CURV: Coherent Uncertainty-Aware Reasoning in Vision-Language Models for X-Ray Report Generation
Authors: Ziao Wang
Sixing Yan
Kejing Yin
Xiaofeng Zhang
William K. Cheung
Venue: NeurIPS 2025 poster ()
Keywords: Chest X-Ray Report Generation
Vision Language Models
Uncertainty-Aware Reasoning
Primary area: applications

Abstract: Vision-language models have been explored for radiology report generation with promising results. Yet, uncertainty elaborated in findings and the reasoning process for reaching clinical impressions are seldom explicitly modeled, reducing the clinical accuracy and trustworthiness of the generated reports. We present CURV, a novel framework that alleviates the limitations through integrated awareness of uncertainty and explicit reasoning capabilities. Our approach consists of three key components: (1) an uncertainty modeling mechanism that teaches the model to recognize and express appropriate levels of diagnostic confidence, (2) a structured reasoning framework that generates intermediate explanatory steps connecting visual findings to clinical impressions, and (3) a reasoning coherence reward that ensures logical consistency among findings, reasoning, and impressions. We implement CURV through a three-stage training pipeline that combines uncertainty-aware fine-tuning, reasoning initialization, and reinforcement learning. In particular, we adopt a comprehensive reward function addresses multiple aspects of report quality, incorporating medical term matching, uncertainty expression evaluation, and semantic coherence evaluation. Experimental results demonstrate that CURV generates clinically relevant reports with appropriate uncertainty expressions and transparent reasoning traces, significantly outperforming previous methods. CURV represents a substantial advancement toward interpretable and trustworthy AI-generated radiology reports, with broader implications for the deployment of vision-language models in high-stakes clinical environments where uncertainty awareness and reasoning transparency are essential.

CURV: Coherent Uncertainty-Aware Reasoning in Vision-Language Models for X-Ray Report Generation
Ziao Wang Sixing Yan', Kejing Yin'; Xiaofeng Zhang", William Cheung'
'Deparmment Computer Science, Hong Kong Baptist University
Institute of Systems Medicine and Health Sciences, Hong Kong Baptist University Department of Computer Science, Harbin Institute of Technology
Abstract
Vision-language models have been explored for radiology report generation with promising results. Yet, uncertainty elaborated in findings and the reasoning pro- cess for reaching clinical impressions are seldom explicitly modeled, reducing the clinical accuracy and trustworthiness of the generated reports. We present CURV, novel framework that alleviates the limitations through integrated aware- ness of uncertainty and explicit reasoning capabilities. Our approach consists of three key components: (1) an uncertainty modeling mechanism that teaches the model to recognize and express appropriate levels of diagnostic confidence, (2) a structured reasoning framework that generates intermediate explanatory steps connecting visual findings to clinical impressions, and (3) reasoning coherence reward that ensures logical consistency among findings, reasoning, and impres- sions. We implement CURV through three-stage training pipeline that combines uncertainty-aware fine-tuning reasoning initialization, and reinforcement learning. In particular, adopt comprehensive reward function that addresses multi- ple aspects of report quality, incorporating medical term matching, uncertainty expression evaluation, and semantic coherence evaluation. Experimental results demonstrate that CURV generates clinically relevant reports with appropriate un- certainty expressions and transparent reasoning traces, significantly outperforming previous methods. CURV2 represents a substantial advancement toward inter- pretable and trustworthy Al-generated radiology reports, with broader implications for the deployment of vision-language models in high-stakes clinical environments where uncertainty awareness and reasoning transparency are essential.
Introduction
Chest X-rays (CXRs) are cornersoone of diagnostic imaging, yet their interpretation is time-intensive, straining healthcare systems amid radiologist shortages. Automated report generation using vision- language models (VLMs) offers promising solution to enhance efficiency and reduce workload [31, 20). Moreover, this technology can help bridge the gap in report quality between large and small hospitals, where physicians in smaller facilities often have less experience compared those in major centers, enabling more consistent and accurate diagnoses across diverse settings.
Medical report generation, unlike general image captioning tasks, imposes unique challenges that current VLMs are not fully equipped to address, particularly in handling diagnostic uncertainty and providing transparent reasoning [35]. Radiologists routinely employ linguistic markers such as "likely"
"Correspondence to: Kejing Yin <eskjyin Beskjyin@comp.hkiee www.mnppp.b........... hdk "Code and Dataset: https: :/:g:://tttt thub. cwm/ http///wwwxx/////p/ww///p// CURV
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Structural Uncertainty (Findings); "Pulmonary nodules in the left upper lobe are also not completely characterized on this study. However, in addition, there is a more hazy widespread opacity projecting over the left mid upper lung which could be compatible with a coinciding pneumonia."
Semantic Uncertainty (Impression): "Increasing left lung opaci- fication which may reflect pneumonia superimposed metastatic disease, although other etiologies such as lymphangitic pattern of metastatic spread could be considered. CT may be helpful to evaluate further if needed clinically."
(a) Chest X-ray image
(b) Corresponding uncertain expressions from the radiology report.
Figure I: Illustration of Structural and Semantic Uncertainty. (a) A hazy opacity in the left mid-upper lung (arrow) generates structural uncertainty regarding its nature. (b) This feeds into the semantic uncertainty in the impression, where multiple etiologies are considered and further investigation (CT) is suggested.
or "possible" to convey varying degrees of diagnostic confidence, ensuring clear communication with referring physicians [32]. As diagnostic uncertainty is multifaceted, as illustrated in Figure 1, it is crucial to distinguish between at least two key types: (1) Structural Uncertainty regarding specific visual findings (c.g., a sentence in the "Findings" section stating "hazy opacity... could be compatible with pncumonia"); : and (2) Semantic Uncertainty stated in the overall "Impression" (e.g., "opacification may reflect pneumonia... although other etiologies could be considered"). Accu- rately modeling these uncertainties, and the reasoning that connects them, is crucial for generating trustworthy reports to support effective clinical decision-making Crucially, our work frames this challenge not as quantifying model's internal statistical confidence, but modeling the linguistic expression of diagnostic uncertainty-tii specific language radiologists use to convey confidence levels. Moreover, robust clinical decision-making relies heavily on explicit reasoning that logically connects such visual findings, with their structural uncertainties, to the more diagnosis-orien medical impressions and their associated semantic uncertainties.
Existing VLMS for CXR report generation often prioritize factual accuracy over modeling diagnostic uncertainty providing explicit reasoning pathways, resulting in reports that lack clinical nuance and transparency [41, 29, 8, 38, 23]. This deficiency poses a significant barrier to their clinical adoption, as physicians require both accurate uncertainty expression and transparent reasoning to trust and effectively utilize Al-generated reports. Consequently, there is a pressing need for developing approaches that address these critical gaps by integrating robust uncertainty awareness and transparent reasoning mechanisms into the report generation process.
To tackle these challenges, introduce CURV, novel framework for uncertainty- vision- language models with explicit reasoning capabilities for CXR report generation. CURV advances the field through three key innovations: (1) a systematic uncertainty modeling approach that enables the model to recognize anatomical structures and express appropriate diagnostic confidence using data-driven fine-tuning strategy and specialized uncertainty reward; (2) a structured reasoning framework that generates intermediate explanatory steps linking visual findings to clinical impressions, thereby enhancing transparency. To enable the development and supervised initialization of this reasoning capability, we created TRACE-CXR (TTransparett Reasoning and Articulation for Clinical Explanations CXR), a novel dataset of 2,000 chest X-ray reports, each augmented with an explicit, L.MM-generaeed "thinking" section that models the reasoning pathway from findings to impression; and (3)= a multi-dimenssonal reward design in reinforcement learning that ensures logical consistency across findings, reasoning, and impressions, addressing the need for coherent and trustworthy reports. Our approach leverages a vision language model, augmented by a meticulously designed training pipeline that embeds uncertainty awareness and reasoning capabilities directly into the model. The uncertainty modeling component developed through targeted fine- tuning with uncertainty-ae -nnootatd data, enabling the model to predict confidence levels for identified pathologies and map them to appropriate linguistic expressions in the generated text. For the reasoning component, we employ a structured generation process that articulates visual findings, produces intermediate reasoning steps, and delivers clinical impressions-ale all while maintaining logical coherence across these elements through a novel optimization strategy.
2

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The contributions of this study are as follows:
We propose framework for uncertainty-aware medical report generation, integrating specialized fine-tuning strategy with curated uncertainty-annotteed data and uncertainty- calibrated reward mechanism to enhance the clinical relevance of Al-generated CXR reports.
We introduce structured reasoning framework that leverages our TRACE-CXR dataset to initialize explicit "thinking" pathways within a tripartite report structure (findings, thinking, impression), supported by a multi-dimennional reward design in reinforcement learning, to ensure transparent ex explanations and logical consistency between radiological observations and clinical impressions.
Through extensive experimentation, we demonstrate that CURV generates clinically relevant CXR reports with appropriate uncertainty expressions and transparent reasoning traces, outperforming existing methods in both qualitative and quantitative metrics, thus advancing trustworthy AI in high-stakes clinical environments.
2 Related Work
2.1 Uncertainty in Medical Report Generation
Expressing diagnostic uncertainty critical in radiology for clear communication and effective clinical decision-making Equipping rision-language models (VLMs) with uncertainty awareness is thus essential for clinical adoption. Existing studies have approached this from various perspectives. Wang et al. [32] used Monte Carlo dropout to estimate visual and textual uncertainty, integrating into a weighted loss function for reliable outputs. Similarly, Yan et al. [35] introduced the Diagnostic Uncertainty Encoding framework encode clinically inspired uncertainty concepts, enhancing report accuracy. Najdenkoska et al. [22] proposed a probabilistic latent variable model with variational topic inference generate diverse CXR reports reflecting multiple interpretations. Additionally, Yang et al. [37] demonstrated that training Bayesian neural networks with uncertain labels increases predictive variance for ambiguous cases, while large-scale VLMs like Med-Gemini [25] employ umcertainty-guidee strategies for clinical reasoning tasks. However, many approaches lack explicit differentiation between uncertainty specific findings (Structural Uncertainty) and overall diagnostic synthesis (Semantic Uncertainty), as noted by [41, 29]. CURV addresses this gap by systematically modeling and expressing both types of uncertainty through specialized reward mechanism and fine-tuning with
data, aiming for clinically nuanced reports.
2.2 Reasoning by Reinforcement Learning
Reinforcement learning (RL) has shown promise in enhancing reasoning capabilities language and vision-language models [40, 18, 34). DeepSeek-R1 [7] leverages Group Relative Policy Optimization (GRPO) [28] to develop reasoning skills via rule-based rewards. Extending RL to VLMs, Huang et al. [12] introduced Vision-RI, achieving strong performance through automated dataset construction and Progressive Thinking Suppression Training with GRPO [26, 39]. In the medical domain, RL is increasingly applied radiology report generation for structured reasoning and trustworthiness [11, 42]. Jing et al. [15] proposed BoxMed- RL, focusing on explainable reports through CoT reasoning and spatially verifiable RL, emphasizing spatial grounding via loU-based rewards. Similarly, Shao et al. [27] used RL to improve alignment between radiology images and reports, targeting linguistic quality and anomaly detection. While these works highlight RL's potential for logical consistency and transparency, CURV uniquely integrates uncertainty modeling with structured reasoning, using a multi-dimensional reward design to ensure coherent and trustworthy Al-generated reports.
3
The Proposed Method
The CURV framework, detailed in this section, is designed to produce radiology reports that embody both diagnostic accuracy and a sophisticated understanding of clinical uncertainty. Our method explicitly models the Structural Uncertainty (tied individual findings) and Semantic Uncertainty (concerning the overall diagnostic impression), along with the coherent reasoning that bridges them.
3

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SFT uncertainty aware data
input: (mage, unceriainty locasion)
Uncertainty SFT
oulput: boox, labol, uncertainty description
Model
Uncortainty aware VI.M
AL data
SFT reasoning intiialzation data input: (Imgge, findings, thiwkng, impression)
input: image
Reasoning rntialicaion SF
Reasoning Initialized VLM
output: findins, Impression
output: <lnccings.... whirkingg......... cimpreesiooooooooooooooooooo
GRPO
KL
Reference Model
C+
Policy Model
C+
Reward
Group Computation
O
Ao
System Prompt:
You are medical expert tasked with generating a detailed radiclogy report bssed on the provided medical image. Andlyoe the image carslully and produce a structured report using the following lagged seclions: <iidingss <hhikings, and cimpressioro-
Formal Reward
Rewlg) (Suila)) Naaly) Naw(p) = Noy 11
Uncertaiity Acouracy Reward:
Thinking
Ruda) 71807)) SUP) J810
Final VLM
Figure 2: The CURV framework architecture. The figure illustrates the three-stage training pipeline, beginning with SFT for uncertainty awareness, followed reasoning initialization, and culminating Reinforcement Learning phase using GRPO to refine the final Vision-Language Model.
3.1 Problem Formulation
The generation of medical reports from chest X-ray (CXR) images using vision-language models (VLMs) is a complex task requiring diagnostic accuracy, uncertainty expression, and transparent reasoning. Formally, given CXR image I and prompt p (e.g., requesting a structured report), the goal is to train a VLM Fa to generate a structured output with three components: visual findings, logical reasoning, and clinical impressions with uncertainty expressions. The objective is optimize report quality across accuracy, uncertainty awareness, and coherence, formulated as:
arg max (I.pp).pp........ (r 1.P]),
where is the dataset image-prompt pairs, the generated report, and r(y. I,p) is multi- dimensional reward function assessing format adherence, medical accuracy, uncertainty expression, and reasoning coherence. This framework, CURV, aims to produce clinically relevant, transparent, and trustworthy Al-generated reports by addressing these critical dimensions in unified manner.
3.2 Empowering Uncertainty Awareness in Vision-Language Models
Current vision-language models (VLMs) often fail capture the inherent uncertainty in medical report generation, a critical limitation for clinical decision-make in radiology. The CURV framework addresses this gap by instilling awareness of Structural Uncertainty, which pertains to specific findings, introduced in Section 1. This initial stage focuses on enabling the model to recognize and articulate appropriate confidence levels for anatomical abnormalities, thereby enhancing the reliability of the "Findings" section in generated reports. The supervised fine-tuning (SFT) process leverages a curated uncertainty-annotaal dataset DD where each instance includes CXR image T, prompt p (c.g., instructing detection of anatomical objects with uncertainties), and ground-truu truth structural

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uncertainty specifications Yg = (f1, f......... Each finding fk (by- Lx. tx)) encapsulates bounding box, anatomical label, and textual uncertainty description. The model Ty trained to generate a serialized token sequence seq(Y gt) using the loss function:
Luncertainty
Σ
log na (seq
where log To (seq(Ygz)) (T ,P) p) is the log- log-probability of producing the target sequence encoding all structural findings. This loss guides the model to detect uncertain regions (via by). identify them (via 1,), and articulate specific Structural Uncertainty (via (4) in a structured, sequentially generated format.
3.3 Reasoning Initialization for Transparent Reporting
Building on the uncertainty awareness developed earlier, the second stage of CURV focuses on initializing basic reasoning capabilities in the model. The objective is to generate transparent reports by articulating logical connections between radiological findings and clinical impressions through an intermediate reasoning path, enhancing interpretability for clinical validation.
To achieve this, report generation task is structured with three components: Findings (F), Reasoning
(R), and Impression (C), with R elucidating the transition F R C' to mimic radiologist's inferential steps. Inspired by frameworks like DeepSeck-RI [7]. which use structured data to improve reasoning patterns, this stage employs supervised fine-tuning with dataset we developed for this purpose, Dreason Each instance includes a CXR image L, a prompt p (e.g., "Generate a detailed radiology report"), and ground-truth structured report Y Yuuctureed (texty; text textc). Here, text, provides a logical narrative linking observations text, to conclusions in texte, detailing abnormalities, suspected conditions, differential diagnoses, and contextual information. The VLM Ta fine-tuned on D Presson to maximize the log- liglliill of the serialized sequence susuucueed), with the loss defined
Lreasoning
Σ
log no (seq (Yssq((mmcuurea))
(I.p. Ysuoouee D
By training the model with Lreasoning we instill foundational capability to generate reports that are not only descriptive but also explanatory, paving the way for more sophisticated coherence and alignment in the subsequent reinforcement learning stage.
3.4 Enhancing Clinical Reasoning with Reinforcement Learning
Building upon the foundational capabilities empowered by the previous stages for uncertainty awareness and structured reasoning, the final CURV stage employs Reinforcement Learning (RL) to refine the model's 's clinical reasoning process. Our SFT-then-RL. methodology is designed to move beyond simple imitation [6]. The preceding SFT stage uses the TRACE-CKS CXR dataset to teach the model the basic tripartite report structure, providing foundational policy for exploration. In this RL phase, however, the model trained on the MIMIC-CXR dataset and is provided with only the ground-truth findings and impression sections. This design avoids the "imitation trap" where models simply reproduce potentially flawed or suboptimal reasoning paths from the SFT data. Instead, forces the model discover functionally coherent reasoning process its own, guided only by the reward signals that measure the logical connection between the human-authoree findings and impressions. This ensures the model learns to generate genuinely coherent reasoning rather than engaging in "pseudo reasoning" by merely mimicking a template [4].
To this end, use Group Relative Policy Optimization (GRPO) [28], guided by novel, multi- component reward function tailored for clinical report generation. A key advantage of this RL phase, particularly through the coherence reward (Rooh), is its ability to guide the model in generating the intermediate "thinking" process that logically connects findings to impressions, without requiring extensive supervised data for this specific reasoning component.
Given an input (I. and ground truth Vgt, GRPO samples G reports {yg} from policy Te.. and updates by maximizing:
1
(()) E
(min(pggy, - e, e)Ag)) 3D (TT [
(4)
G
5

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where (0) represents the objective function, which aims to maximize the expected reward for the policy Ty while maintaining stability in training. The function incorporates a clipped advantage term to limit large policy updates and a -I-diieeeeence penalty term (Dxz) with coefficient / to prevent excessive deviation from a reference policy Te.- The expectation E is taken over sampled reports, with G denoting the number of sampled reports (v₂} from the old policy T And Po is the importance sampling ratio and A, = Reotal (Mg. Mgt) - mean( (yk. }g:) is the advantage. The total reward Rioal (y, Y/g) is a weighted sum of three components:
(5) The total reward Rooaa (y, 1/gr) evaluates the quality of a generated report compared to the ground truth Vg: by combining three distinct reward components with corresponding weights Whnt Waces and Wooh- These components assess format adherence (m))), medical accuracy and uncertainty alignment (Race), and reasoning coherence (Resh), respectively, ensuring a comprehensive evaluation of clinical report quality.
Reocal (y Vg:) = Wimt mm (y) + "'ace Race Vgg)) + weoh (osb
Format Adherence Reward (Bmm):: A binary reward II(+) verifying strict adherence to the tripartite structure ( (ffindings), (thinking), (impression)), ensuring each tag appears exactly once and in order, with no extraneous content.
(6) where Ry) is a binary indicator function I(() that returns only if the generated report y fully adheres to the required structure. Specifically, Suull (v) checks if all required sections are present, while Ny) 1, Nutin (y) 1, and (y) = ensure that each section (findings, thinking, impression) appears exactly once in the correct order.
Rum (v) = 1 (V) A Nind = 1 A Nuinkk (3) = 1 A Nmpp (v) 1),
Findings/Impression Uncertainty Accuracy Reward (Rac)): Evaluates medical accuracy and uncertainty in findings (Fy,) and impression (Iy) sections against yg.. weighted as Ryce = 0.7 RØ 0.3 Ry. Returns 0 if sections missing.
1. Entity Matching (Rg): Average Fi-score of RadGraph--xtractee entities [14] between Fy. Ty and F yo vo
RE Ug) 2 (FI(E(F)), ((((()))))))
where Rg yge) computes the average Fi-score for entity matching between the generated report sections (F, for findings and ly for impression) and the ground truth sections (F,, and ygg) The function E()) extracts entities using RadGraph, and F1 (1,..) measures the overlap between entity sets, equally weighting the performance on findings and impression sections.
2. Uncertainty Alignment (Ry): Average alignment of (entity, term, score) triples P(Sy') between sections.
Ru Ugt) = (MatchPaissP((..)))PPP PAa
(8)
where Ry (y, Ugg) measures the alignment of uncertainty expressions between the generated report sections (Fy, and I,) and the ground truth (Fyy, and Ty.)) The function P(-)) extracts (entity, term, score) triples, and MatchPairs(..-) evaluates their similarity, averaging the results for findings and impression sections. MatchPairs combines semantic term similarity (0.4 weight) and score difference (0.6 weight) for common entities, then combines this average similarity (0.7 weight) with entity coverage (0.3 weight).
Thinking Coherence Reward (Reah): Measures Jaccard similarity J(.-)) between RadGraph- extracted entities in the thinking section E(T,) ) and the union of entities in findings E(F,) and impression E(T)). Returns if sections are missing or entity sets are empty.
(9) where (y) quantifies the coherence of the thinking section (T₂) in the generated report y by computing the Jaccard similarity J(.,-) between entities extracted from Ty (via E(T))) and the union of entities from the findings (F(F,) and impression (E,,,) sections. This ensures that the reasoning process logically connects observations to conclusions. This multifaceted RI. optimization aims for reports that are structurally sound, factually accurate with appropriate uncertainty, and demonstrate transparent clinical reasoning.
Ruch (v) = J (E(T,) E(F,) UE())))

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Experiments and Analysis
4.1 Experimental Setup
We conducted experiments on 4xA100 GPUs using Qwen-2.5-VL-3B as the backbone model, balancing efficiency and performance. Training spanned multiple stages over approximately 100 hours with batch size of 16 and learning rate established rision-language models like LLVVA-1.5-7B and MAIRA-2 under consistent conditions. Full details on configurations and baseline setups are provided in Appendix A.
10-6, CURV was benchmarked against
4.2 Datasets
Our experiments leverage the MIMIC-CXR dataset. key contribution of this work, we curated specialized data subsets: (1) an envertainty-annotattee dataset with 1 112.111 samples, and (2) novel TRACE- CXR dataset (Transparent Reasoning and Articulation for Clinical Explanations in CXR), featuring 2,000 reports with explicit reasoning pathways. Both were developed to support uncertainty modeling and structured reasoning, respectively, significantly improving data utility. The clinical validity of TRACE-CK. dataset subsequently confirmed through formal evaluation with board-certified radiologist, which revealed strong concordance with expert judgment (see Appendix B). Detailed curation processes and statisties for both datasets are also described in Appendix B.
4.3 Evaluation Metric
We assess CURV using standard NLP metries (e.g., BLEU, ROUGE-L, METEOR) for textual quality and climical accuracy metrics (e.g., CheXbert, RadGraph FI-scores) for medical relevance. Additionally, LLM--bssed evaluation protocols are employed to evaluate the unique "Thinking" section and uncertainty expressions (structural and semantic). Complete metric definitions and evaluation protocols are available Appendix
Table 1: Generation metries for radiology report generation across different models
Model
B-1
B-2
B-3
B-4
METEOR
R-L
gritim
LLaVA-115-7B [21]
19.09
7.46
2.81
1.25
19.16
18.36
44.25
ALAVA-1.5-BB-SFT--N
22.58
15.06
9.43
6.13
25.71
28.09
50.28
HuatuoGPT--Vision- [5]
19.33
9.42
4.64
1.93
26.01
20.78
47.32
MAIRA-2 [3]
24.94
14.12
9.01
6.14
26.78
28.65
47.48
Qwen2.5-VL-3B [1]
13.09
5.42
2.08
0.89
20.81
15.23
44.57
Gemini 2.5 pro
12.54
5.20
2.25
1.05
21.19
15.01
40.41
CURV sttgellalllll
14.96
7.08
3.33
1.61
23.47
19.07
45.15
CURV stage2
10.72
5.22
2.43
1.10
18.57
14.59
42.76
CURV
25.38
15.58
9.85
6.18
30.43
31.19
50.48
Table 2: Clinical accuracy metries for radiology report generation across different models
CheXbert
RadGraph
Model
Acc.
Macro F1
Micro F1
Ent. F1
F1
LLaVA-1.5-7B [21]
63.25
4.94
38.54
7.61
4.95
LLaVA-I.5-BB-SFT-C
72.72
5.00
51.51
17.57
13.06
HuauuoPTTVVisoo-775 [5]
71.15
5.34
48.62
15.92
9.06
MAIRA-2 [3]
67.39
6.34
46.53
25.01
17.05
Awen2.5-VL-3B [1]
67.78
4.75
37.66
9.46
4.66
Gemini 2.5 pro
74.35
5.35
48.45
13.09
7.71
CURV stagel
57.59
4.51
30.75
17.00
9.95
CURV stage2
56.57
3.87
26.83
11.11
6.03
CURV
76.93
5.22
57.12
25.95
19.54

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4.4 Main Results
Overall Metric The experimental results in Tables highlight the effectiveness the CURV framework in radiology report generation, with notable insights from both metric interpretations and model scale perspectives. From the metries' standpoint, CURV achieves superior generation quality with top scores in BLEU (c.g.. BLEU-3: 9.85), METEOR (30.43), and ROUGE L (31.19) compared to SOTA method like MAIRA-2 (BLEU-3: 9.01), indicating enhanced fluency and textual relevance. Likewise, CURV's ability to produce medically relevant content is underscored by its strong clinical accuracy. This advantage is particularly pronounced when compared to frontier generalist models; while such models are powerful, CURV's specialized approach significantly outperforms Gemini 2.5 Pro across all clinical metrics, achieving RadGraph F1 score of 19.54 versus 7.71. This demonstrates the critical value of targeted framework for this complex medical task. Finally, from model scale perspective, CURV, with compact 3B parameter size, outperforms larger 7B models like HuatuoGPT- Vision-BB and MAIRA-2, demonstrating that our proposed method enables efficient performance without requiring extensive computational resources.
Table 3: Generation metrics for radiology report generation IU X-ray dataset
Model
B-1
B-2
B-3
B-4
METEOR
R-L
gritim
LLaVA-1.5-7B
16.52
6.60
3.00
1.40
19.65
17.48
45.78
LLLVA-I.5-7B-SFFCCKK
21.42
12.95
8.03
5.20
23.24
26.40
46.21
HuatooGPT-Viiion-777
19.33
10.70
6.28
2.81
31.02
23.42
50.22
MAIRA-2
26.37
15.60
9.64
6.03
25.52
31.18
54.18
Qwen2.5-VL-3B
11.38
5.05
2.28
1.05
21.65
15.01
45.95
CURV stagel
12.51
6,24
3.18
1.57
23.64
18.18
46.76
CURV stage2
10.18
5.32
2.75
1.27
18.64
13.93
44.46
CURV
29.23
18.76
12.08
6.86
38.30
39.08
54.89
Table 4: Clinical accuracy metries for radiology report generation on IÜ X-ray dataset
CheXbert
RadGraph
Model
Acc.
Macro F1
Micro F1
Ent. F1
F1
LLaVA-1.5-7B
72.03
4.66
46.81
13.37
8.76
LLaVA-L.5-BB-FFT-
76.34
5.84
53.33
16.19
10.31
HuatuoGPT-Vision-7B
89.89
5.72
67.07
22.98
13.96
MAIRA-2
88.74
6.22
70.75
34.53
24.01
Qwen2.5-VL-3B
80.64
4.92
49.47
12.70
6.26
CURV CURV_stagel stagel
68.89
4.57
40.30
20.59
12.13
CURV _saage
67.59
3.76
33.67
12.83
7.28
CURV
91.56
5.86
74.36
36.99
25.65
Evaluation To assess the framework's generalization capabilities, we con- ducted rigorous out-of-distribution (OOD) evaluation using the X-ray [9] dataset, which was not used during training. As shown in the results (Tables 3 and Tables 4), CURV maintains its strong performance, outperforming all baseline methods on this new dataset. This successful performance demonstrates that the model's learned capabilities for reasoning and expressing uncertainty and can generalize well beyond the MIMIC-CX CXR dataset was trained on. These findings provide strong evidence that CURV is not overfit to its training data and can be effectively applied to different clinical data sources.
robust
LLM-based Evaluation The L.LM-based evaluation, as shown in Figure demonstrates substantial qualitative improvements in CURV- gegerreee reports throughout the reinforcement learning phase. This evaluation based on criteria defined in Appendix C. The "Thinking" section shows consistent Coherence, Depth of Analysis, Relevance, Evidence- Baaed Nature, and Consistency- with the corresponding scores progressively rising (c.g., Logical Coherence from 2.23 to 4.55 and Consistency from 2.88 to 4.76 by step 3,200).
and significant enhancements across all assessed criteria-Logee

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2.00
230
2.50
Srauk Zargs SaN Srace 1900 Sigg 203 PrpwinG S64-200 Rap43200
370-330
2kng
Skng S0EO
Srap 19KE
Sing
(a) LLM--bsed thinking evaluation
(b) LLMed uncertainty evaluation
Figure 3: LLM-based evaluation of report quality during reinforcement learning. (a) shows the progressive improvement of the 'Thinking' section across five qualitative criteria. (b) tracks the increasing scores for both structural and semantic uncertainty expression, demonstrating the model's refinement over training steps.
This indicates the model's increasing ability to articulate transparent and sound reasoning process. Regarding uncertainty expression, Semantic Uncertainty scores steadily improved from 1.60 to 2.60, indicating better conveyance of overall diagnostic confidence. Structural Uncertainty scores also increased from an initial value 0.60 to 0.76 (following an early dip), signifying progress in artieulating confidence for specific findings, albeit with more complex learning dynamies observed. Collectively, these trends underscore the efficacy of the CURV framework, especially its RL stage, in fostering clinically nuanced reports that more interpretable, trustworthy, and adept expressing diagnostic uncertainty, thereby enhancing clinical utility.
1.00
0.26
0.95
0.90
0.22
0.85
0.20
0.4
0.20
2000
chal Step
(lobel StAp
12 55 2005
Format Reward
(b) Accuracy Reward
Coherence Reward
Figure 4: Reward trends during the reinforcement learning phase. The plots show the rapid conver- gence of the (a) Format Reward, the steady increase of the (b) Accuracy Reward, and the significant late-stage ascent of the (c) Coherence Reward, validating the effectiveness of the multi-component reward function.
Reward Changing The evolution of reward components during reinforcement learning, as depicted Figure 4, underscores the efficacy of the CURV training strategy. The Format Adherence Reward (Rmmt) exhibits rapid increase early in training, quickly reaching near-optimal values (Figure 4a). This indicates the model's swift adoption of the required tripartite report structure. Concurrently, the Accuracy Reward (Raec), encompassing both entity matching and uncertainty alignment, demon- strates a steady, more gradual improvement throughout the training process (Figure 4b). This reflects the nuanced challenge of enhancing medical accuracy and appropriate uncertainty expression in the "Findings" and "Impression" sections. Most notably, the Thinking Coherence Reward (Reok) initially remains low but undergoes significant and steep ascent in later training stages, eventually plateauing at a high level (Figure 4c). This trajectory strongly suggests that the reinforcement learning phase, guided by Rosh, successfully teaches the model to generate logically sound "thinking" process that effectively connects radiological findings to clinical impressions. Collectively, these trends validate the multi-component reward function and the RL approach in progressively refining the model towards generating structurally correct, clinically accurate, and coherently reasoned radiology reports.
Case Study To qualitatively illustrate the model's 's advanced capabilities, we present analysis of Case Study in Figure 5. The model proficiently generated clinically coherent and structured report, comprising distinct "Findings." "Thinking." and "Impression" sections, adhering to the desired output

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Strong Correspondence in Key Findings:
Generated Findings: "...bibasal atelectasis...
There is likely bilateral pleural effusions of mod- extent."
Ground Truth Findings: "GT: ...iiassilrr opacities compatible with... adjacent atelec- tasis. Persistent moderate bilateral pleural ef- fusions..."
Alignment in Overall Clinical Assessment:
costoperanic anglc
Generated Impression: " "...bibusal atelectasis and moderate pleural effussioss... the concern is likely the effects of... management of any underlying atelectasis and effusions..."
(a) Chest X-ray image, the "bibasal atelectasis" mentioned in both findings and impression is cor- responding to right and left lower lung zone as annotated the image, and the "pleural effusions" is related with left and right costophrenic angle.
Ground Truth Impression: "GT: Persistent moderate bilateral pleural effissioss with adja- cent atelectasis."
(b) Generated report sections.
Figure 5: Case Study: Illustaaing robust VLM performance in identifying key clinical findings and generating useful reasoning process. (b) Generated report sections demonstrate strong cor- respondence with ground truth on core observations such as bibasilar atelectasis and moderate pleural effusions, including appropriate use of uncertainty terms (e.g.. "likely"). The model's explicit "<thinking>> section (analyzed in the main text, full content at D) provides a valuable, transparent pathway by linking these findings to patient context (e.g., supine position, HF history) and potential implications, showcasing the utility of structured reasoning in enhancing clinical report generation.
format. Due to page limit, key excerpts are shown, the full case is detailed in Appendix D. In the Findings section, the model accurately identified significant pathologies, including bibasilar atelectasis and moderate bilateral pleural effusions. These observations demonstrated strong correspondence with the ground truth report, and the model appropriately applied structural uncertainty terms (e.g.. "likely"), showcasing its uncertainty- aware learning. The "Thinking" section proved particularly valuable, offering a transparent reasoning pathway. It successfully linked the identified visual findings with crucial patient context, such as supine positioning and history of biventricular heart failure, and considered their clinical implications. This explicit articulation of the thought process significantly enhances report interpretability and trustworthiness. Finally, the "Impression" section aligned well with the ground truth's 's primary assessment, correctly summarizing the key findings of atelectasis and effusions and focusing on their management. This case exemplifies the model's robust ability to produce accurate, contextually-awant and clinically useful radiological interpretations with a clear and valuable reasoning structure, highlighting the strengths of our proposed framework.
Conclusion
We introduced CURV, a novel framework that enhances radiology report generation by integrating uncertainty awareness and explicit reasoning into vision-language models. CURV's key innova- tions-uncertaing, modeling, a structured reasoning module, and a coherence-driven reinforcement learning etrategy-enable the generation of reports that are clinically accurate, transparent, and appropriately express diagnostic confidence. Experimental results demonstrate CURV's superior performance in producing interpretable Al-generated CXR reports. However, CURV's performance relies on the quality of the initial curated datasets for uncertainty and reasoning, and its "thinking" process, while explicit, is learned via LLM-ggnerated data. Furthermore, its generalization other medical imaging modalities requires further investigation. Ultimately, the most critical next step is large-scale clinical validation by expert radiologists to ensure its safe and effective translation into practice. Despite these limitations, CURV marks significant advancement for trustworthy vision- language models in high-stakes clinical applications. further promote research and transparency in this domain, the TRACE-CXR dataset developed for this study will be made publicly available. We believe CURV and the TRACE-CXR dataset will serve as valuable resources for future work where clarity in reasoning and uncertainty is crucial.
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Acknowledgments and Disclosure of Funding
This work is partially supported by the National Natural Science Foundation of China (62302413), the Health and Medical Research Fund (23220312), the General Research Fund RGC/HKBU12206621 from the Research Grant Council, and the Research Matching Grant Scheme RMGS2021 _8_06 06 from the Hong Kong Government.
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The paper proposes novel framework and methodology (CURV) for X- Ray report generation, focusing on its empirical performance and practical contributions. While it uses mathematical formulations to define its components, loss functions, and reward mechanisms, it does not present new theoretical results such as theorems or lemmas accompanied by formal assumptions and proofs. The primary validation is experimental.
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Justification; While statistical validation through error bars or significance testing valuable. it was not included for the main quantitative results in this submission. This paper introduces CURV, a novel and complex multi-stage framework for a challenging task. The primary focus of this foundational study was to establish the framework's 's architecture, demonstrate its unique capabilities uncertainty modeling and explicit reasoning, and show its potential to significantly advance performance on key clinical metrics. Given the extensive compu- tational resources and time required to conduct multiple full end-to-end training runs of such complex system, comprehensive statistical analysis was beyond the scope of this initial investigation. We acknowledge this as limitation and plan to incorporate rigorous statistical validation in future in-depth studies and extensions of this work.
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Section details the positive societal benefits. Potential negative impacts, such as over-reliance or algorithmic bias common Al in healtheare, are acknowledged as areas requiring ongoing attention in the field, though not extensively detailed for CURV specifically.
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A Experimental Setup
In this section, we outline the experimental setup for training and evaluating the CURV framework for radiology report generation and the comparison methods.
Training Configuration Experiments were conducted on a node with 4xA 100-00 GPUs. We uti- lized the Swen-2.55VL-333 the backbone model for its balance of efficiency and performance. The training was configured with a batch size of 16, and group size of 8 for GRPO in the reinforcement learning stage. The entire process spanned 3,200 steps over approximately 100 hours, with learning rate x 10 used for stable convergence.
To manage the memory requirements of training 3B parameter model, we employed different fine-tuning strategies and stae-of-the-an memory optimization techniques across the stages:
Stage and 2 (SFT Uncertainty Aware and Reasoning Initialization): To maximize efficiency, we kept the vision encoder frozen and fine-tuned only the parameters of the multimodal projection layer and the LLM backbone. This stage took approximately 4 hours to complete on 4 GPUs.
Stage 3 (RI. Enhancement): this final stage, the goal was to refine the entire model's behavior based on global reward signals. Therefore, we performed full-parameter fine- tuning of the model checkpoint from Stage 2. To make this computationally intensive step feasible within the 80GB memory of each GPU, we leveraged a combination of standard optimization techniques:
Mixed-Preciste Training: We used bfloat16 (bf16) precision, which halves the memory footprint for model parameters, gradients, and optimizer states compared to standard FP32, with minimal impact training stability.
Parameter and Optimizer State Sharding: We utilized DeepSpeed with ZeRO Stage 3 optimization. This powerful technique partitions not only the optimizer states but also the gradients and the model parameters themselves across all 4 GPUs. This drastically reduces the per-GPU memory load, as each GPU only holds fraction of the total training-related tensors.
To manage memory consumed by activations, we em- ployed activation checkpointing (also known as gradient checkpointing). This technique avoids storing all activations by re-computing them during the backward pass, trading a modest amount of compute time for significant reduction in memory usage.
Activation Checkpointing:
Baseline Models For the baseline models, CURV was benchmarked against established vision- language models, briefly introduced below with their experimental configurations:
LLaVA-1.5-7B [21]: A vision language model for image and language understanding, fine-tuned on Vicuna with GPT-generated data for strong chat capabilities. We used the original model and variant,
1-A-A---5-7B--FFT-CC fine-tuned on a chest X-ray (CXR)
dataset for epoch.
MAIRA-2 [3]: A vision language model for radiology report generation from chest X-rays, used the original model without additional fine-tuning.
HuatuoGPT-Vision-
[5]: A medical vision language model based on Qwen2-7B and LLaVA-v1.5. 55, trained on PubMedVision for medical vision-language tasks. The original model was used without further fine-tuning.
All baselines were evaluated under consistent conditions, aligning with dataset and metric details in Sections 4.2 and 4.3, to ensure fair comparison with CURV for chest X-ray report generation.
B Datasets
We details the datasets and preprocessing steps undertaken to train and evaluate the CURV framework. key contribution of our work is the curation and enhancement of existing datasets to specifically support uncertainty modeling and structured reasoning in radiology report generation.
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Table 5: Dataset Statistics for CURV Training and Evaluation
Statistic
Value
MIMIC-CXR (Original)
Total Reports
227,835
Reports with Findings (REGEX)
65.7%
Reports with Impression (REGEX)
82.3%
Reports with Both Findings & Impression (REGEX)
48.0%
MIMIC-CXR (After LLM-Ennanced Parsing)
Reports with Findings
185,122 (81.25%)
Reports with Impression
193,755 (85.04%)
Reports with Both Findings & Impression
151,048 (66.30%)
Uncertainty Dataset (Puncertainty) )
Samples with Uncertainty Annotations
112,111
Unique Uncertainty Expressions (freq. > 5)
~2,700
TRACE-CXR Dataset (D reason)
Number of Reports in TRACE-CXR
2000
Core Dataset and LLM-Enhanced Parsing Our primary dataset is MIMIC-CR [16, 17, 10], a large-scale collection of 227,835 radiology reports. Initial analysis using REGEX- bEGee- parsing revealed considerable heterogeneity report structure. While "IMPRESSIÓN" sections were present in 82.3% of reports and "FINDINGS" in 65.7%, only 48.0% of reports reliably contained both. This structural variability, coupled with an average raw text length of 634.3 characters (Findings: 335.2 chars, Impression: 175.1 chars), hindered consistent extraction of these crucial sections. To address these limitations and create a more uniform dataset for subsequent annotation and training, employed Large Language Model (LLM) for enhanced parsing of the MIMIC-CXR reports. This step significantly improved the availability of structured data: the proportion of reports with an identifiable "FINDINGS" section increased to 81.25% (185,122 reports), and those with an "IMPRESSION" section rose to 85.04% (193,755 reports). Consequently, the number of reports containing both "FINDINGS" and "IMPRESSION" sections increased substantially from 48.0% to 66.30% 15,044 reports), providing a more robust foundation for our work.
Curating Data for Uncertainty Modeling A core innovation of CURV is its explicit modeling of diagnostic uncertainty. To train this capability (Stage of our pipeline), we created specialized uncertainty- dataset, D Duncertainty- This was achieved by leveraging the Imagenome dataset [33], which provides valuable links between textual phrases radiology reports and corresponding bounding box localizations on CXR images. Building upon these existing spatial annotations from Imagenome, we utilized the Qwen2.5-7B-insttee extraction. This model was specifically prompted to identify and extract phrases directly from the report sentences that Imagenome had linked to specific visual findings. This process allowed Our final incertainty-annottre. identified approximately 2,700 unique uncertainty expressions that occurred with frequency greater than five. The most common expressions included "likely" (131,334 instances), "may" (91,206 instances), and "could" (71,267 instances). For training, these annotations were formatted into structured JSON outputs, each containing the bounding box coordinates, an anatomical label, and the specific uncertainty description related to that finding (e.g., "bbox_2dd. [121, 104, 180, 162], "label": "left lower lung zone", "uncertainty": "Bilateral nodular opacities that most likely represent nipple shadows. ")).
model to perform fine-grained uncertainty
map linguistic expressions of uncertainty to precise image regions. dataset contains 1 112,111 samples. Through this process, we
Generating the TRACE-CXR Dataset (Drewson) for Reasoning Initialization To initialize the basic reasoning capabilities in Stage 2 of the CURV framework, we constructed the TRACE-CXR dataset (also referred as Dresson our methodological descriptions in Section 3). The goal was to create training instances that explicitly model "thinking" process connecting radiological findings to clinical impressions. The exact prompt used to guide the LLM in emulating an experienced radiologist's 's logical, step-by-step reasoning process is shown in Figure 6. Additionally, we validated the generated data using a separate prompt to ensure quality, retaining only those entries with a score higher than 80 out of 100, as shown in 7. Using the LLM-enhanced "FINDINGS" and "IMPRES-
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You highly experienced radiologist tasked with generating detailed reasoning process that explains how specific findings a radiology report lead to a clinical impression. Your goal is to create logical, observed features in medical image to the final diagnosis clinical takeaway. Use precise medical terminology and ensure the reasoning is clear, concise, findings and impression.
explanation that connects the
relevant to the provided
Input:
Findings: [content of findings]
Impression: [content of impression]
Task:
1. Analyze the provided findings and impression.
2. Generate detailed reasoning process that explains how the findings support the impression. Break logical steps, addressing:
the explanation into
What specific abnormalities or features in the findings are most relevant to the impression.
How these features are typically associated with the auspected condition or diagnosis.
Any differential diagnoses or alternative possibilities
considered based on the findings, and why the given impression is the most likely.
If applicable, mention any additional context (e.g., typical clinical presentations, risk factors, or imaging characteristics that supports the reasoning.
3. Enclose your reasoning process in <thinking> tags as follows: <thinking>Your detailed reasoning here</thinking>, Output Format:
<thinking> [Your detailed step by- by--by--sse step reasoning connecting the findings to the impression]
</thinking>
Figure 6: The LLM prompt for generating the 'thinking" section in the TRACE-CXR dataset. This prompt guides the LLM create logical. step-by-step reasoning process that connects the provided findings to the clinical impression, emulating a radiologist's 's thought process.
SION" sections inputs, prompted L.M (grok-3) to generate intermediate "THINKING" section. The LLM was guided by detailed instructions to emulate an experienced radiologist, tasking analyze the provided findings and impression, and then to construct a logical, step-by-step expla- nation of how the findings support the impression, including consideration of differential diagnoses where appropriate. The resulting data for Dremon consists of the CXR image paired with struc- tured report containing three distinct sections: sfindingsDDttailed description of image wetarvaationssffinnnnneeee
based on simpressionnConee summary and recommendations' onsa/ttioon////e /iiiressipssinnn Table 5 summarizes key statisties of the datasets pivotal to CURV's training and evaluation.
BeainkinggReasonnin
and
Table Comprehensive comparison of evaluation scores between Clinicians (Clin), Grok3 (Grok),
and Gemini 2.5 Pro (Gem).
Study
Logical Coherence
Depth of Apalysis
Relevance
Evidence Based
Consistency Grok
Overall
Clin Grok Gem
Clin
Grok Gem
Clin
Grok Gem
Clin
Grok
Gem
Clin
Gem
Grok
Gem
154517467
5.0
551966501
4
4
5
5.0
5.0
<52188295
5
5
5
5.0
<55493024
5
4
4
5
5
5.0
5.0
<51074196
5
5
5
5
4
4
5
5
S
5.0
5.0
657002637
5
5
5
5
5
5
5.0
5.0
550849849
4.2
551215354
5
5.0
5.0
<55228325
5
5
5
5.0
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Clinical Expert Evaluation of TRACE-CXR Dataset To validate the quality of the LLM- generated TRACE-CHE CXR dataset and the reliability of our LLM- based evaluation protocol, we con- ducted formal evaluation with board-certified radiologist. We compared their expert judgments against those of two state-of-the-an LLMs (Grok3 and Gemini 2.5 Pro). For the evaluation, random sample of 10 reports from the TRACE-CXR dataset was scored by all three evaluators using the exact same criteria outlined Appendix (Logical Coherence, Consistency, etc.). The results, summarized in Table 6, reveal strong alignment between the human expert and both LLMs. Specifically, Gemini 2.5 Pro's scores were within one point of the clinician's 's 100% of the time, with a very low Mean Absolute Error (MAE) of 0.380. Similarly, Grok3's s scores were within one point of the expert's s 98.0% of the time, with an MAE of 0.440. This high degree concordance provides strong, facti-- validation for approach. It not only confirms that the LLM LLM-generatee gegeerated reasoning traces in the TRACE-CXR dataset are of high clinical quality but also substantiates that our LLM--ased evaluation metrics serve reliable proxy for expert human judgment.
** Evaluate a chest X-ray (CXR) report 's <thinking> section across five key aspects.
** Instruction::: Assess the <thinking section of chest X-ray report across multiple aspecta. Analyze how well the reasoning links findings to impressions, its depth, clinical relevance,
evidence base, and consistency with other sections.
** Input Format:**
'<findings>': Observations from the CXR image.
'<thinking>': Reasoning or analysis based findings. '<impression>" Summarized conclusions or diagnosis.
*Provided ** Input:**
**Evaluation Criteria:**
Evaluate ALL of the following aspects independently: 1. **Logical Coherence:**
-*oor 5:** Clear, logical flow, seamlessly connecting findings to impressions without doubt.
**Score 0:** Incoherent illogical; reasoning is fragmented fails to connect findings to impressions.
2. **Depth of Analysia:**
**Score 5:** Deep analysis with comprehensive explanations, limitations.
including alternatives
**Score 0:** Superficial or absent analysis; merely restates findings without insight.
3. ** *Reeevance::**
**orr 5:*. Highly relevant, focusing key clinical findings without extraneous content.
**Score 0:** Irrelevant or off-topic reasoning, ignoring clinical context.
4. ** Evidence basiceebbasee based:**
**ooore 5:** Strongly evidence based, tied to medical knoxledge and practices. **Score 0:** Lacking evidence; speculative or contrary to medical standards.
5. **Consistenyy
**Score 5:** Fully consistent
'<findings>', '<thinking>', **Score 0:** Grossly inconsistent with contradictions undermining trustworthiness.
and <<impression>'.
*+Output Format:** Return evaluation JSON object per the provided schema.
Figure 7: The LLM-based evaluation prompt for the 'Thinking" section. This prompt instructs evaluator LLM to score the generated reasoning on five criteria- Loseeria-Lggicaa Coherence, Depth of Analysis, Relevance, Evidence-Based Nature, and Consistency-and and return the assessment in structured JSON format.
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**Task Comprehensively evaluate uncertainty expressions in chest X-ray (CXR) report, comparing generated report with ground truth report.
*IInsructionn You are tasked with rigorously assessing how well the generated report expresses uncertainty compared to the ground truth report. Focus on two key aspects:
1. Structural Uncertainty:** Hedging or ambiguity about specific anatomical regions or findings (typically in findings sections) Example: "a nodule may represent benign lesion malignancy" This appears in descriptions of specific observations overall diagnostic
2. Semantic Uncertainty:** Ambiguity synthesis (typically in impression sections)
Example: "findings are nonspecific and could be consistent with infection" This appears the overall asaessment/cocclssii First determine which parts of each report represent findings vs. impression sections, even if they aren't explicitly labeled. Then compare the uncertainty expressions between generated and ground truth reports.
**Evaluation Criteria:**
1. **Structural Uncertainty:*
**Score 5:** Generated report contains uncertainty expressions
about specific findings highly similar to ground truth in both content and atrength
**Score 3:** Somewhat similar with noticeable differences **Score 0:** No structural uncertainty or completely dissimilar to ground truth
2. ** Semantic Uncertainty:**
**ore 5:** Generated report contains holistic diagnostic uncertainty expressions highly similar to ground truth **Score 3:** Somewhat similar with noticeable differences
**Score 0:** No semantic uncertainty or completely dissimilar to ground truth
**Output Format Return your evaluation as JSON object with two main sections (structura__mcrrtiinyy and somantic_uncertaint each containing:
score (0-5)
explanation (1-2
justifying the score)
criples_comparison (comparison uncertainty triples subject, uncertainty term, interpretation)
uncertainty.streegthh (comparison of strength of uncertainty terms) cateettial.pproopiattnesss (assessment of whether uncertainty is expressed in appropriate contexts)
Also include an overall_score section with the same fields (calculate score as average of the two aspects). **Provided Input:**
Generated Report:
{generated_text) Ground Truth Report: fground.truth.text]
Figure 8: The LLM-based evaluation prompt for uncertainty expression. This prompt directs the evaluator LLM assess and score the generated report's 's handling of both structural dinding-specific) and semantic (diagnostic) uncertainty in comparison to the ground truth report.
C Evaluation Metric
To comprehensively assess the performance of CURV and baseline methods, we employ suite of evaluation metries targeting clinical accuracy, the quality of uncertainty expression, and the coherence of the generated reasoning pathways.
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Clinical Accuracy and NLP Metries For evaluating the factual correctness and fluency of the generated "Findings" and "Impression" sections, we utilize standard Natural Language Processing (NIP) metries, including BLEU (1-4) [24], ROUGE-I. [19], METEOR [2]. Beyond these, we measure clinical accuracy using Fl-scores based on medical entity extraction. Specifically, we employ CheXbert [30] and Radgraph [13] to calculate the fl score of extract entities.
LLM-based Evaluation To evaluate the "Thinking" section generated by CURV, which is not present in standard reports and thus not amenable traditional reférence-based metrics, we leverage an LLM-based evaluation protocol. We prompt capable LLM (Qwen3-32B [36]) to assess the quality of the "Thinking" section based on five crucial aspects: Logical Coherence, Depth of Analysis, Relevance, Syidence-Basee Nature, and Consistency with the "Findings" and "Impression" sections. And we also evaluate the expression of uncertainty, particularly distinguishing between structural and semantic uncertainty, also requires more sophisticated approach than standard metrics. We employ another LLM-basee evaluation protocol for this purpose. The LLM is tasked with comparing the uncertainty expressions two key aspects: Structural Uncertainty and Semantic Uncertainty. The prompt for the LLM-based evaluation is shown in Figure 7 and 8.
generated report against those ground truth report, focusing on
D Full Case Study
This appendix provides the complete details to case study shown in Figure
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CAL
(a) Frontal and Lateral Images
Ground Truth: Findings and Impression
FINDINGS: There persistent bibasilar opacitics compatible with layering effusions with adjacent atclectasis and perhaps mild pulmonary edema. The cardiomediastina silhouette is stable. Atherosclerotic calcifications seen at the aortic arch. No acute osseous abnormalities.
IMPRESSION: Persistent moderate bilateral pleural effusions with adjacent atelectasis. Superimposed infection would be difficult to exclude.
Model Output: Findings, Thinking and Impression
He has bibasal atclectasis, likely related poor mobilization of lung volumes during inspiration. There is likely bilateral pleural effusions of moderate extent. However, there are no pulmonary parenchymal opacities at the apices; the pleural surface appears clear. Given patient's 's history of biventricular HIF, she is supine on the exam, resulting in her moderate and greater than moderate apical lung collapse. No evidence of new cardiomegaly acute pulmonary vascular congestion.
FINDINGS:
THINKING: Since the imaging shows bibasal atclectasis, a clear pleural surface, no new signs of car- diomegaly or pulmonary vascular congestion, the history of biventricular heart failure likely plays a significant role. The patient is described as supine on the exam, which contributes to the bibasal atelectasis but does not suggest acute worsening of the pulmonary condition. The presence of moderate pleural effusions is noted, and their lack of apical opacities could indicate underlying pulmonary parenchymal disease such pncumonia. Therefore, the primary concern is likely the presence and management patient's cardiopulmonary status.
atelectasis and effusions, given the
IMPRESSION: The patient is supine and therefore has bibasal atelectasis and moderate pleural effusions. There evidence of pulmonary parenchymal opacities the apices, and the pleural surface is clear. The presence moderate pleural effusions suggests that the lung volumes were better mobilized during expiration. With patient's 's history of biventricular HP, the concern is likely the effects of the supine positioning on lung volumes and the management of any underlying atelectasis and effusions, though no acute pulmonary or cardiac changes are seen.
Figure 9:
A full, detailed view of the case study. This figure presents the frontal and lateral CXR images, the ground truth report, and the complete, three-part report generated by CURV, showcasing the model's ability to produce clinically coherent and well-structured output.
E Licenses for External Assets
This appendix details the licenses for publicly available datasets and pre-trained models utilized in this work.
Datasets
-MIMICCXR (version 2.1.0):
- Source: Johnson et al. [17].
URL: attps://physiioe http://iiiintttte/oooooooiooeoee coonttet//imicccxr/2222
-License: PhysioNet Credentialed Health Data License 1.5.0. Access requires creden- tialing and signing a data use agreement.
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Chest ImaGenome Dataset (version 1.0.0):
Source: Wu et al. [33].
URL: https: wttp://ettsintt...mmmm
License: Derived from MIMIC-CXR, subject to the PhysioNet Credentialed Health Data License 1.5.0.
Pre-trained Models and Baselines
Qwen-2.5-
--en-225--LL-BB VL-3B (Backbone for CURV):
Source: Yang et al. [36].
URL: https: /ete:://ittttt com comm/QuenLM//wen3
License: Apache 2.0 License
LLaVA-1.5-7B:
Sourre: Liu et al. [21].
-URL: https:://///m http//ggttt.....// homm/haotian-liu/llaa
Liccene: Apache 2.0 License.
MAARA-2:
Source: Bannur al. [3].
-URL
Licenne: MICROSOFT RESEARCH LICENSE TERMS
HnatuoGPT-Vision-PB::
Source: Chen et al. [5].
URL:
HuatuoGPT-Vis Vissoon-E
License: Apache 2.0 License
RadGraph:
Source: Jain et al. [13].
URL: https: https://hugg
License: MIT License.
CheXbert:
Source: Smit et al. [30].
URL: http:///ggggggooooooo/oooaoaat//
License: MIT License.
28


Reviews:
[{"review_id": "uTF9cwFsrp", "reviewer": "Reviewer_ZQB5", "summary": "This paper proposes CURV, a novel vision-language model (VLM) framework for radiology report generation from chest X-ray (CXR) images. CURV targets two critical limitations of existing VLMs in the clinical setting: (1) a lack of uncertainty awareness, and (2) the absence of explicit reasoning that connects image findings to diagnostic impressions. The CURV framework introduces three main components: First, uncertainty modeling – captures both structural (finding-level) and semantic (impression-level) diagnostic uncertainty; second, structured reasoning generation – includes an intermediate $<thinking>$ section to explain the reasoning from findings to impressions; thrid, reinforcement learning (RL) – uses Group Relative Policy Optimization (GRPO) with a multi-dimensional reward function to improve coherence and clinical quality. Experimental results on the MIMIC-CXR dataset demonstrate that CURV outperforms several baselines (e.g., LLaVA-1.5-7B, MAIRA-2) in both standard text generation metrics (e.g., BLEU, ROUGE) and clinical accuracy scores (e.g., CheXbert, RadGraph F1).", "questions": "Questions\n1.\tHow does RL refine the reasoning capability beyond supervised learning? Could the author provide more clarification on how the dataset is used in the supervised finetuning and RL finetuning? If the dataset already provided the thinking chain, would this make the RL a fully supervised training? \n2.\tCould the author provide more details around the training hyper-parameters and training settings? \n3.\tAny plans for expert evaluation? LLM-based evaluations are helpful, but would the authors consider radiologist review in future iterations?", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths\n- Significant clinical motivation: The paper clearly identifies two critical missing components in current VLMs—structured uncertainty and transparent reasoning—which are essential for real-world trust and adoption in medicine.\n- New dataset contribution: The authors introduce TRACE-CXR, a curated dataset of 2,000 CXR reports augmented with explicit LLM-generated reasoning paths, which is valuable for future work on clinical explainability.\n- Strong empirical results: CURV achieves impressive improvements over both base and fine-tuned large models, despite using a relatively small 3B backbone.\n\nWeaknesses\n- Clarity around the RL phase is lacking: If the reasoning path is already given during supervised pretraining, what exactly is being learned or discovered during RL? The TRACE-CXR contains full thinking process. Would this make the training process simply a fully supervised finetuning? How exactly the dataset and which dataset is for the RL finetuning?\n- Possible contradiction in supervision vs. self-discovery: The design implies that the model learns to “reason” from examples rather than discovering reasoning paths on its own, which slightly undermines the RL-based reasoning enhancement claim.\n- The training hyper-parameters and training settings needs more clarification: The model is finetuned using four A100 GPUs, which in my experience is difficult to fit in a 3B model. I am wondering if LoRA is more realistic than the full model finetuning.", "was_revised": true, "final_justification": "During the rebuttal period, the authors resolved all of my concerns regarding SFT, RL, and computation resources. Therefore, I will increase my scores to borderline accept.", "rebuttal": "We thank Reviewer ZQB5 for their thoughtful review and for recognizing our work's \"significant clinical motivation\" and \"strong empirical results\". Based on the valuable comments from all reviewers, we have conducted **three significant new experiments and prepared detailed clarifications** to strengthen our work.\n\n**Summary of Major Updates**:\n\n- **New SOTA Comparison**: We performed a new evaluation against Gemini 2.5 Pro. The results confirm CURV's superior performance, with our model achieving a RadGraph F1 of 25.65 vs. Gemini's 7.71.\n\n- **New Clinical Expert Validation**: To validate our LLM-based evaluation, we conducted a new study with a board-certified radiologist. Their scores showed strong alignment with our LLM evaluators (e.g., MAE of 0.38 vs. Gemini 2.5 Pro), confirming our protocol's reliability.\n\n- **New Out-of-Distribution (OOD) Evaluation**: To test generalization, we ran a new evaluation on the IU X-ray dataset. CURV maintains its strong performance and outperforms all baselines, demonstrating that its learned capabilities are robust and not overfit to MIMIC-CXR.\n\nWe address the reviewer's specific points below.\n\n# Weakness 1 & Weakness 2 & Question 1\nWe sincerely apologize for the lack of clarity on this point. We have two different datasets for the SFT and RL stages, where the reasoning path is only used for the initial SFT. The details of the SFT and RL processes are further clarified as follows:\n\n* **Stage 2 (SFT Reasoning Initialization):** In this stage, the model is fine-tuned based on the complete TRACE-CXR dataset, which includes `<findings>`, `<thinking>`, and `<impression>` sections. This supervised learning step aims to teach the model the basic structure, providing it with a foundational template for what a \"reasoning\" section looks like.\n* **Stage 3 (RL Enhancement):** For the RL phase, the model is provided with only the `<findings>` and `<impression>` sections from the MIMIC-CXR dataset, while the ground-truth `<thinking>` text is not used. The model explores how to discover a better reasoning path guided by the reward signals.\n\nOur SFT-then-RL methodology aligns well with the recent studies indicating that relying solely on SFT can cause models to memorize reasoning patterns and fail to generalize [1]. Our RL stage avoids this imitation trap. More importantly, by withholding the ground-truth `<thinking>` section, our approach prevents the \"pseudo reasoning\" problem where models rigidly imitate suboptimal paths from the SFT data [2]. This process forces the model to move beyond simple mimicry and learn to generate functionally coherent reasoning.\n\n[1] Chu, Tianzhe, et al. \"SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training.\" *Forty-second International Conference on Machine Learning.*\n\n[2] Chen, Hardy, et al. \"Sft or rl? an early investigation into training r1-like reasoning large vision-language models.\" *arXiv preprint arXiv:2504.11468* (2025).\n\n---\n\n# Weakness 3 & Question 2\n\nWe thank the reviewer for this suggestion to improve reproducibility. While full-parameter tuning of a 3B model is indeed memory-intensive, it is entirely feasible on a 4x A100-80G node by leveraging a combination of standard, state-of-the-art memory optimization techniques.\n\nOur training approach was as follows:\n\nFor Stages 1 & 2 (SFT), to maximize efficiency while adapting the model to the new data formats, we kept the vision encoder frozen and fine-tuned only the multimodal projection layer and the LLM backbone.\n\nFor Stage 3 (RL), where the goal was to refine the entire model's behavior based on global reward signals, we performed full-parameter fine-tuning of the model checkpoint from Stage 2. To make this feasible, we employed several key techniques:\n* **Mixed-Precision Training**: We used bfloat16 precision, which halves the memory requirement for model parameters and gradients compared to standard FP32, with minimal impact on training stability.\n* **Optimizer State and Parameter Sharding**: The primary memory bottleneck is often not the model weights, but the optimizer states (e.g., Adam's momentum and variance buffers, which can be 6x the size of the parameters in FP32) and activations. A naive calculation for a 3B model in mixed precision requires approximately 48 GB of VRAM (6 GB for fp16 parameters + 6 GB for gradients + 36 GB for fp32 optimizer states and weight copies). We utilized DeepSpeed with ZeRO Stage 3 optimization. This powerful technique partitions not only the optimizer states but also the gradients and the model parameters themselves across all 4 GPUs. This means each GPU only holds a fraction (~1/4) of the total training-related tensors. This reduces the per-GPU memory requirement from ~48 GB to a much more manageable ~12 GB, which fits comfortably within the 80 GB VRAM of each A100 GPU.\n* **Activation Checkpointing**: The second major memory bottleneck comes from storing activations (the intermediate outputs of each layer), the size of which scales with batch_size * context_length * hidden_size * num_layers. To manage this context-dependent memory usage, we employed activation checkpointing (also known as gradient checkpointing). Instead of storing all activations in memory during the forward pass, this technique discards them and recomputes them during the backward pass where needed. This strategy trades a modest amount of additional compute time for a very significant reduction in activation memory, ensuring that even with a long context length, the memory footprint remains manageable.\n\nThe combination of bfloat16, DeepSpeed ZeRO-3, and activation checkpointing is a standard and robust strategy for full-parameter fine-tuning of multi-billion parameter models, and it allows a 3B model to train comfortably within the 80GB of memory available on each A100 GPU. We will add these specific details about our use of bfloat16 precision, DeepSpeed ZeRO-3, and activation checkpointing to Appendix A in the final version to ensure full reproducibility.\n\n---\n\n# Question 3\n\nThis is an excellent point. We completely agree that a formal evaluation by radiologists is the gold standard and a critical next step for this research. While our automated and LLM-based metrics serve as valuable proxies for development, validating the true clinical utility and safety of CURV-generated reports requires human expert review. This was beyond the scope of this work but is a top priority for our future research agenda. We will add the Limitations section of our paper to explicitly state this and discuss our plans to conduct such an evaluation."}, {"review_id": "a4vlDJWWfQ", "reviewer": "Reviewer_e8n6", "summary": "The paper introduces CURV, a new framework designed to enhance radiology report generation from chest X-ray images by explicitly modeling diagnostic uncertainty and incorporating structured clinical reasoning into the generation process. Traditional vision-language models (VLMs) often fail to capture the nuanced uncertainty present in radiology or provide transparent reasoning that connects image findings to clinical impressions. CURV explicitly teaches the model to recognize and express both structural uncertainty (linked to specific visual findings) and semantic uncertainty (reflected in overall clinical impressions). This is achieved through supervised fine-tuning using a specially annotated uncertainty dataset.", "questions": "1. Can the authors provide empirical evidence (qualitative or quantitative) showing that advanced foundation models like GPT-4.5, Gemini, or Med-PaLM also fail to handle these aspects effectively?\n\n2. Could the authors elaborate in the main text how uncertainty expressions were extracted, verified, and aligned with image regions?\n\n3. Are these reasoning traces written or validated by clinicians, or purely generated by LLMs? What measures were taken to ensure their clinical reliability and logical correctness?\n\n4. Can the authors provide examples or analysis comparing LLM evaluations to expert clinician judgments?", "limitations": "See weakness.", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**Strengths:**\n\n1. **Important Problem**: The paper tackles a highly relevant and underexplored challenge in medical vision-language modeling—generating radiology reports that are not only clinically accurate but also uncertainty-aware and reasoning-transparent. This focus is crucial for increasing trust and adoption of AI in clinical settings.\n\n2. **Clear Writing**: The manuscript is clearly written and well-organized. The motivation, methodology, and experimental results are generally easy to follow, and the technical components are presented with sufficient clarity for readers to grasp the overall pipeline.\n\n---\n\n**Weaknesses:**\n\n1. **Lack of Evaluation Against Frontier Models**: While the paper positions itself as addressing deficiencies in existing VLMs, it does not clearly establish whether state-of-the-art general-purpose models (e.g., GPT-4.5, Gemini, etc.) also suffer from the same issues. No direct evidence or comparative evaluation with such advanced models is provided, which weakens the empirical justification for the claimed gap.\n\n2. **Insufficient Detail on Dataset Construction**: A critical part of the proposed framework is the creation of datasets annotated with uncertainty and reasoning information for supervised fine-tuning. However, the main text gives limited explanation of how these datasets (especially TRACE-CXR and the uncertainty-annotated set) were constructed. These are only briefly described in the appendix, which significantly reduces the reproducibility and credibility of this key contribution.\n\n3. **Ambiguity in Reasoning Component**: The paper does not make it sufficiently clear whether the reasoning component is trained against reliable, human-authored reasoning ground truth or only LLM-generated pseudo-labels. This raises questions about the quality and validity of the reasoning supervision signal.\n\n4. **Limited Baseline Comparisons**: Experimental comparisons are conducted primarily against a few relatively lightweight medical or vision-language models (e.g., MAIRA-2, HuatuoGPT-Vision-7B). The paper does not evaluate CURV against state-of-the-art generalist models that may already possess strong reasoning or uncertainty modeling capabilities. This limits the ability to assess the real advantage of CURV.", "was_revised": true, "final_justification": "I appreciate the authors' rebuttal, which has addressed most of my concerns. At this stage, I intend to maintain my original score and would be interested in seeing the feedback from the other reviewers.", "rebuttal": "We sincerely thank Reviewer for their constructive feedback. Based on the valuable comments from all reviewers, we have conducted **three significant new experiments and prepared detailed clarifications** to strengthen our work.\n\n**Summary of Major Updates**:\n\n- **New SOTA Comparison**: We performed a new evaluation against Gemini 2.5 Pro. The results confirm CURV's superior performance, with our model achieving a RadGraph F1 of 25.65 vs. Gemini's 7.71.\n\n- **New Clinical Expert Validation**: To validate our LLM-based evaluation, we conducted a new study with a board-certified radiologist. Their scores showed strong alignment with our LLM evaluators (e.g., MAE of 0.38 vs. Gemini 2.5 Pro), confirming our protocol's reliability.\n\n- **New Out-of-Distribution (OOD) Evaluation**: To test generalization, we ran a new evaluation on the IU X-ray dataset. CURV maintains its strong performance and outperforms all baselines, demonstrating that its learned capabilities are robust and not overfit to MIMIC-CXR.\n\nWe address the reviewer's specific points below.\n\n# Weakness 1 & Weakness 4 & Question 1\n\nWe sincerely thank the reviewer for this excellent suggestion. We agree that comparing CURV to frontier foundation models is a crucial test of its capabilities. As suggested, we conducted a new set of experiments evaluating a state-of-the-art generalist model, Gemini 2.5 Pro, on our test set. The results, summarized in the table below, confirm our central hypothesis: while powerful generalist models perform strongly on average, they lack the specialized mechanisms for generating clinically nuanced reports.\n\n*Table 1: Comparison between Gemini 2.5 pro and CURV on generation metrics*\n| **Model** | **B-1** | **B-2** | **B-3** | **B-4** | **METEOR** | **R-L** | **gritlm** |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| Gemini 2.5 pro | 12.54 | 5.20 | 2.25 | 1.05 | 21.19 | 15.01 | 40.41 |\n| CURV | **29.23** | **18.76** | **12.08** | **6.86** | **38.30** | **39.08** | **54.89** |\n\n*Table 2: Comparison between Gemini 2.5 pro and CURV on clinical accuracy metrics*\n| Model | f1chexbert | | | radgraph | | | |\n|:---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n| | Acc. | Macro F1 | Micro F1 | Ent. F1 | F1 | Prec. | Rec. |\n| Gemini 2.5 pro | 74.35 | 5.35 | 48.45 | 13.09 | 7.71 | 26.76 | 11.56 |\n| CURV | **91.56** | **5.86** | **74.36** | **36.99** | **25.65** | **48.80** | **35.28** |\n\nAs the results clearly show, CURV significantly outperforms Gemini 2.5 Pro across all metrics, especially in clinical accuracy (e.g., RadGraph F1 of 25.65 vs. 7.71). This new evidence demonstrates that generating high-quality, trustworthy radiology reports is a specialized task that benefits immensely from CURV's targeted framework. \n\nWe will add this new baseline comparison with Gemini 2.5 Pro, along with this analysis, to the final version of our paper to further strengthen our claims and provide a more comprehensive evaluation.\n\n# Weakness 2 & Question 2\nWe apologize that placing these details in the appendix reduced the paper's self-contained clarity. To address this, we will move the essential details of our dataset curation process from Appendix B into the main paper. This will improve the paper's clarity and reproducibility. For the uncertainty expression related details:\n1.  **Alignment with Image Regions:** This process began by using the Imagenome dataset [1]. This dataset provides pre-existing links between specific textual phrases in MIMIC-CXR radiology reports and their corresponding bounding box localizations on chest X-ray images. This step established the alignment between a finding described in the text and a region on the image.\n2.  **Extraction of Uncertainty Expressions:** With the text-to-image alignment established, we then used the Qwen2.5-7B-instruct model to perform an extraction of uncertainty language. The model was prompted to identify and pull out specific uncertainty-expressing phrases (e.g., \"likely\") directly from the report sentences that Imagenome had already linked to visual findings. This step maps linguistic uncertainty to precise anatomical regions. The prompt we used to extract uncertainty expression is: *\"You are a medical text analyzer that extracts uncertainty expressions. Extract any words or phrases expressing uncertainty from the following medical text. Return ONLY the uncertainty expressions, one per line. If none exist, return NONE.\"*\n\n[1] J. Wu, et al. Chest imagenome dataset, 2021.\n\n# Weakness 3 & Questions 3 & 4\nWe thank the reviewer for their insightful comment regarding the validation of our LLM-generated TRACE-CXR dataset. The reasoning traces are purely generated by LLMs. On one hand, the practice of generating data to achieve better model learning is becoming more common in recent research works. Yet, ensuring the quality of the generated data and how to use it properly is also vital. This valuable feedback prompted us to conduct a new, formal evaluation with a board-certified radiologist to rigorously assess our methodology. We elaborate our arguments and present our new experimental results as follows.\n\n**1. On the Validity of LLM-Generated Training Data**\n* **Precedent and Community Standard:** Using LLMs to generate training data is now standard practice, underpinning widely-used datasets like Alpaca [1] and WizardLM [2]. The seminal Self-Instruct [3] paper established that a model fine-tuned on purely synthetically generated instructions could match models trained on expensive, human-annotated data, demonstrating that the utility of a large synthetic dataset can overcome point-wise imperfections. Furthermore, recent studies like the Vision-R1 (2025) model [4] use \"automated reasoning dataset construction\" without human validation, confirming this is a state-of-the-art approach.\n* **RL as a Corrective Mechanism Grounded in Human Data:** Crucially, our SFT-then-RL pipeline is designed to mitigate risks from synthetic data. The LLM-generated TRACE-CXR dataset is only used during the SFT \"reasoning initialization\" stage (Stage 2). Its purpose is to teach the model the basic tripartite structure and provide a policy suitable for exploration. In the subsequent RL phase (Stage 3), the model does not make use of any ground-truth `<thinking>` text. Instead, it is rewarded for generating a coherent `<thinking>` section that connects well to the ground-truth, human-authored `<findings>` and `<impression>` sections from the MIMIC-CXR reports. This design means the model is not learning to simply mimic potentially flawed LLM text. Instead, the RL process forces the model to discover a good reasoning path, guided only by a coherence reward tied to genuine clinical endpoints. The RL stage thereby acts as an implicit, task-based validation and refinement mechanism, reducing the dependency on the quality of the initial SFT data.\n\n[1] Taori, et al. \"Stanford alpaca: An instruction-following llama model.\" 30 Jun. 2023. \n\n[2] Xu, Can, et al. \"WizardLM: Empowering large pre-trained language models to follow complex instructions.\" ICLR, 2024. \n\n[3] Wang, et al. \"Self-Instruct: Aligning Language Models with Self-Generated Instructions.\" ACL, 2023. \n\n[4] Huang, et al. \"Vision-r1: Incentivizing reasoning capability in multimodal large language models.\" *arXiv preprint arXiv:2503.06749* (2025).\n\n**2. New Clinical Evaluation: Comparing LLM Evaluators with an Expert Radiologist**\n\n*Table 3: Comprehensive comparison of evaluation scores between Clinicians (Clin), Grok3 (Grok), and Gemini 2.5 Pro (Gem).*\n\n| **Study ID** | **Logical Coherence** | | | **Depth of Analysis** | | | **Relevance** | | | **Evidence Based** | | | **Consistency** | | |\n|:---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n| | **Clin** | **Grok** | **Gem** | **Clin** | **Grok** | **Gem** | **Clin** | **Grok** | **Gem** | **Clin** | **Grok** | **Gem** | **Clin** | **Grok** | **Gem** |\n| **s54517467** | 4 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 4 | 5 | 5 | 4 | 5 | 5 |\n| **s51966501** | 4 | 5 | 5 | 5 | 5 | 5 | 4 | 5 | 5 | 4 | 5 | 5 | 4 | 5 | 5 |\n| **s52188295** | 5 | 5 | 5 | 5 | 5 | 5 | 4 | 5 | 5 | 4 | 5 | 5 | 5 | 5 | 5 |\n| **s55493024** | 4 | 5 | 5 | 5 | 5 | 5 | 4 | 5 | 5 | 4 | 5 | 5 | 5 | 5 | 5 |\n| **s51074196** | 4 | 5 | 5 | 5 | 5 | 5 | 4 | 5 | 5 | 4 | 5 | 5 | 5 | 5 | 5 |\n| **s56689492** | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 |\n| **s57002637** | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 |\n| **s50849849** | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 4 | 5 | 5 | 4 | 5 | 5 | 3 | 4 |\n| **s51215354** | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 |\n| **s52528325** | 4 | 5 | 5 | 5 | 5 | 5 | 4 | 5 | 5 | 4 | 5 | 5 | 5 | 5 | 5 |\n\nWe agree that direct empirical validation is the strongest form of evidence. To that end, we conducted a new formal evaluation with a board-certified radiologist to directly compare their judgments against our LLM-based evaluation protocol. To be maximally thorough, we tested two different SOTA LLMs (Grok3 and Gemini 2.5 Pro).\n* **Methodology**: A board-certified radiologist, Grok3, and Gemini 2.5 Pro scored the same set of 10 reports randomly selected in the TRACE-CXR dataset, using the exact same criteria from our paper (Logical Coherence, Consistency, etc.).\n* **Results**: As shown in Table 3, both LLMs show strong alignment with the human expert, confirming that our evaluation protocol is reliable. Both LLMs demonstrated a high degree of alignment. For gemini 2.5 pro, scores were within 1 point of the clinician's 100% of the time and with a very low Mean Absolute Error (MAE) of 0.380 compared to Clinicians. For Grok 3, scores were within 1 point of the clinician's 98.0% of the time, with a low MAE of 0.440.\n\nIn summary, this new clinical evaluation, combined with our framework's inherent methodological safeguards, provides strong, multi-faceted validation for our approach and confirms that our LLM-based metrics are a reliable proxy for expert judgment."}, {"review_id": "v1lptfsKXO", "reviewer": "Reviewer_Nede", "summary": "This paper introduces CURV, a framework for generating radiology reports that incorporates uncertainty awareness and explicit reasoning capabilities. The approach consists of three main components: (1) uncertainty modeling through fine-tuning with annotated data to express appropriate diagnostic confidence, (2) structured reasoning that generates intermediate \"thinking\" steps between findings and impressions, and (3) a coherence reward to ensure logical consistency. The authors create TRACE-CXR, a dataset with 2,000 reports augmented with LLM-generated reasoning sections, and evaluate on MIMIC-CXR data, showing improvements over baselines.", "questions": "1. How do you validate that the LLM-generated reasoning in TRACE-CXR is clinically accurate and appropriate? Were any medical experts involved in validation?\n2. Why use simple fine-tuning for uncertainty rather than more principled approaches like Bayesian neural networks or ensemble methods?\n3. Can you provide examples where the model's uncertainty expression changes appropriately based on image quality or ambiguity?\n4. How does the coherence reward ensure actual logical reasoning rather than just entity matching?", "limitations": "The authors acknowledge some limitations but understate critical issues: the heavy dependence on LLM-generated data, the narrow disease scope, and the lack of principled uncertainty quantification.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths: \n1. Addresses the critical need for uncertainty expression and transparent reasoning in medical AI, which is essential for clinical adoption.\n2. TRACE-CXR provides explicit reasoning pathways that could be valuable for future research.\n\nWeaknesses: \n1. The core technical contributions are incremental. Uncertainty modeling uses standard fine-tuning with annotated data, reasoning uses supervised learning on LLM-generated data, and the RL component uses standard GRPO with a straightforward reward design.\n2. Both the reasoning sections in TRACE-CXR and uncertainty annotations are generated by LLMs (grok-3, Qwen2.5-7B). This raises concerns about:\n- Quality and accuracy of the generated reasoning\n- Potential propagation of LLM biases/errors\n- Limited validation of the generated content (only score-based filtering)\n3. The approach to uncertainty is simplistic - just fine-tuning on annotated phrases like \"likely\" or \"possible\". There's no principled uncertainty quantification or calibration, making the distinction between \"structural\" and \"semantic\" uncertainty seem artificial.", "was_revised": true, "final_justification": "Thank you for the rebuttal. I have ajusted my rating.", "rebuttal": "We thank Reviewer Nede for their thoughtful feedback, which helps us clarify and strengthen our paper. Based on the valuable comments from all reviewers, we have conducted **three significant new experiments and prepared detailed clarifications** to strengthen our work.\n\n**Summary of Major Updates**:\n\n- **New SOTA Comparison**: We performed a new evaluation against Gemini 2.5 Pro. The results confirm CURV's superior performance, with our model achieving a RadGraph F1 of 25.65 vs. Gemini's 7.71.\n\n- **New Clinical Expert Validation**: To validate our LLM-based evaluation, we conducted a new study with a board-certified radiologist. Their scores showed strong alignment with our LLM evaluators (e.g., MAE of 0.38 vs. Gemini 2.5 Pro), confirming our protocol's reliability.\n\n- **New Out-of-Distribution (OOD) Evaluation**: To test generalization, we ran a new evaluation on the IU X-ray dataset. CURV maintains its strong performance and outperforms all baselines, demonstrating that its learned capabilities are robust and not overfit to MIMIC-CXR.\n\nWe address the reviewer's specific points below.\n\n# Weakness 1\nWe thank the reviewer for this perspective. We agree that the individual components (SFT, RL) are established. However, we respectfully argue that our primary novelty lies not in the components themselves, but in their unique integration into a cohesive, three-stage framework to solve a specific clinical challenge. The core idea—that a model can be taught to first express fine-grained uncertainty, then learn a reasoning structure, and finally be rewarded for the coherence of that reasoning—is a new, structured approach in this domain. As our results in Tables 1 and 2 show, the full CURV model significantly outperforms the intermediate $CURV\\_{stage1/2}$, demonstrating the strong synergistic effect and necessity of our full pipeline.\n\n-----\n# W 2 & Question 1\nWe thank the reviewer for raising this point about data quality. On one hand, the practice of generating data to achieve better model learning is becoming more common in recent research works. Yet, ensuring the quality of the generated data and how to use it properly is also vital. This valuable feedback prompted us to conduct a new, formal evaluation with a board-certified radiologist to rigorously assess our methodology. We elaborate our arguments and present our new experimental results as follows.\n\n**1. On the Validity of LLM-Generated Training Data**\n\nFirst, our use of LLMs to generate training data for SFT aligns with established, state-of-the-art practice (e.g., Alpaca[1], WizardLM[2], Self-Instruct[3], Vision-R1[4]). Second, and more importantly, our pipeline has a built-in corrective mechanism. The LLM-generated TRACE-CXR data is only used for SFT to initialize the model with a basic reasoning structure. The subsequent RL stage does not use the synthetic thinking text. Instead, the model is rewarded for discovering a coherent path that connects the human-authored, ground-truth findings and impression from MIMIC-CXR. This design forces the model to move beyond mimicking synthetic data and grounds its learning in genuine clinical endpoints.\n\n[1] Taori, Rohan, et al. \"Stanford alpaca: An instruction-following llama model.\" 30 Jun. 2023. \n\n[2] Xu, Can, et al. \"WizardLM: Empowering large pre-trained language models to follow complex instructions.\" ICLR, 2024. \n\n[3] Wang, Yizhong, et al. \"Self-Instruct: Aligning Language Models with Self-Generated Instructions.\" ACL, 2023. \n\n[4] Huang, Wenxuan, et al. \"Vision-r1: Incentivizing reasoning capability in multimodal large language models.\" *arXiv preprint arXiv:2503.06749* (2025).\n\n**2. New Clinical Evaluation: Comparing LLM Evaluators with an Expert Radiologist**\n\n*Table: Comparison of evaluation scores between Clinicians, Grok3 , and Gemini 2.5 Pro .*\n\n| Study ID | Clinician Score | Grok-3 Score | Gemini 2.5 Pro Score |\n|:---|:---:|:---:|:---:|\n| s54517467 | 4.4 | 5.0 | 5.0 |\n| s51966501 | 4.2 | 5.0 | 5.0 |\n| s52188295 | 4.6 | 5.0 | 5.0 |\n| s55493024 | 4.4 | 5.0 | 5.0 |\n| s51074196 | 4.4 | 5.0 | 5.0 |\n| s56689492 | 5.0 | 5.0 | 5.0 |\n| s57002637 | 5.0 | 5.0 | 5.0 |\n| s50849849 | 5.0 | 4.2 | 4.8 |\n| s51215354 | 5.0 | 5.0 | 5.0 |\n| s52528325 | 4.4 | 5.0 | 5.0 |\n\n\nWe agree that direct empirical validation is the strongest evidence. To that end, we conducted a new formal evaluation with a board-certified radiologist to directly compare their judgments against our LLM-based evaluation protocol.\n\n- Setup: A board-certified radiologist, Grok3, and Gemini 2.5 Pro scored the same set of 10 reports randomly selected in the TRACE-CXR dataset, using the exact same criteria from our paper (Logical Coherence, Consistency, etc.).\n\n- Results: As shown in the above table, both LLMs show strong alignment with the human expert, confirming that our evaluation protocol is reliable. Both of the LLM demonstrated a high degree of alignment. For gemini 2.5 pro, scores were within 1 point of the clinician’s 100% of the time and with a very low Mean Absolute Error (MAE) of 0.380 compared to Clinicians. For Grok 3, scores were within 1 point of the clinician’s 98.0% of the time, with a low MAE of 0.440.\n\nIn summary, this new clinical evaluation, combined with our framework’s inherent methodological safeguards, provides strong, multi-faceted validation for our approach and confirms that our LLM-based metrics are a reliable proxy for expert judgment.\n\n-----\n# W3 & Q2\nWe thank the reviewer for this insightful comment, which allows us to clarify our approach to uncertainty. \n\n- **Clinical Linguistic Expression vs. Model-Internal Uncertainty**. We aim to model **linguistic uncertainty**—the nuanced language radiologists use to convey diagnostic confidence (e.g., \"likely,\" \"could represent\")—rather than quantifying the model's internal statistical confidence, which is the focus of methods like Bayesian NNs. This is a clinically vital distinction; as demonstrated by [1] that patients whose CT reports used low-certainty language (e.g., \"findings may represent...\") were six times more likely to receive unnecessary antibiotic therapy compared to those with high-certainty reports (e.g., \"findings are diagnostic of...\"). A model that cannot master this distinction is not just stylistically lacking, but clinically unsafe. Given this objective, supervised fine-tuning on clinically annotated phrases is the most direct and appropriate methodology for teaching a model how to communicate these critical nuances in a human-like manner.\n\n- **''Structural'' and  ''Semantic'' Uncertainty**. We respectfully maintain that the distinction between Structural and Semantic uncertainty is clinically meaningful and models the radiologist's reasoning process. This is not an artificial construct but reflects how ambiguity about specific findings propagates to the overall diagnostic conclusion. Structural Uncertainty is tied to specific, localized visual findings. For example, ''a hazy opacity... which could be compatible with pneumonia'' reflects ambiguity about a particular observation. Semantic Uncertainty relates to the overall diagnostic impression, which synthesizes multiple findings. For example,  ''opacification which may reflect pneumonia... although other etiologies... could be considered''. As illustrated in Figure 1 of our paper, uncertainty about a specific structure on the X-ray directly informs the broader semantic uncertainty in the final diagnosis, where differential diagnoses are weighed. This two-level distinction is crucial for generating transparent reports that reflect the real-world diagnostic process.\n\nWe recognize that our manuscript did not make this crucial distinction between modeling linguistic expression and quantifying model uncertainty sufficiently clear. In the final version, we will revise the Introduction (Section 1) and our Methods (Section 3.2) to explicitly define our goal of modeling clinical linguistic uncertainty, supported by citations, and better motivate why our approach is appropriately tailored to this specific, high-stakes problem.\n\n[1] Almeida, Renata R et al. “Impact of Radiology Report Wording on Care of Patients With Acute Epiploic Appendagitis.” AJR. American journal of roentgenology vol. 212,6 (2019).\n\n-----\n# Q3\nThis is an excellent question that helps us clarify the scope of our work. Our framework is designed to model intrinsic diagnostic ambiguity, not extrinsic ambiguity from factors like poor image quality. A classic example is a small pulmonary nodule on a perfectly clear image; the image is unambiguous, but the finding is diagnostically uncertain. CURV learns to generate appropriate linguistic hedges by correlating visual findings with the uncertainty language present in the ground-truth reports. For instance, in our case study (Fig. 9), the model generates \"likely bilateral pleural effusions\" because the visual patterns for atelectasis often carry this type of diagnostic uncertainty, not because the image is of poor quality. We agree that modeling uncertainty from image quality is a valuable and complementary research direction. We will explicitly define our scope in the final manuscript and add this as an area for future work.\n\n-----\n# Q4\nWhile the reward is calculated using entity overlap, it incentivizes the model to generate a fluent, logical narrative that explains the connections between those entities. The model is not simply rewarded for listing entities. It must generate a full Thinking section in natural language. The $R_{coh}$ reward guides the model to select and connect concepts from the  Findings to those in the Impression. To maximize this reward, the model learns to generate explanatory text that justifies these connections, which goes beyond simple entity matching and promotes logical flow. The qualitative improvements shown in the LLM-based evaluation (Figure 3a) support this claim, as scores for Logical Coherence and Depth of Analysis improve significantly during RL training."}, {"review_id": "vwzuaxbgx5", "reviewer": "Reviewer_qpYn", "summary": "This paper presents CURV, a vision-language framework for radiology report generation that jointly models diagnostic uncertainty and explicit reasoning. CURV follows a three-stage training process: it first uses supervised fine-tuning on an uncertainty-annotated dataset to help the model express appropriate diagnostic confidence in the findings section; then it initializes reasoning ability with a curated dataset (TRACE-CXR) that contains structured reasoning segments connecting findings to impressions; finally, it applies reinforcement learning with a multi-dimensional reward function to improve format adherence, clinical accuracy, and logical consistency. Experiments on MIMIC-CXR show that CURV outperforms strong baselines across both standard NLP metrics and clinical evaluation scores. The authors also release the TRACE-CXR dataset to support future research on interpretable medical report generation.", "questions": "1. Could the authors clarify whether the full vision-language model (including the vision encoder and language backbone) was fine-tuned during the two supervised training stages? Were any components (e.g., the vision encoder or projection layers) frozen or adapted with techniques such as LoRA? This information is important given the relatively short training time reported.\n\n2. Have the authors conducted any out-of-distribution (OOD) or cross-dataset evaluations to assess whether the proposed fine-tuning strategy degrades the pretrained model's generalization ability? If not, could they comment on how CURV performs beyond MIMIC-CXR, especially in non-matched or clinically diverse settings?\n\n3. Why did the authors choose to rely on RadGraph-based rewards and evaluation metrics, rather than a fully LLM-based evaluation framework for assessing clinical correctness and uncertainty expression? Given the growing use of large medical LLMs as evaluators, a comparison or justification would help clarify this design choice.", "limitations": "The paper does not explicitly discuss its limitations or potential negative societal impact. It would strengthen the work if the authors could include a discussion on possible risks, such as model hallucination of clinical findings or uncertainty expressions, overreliance on LLM-generated reasoning traces, and the lack of validation on diverse or out-of-distribution datasets. Acknowledging these points would help clarify the model’s reliability and real-world applicability, especially in high-stakes clinical scenarios.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strength\n\n1. The paper addresses two critical limitations in current VLM-based medical report generation—diagnostic uncertainty and reasoning transparency—which are highly relevant in real-world clinical applications.\n\n2. The focus on learning to express diagnostic uncertainty is clinically meaningful and well-grounded, as radiologists often rely on hedging language to convey confidence levels.\n\n3. The creation of a reasoning-labeled dataset based on LLM augmentation is a practical and valuable contribution, enabling supervised learning of clinical reasoning traces.\n\nWeakness\n\n1. The paper does not clearly state whether the full vision-language model, including the vision encoder and LLM backbone, was fine-tuned during the supervised training stages, or whether certain components (e.g., the vision encoder or projection layers) were frozen. Given the short training time reported for Stages 1 and 2, clarification is needed.\n\n2. It remains unclear whether the proposed fine-tuning strategy degrades or overfits the pretrained model's general capability. An out-of-distribution (OOD) evaluation or cross-dataset test (e.g., on non-MIMIC data) would help validate the robustness of CURV’s training strategy.\n\n3. While the paper uses RadGraph-based rewards and metrics to evaluate clinical correctness, it is unclear why the authors did not adopt a fully LLM-based evaluation pipeline to assess report quality more holistically. Given the increasing adoption of large medical LLMs as clinical evaluators, such approaches may provide more interpretable and direct judgments than rule-based entity matching.", "was_revised": true, "final_justification": "After reading the authors' rebuttal, most of my concern have been resoloved. I will keep my positive score.", "rebuttal": "We sincerely thank Reviewer qpYn for their positive assessment, supportive feedback, and constructive questions. Based on the valuable comments from all reviewers, we have conducted **three significant new experiments and prepared detailed clarifications** to strengthen our work.\n\n**Summary of Major Updates**:\n\n- **New SOTA Comparison**: We performed a new evaluation against Gemini 2.5 Pro. The results confirm CURV's superior performance, with our model achieving a RadGraph F1 of 25.65 vs. Gemini's 7.71.\n\n- **New Clinical Expert Validation**: To validate our LLM-based evaluation, we conducted a new study with a board-certified radiologist. Their scores showed strong alignment with our LLM evaluators (e.g., MAE of 0.38 vs. Gemini 2.5 Pro), confirming our protocol's reliability.\n\n- **New Out-of-Distribution (OOD) Evaluation**: To test generalization, we ran a new evaluation on the IU X-ray dataset. CURV maintains its strong performance and outperforms all baselines, demonstrating that its learned capabilities are robust and not overfit to MIMIC-CXR.\n\nWe address the reviewer's specific points below.\n\n# Question 1\n\nThank you for this important clarifying question. We used full model tuning for both the SFT stages and the RL stage.\n\n* **For Stage 1 & 2 (SFT):** To make training feasible, we kept the vision encoder frozen and only fine-tuned the parameters of the multimodal projection layer and the LLM backbone. We utilized DeepSpeed with Zero-3 optimization and bfloat16 precision.\n* **For Stage 3 (RL):** For the reinforcement learning phase, we used the ms-swift [1] rlhf library to implement GRPO. In this final stage, we performed full-parameter fine-tuning of the model checkpoint from Stage 2 to allow the entire model to adapt to the reward signals.\n\n[1] Zhao, Yuze, et al. \"Swift: a scalable lightweight infrastructure for fine-tuning.\" *Proceedings of the AAAI Conference on Artificial Intelligence.* Vol. 39. No. 28. 2025.\n\n# Question 2\n\nYes, in response to this feedback, we have conducted a new OOD evaluation on the IU X-ray [1] dataset. The IU X-ray dataset is a public radiography dataset collected by Indiana University with 7,470 chest X-ray images and 3,955 reports. We randomly select 1,000 of the images and reports as test set. Our CURV framework maintains its strong performance and outperforms the baselines, demonstrating that its learned capabilities for reasoning and uncertainty expression are robust and can generalize. The results presented below show that CURV’s advantages are robust and generalize. Our framework maintains its strong performance, outperforming the baselines even on this new dataset. This new result provides strong evidence that the capabilities learned by CURV are not overfit to MIMIC-CXR.\n\n**Table 1:** Generation metrics for radiology report generation on IU X-ray dataset\n| **Model** | **B-1** | **B-2** | **B-3** | **B-4** | **METEOR** | **R-L** | **gritlm** |\n| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n| LLaVA-1.5-7B | 16.52 | 6.60 | 3.00 | 1.40 | 19.65 | 17.48 | 45.78 |\n| LLaVA-1.5-7B-SFT-CXR | 21.42 | 12.95 | 8.03 | 5.20 | 23.24 | 26.40 | 46.21 |\n| HuatuoGPT-Vision-7B | 19.33 | 10.70 | 6.28 | 2.81 | 31.02 | 23.42 | 50.22 |\n| MAIRA-2 | 26.37 | 15.60 | 9.64 | 6.03 | 25.52 | 31.18 | 54.18 |\n| Qwen2.5-VL-3B | 11.38 | 5.05 | 2.28 | 1.05 | 21.65 | 15.01 | 45.95 |\n| CURV\\_stage1 | 12.51 | 6.24 | 3.18 | 1.57 | 23.64 | 18.18 | 46.76 |\n| CURV\\_stage2 | 10.18 | 5.32 | 2.75 | 1.27 | 18.64 | 13.93 | 44.46 |\n| **CURV** | **29.23** | **18.76** | **12.08** | **6.86** | **38.30** | **39.08** | **54.89** |\n\n**Table 2:** Clinical accuracy metrics for radiology report generation on IU X-ray dataset\n| **Model** | **Acc. (f1chexbert)** | **Macro F1 (f1chexbert)** | **Micro F1 (f1chexbert)** | **Ent. F1 (radgraph)** | **F1 (radgraph)** | **Prec. (radgraph)** | **Rec. (radgraph)** |\n| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n| LLaVA-1.5-7B | 72.03 | 4.66 | 46.81 | 13.37 | 8.76 | 25.54 | 12.38 |\n| LLaVA-1.5-7B-SFT-CXR | 76.34 | 5.84 | 53.33 | 16.19 | 10.31 | 28.95 | 14.36 |\n| HuatuoGPT-Vision-7B | 89.89 | 5.72 | 67.07 | 22.98 | 13.96 | 35.44 | 20.80 |\n| MAIRA-2 | 88.74 | **6.22** | 70.75 | 34.53 | 24.01 | 45.81 | 32.55 |\n| Qwen2.5-VL-3B | 80.64 | 4.92 | 49.47 | 12.70 | 6.26 | 20.84 | 11.41 |\n| CURV\\_stage1 | 68.89 | 4.57 | 40.30 | 20.59 | 12.13 | 31.71 | 18.79 |\n| CURV\\_stage2 | 67.59 | 3.76 | 33.67 | 12.83 | 7.28 | 21.54 | 11.47 |\n| **CURV** | **91.56** | 5.86 | **74.36** | **36.99** | **25.65** | **48.80** | **35.28** |\n\n[1] Demner-Fushman, Dina et al. “Preparing a collection of radiology examinations for distribution and retrieval.” Journal of the American Medical Informatics Association : JAMIA vol. 23,2 (2016): 304-10. doi:10.1093/jamia/ocv080\n\n# Question 3\n\nWe appreciate the question about our evaluation methodology. We agree that LLM-based evaluation is a powerful and increasingly important tool. We used a hybrid evaluation strategy, intentionally leveraging the distinct strengths of both traditional, objective metrics and modern LLM-based evaluations. \n* **LLM-based Evaluation**: We used LLM-based evaluation to assess the novel components of our output that lack a ground-truth reference: the \"Thinking\" section and the quality of Uncertainty Expression. As shown in Figure 3 in our paper, this LLM-based evaluation allowed us to track the significant improvements in qualities like \"Logical Coherence\" and \"Semantic Uncertainty\" throughout the RL training process.\n* **Objective Metrics for Factual Correctness**: For the \"Findings\" and \"Impression\" sections, where ground-truth text is available, we used established and objective metrics like CheXbert and RadGraph F1-scores. These metrics are standard in the field, reproducible, and provide a non-biased, quantitative measure of factual correctness that complements the qualitative nature of LLM evaluations.\n\nWe believe this hybrid approach provides a more comprehensive and robust assessment.\n\n# Response to \"Limitations\" Comment\n\nThis is a very important point, and we agree that a more explicit discussion of limitations is needed. We will expand our Conclusion (Section 5) to include a dedicated \"Limitations and Future Work\" subsection. In it, we will address the points you raised: the potential for model hallucination, the reliance on LLM-generated data for training, the need for broader OOD and cross-dataset validation, and the importance of future validation by clinical experts. Thank you for helping us make the paper more transparent and balanced."}]

Author rebuttal:

Meta-review: The paper gets reviewing comments from four experts. Three experts are positive or slightly positive about the paper and one expert (Reviewer e8n6) is slightly negative.

Reviewer e8n6 also admits that his original concerns are mostly answered by the rebuttal and is willing to hear the feedbacks from other reviewers, who are all positve.  Thus, the AC decides to downweigh his/her comments and recommends to accept the paper as a poster.
Decision: Accept (poster)