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arxiv: 2605.16530 · v2 · pith:HAFK2V36new · submitted 2026-05-15 · 💻 cs.CV

SWoMo: Neuro-Symbolic World Model for Cataract Surgery Simulation

Ssharvien Kumar Sivakumar , Akwele Johnson , Anirudh Dhingra , Yannik Frisch , Ghazal Ghazaei , Anirban Mukhopadhyay This is my paper

Pith reviewed 2026-05-21 07:33 UTC · model grok-4.3

classification 💻 cs.CV
keywords neuro-symbolic world modelcataract surgery simulationdiffusion modelsim-to-real translationscene graphsurgical phase detectionvideo style transferrule-based simulator
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The pith

SWoMo separates rule-based motion from diffusion visuals to build better cataract surgery simulators.

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

The paper introduces a world model called SWoMo that splits the task of simulating cataract surgery into two parts. A symbolic rule-based simulator with scene graphs generates the physical motions and tool-tissue interactions, while a diffusion model handles realistic textures, deformations, and appearances. To train the visual model they reconstruct real videos inside the simulator to create paired data for sim-to-real translation. This design is shown to generalize to new geometries, boost downstream phase detection, and support style transfer. Readers would care because accurate simulations help train surgeons and develop autonomous surgical systems.

Core claim

We introduce SWoMo, a neuro-symbolic world model for cataract surgery simulation that decouples motion generation from visual realism. The symbolic component, consisting of a rule-based simulator and scene graph representations, models motion dynamics and tool-tissue interactions, while a diffusion model produces realistic visual appearance, including textures and tissue deformations. We propose an inverse pairing strategy that reconstructs real surgical videos in the simulator to obtain paired simulated and real videos, which are then used to train our video diffusion model for the reverse objective of sim-to-real translation.

What carries the argument

Neuro-symbolic decoupling: a rule-based simulator with scene graphs for motion and interactions paired with a video diffusion model for appearance, trained via inverse pairing of real videos to simulated scenes.

If this is right

  • The simulator produces valid motions and interactions for geometries never seen in training.
  • Features extracted from the simulator improve accuracy on downstream surgical phase detection tasks.
  • The trained diffusion model enables unsupervised transfer of visual style between different surgical video domains.
  • Both visual quality and interaction fidelity exceed those of prior non-neuro-symbolic simulators.

Where Pith is reading between the lines

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

  • The same split between symbolic dynamics and learned appearance could shorten development time for simulators of other endoscopic procedures.
  • Because motion rules stay explicit, the system might need far less paired video data than end-to-end learned world models.
  • Real-time surgical planning agents could query the symbolic component directly for physically safe action proposals.

Load-bearing premise

The inverse pairing strategy can accurately reconstruct real surgical videos inside the rule-based simulator to produce high-quality paired simulated and real data suitable for training the diffusion model without introducing reconstruction errors or biases.

What would settle it

Run the simulator on interaction geometries absent from training videos and check whether tool-tissue contact points and deformation patterns match real surgery footage within measurement error.

Figures

Figures reproduced from arXiv: 2605.16530 by Akwele Johnson, Anirban Mukhopadhyay, Anirudh Dhingra, Ghazal Ghazaei, Ssharvien Kumar Sivakumar, Yannik Frisch.

Figure 1
Figure 1. Figure 1: Neuro-symbolic World Model for interactive cataract surgery simulation that decouples surgical interaction dynamics from visual appearance [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of SWoMo’s two-stage video diffusion training. In the second stage, we enable conditioning on the simulated sequence x¯1:n. For that, we freeze the parameters θ of the pre-trained diffusion backbone ǫθ from the previous stage and create a separate, trainable copy of its encoder with parameters θc. The frozen backbone ǫθ and the trainable encoder are connected through zero-initialised convolutional… view at source ↗
Figure 3
Figure 3. Figure 3: Qualitative Results of Sim-to-Real Video Transfer. [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Unsupervised Video Style Transfer and Generalisation to Novel Tool Motion Downstream Evaluation on Phase Recognition: We use synthesised videos to augment the training data of a downstream model for phase recognition during cataract surgery. The phase recognition is performed using MS-TCN++ [22] trained on DINO features [5]. To generate a synthesised video, two real videos are randomly selected from the tr… view at source ↗
read the original abstract

Realistic surgical simulation plays a crucial role in training novice surgeons and in the development of autonomous agents. World models can scale such simulation environments to realistic and diverse procedures by predicting future patient states conditioned on current observations and surgical actions. However, current state-of-the-art approaches often fail to satisfy key criteria required for clinical applicability, including visual realism, physically grounded interactions, and the ability to simulate scenarios beyond the training distribution. Hence, we introduce SWoMo, a neuro-symbolic world model for cataract surgery simulation that decouples motion generation from visual realism. The symbolic component, consisting of a rule-based simulator and scene graph representations, models motion dynamics and tool-tissue interactions, while a diffusion model produces realistic visual appearance, including textures and tissue deformations. We propose an inverse pairing strategy that reconstructs real surgical videos in the simulator to obtain paired simulated and real videos, which are then used to train our video diffusion model for the reverse objective of sim-to-real translation. Our experiments show both qualitative and quantitative improvements over prior work. We demonstrate that our simulator further satisfies the key criteria, including generalisation to unseen interaction geometries, improvements in downstream phase detection, and unsupervised video style transfer. The code, data, and model weights are available at: https://ssharvienkumar.github.io/SWoMo/

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 2 minor

Summary. The paper introduces SWoMo, a neuro-symbolic world model for cataract surgery simulation. It decouples motion generation and tool-tissue interactions (via a rule-based simulator and scene-graph representations) from visual realism (via a video diffusion model). An inverse pairing strategy reconstructs real surgical videos inside the symbolic simulator to produce paired sim-real data for training the diffusion model on sim-to-real translation. Experiments claim qualitative and quantitative improvements over prior work, plus generalization to unseen interaction geometries, gains in downstream phase detection, and unsupervised video style transfer. Code, data, and weights are released.

Significance. If the central claims hold, the neuro-symbolic decoupling offers a principled route to physically grounded yet visually realistic surgical simulation, addressing limitations of pure neural world models. The open release of code and data strengthens reproducibility. The approach could support surgeon training and autonomous agent development, provided the inverse reconstruction step yields sufficiently accurate paired data.

major comments (2)
  1. [§3.2] §3.2 (Inverse Pairing Strategy): The reconstruction procedure is presented as yielding high-fidelity paired training data, yet no quantitative metrics (e.g., tool-position error, tissue-deformation L2, occlusion IoU, or temporal alignment scores) are reported for how closely the symbolic scene graphs recover real video geometry and dynamics. This is load-bearing for the sim-to-real diffusion training and the claimed generalization to unseen geometries.
  2. [§4] §4 (Experiments): The abstract and results claim both qualitative and quantitative improvements plus downstream benefits, but the provided text supplies no concrete numbers, baselines, or ablation tables (e.g., FID, PSNR, phase-detection F1, or cross-geometry success rates). Without these, the strength of the empirical support cannot be evaluated.
minor comments (2)
  1. Notation for scene-graph nodes and diffusion conditioning variables is introduced without a consolidated table; a single reference table would improve readability.
  2. The claim of 'parameter-free' motion dynamics should be cross-checked against any learned components inside the rule-based simulator.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their thorough review and constructive feedback. We address each major comment below and describe the revisions we will make to improve the manuscript.

read point-by-point responses

  1. Referee: [§3.2] §3.2 (Inverse Pairing Strategy): The reconstruction procedure is presented as yielding high-fidelity paired training data, yet no quantitative metrics (e.g., tool-position error, tissue-deformation L2, occlusion IoU, or temporal alignment scores) are reported for how closely the symbolic scene graphs recover real video geometry and dynamics. This is load-bearing for the sim-to-real diffusion training and the claimed generalization to unseen geometries.

    Authors: We agree that direct quantitative metrics on reconstruction fidelity would strengthen the justification for the paired data and the generalization results. The current manuscript supports the inverse pairing through qualitative inspection, downstream task performance, and cross-geometry experiments. In the revised version we will add a dedicated evaluation subsection reporting tool-position error, tissue-deformation L2, occlusion IoU, and temporal alignment scores on a held-out set of reconstructed videos. revision: yes

  2. Referee: [§4] §4 (Experiments): The abstract and results claim both qualitative and quantitative improvements plus downstream benefits, but the provided text supplies no concrete numbers, baselines, or ablation tables (e.g., FID, PSNR, phase-detection F1, or cross-geometry success rates). Without these, the strength of the empirical support cannot be evaluated.

    Authors: We acknowledge that the numerical results and comparison tables should be presented more explicitly. Although the experiments section describes the improvements and includes supporting figures, we will revise the manuscript to include clear tables with all concrete metric values (FID, PSNR, phase-detection F1, cross-geometry success rates), baselines, and ablations so that the quantitative claims can be directly evaluated. revision: yes

Circularity Check

0 steps flagged

No significant circularity; components and training remain independent of self-referential inputs

full rationale

The paper's derivation separates the symbolic rule-based simulator and scene-graph modeling of motion dynamics and tool-tissue interactions from the diffusion model for visual appearance. The inverse pairing strategy reconstructs real surgical videos inside the simulator to generate paired data drawn from external real videos for training the diffusion model. No equations, predictions, or central claims reduce by construction to fitted parameters, self-definitions, or self-citation chains that bear the load of the results. The architecture and evaluation against generalization, phase detection, and style transfer are self-contained with independent external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests primarily on the domain assumption that the rule-based simulator faithfully captures tool-tissue dynamics; no explicit free parameters or new invented entities are described in the abstract. The approach assembles standard components (scene graphs, diffusion models) from prior work.

axioms (1)
  • domain assumption The rule-based simulator and scene graph representations accurately model motion dynamics and tool-tissue interactions for cataract surgery procedures.
    This premise underpins the symbolic component's ability to provide physically grounded interactions and generalization, as stated in the abstract.

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Reference graph

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