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arxiv: 2604.22156 · v1 · submitted 2026-04-24 · 💻 cs.LG · cs.CV

Sum-of-Checks: Structured Reasoning for Surgical Safety with Large Vision-Language Models

Weiqiu You , Cassandra Goldberg , Amin Madani , Daniel A. Hashimoto , Eric Wong This is my paper

Pith reviewed 2026-05-08 12:32 UTC · model grok-4.3

classification 💻 cs.LG cs.CV
keywords critical view of safetystructured reasoninglarge vision-language modelssurgical AIlaparoscopic cholecystectomyverification checksbile duct injuryauditable AI
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The pith

Decomposing surgical safety criteria into expert verification checks allows large vision-language models to assess the Critical View of Safety more accurately and transparently.

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

The paper tries to establish that breaking each criterion of the Critical View of Safety into a set of specific expert-defined checks lets large vision-language models produce more accurate and reviewable judgments on laparoscopic images. A sympathetic reader would care because bile duct injuries remain a serious risk in gallbladder surgery, and flexible AI models have so far been hard to trust or audit in the operating room. The method has the model first judge individual pieces of visual evidence before combining those judgments, rather than asking for an overall answer directly. This separation of evidence collection from final scoring turns out to be the key step that lifts performance and makes the reasoning legible to surgeons.

Core claim

The Sum-of-Checks framework decomposes each of the three CVS criteria into expert-defined reasoning checks that reflect clinically relevant visual evidence. For any given laparoscopic frame the model returns a binary judgment plus justification for every check; those outcomes are then combined through fixed weighted aggregation to produce a score for each criterion. When tested on the Endoscapes2023 benchmark against direct prompting, chain-of-thought, and sub-question baselines, the structured method raises average frame-level mean average precision by 12-14 percent across three frontier models.

What carries the argument

The Sum-of-Checks framework, which decomposes each CVS criterion into multiple expert-defined binary checks whose outcomes are aggregated with fixed weights to produce criterion-level scores.

Load-bearing premise

The manually chosen checks and their fixed weights correctly capture all the clinically important visual evidence and the relative importance of each piece of evidence for every safety criterion.

What would settle it

A collection of images in which surgeons identify an unsafe view due to an anatomical feature the checks never ask the model to examine, yet the model still reports a passing score.

read the original abstract

Purpose: Accurate assessment of the Critical View of Safety (CVS) during laparoscopic cholecystectomy is essential to prevent bile duct injury, a complication associated with significant morbidity and mortality. While large vision-language models (LVLMs) offer flexible reasoning, their predictions remain difficult to audit and unreliable on safety-critical surgical tasks. Methods: We introduce Sum-of-Checks, a framework that decomposes each CVS criterion into expert-defined reasoning checks reflecting clinically relevant visual evidence. Given a laparoscopic frame, an LVLM evaluates each check, producing a binary judgment and justification. Criterion-level scores are computed via fixed, weighted aggregation of check outcomes. We evaluate on the Endoscapes2023 benchmark using three frontier LVLMs, comparing against direct prompting, chain-of-thought, and sub-question decomposition, each with and without few-shot examples. Results: Sum-of-Checks improves average frame-level mean average precision by 12--14% relative to the best baseline across all three models and criteria. Analysis of individual checks reveals that LVLMs are reliable on observational checks (e.g., visibility, tool obstruction) but show substantial variability on decision-critical anatomical evidence. Conclusion: Structuring surgical reasoning into expert-aligned verification checks improves both accuracy and transparency of LVLM-based CVS assessment, demonstrating that explicitly separating evidence elicitation from decision-making is critical for reliable and auditable surgical AI systems. Code is available at https://github.com/BrachioLab/SumOfChecks.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 2 minor

Summary. The manuscript introduces Sum-of-Checks, a framework that decomposes each Critical View of Safety (CVS) criterion into expert-defined reasoning checks for large vision-language models (LVLMs). For a given laparoscopic frame, the LVLM produces binary judgments and justifications on each check; criterion scores are obtained by fixed weighted aggregation of these outcomes. The method is evaluated on the Endoscapes2023 benchmark against direct prompting, chain-of-thought, and sub-question decomposition baselines (with and without few-shot examples) using three frontier LVLMs, reporting a 12-14% relative improvement in frame-level mean average precision.

Significance. If the central claim holds, the work offers a concrete, auditable way to structure LVLM reasoning for safety-critical surgical tasks by separating evidence elicitation from decision-making. The public code release at https://github.com/BrachioLab/SumOfChecks is a clear strength that supports reproducibility. The reported variability of LVLMs on decision-critical anatomical checks versus observational checks could usefully guide future model development in medical AI.

major comments (3)
  1. [§4 (Evaluation)] §4 (Evaluation): The abstract and results claim a 12-14% relative mAP improvement across models and criteria, yet no statistical significance tests, confidence intervals, exact baseline prompt implementations, or data-split details (e.g., train/test frame partitioning or leakage controls) are provided. These omissions are load-bearing because they prevent ruling out confounds and confirming that gains are attributable to the Sum-of-Checks decomposition rather than implementation artifacts.
  2. [§3.2 (Weighted Aggregation)] §3.2 (Weighted Aggregation): The fixed weighted aggregation of binary check outcomes is introduced without ablation on the weight values or any reported correlation between the resulting criterion scores and expert-provided ground-truth criterion ratings. This directly threatens the central claim, as the observed mAP gains could arise from longer or more structured prompts rather than faithful capture of clinically relevant visual evidence.
  3. [§5 (Analysis of Individual Checks)] §5 (Analysis of Individual Checks): While the paper notes substantial LVLM variability on decision-critical anatomical checks, it provides no quantitative check-level accuracy metrics against expert annotations, inter-rater agreement, or expert validation that the defined checks accurately encode the clinically relevant evidence for each CVS criterion. This is required to substantiate the claim of improved reliability and auditability.
minor comments (2)
  1. [Abstract] Abstract: The three frontier LVLMs are not named; listing them (e.g., GPT-4V, Gemini, etc.) would improve clarity and allow readers to assess generalizability.
  2. [§3.1 (Check Definition)] §3.1 (Check Definition): The expert-defined checks are described at a high level; providing the full list of checks per criterion in a table or appendix would aid reproducibility and allow independent clinical review.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their thorough review and constructive suggestions. We address each of the major comments below and outline the revisions we will make to strengthen the manuscript.

read point-by-point responses

  1. Referee: [§4 (Evaluation)] The abstract and results claim a 12-14% relative mAP improvement across models and criteria, yet no statistical significance tests, confidence intervals, exact baseline prompt implementations, or data-split details (e.g., train/test frame partitioning or leakage controls) are provided. These omissions are load-bearing because they prevent ruling out confounds and confirming that gains are attributable to the Sum-of-Checks decomposition rather than implementation artifacts.

    Authors: We agree that these details are important for reproducibility and to confirm the robustness of our results. In the revised manuscript, we will add statistical significance testing using bootstrap resampling to compute confidence intervals for the mAP differences. We will also include the exact prompt templates for all baselines in the supplementary material and provide a detailed description of the data partitioning used in Endoscapes2023, including confirmation of no data leakage between training and evaluation sets. These additions will be incorporated in the next version. revision: yes

  2. Referee: [§3.2 (Weighted Aggregation)] The fixed weighted aggregation of binary check outcomes is introduced without ablation on the weight values or any reported correlation between the resulting criterion scores and expert-provided ground-truth criterion ratings. This directly threatens the central claim, as the observed mAP gains could arise from longer or more structured prompts rather than faithful capture of clinically relevant visual evidence.

    Authors: The weights were determined through consultation with surgical experts to prioritize checks that are most indicative of each CVS criterion. However, we acknowledge the value of an ablation study. We will add an ablation on the weight values in the revised paper, showing performance under uniform weights and perturbed weights. Additionally, we will compute and report the correlation between the aggregated criterion scores and the expert ground-truth ratings available in the benchmark to demonstrate alignment with clinical judgment. This will help isolate the contribution of the structured decomposition. revision: yes

  3. Referee: [§5 (Analysis of Individual Checks)] While the paper notes substantial LVLM variability on decision-critical anatomical checks, it provides no quantitative check-level accuracy metrics against expert annotations, inter-rater agreement, or expert validation that the defined checks accurately encode the clinically relevant evidence for each CVS criterion. This is required to substantiate the claim of improved reliability and auditability.

    Authors: We appreciate this point. The current analysis in §5 is based on qualitative observations of model outputs. To address this, we will include quantitative check-level performance metrics by comparing LVLM judgments to available annotations where possible. However, the Endoscapes2023 benchmark provides criterion-level but not check-level expert annotations, so full validation would require additional expert review. We will add a discussion of this limitation and report inter-annotator agreement for the checks we can evaluate. We believe this will further support the auditability claim by showing where models succeed or fail on specific evidence. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical evaluation on external benchmark with independent baselines

full rationale

The paper defines Sum-of-Checks via expert-specified checks and fixed (non-learned) weighted aggregation, then reports frame-level mAP gains on the held-out Endoscapes2023 benchmark against direct prompting, CoT, and sub-question baselines. No parameters are fitted to evaluation data, no self-citations underpin the central claims, and no step equates the reported improvement to the input definitions by construction. The derivation is therefore self-contained against external data.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

Abstract does not detail specific free parameters beyond mentioning fixed weights; relies on domain assumption that expert checks capture relevant evidence.

free parameters (1)
  • aggregation weights
    Fixed weights used to combine binary check outcomes into criterion-level scores; choice of weights is not derived from data in the abstract.
axioms (1)
  • domain assumption Expert-defined checks reflect clinically relevant visual evidence for CVS criteria
    Invoked in the framework design to justify the decomposition.

pith-pipeline@v0.9.0 · 5576 in / 1304 out tokens · 65295 ms · 2026-05-08T12:32:22.174876+00:00 · methodology

discussion (0)

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Reference graph

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