EmoMAS: Emotion-Aware Multi-Agent System for High-Stakes Edge-Deployable Negotiation with Bayesian Orchestration

Researchers introduce EmoMAS, a Bayesian multi-agent framework that enables small language models to perform sophisticated negotiation by treating emotional intelligence as a strategic variable. The system coordinates game-theoretic, reinforcement learning, and psychological agents to optimize negotiation outcomes while maintaining privacy through edge deployment, demonstrating performance comparable to larger models across high-stakes domains.