# Surgical-MambaLLM: Mamba2-enhanced Multimodal Large Language Model for VQLA in Robotic Surgery

arXiv: https://arxiv.org/abs/2509.16618 · HTML: https://mlsanity.com/paper/2509.16618/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Medical vision-language models, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 15 of its 35 references that are in the map.
- First posted on arXiv 20 Sep 2025.

## Placed in

- Multimodal LLMs / vision-language models / [Medical vision-language models](https://mlsanity.com/mllm/medical-vision-language-models/)

## Builds on (most cited first, 15 of 15)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality](https://mlsanity.com/paper/2405.21060/) (2024)
- [Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model](https://mlsanity.com/paper/2401.09417/) (2024)
- [VMamba: Visual State Space Model](https://mlsanity.com/paper/2401.10166/) (2024)
- [SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation](https://mlsanity.com/paper/2401.13560/) (2024)
- [2017 Robotic Instrument Segmentation Challenge](https://mlsanity.com/paper/1902.06426/) (2019)
- [2018 Robotic Scene Segmentation Challenge](https://mlsanity.com/paper/2001.11190/) (2020)
- [Cobra: Extending Mamba to Multi-Modal Large Language Model for Efficient Inference](https://mlsanity.com/paper/2403.14520/) (2024)
- [VL-Mamba: Exploring State Space Models for Multimodal Learning](https://mlsanity.com/paper/2403.13600/) (2024)
- [Dual-path Mamba: Short and Long-term Bidirectional Selective Structured State Space Models for Speech Separation](https://mlsanity.com/paper/2403.18257/) (2024)
- [LLM-Assisted Multi-Teacher Continual Learning for Visual Question Answering in Robotic Surgery](https://mlsanity.com/paper/2402.16664/) (2024)
- [Surgery-R1: Advancing Surgical-VQLA with Reasoning Multimodal Large Language Model via Reinforcement Learning](https://mlsanity.com/paper/2506.19469/) (2025)

## Built on by (most cited first, 3 of 3)

- [Decoding the Surgical Scene: A Scoping Review of Scene Graphs in Surgery](https://mlsanity.com/paper/2509.20941/) (2025)
- [NeuroABench: A Multimodal Evaluation Benchmark for Neurosurgical Anatomy Identification](https://mlsanity.com/paper/2512.06921/) (2025)
- [Object Tokens as a Bridge Between Segmentation and Visual Question Answering in Robotic Surgery](https://mlsanity.com/paper/2606.15861/) (2026)

## Abstract (arXiv)

In recent years, Visual Question Localized-Answering in robotic surgery (Surgical-VQLA) has gained significant attention for its potential to assist medical students and junior doctors in understanding surgical scenes. Recently, the rapid development of Large Language Models (LLMs) has provided more promising solutions for this task. However, current methods struggle to establish complex dependencies between text and visual details, and have difficulty perceiving the spatial information of surgical scenes. To address these challenges, we propose a novel method, Surgical-MambaLLM, which is the first to combine Mamba2 with LLM in the surgical domain, that leverages Mamba2's ability to effectively capture cross-modal dependencies and perceive spatial information in surgical scenes, thereby enhancing the LLMs' understanding of surgical images. Specifically, we propose the Cross-modal Bidirectional Mamba2 Integration (CBMI) module to leverage Mamba2 for effective multimodal fusion, with its cross-modal integration capabilities. Additionally, tailored to the geometric characteristics of surgical scenes, we design the Surgical Instrument Perception (SIP) scanning mode for Mamba2 to scan the surgical images, enhancing the model's spatial understanding of the surgical scene. Extensive experiments demonstrate that our Surgical-MambaLLM model outperforms the state-of-the-art methods on the EndoVis17-VQLA and EndoVis18-VQLA datasets, significantly improving the performance of the Surgical-VQLA task.