{"slug": "towards-a-data-centric-understanding-of-spatial-reasoning-in-multimodal-large", "title": "Towards a Data-Centric Understanding of Spatial Reasoning in Multimodal Large Language Models", "summary": "A new arXiv survey (arXiv:2610.19108v1) examines data-centric approaches to fine-tuning pretrained Multimodal Large Language Models (MLLMs) for spatial reasoning, covering methods from supervised to reinforcement learning with an emphasis on the datasets used. The survey categorizes research into dataset design, dataset construction, and evaluation, and identifies four main methods for constructing fixed spatial annotations: rules and templates, LLM prompting, procedural generation, and human-in-the-loop. It also reports substantial variation in evaluation approaches, including different scoring methods, tolerance metrics, and benchmark types, and points to future work in unified dynamic annotation, efficient spatial reasoning, evaluation standardization, expanded spatial range, and human-centered spatial interactions.", "body_md": "arXiv:2610.19108v1 Announce Type: new \nAbstract: Multimodal Large Language Models (MLLMs) have been widely applied in embodied agents and robotics for vision tasks. However, MLLMs suffer from limitations in spatial reasoning and perception. Post-training techniques have shown promise for improving these capabilities; yet, there are limited data-driven surveys of such approaches. In this survey, we consider approaches to fine-tuning pretrained MLLMs for spatial reasoning tasks, ranging from supervised to reinforcement learning, with an emphasis on the datasets used. We investigate themes of dataset construction, data modality and annotation, and evaluation of the resulting models. Based on these themes, we categorize research into three groups: dataset design, dataset construction and evaluation. We find four main methods for constructing fixed spatial annotations: rules and templates, LLM prompting, procedural generation, and human-in-the-loop. We also observe substantial variation in evaluation approaches, including different scoring methods, tolerance metrics, and benchmark types. Finally, we identify opportunities for future research in unified dynamic annotation, efficient spatial reasoning, standardization of evaluation, expanded spatial range, and human-centered spatial interactions.", "url": "https://wpnews.pro/news/towards-a-data-centric-understanding-of-spatial-reasoning-in-multimodal-large", "canonical_source": "https://arxiv.org/abs/2610.19108", "published_at": "2026-10-15 04:00:00+00:00", "updated_at": "2026-10-15 05:56:02.348662+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "large-language-models", "ai-research", "robotics"], "entities": ["arXiv", "Multimodal Large Language Models", "MLLMs"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/towards-a-data-centric-understanding-of-spatial-reasoning-in-multimodal-large", "markdown": "https://wpnews.pro/news/towards-a-data-centric-understanding-of-spatial-reasoning-in-multimodal-large.md", "text": "https://wpnews.pro/news/towards-a-data-centric-understanding-of-spatial-reasoning-in-multimodal-large.txt", "jsonld": "https://wpnews.pro/news/towards-a-data-centric-understanding-of-spatial-reasoning-in-multimodal-large.jsonld"}}