# SVG Decomposition for Enhancing Large Multimodal Models Visualization Comprehension: A Study with Floor Plans

arXiv: https://arxiv.org/abs/2511.03478 · HTML: https://mlsanity.com/paper/2511.03478/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning.
- Builds on 17 of its 36 references that are in the map.
- First posted on arXiv 5 Nov 2025.

## Placed in

- LLM reasoning / [Spatial and embodied reasoning](https://mlsanity.com/lr/spatial-and-embodied-reasoning/)
- Multimodal LLMs / vision-language models / [Embodied and 3D vision-language models](https://mlsanity.com/mllm/embodied-and-3d-vision-language-models/)

## Builds on (most cited first, 17 of 17)

- [ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning](https://mlsanity.com/paper/2203.10244/) (2022)
- [SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities](https://mlsanity.com/paper/2401.12168/) (2024)
- [What's "up" with vision-language models? Investigating their struggle with spatial reasoning](https://mlsanity.com/paper/2310.19785/) (2023)
- [CubiCasa5K: A Dataset and an Improved Multi-Task Model for Floorplan Image Analysis](https://mlsanity.com/paper/1904.01920/) (2019)
- [TopViewRS: Vision-Language Models as Top-View Spatial Reasoners](https://mlsanity.com/paper/2406.02537/) (2024)
- [Can Large Vision Language Models Read Maps Like a Human?](https://mlsanity.com/paper/2503.14607/) (2025)
- [Deep Floor Plan Recognition Using a Multi-Task Network with Room-Boundary-Guided Attention](https://mlsanity.com/paper/1908.11025/) (2019)
- [Advancing Multimodal Large Language Models in Chart Question Answering with Visualization-Referenced Instruction Tuning](https://mlsanity.com/paper/2407.20174/) (2024)
- [How Good is ChatGPT in Giving Advice on Your Visualization Design?](https://mlsanity.com/paper/2310.09617/) (2023)
- [Chart-based Reasoning: Transferring Capabilities from LLMs to VLMs](https://mlsanity.com/paper/2403.12596/) (2024)
- [Exploring the Capability of LLMs in Performing Low-Level Visual Analytic Tasks on SVG Data Visualizations](https://mlsanity.com/paper/2404.19097/) (2024)
- [ImplicitAVE: An Open-Source Dataset and Multimodal LLMs Benchmark for Implicit Attribute Value Extraction](https://mlsanity.com/paper/2404.15592/) (2024)
- [Leveraging Large Language Models for Scalable Vector Graphics-Driven Image Understanding](https://mlsanity.com/paper/2306.06094/) (2023)
- [FloNa: Floor Plan Guided Embodied Visual Navigation](https://mlsanity.com/paper/2412.18335/) (2024)
- [Eliminating Rasterization: Direct Vector Floor Plan Generation with DiffPlanner](https://mlsanity.com/paper/2508.13738/) (2025)
- [Research on Navigation Methods Based on LLMs](https://mlsanity.com/paper/2504.15600/) (2025)
- [Knowledge driven Description Synthesis for Floor Plan Interpretation](https://mlsanity.com/paper/2103.08298/) (2021)

## Abstract (arXiv)

Large multimodal models (LMMs) are increasingly capable of interpreting visualizations, yet they continue to struggle with spatial reasoning. One proposed strategy is decomposition, which breaks down complex visualizations into structured components. In this work, we examine the efficacy of scalable vector graphics (SVGs) as a decomposition strategy for improving LMMs' performance on floor plans comprehension. Floor plans serve as a valuable testbed because they combine geometry, topology, and semantics, and their reliable comprehension has real-world applications, such as accessibility for blind and low-vision individuals. We conducted an exploratory study with three LMMs (GPT-4o, Claude 3.7 Sonnet, and Llama 3.2 11B Vision Instruct) across 75 floor plans. Results show that combining SVG with raster input (SVG+PNG) improves performance on spatial understanding tasks but often hinders spatial reasoning, particularly in pathfinding. These findings highlight both the promise and limitations of decomposition as a strategy for advancing spatial visualization comprehension.