MLLMは人間のように多義図形を報告するか？
(How) Do MLLMs Report Bistable Images Like Humans?
マルチモーダル大規模言語モデル（MLLM）が、アヒルとウサギの多義図形のような二重安定画像に対して、人間と同様に一度に一つの解釈を報告するか、また視覚的手がかりや言語的バイアスで報告が変わるかを調べ、その内部計算機構を解析した。
著者: Ryota Takatsuki, Tomoki Doi, Amane Watahiki, Anil K. Seth, Hitomi Yanaka
分類: cs.CV, cs.AI
原文アブストラクト
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. Code and data are available at https://github.com/rtakatsky/mllm-bistable-images.