Paper Detail
Saisha Shetty, Satvik Tripathi, Austin Lin, Colin Zhao, Theodore Kim, Don Enwerem, Jacinta Arnold, Shahriar Faghani, Tessa S Cook
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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@article{shetty2026marc,
title = {MARC v1: An Open-Source Multi-Agent Framework for Clinical AI Reasoning and Coordination},
author = {Saisha Shetty and Satvik Tripathi and Austin Lin and Colin Zhao and Theodore Kim and Don Enwerem and Jacinta Arnold and Shahriar Faghani and Tessa S Cook},
year = {2026},
abstract = {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 pla},
url = {https://arxiv.org/abs/2608.13476},
keywords = {cs.AI, cs.CL},
eprint = {2608.13476},
archiveprefix = {arXiv},
}
{}