The paper investigates how vision encoders and Vision‑Language Models (VLMs) encode conceptual information by using canonical color as a test case. By creating a dataset of objects with canonical colors and probing encoders with both color and grayscale images, the authors show that canonical color can still be decoded from grayscale inputs and is linked to predicted object identity. They further demonstrate that post‑training of VLMs can significantly influence color decodability within the vision encoder, suggesting that canonical color is a useful tool for tracing conceptual semantics in these models.
arXiv:2609.09124v1 Announce Type: cross Abstract: Visual encoders construct a representation of the image input for Vision-Language models. How much conceptual, as opposed to immediately visible, inf...
arXiv:2607. 13647v1 Announce Type: cross Abstract: Do vision models see colors the way humans do?
arXiv:2604.03114v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) may need to forget visual concepts after deployment because of privacy, copyright, licensing, safety, or policy...
arXiv:2603. 06054v2 Announce Type: replace-cross Abstract: The use of Vision-Language Models (VLMs) in automated driving applications is becoming increasingly common, with the aim of leveraging their reasoning and generalisation capabilities to handle long-tail scenarios.
arXiv:2604.08884v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) have achieved strong performance on RGB image understanding, yet their ability to use spectral evide...
arXiv:2511. 19418v3 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) excel at reasoning in linguistic space but struggle with perceptual understanding that requires dense visual perception, e.
arXiv:2606. 20077v1 Announce Type: cross Abstract: Visual tokens enter Large Language Models (LLMs) as raw, foreign signals.
The paper introduces the Graded Color Attribution (GCA) dataset, a benchmark that tests whether Vision‑Language Models (VLMs) and humans can articulate and follow a threshold rule for labeling objects by color. In experiments, humans consistently adhere to their stated rules, while VLMs—despite accurately estimating color coverage—often violate their own introspective rules, especially when world‑knowledge priors are present. This discrepancy highlights a miscalibration in VLM self‑knowledge that differs from human cognition.
arXiv:2608. 14286v1 Announce Type: cross Abstract: Vision language models (VLMs) are increasingly used in industrial decision-making systems, such as recruitment support and recommendation.
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.
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.
arXiv:2603. 23867v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) have been applied to a wide range of reasoning tasks, yet it remains unclear whether they can reason robustly under distribution shifts.