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How Do LLMs and VLMs Understand Viewpoint Rotation Without Vision? An Interpretability Study

Paper · 2026 · Zhen Yang, Ping Jian, Zhongbin Guo et al.

Summary

The paper asks whether language alone, without images, lets LLMs and VLMs track how a viewpoint changes after a series of turns. The authors built VRUBench, a synthetic text-only benchmark. Each instance describes several left or right rotations in multiples of 90°, paired with objects seen along the way. The model must name the object facing it after the last turn, or answer "unknown" if that direction was never observed. Humans solve it perfectly, but most models do poorly. Even Qwen3-VL reaches only about 60%.

To find out why, the authors combine layer-wise probing with head-wise causal intervention (path patching). Models do represent each rotation step and the resulting absolute orientation in their hidden states. However, a small group of attention heads in the later layers fails to connect that orientation to the object seen there, which leads to hallucinated answers. Fine-tuning only these heads substantially improves VRU accuracy and avoids the loss of general skills caused by full fine-tuning. The work is an early mechanistic look at text-based spatial reasoning.

Key points

  • VRUBench composition: 19,591 instances built from 100 indoor objects, split into 4,614 two-step, 4,977 three-step, and 5,000 each of four- and five-step rotation sequences.
  • Benchmark findings:
    • VLMs consistently beat their text-only counterparts (e.g., Qwen3-VL-8B vs Qwen3-8B).
    • Think-then-answer (CoT) prompting helps, which differs from earlier results on the visual dataset VSI-Bench.
  • Probing results:
    • Rotation direction and angle are encoded strongly at every step.
    • Absolute orientation emerges in early-to-middle layers and fades in later ones.
  • Path patching results: Key heads in middle-to-late layers fall into three types:
    • "proposal" heads that collect candidate answers;
    • "answer decision" heads that pick one;
    • an "unknown" head reflecting cautious behavior, possibly a product of alignment training.
  • Selective SFT of the top-32 heads (Qwen2.5-VL-3B, about 0.03B tuned parameters):
    • VRUBench accuracy rises from 37.6 to 80.1, using roughly half the GPU hours of full SFT.
    • MMLU and BBH stay stable, while full SFT drops BBH from 25.5 to 19.7.
    • Performance on the visual OOD benchmark SpinBench also improves slightly (and by +3.6 for the 7B model), suggesting text-only training carries over to visual tasks.

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

 Institutions

🎓Beijing Institute of TechnologyBeijing · China43 works · 3,652 citations🎓Qilu University of TechnologyJinan · China7 works · 3 citations🎓Shandong Academy of SciencesJinan · China4 works · 1 citation🔬National Supercomputing Center in JinanJinan · China3 works

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksComputer Vision and Pattern Recognition · 3,486 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 3,894 worksInterpretability · 149 works

Cite

Cite as Zhen Yang, Ping Jian, Zhongbin Guo, Zuming Zhang, Chengzhi Li, Yonghong Deng, Xinyue Zhang and Wenpeng Lu. 2026. How Do LLMs and VLMs Understand Viewpoint Rotation Without Vision? An Interpretability Study. 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. DOI 10.18653/v1/2026.acl-long.496

@inproceedings{yang2026how,
  title = {{How Do LLMs and VLMs Understand Viewpoint Rotation Without Vision? An Interpretability Study}},
  author = {Zhen Yang, Ping Jian, Zhongbin Guo, Zuming Zhang, Chengzhi Li, Yonghong Deng, Xinyue Zhang and Wenpeng Lu},
  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.496},
  url = {https://doi.org/10.18653/v1/2026.acl-long.496}
}

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