only and No-Social-Learning baselines in Task Completion Rate and Time to Completion, particularly in dynamic problem-solving scenarios. Ablation studies confirm the necessity of both multimodality and socialized learning. Our analysis reveals the emergence of efficient communication protocols integrating visual pointers with concise text, alongside rapid role specialization leading to stable labor division. Qualitative case studies demonstrate agents' abilities for shared awareness, dynamic re-planning, and adaptive problem-solving, suggesting a nascent form of machine social cognition. These findings indicate that integrating multimodal perception with explicit social learning is critical for developing human-like collaborative intelligence in multi-agent systems.