---
title: "Seeing the Other Side — Takenaka 2026"
description: "The paper tests whether large vision–language models (LVLMs) can reason about the hidden sides of an object from given views, an ability people use easily."
authors: ["Makoto Takenaka", "Hitomi Yanaka"]
year: 2026
venue: "Proceedings of the Fifteenth Language Resources and Evaluation Conference"
source: "https://aclanthology.org/2026.lrec-1.737/"
doi: "10.63317/2b34cz9k64ug"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/takenaka-2026-seeing-other-side-diagnostic-tasks/"
---
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# Seeing the Other Side: Diagnostic Tasks for Viewpoint Reasoning in Vision–Language Models

**Authors:** Makoto Takenaka, [Hitomi Yanaka](https://md.research.elamur.ai/free/en/authors/hitomi-yanaka/) · **Year:** 2026 · **Venue:** Proceedings of the Fifteenth Language Resources and Evaluation Conference · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.lrec-1.737/) · [PDF](https://aclanthology.org/2026.lrec-1.737.pdf) · [DOI 10.63317/2b34cz9k64ug](https://doi.org/10.63317/2b34cz9k64ug) · [OpenAlex](https://openalex.org/W7160027374)

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

## Summary
The paper tests whether large vision–language models (LVLMs) can reason about the hidden sides of an object from given views, an ability people use easily. Most existing benchmarks only check content that is directly visible. The authors propose two multiple-choice VQA diagnostics. Opposite-Side Reasoning (OSR) asks whether two views show an object from opposing directions. Viewpoint Identification (VI) gives a labelled reference view and asks the model to name the viewpoint of a target image. A control variant, VI (no-ref), removes the reference image and its label. Comparing it with VI shows which correct answers do not actually depend on the reference. Images were rendered from Google Scanned Objects. The evaluated models include proprietary systems and a range of open multi-image models. All models remain well below human performance. Even strong proprietary models keep many correct answers when the reference is removed, which points to reliance on language or dataset priors rather than grounded spatial inference. The resulting datasets help separate genuine viewpoint reasoning from shortcut behaviour.

## Key points
- **Data source and rendering:** The data come from Google Scanned Objects (GSO), which contains about 1,000 household 3D models under CC BY 4.0. The authors selected 194 objects and rendered opposing views in Blender at 512×512. Each view carries one of six labels: left, right, top, bottom, front or rear.
- **OSR dataset:** 1,164 items, split evenly into 582 opposite pairs and 582 non-opposite pairs. Negatives were made by swapping in a non-opposing view.
- **VI dataset:** Built from the OSR positive pairs, with each pair also used in reverse direction (1,164 items). Each item offers five candidate answers. VI (no-ref) offers all six viewpoints.
- **Models evaluated:**
  - Proprietary: GPT-5, Claude Sonnet-4 and Gemini-2.5.
  - Open multi-image (13 models): InternVL3_5, Qwen2.5-VL, Deepseek_vl2, Idefics3, Mantis, Pixtral and LLaVA-next.
  - Single-image (2 models): LLaVA-v1.5 and MiniCPM-Llama3-V, which received the images stitched into one composite.
- **Protocol and release:** Accuracy is averaged over three random shuffles of the answer options to reduce selection bias. The datasets are planned for release on the Hugging Face Hub (mtake/Seeing-the-Other-Side).

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

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/)

Topics: [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) · [Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/) · [Vision & language](https://md.research.elamur.ai/free/en/topics/multimodal/)

## Cite

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

```bibtex
@inproceedings{takenaka2026seeing,
  title = {{Seeing the Other Side: Diagnostic Tasks for Viewpoint Reasoning in Vision–Language Models}},
  author = {Takenaka, Makoto and Yanaka, Hitomi},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/2b34cz9k64ug},
  url = {https://doi.org/10.63317/2b34cz9k64ug}
}
```

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