---
title: "Ascending the Infinite Ladder — paper by Zhang et al. (2026)"
description: "The authors present Inf-Bench, which targets this gap. 47 references. Among them: 0."
authors: ["Jiahuan Zhang", "Shunwen Bai", "Tianheng Wang", "KaiWen Guo", "Zijia Song", "Hanqing WU", "Guozheng Rao", "Kai Han", "Kaicheng Yu"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.1119/"
doi: "10.18653/v1/2026.acl-long.1119"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zhang-2026-ascending-infinite-ladder-benchmarking-spatial/"
---
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# Ascending the Infinite Ladder: Benchmarking Spatial Deformation Reasoning in Vision-Language Models

**Authors:** [Jiahuan Zhang](https://md.research.elamur.ai/free/en/authors/jiahuan-zhang/), Shunwen Bai, [Tianheng Wang](https://md.research.elamur.ai/free/en/authors/tianheng-wang/), KaiWen Guo, Zijia Song, [Hanqing WU](https://md.research.elamur.ai/free/en/authors/hanqing-wu/), [Guozheng Rao](https://md.research.elamur.ai/free/en/authors/guozheng-rao/), [Kai Han](https://md.research.elamur.ai/free/en/authors/kai-han/) and [Kaicheng Yu](https://md.research.elamur.ai/free/en/authors/kaicheng-yu/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.1119/) · [PDF](https://aclanthology.org/2026.acl-long.1119.pdf) · [DOI 10.18653/v1/2026.acl-long.1119](https://doi.org/10.18653/v1/2026.acl-long.1119) · [OpenAlex](https://openalex.org/W4415343554)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Vision-language models are increasingly marketed as spatially capable, but existing spatial benchmarks mostly test static relations such as relative height or path finding. They rarely test whether a model can follow an object's shape as it is cut, rotated, stacked or recolored. The authors present Inf-Bench, which targets this gap. It covers three levels: 2D and 2.5D tasks based on the factory game Shapez, and 3D tasks built on the Rubik's Cube. Each level has forward questions (predict the result of given operations) and inverse questions (recover the operations linking two states).

An automatic generator produces unlimited new items, which avoids data leakage. Evaluation uses an "Infinite Ladder" protocol: models start with one-step tasks and move to longer deformation chains as they succeed. Their score is the highest rung they reach, so the benchmark should not saturate.

Humans beat every tested model, and the gap is widest in 3D, where even OpenAI o3 fails basic items. Fine-tuning and prompting-based reasoning aids help little. The authors conclude that deformation reasoning remains an open weakness of current VLMs.

## Key points
- **Task design:**
  - 2D shapes have four quadrants and six operations (cutting, rotating, coloring and similar).
  - 2.5D adds up to four stacked layers and uses seven operations.
  - 3D uses a 27-unit cube with 54 visible faces and all 54 basic rotations.
- **Main result:** In the extracted Table 4 numbers, GPT-5 reaches mean top ranks of roughly 8–10 on 2D/2.5D over 10 ladder runs. It drops to about 1 on 3D forward and 0 on 3D inverse. Llama 4-scout stays below 1 on every task.
- **Model strategies:** The authors identify two approaches, "direct pairing" (intuitive pattern matching) and step-by-step encoding of the figure. Both hit clear performance limits.
- **Fine-tuning:** Supervised fine-tuning helps models generalize on low-dimensional tasks. On 3D forward tasks, models stay below about seven steps even when trained on sequences of up to 10 steps. Training on forward tasks transfers to inverse tasks better than the reverse.
- **Reasoning enhancements:** CoT, few-shot prompting, self-reflection, tool use and ReAct give marginal or negative changes. For example, self-reflection lowers GPT-5's 2D forward score by about 5.3 ranks.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Jiahuan Zhang, Shunwen Bai, Tianheng Wang, KaiWen Guo, Zijia Song, Hanqing WU, Guozheng Rao, Kai Han and Kaicheng Yu. 2026. Ascending the Infinite Ladder: Benchmarking Spatial Deformation Reasoning in Vision-Language Models. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{zhang2026ascending,
  title = {{Ascending the Infinite Ladder: Benchmarking Spatial Deformation Reasoning in Vision-Language Models}},
  author = {Jiahuan Zhang, Shunwen Bai, Tianheng Wang, KaiWen Guo, Zijia Song, Hanqing WU, Guozheng Rao, Kai Han and Kaicheng Yu},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1119},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1119}
}
```

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*Updated 2 November 2026*