mlsanityData to 14 Oct 2026

MARC v1: An Open-Source Multi-Agent Framework for Clinical AI Reasoning and Coordination

arXiv 2608.13476 ↗First posted 13 Aug 2026cs.AI cs.CLcited by 0

On the map: LLM reasoning → Clinical and biomedical applications

also in 1 more
Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning. Builds on 8 of its 22 references that are in the map. First posted on arXiv 13 Aug 2026.

Builds on

8 of 22 references.

Closest ancestors · 3

Same direction, newest first

  1. 2025Capabilities of GPT-5 on Multimodal Medical Reasoning11 Aug
  2. 2025Multi-step retrieval and reasoning improves radiology question answering with large language models1 Aug
  3. 2025RadFabric: Agentic AI System with Reasoning Capability for Radiology17 Jun

Other references · 5

  1. 2023Capabilities of GPT-4 on Medical Challenge Problemscited by 638
  2. 2025MedGemma Technical Reportcited by 436
  3. 2025Generative to Agentic AI: Survey, Conceptualization, and Challengescited by 33
  4. 2024Best Practices for Large Language Models in Radiologycited by 5
  5. 2025Retrieval-Augmented Generation in Medicine: A Scoping Review of Technical Implementations, Clinical Applications, and Ethical Considerationscited by 5

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025TeamMedAgents: Pareto-Efficient Multi-Agent Medical Reasoning Through Teamwork Theory
  2. 2026ClinicalAgents: Multi-Agent Orchestration for Clinical Decision Making with Dual-Memory
  3. 2026TARSE: Test-Time Adaptation via Retrieval of Skills and Experience for Reasoning Agents
  4. 2025CodeAgents: A Token-Efficient Framework for Codified Multi-Agent Reasoning in LLMs
  5. 2025MedLA: A Logic-Driven Multi-Agent Framework for Complex Medical Reasoning with Large Language Models
  6. 2024ArgMed-Agents: Explainable Clinical Decision Reasoning with LLM Disscusion via Argumentation Schemes
  7. 2025Reasoning LLMs in the Medical Domain: A Literature Survey
  8. 2024AgentClinic: a multimodal agent benchmark to evaluate AI in simulated clinical environments