Collective Reasoning, Single-Writer Execution: Fusion-MoA for Long-Horizon Agents

Abstract

Mixture-of-Agents systems combine several language models into a stronger response, but tool-using agents act over long horizons in which every step changes external state and the history keeps growing. We present Fusion-MoA, a serving architecture that brings multi-model collaboration into this loop through executor-anchored consultation. One Executor model holds the full context and emits every text and tool call; when consultation is admitted, three specialist models review its proposed next action through a compact view under a shared 120-second deadline. The fleet sits behind one OpenAI-compatible endpoint, so an unmodified agent client drives it as a single model. Fusion-MoA serves an in-car assistant and an in-car personal agent for intelligent cockpits and is deployed on MetaX C550 servers. Evaluated as four quantized open-weight models on five NVIDIA L40S GPUs and driven by Claude Code, it passes 78 of the 89 Terminal-Bench 2.1 tasks, 16 more than its Executor alone and 29 more than a self-hosted quantized Kimi K3 on the same client. On the 63 tasks that both it and the API-served LongCat-2.5-Preview pass, it needs 14.5 minutes on average against 17.5. Specialist reviews use 0.4% of the Executor's tokens, and every tool call comes from the Executor.

arXiv comment9 pages, 2 figures, 9 tables