DiffMAS framework teaches agents to communicate via latent representations

Most multi-agent LLM systems treat inter-agent communication as a fixed interface, leaving it unoptimized alongside reasoning. The paper by Ye Yu, Heming Liu, and Haibo Jin proposes DiffMAS, a training framework that treats latent communication — via internal representations such as key-value caches — as a learnable component. DiffMAS applies parameter-efficient supervised training over multi-agent latent trajectories so agents jointly learn how to encode and interpret information across interactions. On benchmarks spanning mathematical reasoning, scientific QA, code generation, and commonsense tasks, DiffMAS outperforms single-agent inference, text-based multi-agent systems, and prior latent communication methods, achieving 26.7% on AIME24 and 20.2% on GPQA-Diamond.