SurgTEMP: Temporal-Aware Surgical Video Question Answering with Text-guided Visual Memory for Laparoscopic Cholecystectomy
Pith reviewed 2026-05-13 23:48 UTC · model grok-4.3
The pith
SurgTEMP models temporal information in surgical videos through query-guided memory banks and staged training to improve video-based VQA.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
SurgTEMP shows that a query-guided token selection module constructing hierarchical visual memory banks, paired with a Surgical Competency Progression training scheme, enables effective processing of variable-length surgical videos while preserving procedure-relevant cues and temporal coherence across perception, assessment, and reasoning tasks.
What carries the argument
The query-guided token selection module that builds hierarchical visual memory banks (spatial and temporal) together with the Surgical Competency Progression (SCP) training scheme.
If this is right
- The framework supports downstream tasks ranging from basic instrument perception to Critical View of Safety assessment and adverse-event detection.
- Variable-length videos can be handled without losing temporal coherence needed for intraoperative reasoning.
- A single model can address the full hierarchy from perception to high-level skill and difficulty evaluation.
- The released dataset provides a standardized benchmark for comparing future surgical video VQA systems.
Where Pith is reading between the lines
- The memory-bank approach could transfer to other long-form medical video domains such as endoscopy or interventional radiology.
- Real-time deployment during live procedures would require additional latency testing on streaming input.
- The three-level task hierarchy offers a template for curriculum design in other sequential medical decision models.
- Expanding the dataset to additional surgical procedures would test whether the memory mechanism generalizes beyond cholecystectomy.
Load-bearing premise
The query-guided token selection module and SCP training scheme can effectively model variable-length surgical videos while preserving procedure-relevant cues and temporal coherence across diverse analytical needs.
What would settle it
An evaluation on CholeVidQA-32K in which SurgTEMP shows no statistically significant gain over standard fine-tuned video LLMs of comparable size would falsify the central performance claim.
read the original abstract
Surgical procedures are inherently complex and risky, requiring extensive expertise and constant focus to navigate evolving intraoperative scenes. Computer-assisted systems such as surgical visual question answering (VQA) offer promises for education and intraoperative support. Current surgical VQA research largely focuses on static frame analysis, overlooking rich temporal semantics. Surgical video question answering is further challenged by low visual contrast, its highly knowledge-driven nature, diverse analytical needs spanning scattered temporal windows, and the hierarchy from basic perception to high-level intraoperative assessment. To address these challenges, we propose SurgTEMP, a multimodal LLM framework featuring (i) a query-guided token selection module that builds hierarchical visual memory (spatial and temporal memory banks) and (ii) a Surgical Competency Progression (SCP) training scheme. Together, they enable effective modeling of variable-length surgical videos while preserving procedure-relevant cues and temporal coherence, and better support diverse downstream assessment tasks. To support model development, we introduce CholeVidQA-32K, a surgical video question answering dataset comprising 32K open-ended QA pairs and 3,855 video segments (approximately 128 h total) from laparoscopic cholecystectomy. The dataset is organized into a three-level hierarchy -- Perception, Assessment, and Reasoning -- spanning 11 tasks from instrument/action/anatomy perception to Critical View of Safety (CVS), intraoperative difficulty, skill proficiency, and adverse event assessment. In comprehensive evaluations against state-of-the-art open-source multimodal and video LLMs (fine-tuned and zero-shot), SurgTEMP achieves substantial performance improvements, advancing the state of video-based surgical VQA. The project page is available at: https://camma-public.github.io/SurgTEMP/
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper proposes SurgTEMP, a multimodal LLM framework for surgical video question answering that incorporates a query-guided token selection module to construct hierarchical spatial and temporal visual memory banks, along with a Surgical Competency Progression (SCP) training scheme. It introduces the CholeVidQA-32K dataset comprising 32K open-ended QA pairs across 3,855 video segments (~128 hours) from laparoscopic cholecystectomy procedures, structured in a three-level hierarchy (Perception, Assessment, Reasoning) covering 11 tasks from basic instrument/action/anatomy perception to Critical View of Safety (CVS), difficulty, skill, and adverse event assessment. Comprehensive evaluations against fine-tuned and zero-shot state-of-the-art multimodal and video LLMs report substantial performance improvements on the dataset.
Significance. If the reported gains hold under rigorous evaluation, this advances video-based surgical VQA by explicitly addressing temporal semantics, variable-length videos, and the progression from perception to high-level reasoning in knowledge-driven, low-contrast surgical scenes. The CholeVidQA-32K dataset is a substantial contribution that enables standardized benchmarking across diverse intraoperative tasks, with potential impact on surgical education and computer-assisted systems. The memory-bank and SCP innovations provide a concrete extension of multimodal LLM techniques to the surgical domain.
major comments (1)
- [§4] §4 (Experiments) and associated tables: the abstract and high-level claims of 'substantial performance improvements' require explicit reporting of per-task metrics (e.g., accuracy, BLEU, or task-specific scores), error bars, train/val/test splits, and ablation results for the token-selection module and SCP scheme; without these, the central claim that the proposed components drive the gains cannot be fully assessed.
minor comments (3)
- [§3.2] §3.2: clarify the exact mechanism by which query-guided selection populates the temporal memory bank for videos exceeding the context window, including any length-dependent hyperparameters.
- [Dataset] Dataset section: provide the precise distribution of the 32K QA pairs across the 11 tasks and three hierarchy levels, plus any inter-annotator agreement statistics.
- [§3.1] Figure 2 and §3.1: ensure the diagram and text consistently label the spatial vs. temporal memory banks and their interaction with the LLM decoder.
Simulated Author's Rebuttal
We thank the referee for the positive assessment, detailed summary, and recommendation for minor revision. We appreciate the focus on strengthening the experimental reporting and will incorporate the requested details to better support our claims.
read point-by-point responses
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Referee: [§4] §4 (Experiments) and associated tables: the abstract and high-level claims of 'substantial performance improvements' require explicit reporting of per-task metrics (e.g., accuracy, BLEU, or task-specific scores), error bars, train/val/test splits, and ablation results for the token-selection module and SCP scheme; without these, the central claim that the proposed components drive the gains cannot be fully assessed.
Authors: We agree that granular per-task metrics, error bars, explicit splits, and targeted ablations are essential for rigorously validating the contributions of the query-guided token selection and SCP scheme. In the revised manuscript, Section 4 and associated tables will be expanded to report per-task scores (accuracy for perception/assessment tasks and BLEU/ROUGE for reasoning tasks) across all 11 tasks in the Perception-Assessment-Reasoning hierarchy. We will include mean performance with standard deviations from multiple runs as error bars, explicitly document the video-level train/val/test splits (ensuring no temporal leakage), and add ablation tables isolating the token-selection module and SCP training. These changes will directly address the concern and strengthen the evidence for our performance claims. revision: yes
Circularity Check
No significant circularity in derivation chain
full rationale
The paper introduces an architectural framework (query-guided token selection with hierarchical memory banks plus SCP training) and a new dataset (CholeVidQA-32K) whose construction and evaluation are described independently of the target performance claims. No equations, derivations, or fitted parameters are presented that reduce by construction to the inputs; results are shown via empirical tables against external baselines. This matches the expected non-circular outcome for an applied ML architecture paper.
Axiom & Free-Parameter Ledger
axioms (1)
- domain assumption Multimodal LLMs can be extended with query-guided memory banks to handle long surgical videos
discussion (0)
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