用多智能体协作提升临床AI推理可解释性与可靠性
MARC v1: An Open-Source Multi-Agent Framework for Clinical AI Reasoning and Coordination
- 分工协作的多智能体架构替代单一提示，支持任务分解与过程追踪
- 可自动从自然语言生成专用提示，无需手动调优
- 无需编程即可配置，适合临床专家使用
我们提出多智能体推理与协调（MARC）框架，以确定性多智能体编排取代传统的单体大模型提示，用于临床推理。MARC通过角色专精的智能体协同完成信息提取、推理、答案生成与评估，并实现显式上下文传递与可追溯的中间输出，支持阶段式故障归因。我们还引入分解器模块，可将自然语言描述自动转换为任务特定的智能体提示，消除手动提示工程。该框架支持基于API和本地CPU部署，完全通过YAML配置，无需代码修改。MARC设计为模型无关、可解释且对无编程经验的临床领域专家友好。完整框架已开源：https://github.com/Penn-RAIL/MARC-v1。
原文摘要 · Abstract (English)
We present Multi-Agent Reasoning and Coordination (MARC), an open-source framework that replaces monolithic LLM prompting with deterministic multi-agent orchestration for clinical reasoning. MARC coordinates role-specialized agents for extraction, reasoning, answer generation, and evaluation, with explicit context passing and traceable intermediate outputs, enabling stage-wise failure attribution. We additionally introduce a Decomposer module that generates task-specific agent prompts from a plain-language description, eliminating manual prompt engineering. The framework supports both API-based and local CPU-compatible deployments and is entirely configurable via YAML, without code modifications. MARC is designed to be model-agnostic, interpretable, and accessible to clinical domain experts without programming expertise. The full framework is available at https://github.com/Penn-RAIL/MARC-v1.
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