SPACENUM: Revisiting Spatial Numerical Understanding in VLMs
The paper titled 'SPACENUM: Revisiting Spatial Numerical Understanding in VLMs' explores the capabilities of Vision-Language Models (VLMs) in producing numerical outputs related to spatial perception. The authors introduce a framework to evaluate how well these models understand the relationship between spatial structures and numerical representations. Their findings indicate that current VLMs struggle to accurately ground numerical values in spatial contexts, often performing close to random guessing.
- ▪The study focuses on evaluating Vision-Language Models in embodied environments.
- ▪Two tasks, Num2Space and Space2Num, are formulated to assess the models' understanding of spatial numerical relationships.
- ▪Results show that VLMs largely fail to ground numbers in spatial meaning and rely on shallow spatial cues.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.23898 |
| Publication time | Mon, 25 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-25T04:07:35.648Z |
| Last seen | 2026-05-25T04:07:35.648Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | Uh8_GgpWLLbK |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2605.23898 (cs) [Submitted on 22 May 2026] Title:SPACENUM: Revisiting Spatial Numerical Understanding in VLMs Authors:Jianshu Zhang, Yijiang Li, Huifeixin Chen, Haoran Lu, Letian Xue, Bingyang Wang, Han Liu View a PDF of the paper titled SPACENUM: Revisiting Spatial Numerical Understanding in VLMs, by Jianshu Zhang and 6 other authors View PDF HTML (experimental) Abstract:Vision-Language Models (VLMs) are increasingly deployed in embodied environments, where they need produce numerical outputs such as action magnitudes and spatial coordinates. Although these numbers appear meaningful, it remains unclear whether these numerical outputs are genuinely grounded in spatial perception.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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