@inproceedings{takatsuki-etal-2026-mllms,
title = "(How) Do {MLLM}s Report Bistable Images Like Humans?",
author = "Takatsuki, Ryota and
Doi, Tomoki and
Watahiki, Amane and
Seth, Anil K and
Yanaka, Hitomi",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1744/",
pages = "39784--39814",
ISBN = "979-8-89176-459-0",
abstract = "Bistable images such as the duck-rabbit are classic stimuli in which one image supports multiple mutually incompatible interpretations, typically reported one at a time in humans. We ask whether multimodal large language models (MLLMs) show similar report behavior and what internal computations support it. Using the LLaVA family, we study two tractable dimensions: modulability, whether reports can be biased by bottom-up visual cues and top-down linguistic priors, and exclusivity, whether responses commit to a single interpretation. We test both on the canonical duck-rabbit and on synthetic Visual Anagrams to mitigate memorization confounds. Behaviorally, both visual and linguistic manipulations systematically shift reports in human-consistent ways, while responses remain predominantly exclusive. Mechanistically, these effects arise from competing image-token representations, distinct pathways for bottom-up and top-down modulation, and a link between exclusive reporting and object-count encoding."
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<abstract>Bistable images such as the duck-rabbit are classic stimuli in which one image supports multiple mutually incompatible interpretations, typically reported one at a time in humans. We ask whether multimodal large language models (MLLMs) show similar report behavior and what internal computations support it. Using the LLaVA family, we study two tractable dimensions: modulability, whether reports can be biased by bottom-up visual cues and top-down linguistic priors, and exclusivity, whether responses commit to a single interpretation. We test both on the canonical duck-rabbit and on synthetic Visual Anagrams to mitigate memorization confounds. Behaviorally, both visual and linguistic manipulations systematically shift reports in human-consistent ways, while responses remain predominantly exclusive. Mechanistically, these effects arise from competing image-token representations, distinct pathways for bottom-up and top-down modulation, and a link between exclusive reporting and object-count encoding.</abstract>
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%0 Conference Proceedings
%T (How) Do MLLMs Report Bistable Images Like Humans?
%A Takatsuki, Ryota
%A Doi, Tomoki
%A Watahiki, Amane
%A Seth, Anil K.
%A Yanaka, Hitomi
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F takatsuki-etal-2026-mllms
%X Bistable images such as the duck-rabbit are classic stimuli in which one image supports multiple mutually incompatible interpretations, typically reported one at a time in humans. We ask whether multimodal large language models (MLLMs) show similar report behavior and what internal computations support it. Using the LLaVA family, we study two tractable dimensions: modulability, whether reports can be biased by bottom-up visual cues and top-down linguistic priors, and exclusivity, whether responses commit to a single interpretation. We test both on the canonical duck-rabbit and on synthetic Visual Anagrams to mitigate memorization confounds. Behaviorally, both visual and linguistic manipulations systematically shift reports in human-consistent ways, while responses remain predominantly exclusive. Mechanistically, these effects arise from competing image-token representations, distinct pathways for bottom-up and top-down modulation, and a link between exclusive reporting and object-count encoding.
%U https://aclanthology.org/2026.emnlp-main.1744/
%P 39784-39814
Markdown (Informal)
[(How) Do MLLMs Report Bistable Images Like Humans?](https://aclanthology.org/2026.emnlp-main.1744/) (Takatsuki et al., EMNLP 2026)
ACL
- Ryota Takatsuki, Tomoki Doi, Amane Watahiki, Anil K Seth, and Hitomi Yanaka. 2026. (How) Do MLLMs Report Bistable Images Like Humans?. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 39784–39814, Budapest, Hungary. Association for Computational Linguistics.