C-MIG: Multi-view Information Gain-based Retrieval-Augmented Generation for Clinical Diagnosis Reasoning
arXiv:2605.27860v1 Announce Type: new Abstract: Retrieval-augmented generation combined with reinforcement learning has shown promise for grounding large language models in trustworthy medical evidence. However, existing methods rely on exact-match binary rewards, which in clinical diagnosis cause two issues: (i) semantically relevant but non-verbatim steps receive zero signal, discarding valuable learning signals; and (ii) uni-dimensional rewards cannot effectively supervise heterogeneous reasoning capabilities. To address these issues, we propose C-MIG, a Multi-view Information Gain-based re
The paper addresses current limitations in Retrieval-Augmented Generation (RAG) when applied to complex, high-stakes domains like clinical diagnosis, building on recent advancements in large language models and reinforcement learning.
Improving RAG techniques for medical evidence grounding can significantly enhance the reliability and trustworthiness of AI in healthcare, potentially leading to better diagnostic tools and patient outcomes.
The proposed C-MIG method introduces a multi-view, information gain-based reward system that moves beyond binary exact-match rewards, enabling more nuanced and effective supervision of heterogeneous reasoning capabilities in RAG systems.
- · AI in healthcare developers
- · Medical professionals (diagnosis support)
- · Patients (improved diagnostic accuracy)
- · Large Language Model researchers
- · Developers relying solely on simple RAG reward mechanisms
- · Systems with high false-positive rates in clinical AI
More robust and accurate AI tools for clinical diagnosis become available.
Reduced diagnostic errors and improved healthcare efficiency lead to better patient care and resource allocation.
Increased public and professional trust in AI-driven medical decisions could accelerate broader AI adoption across critical sectors.
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Read at arXiv cs.AI