Mimosa Framework: Toward Evolving Multi-Agent Systems for Scientific Research
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 ...
| Authors | Martin Legrand, Tao Jiang, Matthieu Feraud et al. |
|---|---|
| Primary category | cs.AI |
| Published | 30 March 2026 |
| Last updated at source | 30 March 2026 |
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