Why Do MLLMs Struggle with Spatial Understanding? A Systematic Analysis from Data to Architecture
- URL: http://arxiv.org/abs/2509.02359v1
- Date: Tue, 02 Sep 2025 14:22:43 GMT
- Title: Why Do MLLMs Struggle with Spatial Understanding? A Systematic Analysis from Data to Architecture
- Authors: Wanyue Zhang, Yibin Huang, Yangbin Xu, JingJing Huang, Helu Zhi, Shuo Ren, Wang Xu, Jiajun Zhang,
- Abstract summary: We present a systematic analysis of spatial understanding from both data and architectural perspectives.<n>From the data perspective, the performance of spatial understanding converges quickly as the training data increases.<n>From the architectural perspective, we find that spatial understanding relies more heavily on the positional encoding within the visual encoder than within the language model.
- Score: 16.15618237704827
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Spatial understanding is essential for Multimodal Large Language Models (MLLMs) to support perception, reasoning, and planning in embodied environments. Despite recent progress, existing studies reveal that MLLMs still struggle with spatial understanding. However, existing research lacks a comprehensive and systematic evaluation of these limitations, often restricted to isolated scenarios, such as single-view or video. In this work, we present a systematic analysis of spatial understanding from both data and architectural perspectives across three representative scenarios: single-view, multi-view, and video. We propose a benchmark named MulSeT (Multi-view Spatial Understanding Tasks), and design a series of experiments to analyze the spatial reasoning capabilities of MLLMs. From the data perspective, the performance of spatial understanding converges quickly as the training data increases, and the upper bound is relatively low, especially for tasks that require spatial imagination. This indicates that merely expanding training data is insufficient to achieve satisfactory performance. From the architectural perspective, we find that spatial understanding relies more heavily on the positional encoding within the visual encoder than within the language model, in both cascaded and native MLLMs. Moreover, we explore reasoning injection and envision future improvements through architectural design to optimize spatial understanding. These insights shed light on the limitations of current MLLMs and suggest new directions for improving spatial reasoning capabilities through data scaling and architectural tuning.
Related papers
- From Indoor to Open World: Revealing the Spatial Reasoning Gap in MLLMs [65.04549036809557]
We introduce a benchmark built from pedestrian-perspective videos captured with synchronized stereo cameras, LiDAR, and IMU/GPS sensors.<n>This dataset provides metrically precise 3D information, enabling the automatic generation of spatial reasoning questions.<n> Evaluations reveal that the performance gains observed in structured indoor benchmarks vanish in open-world settings.
arXiv Detail & Related papers (2025-12-22T18:58:12Z) - SpatialThinker: Reinforcing 3D Reasoning in Multimodal LLMs via Spatial Rewards [37.39035418889281]
We introduce SpatialThinker, a 3D-aware MLLM trained with RL to integrate structured spatial grounding with multi-step reasoning.<n>The model simulates human-like spatial perception by constructing a scene graph of task-relevant objects and spatial relations, and reasoning towards an answer via dense spatial rewards.
arXiv Detail & Related papers (2025-11-10T18:52:47Z) - Actial: Activate Spatial Reasoning Ability of Multimodal Large Language Models [75.45940282834327]
We introduce Viewpoint Learning, a task designed to evaluate and improve the spatial reasoning capabilities of MLLMs.<n>We present the Viewpoint-100K dataset, consisting of 100K object-centric image pairs with diverse viewpoints and corresponding question-answer pairs.<n>Our approach employs a two-stage fine-tuning strategy, resulting in significant improvements across multiple tasks.
arXiv Detail & Related papers (2025-11-03T14:27:00Z) - Abstractive Visual Understanding of Multi-modal Structured Knowledge: A New Perspective for MLLM Evaluation [48.462734327375536]
Multi-modal large language models (MLLMs) incorporate heterogeneous modalities into LLMs, enabling a comprehensive understanding of diverse scenarios and objects.<n>Despite the proliferation of evaluation benchmarks and leaderboards for MLLMs, they predominantly overlook the critical capacity of MLLMs to comprehend world knowledge with structured abstractions that appear in visual form.<n>We propose M3STR, an innovative benchmark grounded in the Multi-Modal Map for STRuctured understanding.<n>Our findings reveal persistent deficiencies in processing abstractive visual information with structured knowledge, thereby charting a pivotal trajectory for advancing MLLMs' holistic reasoning capacities.
arXiv Detail & Related papers (2025-06-02T04:00:35Z) - Spatial-MLLM: Boosting MLLM Capabilities in Visual-based Spatial Intelligence [13.168559963356952]
We present Spatial-MLLM, a novel framework for visual-based spatial reasoning from purely 2D observations.<n>Our key insight is to unleash the strong structure prior to the feed-forward visual geometry foundation model.<n>A connector then integrates both features into unified visual tokens for enhanced spatial understanding.
arXiv Detail & Related papers (2025-05-29T17:59:04Z) - SpatialScore: Towards Unified Evaluation for Multimodal Spatial Understanding [64.15606979785355]
Multimodal large language models (MLLMs) have achieved impressive success in question-answering tasks, yet their capabilities for spatial understanding are less explored.<n>This work investigates a critical question: do existing MLLMs possess 3D spatial perception and understanding abilities?
arXiv Detail & Related papers (2025-05-22T17:59:03Z) - Scaling and Beyond: Advancing Spatial Reasoning in MLLMs Requires New Recipes [84.1059652774853]
Multimodal Large Language Models (MLLMs) have demonstrated impressive performance in general vision-language tasks.<n>Recent studies have exposed critical limitations in their spatial reasoning capabilities.<n>This deficiency in spatial reasoning significantly constrains MLLMs' ability to interact effectively with the physical world.
arXiv Detail & Related papers (2025-04-21T11:48:39Z) - EmbodiedVSR: Dynamic Scene Graph-Guided Chain-of-Thought Reasoning for Visual Spatial Tasks [24.41705039390567]
EmbodiedVSR (Embodied Visual Spatial Reasoning) is a novel framework that integrates dynamic scene graph-guided Chain-of-Thought (CoT) reasoning.<n>Our method enables zero-shot spatial reasoning without task-specific fine-tuning.<n>Experiments demonstrate that our framework significantly outperforms existing MLLM-based methods in accuracy and reasoning coherence.
arXiv Detail & Related papers (2025-03-14T05:06:07Z) - GSR-BENCH: A Benchmark for Grounded Spatial Reasoning Evaluation via Multimodal LLMs [3.2688425993442696]
The ability to understand and reason about spatial relationships between objects in images is an important component of visual reasoning.
We extend the previously released What'sUp dataset and propose a novel comprehensive evaluation for spatial relationship understanding.
arXiv Detail & Related papers (2024-06-19T06:15:26Z) - LLM Inference Unveiled: Survey and Roofline Model Insights [62.92811060490876]
Large Language Model (LLM) inference is rapidly evolving, presenting a unique blend of opportunities and challenges.
Our survey stands out from traditional literature reviews by not only summarizing the current state of research but also by introducing a framework based on roofline model.
This framework identifies the bottlenecks when deploying LLMs on hardware devices and provides a clear understanding of practical problems.
arXiv Detail & Related papers (2024-02-26T07:33:05Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.