[2603.28986] Mimosa Framework: Toward Evolving Multi-Agent Systems for Scientific Research

Computer Science > Artificial Intelligence arXiv:2603.28986 (cs) [Submitted on 30 Mar 2026] Title:Mimosa Framework: Toward Evolving Multi-Agent Systems for Scientific Research Authors:Martin Legrand, Tao Jiang, Matthieu Feraud, Benjamin Navet, Yousouf Taghzouti, Fabien Gandon, Elise Dumont, Louis-Félix Nothias View a PDF of the paper titled Mimosa Framework: Toward Evolving Multi-Agent Systems for Scientific Research, by Martin Legrand and 7 other authors View PDF HTML (experimental) Abstract:Current Autonomous Scientific Research (ASR) systems, despite leveraging large language models (LLMs) and agentic architectures, remain constrained by fixed workflows and toolsets that prevent adaptation to evolving tasks and environments. We introduce Mimosa, an evolving multi-agent framework that automatically synthesizes task-specific multi-agent workflows and iteratively refines them through experimental feedback. Mimosa leverages the Model Context Protocol (MCP) for dynamic tool discovery, generates workflow topologies via a meta-orchestrator, executes subtasks through code-generating agents that invoke available tools and scientific software libraries, and scores executions with an LLM-based judge whose feedback drives workflow refinement. On ScienceAgentBench, Mimosa achieves a success rate of 43.1% with DeepSeek-V3.2, surpassing both single-agent baselines and static multi-agent configurations. Our results further reveal that models respond heterogeneously to multi-agent decom...