{"slug": "chain-of-spatial-thoughts-modality-agnostic-spatial-grounding-for-vision-models", "title": "Chain of Spatial Thoughts: Modality-Agnostic Spatial Grounding for Vision Language Models", "summary": "Researchers introduce Space Tokens, a lightweight, architecture-agnostic framework that equips vision-language models (VLMs) with explicit continuous spatial representations without additional inference-time modules, improving spatial reasoning. On VSI-Bench, the method improves Qwen3-VL-8B by 4.3% and SenseNova-SI-1.3 by 1.3%, achieving state-of-the-art performance on object size (79.2%) and room size estimation (75.7%). The framework distills scene-level 3D geometry and object-centric spatial attributes into continuous latent tokens that integrate into chain-of-thought reasoning and can be explicitly decoded for verification.", "body_md": "arXiv:2608.10278v1 Announce Type: new\nAbstract: Spatial understanding is fundamental to embodied intelligence, underpinning applications such as robotic manipulation, embodied navigation, and autonomous driving. Although recent vision-language models (VLMs) have achieved impressive performance on spatial reasoning benchmarks, state-of-the-art approaches typically rely on additional spatial encoders or architectural modifications during inference, increasing computational cost. We introduce Space Tokens, a lightweight, architecture-agnostic framework that equips VLMs with explicit continuous spatial representations without requiring additional inference-time modules. By distilling scene-level 3D geometry and object-centric spatial attributes into continuous latent tokens, our method enables these modalities to be directly incorporated into a chain-of-thought reasoning process, thereby improving the VLM's spatial reasoning capabilities. At the same time, the learned representations can be explicitly decoded to verify that they encode meaningful geometric information, while the unified token interface remains extensible to additional modalities. Experiments on VSI-Bench improve Qwen3-VL-8B by 4.3% and SenseNova-SI-1.3 by 1.3%, while achieving state-of-the-art performance on object size (79.2%) and room size estimation (75.7%). These results demonstrate that continuous spatial tokens provide an effective, interpretable, and computationally efficient mechanism for integrating geometric reasoning into large vision-language models.", "url": "https://wpnews.pro/news/chain-of-spatial-thoughts-modality-agnostic-spatial-grounding-for-vision-models", "canonical_source": "https://arxiv.org/abs/2608.10278", "published_at": "2026-08-12 04:00:00+00:00", "updated_at": "2026-08-12 04:11:22.288675+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "computer-vision", "ai-research"], "entities": ["Space Tokens", "Qwen3-VL-8B", "SenseNova-SI-1.3", "VSI-Bench"], "alternates": {"html": "https://wpnews.pro/news/chain-of-spatial-thoughts-modality-agnostic-spatial-grounding-for-vision-models", "markdown": "https://wpnews.pro/news/chain-of-spatial-thoughts-modality-agnostic-spatial-grounding-for-vision-models.md", "text": "https://wpnews.pro/news/chain-of-spatial-thoughts-modality-agnostic-spatial-grounding-for-vision-models.txt", "jsonld": "https://wpnews.pro/news/chain-of-spatial-thoughts-modality-agnostic-spatial-grounding-for-vision-models.jsonld"}}