MH-MuG: Collaborative Music Generation Game Between AI Agents Toward Emergent Musical Creativity

Koki Sakurai, Haruto Uenoyama, Akira Taniguchi, Tadahiro Taniguchi

Abstract: This study explores collaborative music generation using multiple symbolic music generation AI agents, grounding the process in the Systems Model of Creativity. We propose the Metropolis-Hastings Music Generation Game (MH-MuG), which integrates latent diffusion models (LDMs) and formulates the collaboration as a decentralized Bayesian inference process. In MH-MuG, agents with distinct musical knowledge (pre-trained on Classical and Jazz) alternate between composer and listener roles. This interaction functions as a Markov Chain Monte Carlo (MCMC) method, enabling agents to collectively sample from a joint distribution that integrates their knowledge. We compared two variants: one without fine-tuning (w/o f.t.), modeling a fixed-knowledge game, and one with fine-tuning (w/ f.t.), modeling mutual adaptation. Our experiments yielded two key findings: (1) The (w/o f.t.) variant, functioning as a collaborative music generation game, successfully generated high-quality, stylistically fused music. (2) Conversely, the (w/ f.t.) variant led to a significant reduction in diversity. We interpret this not as a failure, but as a computational demonstration of the “siloing” phenomenon that occurs when creative interactions are limited to a closed loop.

External IDs:doi:10.1109/access.2026.3666234