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arxiv: 2605.27860 · v1 · pith:UCFHIZEBnew · submitted 2026-05-27 · 💻 cs.AI

C-MIG: Multi-view Information Gain-based Retrieval-Augmented Generation for Clinical Diagnosis Reasoning

Yuwei Miao , Gen Li , Yunsheng Zeng , Xiandong Li , Yujin Wang , Siyu Chen , Luning Wang , Yunhao Qiao
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Junfeng Wang Jianwei Lv Bo Yuan
This is my paper

Pith reviewed 2026-06-29 12:41 UTC · model grok-4.3

classification 💻 cs.AI
keywords retrieval-augmented generationclinical diagnosisinformation gainreinforcement learningmulti-view supervisionmedical reasoningRAG-RL methods
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The pith

C-MIG estimates information gain from retrieved documents and their refinements to jointly supervise retrieval and refinement in clinical diagnosis reasoning.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces C-MIG to address limitations in retrieval-augmented generation combined with reinforcement learning for medical diagnosis. Existing approaches rely on exact-match binary rewards that assign zero value to semantically relevant but non-verbatim steps and cannot supervise varied reasoning types effectively. C-MIG instead calculates information gain from two views—retrieved documents and document refinements—using a frozen reference model to provide continuous signals for both what to retrieve and how to refine. It adds a multi-subquery retrieval strategy to improve knowledge coverage in diagnostic scenarios. Experiments across four medical benchmarks show the approach yields the highest results among RAG-RL methods on in-domain and out-of-domain data while surpassing leading general-purpose language models.

Core claim

C-MIG estimates information gain under a frozen reference model from the retrieved-document view and the document-refinement view to jointly guide retrieval and refinement, thereby reducing loss of valuable learning signals from non-exact matches and improving credit assignment across heterogeneous clinical reasoning steps, while incorporating multi-subquery retrieval to boost knowledge recall coverage.

What carries the argument

Multi-view information gain estimation, which quantifies added value from both retrieved documents and their refinements to supply supervision signals for retrieval and refinement.

If this is right

  • Semantically relevant but non-verbatim reasoning steps receive positive learning signals instead of zero reward.
  • Heterogeneous reasoning capabilities receive multi-dimensional supervision rather than uni-dimensional binary feedback.
  • Multi-subquery retrieval increases coverage of relevant medical knowledge in diagnostic queries.
  • Performance improves on both in-domain and out-of-domain sets relative to prior RAG-RL methods.
  • Clinical diagnosis accuracy exceeds that of state-of-the-art general-purpose large language models.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same two-view information gain structure could be tested in other evidence-grounded domains such as legal case analysis or scientific hypothesis generation.
  • Because the reference model remains frozen, periodic replacement with updated models may be needed to keep the gain estimates aligned with current medical knowledge.
  • If the approach scales, it could support finer-grained evaluation of retrieval quality in any RAG system where exact string matches are poor proxies for usefulness.

Load-bearing premise

Information gain calculated from the two views under a frozen reference model can supervise retrieval and refinement without discarding useful signals or introducing new biases in clinical reasoning.

What would settle it

A controlled test on held-out clinical diagnosis cases in which C-MIG produces no accuracy improvement over binary-reward RAG-RL baselines despite higher measured information gain would show the multi-view signals fail to provide effective supervision.

Figures

Figures reproduced from arXiv: 2605.27860 by Bo Yuan, Gen Li, Jianwei Lv, Junfeng Wang, Luning Wang, Siyu Chen, Xiandong Li, Yujin Wang, Yunhao Qiao, Yunsheng Zeng, Yuwei Miao.

Figure 1
Figure 1. Figure 1: Hard process rewards assign zero signal to se [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of C-MIG. The framework integrates multi-turn RAG trajectory generation with multi-view [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Training dynamics of (a) diagnostic accuracy [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Training dynamics across all methods (after smoothing). (a) Sequence entropy. (b) Gradient norm. (c) [PITH_FULL_IMAGE:figures/full_fig_p015_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Process reward curves. (a) C-MIG soft refine information gain. (b) AutoRefine hard refine reward. (c) [PITH_FULL_IMAGE:figures/full_fig_p015_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Case study. C-MIG performs step-by-step diagnostic reasoning. [PITH_FULL_IMAGE:figures/full_fig_p018_6.png] view at source ↗
read the original 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 retrieval-augmented generation framework for Clinical diagnosis. C-MIG estimates information gain under a frozen reference model from two complementary views, retrieved-document and document-refinement, to jointly guide what to retrieve and how to refine, alleviating the issues of valuable reward signal loss and credit assignment. We further design a multi-subquery retrieval augmentation strategy that improves knowledge recall coverage in clinical diagnostic scenarios. Comprehensive experiments on four medical benchmarks demonstrate that C-MIG achieves the best performance among all RAG-RL methods on both in-domain and out-of-domain sets, and outperforms state-of-the-art general-purpose LLMs for clinical diagnosis.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper proposes C-MIG, a multi-view information gain-based RAG-RL framework for clinical diagnosis reasoning. It estimates information gain under a frozen reference model from retrieved-document and document-refinement views to jointly supervise retrieval and refinement, addressing the loss of learning signals from binary exact-match rewards and the limitations of uni-dimensional rewards for heterogeneous reasoning. A multi-subquery retrieval augmentation strategy is introduced to improve knowledge recall. Experiments on four medical benchmarks are claimed to show that C-MIG achieves the best performance among RAG-RL methods on both in-domain and out-of-domain sets while outperforming state-of-the-art general-purpose LLMs.

Significance. If the performance claims are substantiated with rigorous experiments, the approach could provide a more effective way to deliver dense, multi-dimensional supervision signals in RAG-RL for medical tasks, potentially improving the grounding and reliability of LLMs in clinical diagnosis without relying on verbatim matches.

major comments (2)
  1. Abstract: The central empirical claim (best performance among RAG-RL methods and outperformance of SOTA LLMs) is asserted without any description of experimental setup, baselines, datasets, statistical tests, error analysis, or ablation studies. This absence prevents evaluation of whether the reported gains are robust or load-bearing for the contribution.
  2. Abstract (Methods description): The information-gain reward is defined with respect to a frozen reference model; without the full methods section it remains unclear whether the multi-view estimates are independent of the fitting process or risk circularity in credit assignment for heterogeneous clinical reasoning steps.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their comments. We address the major comments point by point below.

read point-by-point responses

  1. Referee: Abstract: The central empirical claim (best performance among RAG-RL methods and outperformance of SOTA LLMs) is asserted without any description of experimental setup, baselines, datasets, statistical tests, error analysis, or ablation studies. This absence prevents evaluation of whether the reported gains are robust or load-bearing for the contribution.

    Authors: Abstracts are concise summaries by design and do not include full experimental details. The manuscript provides comprehensive descriptions of the experimental setup, baselines, datasets (four medical benchmarks), ablations, and analyses in Sections 4 and 5, including in-domain and out-of-domain evaluations. The performance claims are substantiated with these results. We do not view changes to the abstract as necessary. revision: no

  2. Referee: Abstract (Methods description): The information-gain reward is defined with respect to a frozen reference model; without the full methods section it remains unclear whether the multi-view estimates are independent of the fitting process or risk circularity in credit assignment for heterogeneous clinical reasoning steps.

    Authors: The abstract states that information gain is estimated under a frozen reference model. This design choice ensures the estimates are independent of the policy being optimized during training, with the reference model held fixed to provide stable supervision. The multi-view estimates (retrieved-document and document-refinement) therefore introduce no circularity in credit assignment. Full details appear in the Methods section. revision: no

Circularity Check

0 steps flagged

No significant circularity; empirical claims rest on benchmarks, not self-referential derivation

full rationale

The paper introduces C-MIG as a framework that computes information gain from a frozen reference model across two views to supervise retrieval and refinement in RAG-RL. All load-bearing claims concern measured performance on four medical benchmarks (in-domain and out-of-domain), not a closed mathematical derivation. No equations, fitted parameters renamed as predictions, self-citations used to justify uniqueness, or ansatzes smuggled via prior work appear in the text. The reward signal is explicitly external (frozen model) and the evaluation is benchmark-driven, rendering the method self-contained against external data rather than reducing to its own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

Abstract-only review; no explicit free parameters, axioms, or invented entities are stated. The framework implicitly assumes information gain from a frozen model is a reliable unsupervised signal.

axioms (2)
  • domain assumption Exact-match binary rewards discard valuable learning signals for semantically relevant but non-verbatim reasoning steps in clinical diagnosis.
    Stated as issue (i) motivating the work.
  • domain assumption Uni-dimensional rewards cannot effectively supervise heterogeneous reasoning capabilities.
    Stated as issue (ii) motivating the work.

pith-pipeline@v0.9.1-grok · 5751 in / 1280 out tokens · 32187 ms · 2026-06-29T12:41:09.948684+00:00 · methodology

discussion (0)

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Reference graph

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