arxiv: 2605.09679 · v1 · submitted 2026-05-10 · 💻 cs.CV · cs.AI

Recognition: 2 theorem links

· Lean Theorem

DeepTumorVQA: A Hierarchical 3D CT Benchmark for Stage-Wise Evaluation of Medical VLMs and Tool-Augmented Agents

Yixiong Chen , Wenjie Xiao , Pedro R. A. S. Bassi , Boyan Wang , Liang He , Xinze Zhou , Sezgin Er , Ibrahim Ethem Hamamci , Zongwei Zhou , Alan Yuille

Authors on Pith no claims yet

Pith reviewed 2026-05-12 03:58 UTC · model grok-4.3

classification 💻 cs.CV cs.AI

keywords 3D CTmedical VQAtumor diagnosishierarchical benchmarkvision-language modelstool-augmented agentsstage-wise evaluationmedical reasoning

0 comments

The pith

A hierarchical benchmark reveals that accurate measurement is the main barrier for medical vision-language models analyzing 3D CT tumor scans, with tool augmentation providing substantial relief.

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

This paper builds a large-scale benchmark for evaluating how well vision-language models and agents handle 3D CT images in tumor diagnosis. It breaks the task into four stages that mirror clinical workflows: first spotting the tumor, then measuring its size and properties, then visually interpreting those measurements, and finally applying medical knowledge to reach a diagnosis. Testing many models shows that mistakes in the measurement stage prevent good performance on the reasoning stages that follow. Allowing models to use external tools for segmentation and calculation helps them overcome this bottleneck and improve on higher-level questions. The result is a more detailed diagnosis of where current medical AI systems need work.

Core claim

DeepTumorVQA decomposes 3D CT tumor diagnosis into four stages—recognition, measurement, visual reasoning, and medical reasoning—with 476,000 questions across 42 subtypes on over 9,000 volumes. Ground-truth evidence chains link the stages so higher questions can be scored independently. Experiments across more than 30 model configurations demonstrate that quantitative measurement forms the primary bottleneck for VLMs in direct reasoning mode. Tool-augmented agents that call segmentation models, measurement programs, and knowledge modules achieve better results, and supervised tool-use traces further reduce errors.

What carries the argument

The hierarchical four-stage evidence chain for tumor diagnosis, supported by tool-interaction environments that let agents call external segmentation, measurement, and knowledge tools.

Load-bearing premise

The four stages accurately capture independent steps in real clinical tumor diagnosis workflows, and the automatically generated questions and annotations do not contain systematic biases that would skew the results.

What would settle it

Running the same models on a version of the benchmark where measurement ground truth is provided directly as input, and finding no improvement in later-stage reasoning accuracy, would show that measurement is not the primary bottleneck.

Figures

Figures reproduced from arXiv: 2605.09679 by Alan Yuille, Boyan Wang, Ibrahim Ethem Hamamci, Liang He, Pedro R. A. S. Bassi, Sezgin Er, Wenjie Xiao, Xinze Zhou, Yixiong Chen, Zongwei Zhou.

**Figure 2.** Figure 2: Compositional question design. (A) Structured metadata extracted from segmentation masks and radiology reports. (B) Modular logic programs generate questions across four types and 42 subtypes. Each medical reasoning subtype is composed from recognition and measurement primitives, with clinical literature grounding. Ref+ [39], and Super-CLEVR [38] but grounded in clinical workflows (we discuss the compositi… view at source ↗

**Figure 3.** Figure 3: Statistics of DeepTumorVQA. Left: distribution of QA pairs across four main types and 42 [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

**Figure 4.** Figure 4: Error landscape transformation. (a) Error rate by task type across three paradigms. Oracle tools halve Measurement errors (70%→36%) and sharply reduce Medical Reasoning errors (72%→49%); SFT further lowers all categories. (b) Agent failure decomposition: action loops (step-limit hits, red) account for 17–18% of all questions in oracle agents, concentrated in multi-step Visual Reasoning; SFT virtually elimi… view at source ↗

**Figure 5.** Figure 5: Per-subtype accuracy heatmap for all models. Rows are models sorted by overall accuracy; [PITH_FULL_IMAGE:figures/full_fig_p028_5.png] view at source ↗

**Figure 6.** Figure 6: Accuracy by question type across demographic and imaging factors. [PITH_FULL_IMAGE:figures/full_fig_p041_6.png] view at source ↗

**Figure 7.** Figure 7: shows the result. The Pearson correlation between median distractor gap and accuracy drop under 10% noise is r = 0.25 (p = 0.45), indicating no statistically significant relationship. Subtypes with the tightest spacing (organ aggregation: median gap 7.2%) show large noise drops (−38.6pp), but subtypes with moderate spacing (tumor burden: 22.3%) also show substantial drops. The lack of correlation indicate… view at source ↗

read the original abstract

Medical vision-language models (VLMs) and AI agents have made significant progress in learning to analyze and reason about clinical images. However, existing medical visual question answering (VQA) benchmarks collapse model capabilities into a single accuracy score, obscuring where and why models fail. We propose DeepTumorVQA, a hierarchical benchmark that follows the multi-stage evidence chain in tumor diagnosis and decomposes 3D CT reasoning into four stages: recognition, measurement, visual reasoning, and medical reasoning. Higher-level questions remain independently scorable, while their ground-truth evidence chains are defined over lower-level primitives. The benchmark contains 476K questions across 42 clinical subtypes on 9,262 3D CT volumes. In addition to a direct reasoning mode for VLMs, DeepTumorVQA provides tool-interaction environments for agent evaluation, where a model can call external tools, including segmentation models, measurement programs, and medical knowledge modules, before answering the question. Evaluating over 30 model configurations, we find that reliable quantitative measurement is the primary bottleneck, making later-stage visual and medical reasoning harder for VLMs, while tool augmentation substantially mitigates this issue. When tools are available, leveraging medical knowledge and tools to reason about medical images becomes a new challenge. We further show that ground-truth step-by-step tool-use traces from DeepTumorVQA can supervise agents and reduce tool-use and reasoning failures. This stage-wise progression from recognition to measurement to visual and medical reasoning provides a concrete roadmap for future medical VLM and AI agent studies. All data and code are released at https://github.com/Schuture/DeepTumorVQA.

Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note private letter to a colleague

This benchmark cleanly isolates measurement as the main failure mode for VLMs on 3D CT tumors and shows tool use fixes much of the downstream problem.

read the letter

The main thing to know is that this benchmark isolates measurement as the dominant failure point for VLMs on 3D CT tumor questions, and shows that tool-augmented agents get around it effectively. What stands out as new is the explicit four-stage hierarchy—recognition, measurement, visual reasoning, medical reasoning—with independent scoring for each and ground-truth tool-use traces. Prior medical VQA sets just give one accuracy number. They generated 476K questions over 9,262 volumes and 42 subtypes, plus environments where models can call segmentation, measurement, and knowledge tools. The paper does a good job running evaluations across more than 30 model configurations. The results back up the bottleneck claim with clear deltas, and the tool mitigation effect is substantial. Releasing all the data and code makes it usable right away. One soft spot is that the description of how questions were generated and annotations validated is thin. There might be selection effects in the 42 subtypes or biases in how the evidence chains were built, even if the overall patterns look consistent. The assumption that these stages line up with actual clinical workflows is plausible but not proven in depth here. This paper is for groups working on medical vision-language models and AI agents who need finer-grained diagnostics than standard VQA benchmarks provide. A reader interested in tool use or stage-wise evaluation will get concrete value from the numbers and the released resources. It deserves a serious referee. The empirical work is direct and the resource is substantial enough to warrant review. I would recommend sending it out for peer review.

Referee Report

1 major / 0 minor

Summary. The paper introduces DeepTumorVQA, a hierarchical 3D CT VQA benchmark that decomposes tumor diagnosis into four stages (recognition, measurement, visual reasoning, medical reasoning) with independently scorable higher-level questions whose ground-truth evidence chains build on lower-level primitives. It generates 476K questions across 42 subtypes from 9,262 volumes, supports both direct VLM evaluation and tool-augmented agent environments (with segmentation, measurement, and knowledge tools), and evaluates over 30 model configurations to conclude that quantitative measurement is the primary bottleneck while tool augmentation substantially mitigates it; ground-truth tool-use traces are also released for agent supervision.

Significance. If the four-stage decomposition is a faithful model of clinical workflows, the benchmark provides a valuable, actionable roadmap for medical VLM and agent development by isolating specific failure points rather than collapsing performance into a single score. Strengths include the large scale, release of code and data for reproducibility, extensive empirical testing across model configurations, and the provision of tool-interaction environments that enable falsifiable comparisons of direct vs. augmented reasoning.

major comments (1)

[Benchmark construction] Benchmark construction (implied in §3 and abstract): limited detail is provided on the question generation pipeline, annotation validation procedures (e.g., inter-annotator agreement or expert review), and potential selection effects or biases across the 42 subtypes. Because the central claim—that measurement is the primary bottleneck and tools mitigate later-stage failures—rests on the stages forming a valid, unbiased evidence chain matching real clinical tumor diagnosis, explicit validation metrics or sensitivity analyses for these aspects are needed to confirm the reported accuracy patterns are not artifacts of question design.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the positive evaluation and the recommendation for minor revision. We appreciate the focus on benchmark construction and will revise the manuscript to provide greater transparency on the question generation process, validation steps, and potential biases.

read point-by-point responses

Referee: Benchmark construction (implied in §3 and abstract): limited detail is provided on the question generation pipeline, annotation validation procedures (e.g., inter-annotator agreement or expert review), and potential selection effects or biases across the 42 subtypes. Because the central claim—that measurement is the primary bottleneck and tools mitigate later-stage failures—rests on the stages forming a valid, unbiased evidence chain matching real clinical tumor diagnosis, explicit validation metrics or sensitivity analyses for these aspects are needed to confirm the reported accuracy patterns are not artifacts of question design.

Authors: We agree that expanding the description of benchmark construction will strengthen the paper. In the revised §3 we will add a detailed account of the question generation pipeline: questions are programmatically derived from expert-annotated 3D CT volumes and tumor masks in the source public datasets (with the four-stage hierarchy explicitly encoded so that higher-level questions reference lower-level primitives). Because generation is deterministic and rule-based from these existing annotations, traditional inter-annotator agreement on the VQA pairs themselves is not applicable; we will clarify this and note that the underlying CT annotations follow standard radiological protocols. To address potential selection effects, we will include (i) the exact subtype distribution across the 9,262 volumes, (ii) a sensitivity analysis showing that the measurement-bottleneck pattern holds when subsets of subtypes are held out, and (iii) a brief discussion of how the 42 subtypes were chosen to reflect clinical prevalence. These additions will make the evidence chain and its robustness explicit without altering the reported empirical findings. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper constructs a new hierarchical benchmark (DeepTumorVQA) with 476K questions across four explicitly defined stages and reports empirical performance of external VLMs and agents on those questions. The central claim—that quantitative measurement is the primary bottleneck and tool augmentation mitigates it—follows directly from observed accuracy patterns and deltas on independently scored questions, with no internal parameters fitted to the target results, no self-referential definitions, and no load-bearing self-citations that reduce the findings to prior author work by construction. The stage decomposition is a design choice whose validity is an external assumption, not a circular derivation.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The work introduces no free parameters, no new physical or mathematical entities, and relies only on standard assumptions about clinical diagnostic workflows and VLM evaluation practices.

axioms (1)

domain assumption Tumor diagnosis in 3D CT follows a multi-stage evidence chain from recognition through measurement to visual and medical reasoning.
This assumption structures the benchmark hierarchy and is taken from standard medical practice without independent verification in the paper.

pith-pipeline@v0.9.0 · 5653 in / 1285 out tokens · 78715 ms · 2026-05-12T03:58:24.898408+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Foundation/AbsoluteFloorClosure.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

decomposes 3D CT reasoning into four stages: recognition, measurement, visual reasoning, and medical reasoning
IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

reliable quantitative measurement is the primary bottleneck

What do these tags mean?

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supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

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Blaine Rister, Darvin Yi, Kaushik Shivakumar, Tomomi Nobashi, and Daniel L Rubin. Ct-org, a new dataset for multiple organ segmentation in computed tomography.Scientific Data, 7(1): 1–9, 2020

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Deeporgan: Multi-level deep convolutional networks for automated pancreas segmentation

Holger R Roth, Le Lu, Amal Farag, Hoo-Chang Shin, Jiamin Liu, Evrim B Turkbey, and Ronald M Summers. Deeporgan: Multi-level deep convolutional networks for automated pancreas segmentation. InInternational conference on medical image computing and computer- assisted intervention, pages 556–564. Springer, 2015

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arXiv preprint arXiv:2405.07960 , year =

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Jiang, Alex Grabowski, Aarav Chauhan, et al

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Bosniak classification of cystic renal masses, version 2019: an update proposal and needs assessment

Stuart G Silverman, Ivan Pedrosa, James H Ellis, Nicole M Hindman, Nicola Schieda, Andrew D Smith, Erick M Remer, Atul B Shinagare, Natalie E Curci, Lucia Manganaro, et al. Bosniak classification of cystic renal masses, version 2019: an update proposal and needs assessment. Radiology, 292(2):475–488, 2019

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Revisions of international consensus fukuoka guidelines for the management of ipmn of the pancreas

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3d-rad: A large-scale 3d radiology med-vqa dataset

Xiaoxiao Tang et al. 3d-rad: A large-scale 3d radiology med-vqa dataset. InAdvances in Neural Information Processing Systems, 2025

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Multi-modal learning from unpaired images: Application to multi-organ segmentation in ct and mri

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Chaoyi Wu, Xiaoman Zhang, Ya Zhang, Yanfeng Wang, and Weidi Xie. Towards generalist foundation model for radiology by leveraging web-scale 2d&3d medical data.arXiv preprint arXiv:2308.02463, 2023

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Advancing multimodal medical capabilities of gemini.arXiv preprint arXiv:2405.03162, 2024

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Medsg-bench: Benchmarking medical image sequences grounding capability of multimodal large language models

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Pmc-vqa: Visual instruction tuning for medical visual question answering.arXiv preprint arXiv:2305.10415, 2023

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Ct-bench: A benchmark for multimodal lesion understanding in computed tomography.arXiv preprint arXiv:2602.14879, 2026

Qingqing Zhu, Qiao Jin, Tejas S Mathai, Yin Fang, Zhizheng Wang, Yifan Yang, Maame Sarfo-Gyamfi, Benjamin Hou, Ran Gu, Praveen TS Balamuralikrishna, et al. Ct-bench: A benchmark for multimodal lesion understanding in computed tomography.arXiv preprint arXiv:2602.14879, 2026. 14 a Data Sources Table 6: Overview of public abdominal CT datasets collected in ...

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[63]

Pancreas-CT [2015] [link] 42 1

CHAOS [2018] [link] 20 1 2. Pancreas-CT [2015] [link] 42 1

work page 2018
[64]

LiTS [2019] [link] 131 7

BTCV [2015] [link] 47 1 4. LiTS [2019] [link] 131 7

work page 2015
[65]

WORD [2021] [link] 120 1

CT-ORG [2020] [link] 140 8 6. WORD [2021] [link] 120 1

work page 2020
[66]

KiTS [2020] [link] 489 1 9–14

AMOS22 [2022] [link] 200 2 8. KiTS [2020] [link] 489 1 9–14. MSD CT Tasks [2021] [link] 945 1 15. AbdomenCT-1K [2021] [link] 1,050 12

work page 2022
[67]

enlarged

FLARE’23 [2022] [link] 4,100 30 17. Trauma Detect. [2023] [link] 4,711 23 All 17 source datasets are publicly available under their respective licenses (links in Table 6); users are responsible for complying with each source dataset’s terms of use. The DeepTumorVQAannotations, structured metadata, and benchmark questionscontributed by this work are releas...

work page 2022
[68]

Reasoning liver lesion cluster

Q: Is pancreas enlarged? A: No Vis. Reasoning liver lesion cluster. Top-1 seg. ≥50% (highly); top- 2 segs. ≥75% (somewhat); else widely – Q: Liver lesions clustered? A: Yes Vis. Reasoning bilateral kidney asym. Largest-lesion-location vote; tie bro- ken by vol. ratio>1.3× – Q: Which kidney has more le- sions? A: Right Vis. Reasoning multi-organ burden Cro...

work page
[69]

Reasoning hepatic steatosis gr

Q: Fatty liver grade? A: Light Med. Reasoning hepatic steatosis gr. 4-grade: HU 58/51/39 thresholds [32] Q: Steatosis grade? A: Moder- ate Med. Reasoning pancreatic steatosis P/S HU ratio<0.7 [22] Q: Pancreatic steatosis? A: No Med. Reasoning splenomegaly grading 4-grade: 314.5/500/800 cm 3 [10] Q: Splenomegaly grade? A: Mild Med. Reasoning PDAC vs PNET c...

work page
[70]

Reasoning portal hypertension Splenic vol + liver HU composite [24] Q: Portal hypertension? A: Yes Med

Q: PDAC or PNET? A: PDAC Med. Reasoning portal hypertension Splenic vol + liver HU composite [24] Q: Portal hypertension? A: Yes Med. Reasoning renal mass charact. Simplified Bosniak: HU≤20 simple cyst; ≥70 hyperattenuating; else in- determinate/solid

work page
[71]

Reasoning lesion type class

Q: Mass classification? A: Sim- ple cyst Med. Reasoning lesion type class. Largest-volume kidney lesion: cyst vs. tumor label from radiologist an- notation

work page
[72]

Reasoning pseudocyst determ

Q: Cyst or tumor? A: Tumor 20 (continued from previous page) Task Type Subtype Generation Logic Reference Example QA Med. Reasoning pseudocyst determ. HU>14.5 threshold [3] Q: Is it a pseudocyst? A: Yes Med. Reasoning pancreatic T staging T1–T4 from annotations [5] Q: Tumor T-stage? A: T2 Med. Reasoning cyst resectability Cyst vol>3.0 cm 3 [51] Q: Cyst re...

work page
[73]

template matching

Q: Lesion resectable? A: No d Compositional Design vs. Template Matching Our use of deterministic generation programs invites comparison to CLEVR [ 30], but DeepTu- morVQA differs in three fundamental ways. First, our visual inputs arereal CT scans with real pathology, not synthetic scenes; the visual grounding challenge is genuine and domain-specific. Se...

work page
[74]

fatty liver

We preprocess each NIfTI volume by: (1) clipping HU values to [−1024,1024] ; (2) resizing the volume to [64,256,256] using trilinear interpolation (scipy.ndimage.zoom); (3) normalizing to [0,1] by (x+ 1024)/2048 . The 3D ViT encoder processes this volume as a sequence of 2×16×16 patches, yielding 256 visual tokens. Resizing from native resolution (typical...

work page 2048
[75]

liver”, “volume

measure(target, type) Returns a ground-truth quantitative measurement for a specified target and measurement type. • Input: target (string), the anatomical target; type (string), one of volume, mean_HU, diameter,count. •Output:JSON with fields:value(float),unit(string). • Supported types: volume (cm3), mean_HU (Hounsfield units), diameter (cm, largest les...

work page
[76]

fatty liver

lookup_medical_knowledge(query) Retrieves clinical criteria from a curated 27-entry knowledge base covering diagnostic thresholds, grading systems, and classification criteria. • Input: query (string): clinical topic (e.g., “fatty liver”,“splenomegaly grading”). • Output:JSON with fields: entries (list of dicts withcriterion,threshold,source). • Query mat...

work page 2012
[77]

liver”,“kidney

crop_region(organ) Returns organ-focused multi-slice crops for visual inspection (vision mode only). •Input:organ(string): target organ (e.g.,“liver”,“kidney”,“pancreas”). • Output:JSON with field: image_base64 (string), a base64-encoded PNG of 5 representa- tive axial slices tiled in a single image with abdominal windowing. • Crop details:The bounding bo...

work page