arXiv:2603. 06828v2 Announce Type: replace-cross Abstract: We uncover a behavioral law of long-horizon vision-language models: models that maintain temporally grounded beliefs generalize better.
By Md Ashikur Rahman, Md Arifur Rahman, Niamul Hassan Samin, Abdullah Ibne Hanif Arean, Juena Ahmed Noshin
arXiv:2608. 13267v1 Announce Type: cross Abstract: Existing vision-language model (VLM) benchmarks emphasize perception and reasoning accuracy (how well VLMs describe and reason about what they see in an image), with limited attention to behavioral reliability under uncertainty (how they behave when visual evidence is missing or misleading).
By Paul Osemudiame Oamen, Owusu-Banahene Osei, Ananya Mukherjee, Christian Greisinger, Steffen Eger, Pius Onobhayedo, Wei Zhao
The paper introduces Embedded Stroop, a diagnostic test that embeds text prompts directly into images to study interference in multimodal large language models (MLLMs). Using the What-Color-Is-the-Text (WCIT) benchmark, which tests 59 fine‑grained colors in standard, flipped, and masked conditions, the authors evaluate 16 models and find that while exact color accuracy is low (6.3%), models still recognize coarse color families (38.4%) but frequently hallucinate the embedded word instead of the true color (Stroop Hallucination Rate of 21.6%). Masking or flipping the embedded text reduces hallucinations, indicating that semantic legibility can dominate visual color perception in MLLMs.
By Jinkun Zhao, Lei Huang, Haixin Ge, Wenjun Wu
arXiv:2604. 14888v3 Announce Type: replace-cross Abstract: Recent advances in vision language models (VLMs) offer reasoning capabilities, yet how these unfold and integrate visual and textual information remains unclear.
By Danae S\'anchez Villegas, Samuel Lewis-Lim, Nikolaos Aletras, Desmond Elliott
Large vision-language models can recognize the objects and attributes in a crowded scene yet assign an attribute to the wrong same-class instance. Generic visual-question-answering accuracy marks the...
arXiv:2608. 10195v1 Announce Type: cross Abstract: Human vision organizes what it sees into wholes: same-colored points group into series, similar marks cohere into categories, and shapes complete into recognizable objects.
By Sudhanva Manjunath Athreya, Sai Phani Kumar Malladi