@inproceedings{wang-etal-2026-readable,
title = "Readable Yet Unpredictable: Rotated-Outcome Prediction in Vision-Language Models",
author = "Wang, Lexin and
Liu, Shenghua and
Wang, Yiwei and
Guo, Jiafeng and
Cheng, Xueqi",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1043/",
pages = "22095--22111",
ISBN = "979-8-89176-462-0",
abstract = "Can vision-language models predict what a 180{\textdegree} rotation would reveal from the original image alone? We study this ability through Rotated-Outcome Prediction: given an original image, a model must answer what would be seen or read after a 180{\textdegree} in-plane rotation, without directly observing the rotated target. To study this gap, we introduce ROTOUTBENCH, a paired diagnostic benchmark spanning open visual cases and controlled text-image rotations. A sharp pattern emerges: many VLMs can recognize the relevant content when directly given either the original or rotated image, yet fail to infer the rotated result from the original image alone. On controlled text-image rotations, predicted-rotation accuracy collapses to near zero even for models with high direct-reading accuracy. A model-level diagnostic further shows that the prediction state can approach a rotated-image reading state, while the final readout still shifts toward the original string. Current VLMs can recognize a transformed visual state when it is shown, but often fail to predict that state from the original view."
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<abstract>Can vision-language models predict what a 180° rotation would reveal from the original image alone? We study this ability through Rotated-Outcome Prediction: given an original image, a model must answer what would be seen or read after a 180° in-plane rotation, without directly observing the rotated target. To study this gap, we introduce ROTOUTBENCH, a paired diagnostic benchmark spanning open visual cases and controlled text-image rotations. A sharp pattern emerges: many VLMs can recognize the relevant content when directly given either the original or rotated image, yet fail to infer the rotated result from the original image alone. On controlled text-image rotations, predicted-rotation accuracy collapses to near zero even for models with high direct-reading accuracy. A model-level diagnostic further shows that the prediction state can approach a rotated-image reading state, while the final readout still shifts toward the original string. Current VLMs can recognize a transformed visual state when it is shown, but often fail to predict that state from the original view.</abstract>
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%0 Conference Proceedings
%T Readable Yet Unpredictable: Rotated-Outcome Prediction in Vision-Language Models
%A Wang, Lexin
%A Liu, Shenghua
%A Wang, Yiwei
%A Guo, Jiafeng
%A Cheng, Xueqi
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F wang-etal-2026-readable
%X Can vision-language models predict what a 180° rotation would reveal from the original image alone? We study this ability through Rotated-Outcome Prediction: given an original image, a model must answer what would be seen or read after a 180° in-plane rotation, without directly observing the rotated target. To study this gap, we introduce ROTOUTBENCH, a paired diagnostic benchmark spanning open visual cases and controlled text-image rotations. A sharp pattern emerges: many VLMs can recognize the relevant content when directly given either the original or rotated image, yet fail to infer the rotated result from the original image alone. On controlled text-image rotations, predicted-rotation accuracy collapses to near zero even for models with high direct-reading accuracy. A model-level diagnostic further shows that the prediction state can approach a rotated-image reading state, while the final readout still shifts toward the original string. Current VLMs can recognize a transformed visual state when it is shown, but often fail to predict that state from the original view.
%U https://aclanthology.org/2026.findings-emnlp.1043/
%P 22095-22111
Markdown (Informal)
[Readable Yet Unpredictable: Rotated-Outcome Prediction in Vision-Language Models](https://aclanthology.org/2026.findings-emnlp.1043/) (Wang et al., Findings 2026)
ACL