← Back to all news
arXiv Computer Vision By Luca Bux, Thiago Rios, Ingo Scholtes, Stefan Menzel

Do Vision-Language Models Agree on the Affective Qualities of Shape? A Cross-Model Audit for Generative Design Interfaces

Read the original on arXiv Computer Vision →

The study audits six vision‑language models (VLMs) to assess whether they consistently encode affective qualities of 3D shapes, using Kansei adjective pairs as affective axes. Across ten ShapeNet categories, models show moderate agreement (mean rank correlation 0.36) that is lower than geometric controls but higher than unrelated adjective pairs, with convergence varying widely by category and axis. The authors demonstrate how this audit informs a UI prototype that selectively exposes Kansei descriptors for generative design interfaces.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

Related stories

arXiv Machine Learning
Sep 22

Replicating the Geometry of Emotion Representations in a Base Open-Weights Model

Sofroniew et al. (2026) showed that emotion concepts in Claude Sonnet 4.5 are encoded as vectors whose geometry mirrors human affect psychology. This study replicates that finding using the base pretrained model google/gemma-2-27b, generating 205,200 Claude Sonnet 4.5 stories, extracting 171 emotion vectors, and recovering a similar affective circumplex with principal components explaining comparable variance. The analysis further identifies a sharp geometric seam at layers 22‑26, demonstrates that much of the geometry already exists in static token embeddings, and shows that the geometry predicts token‑level co‑activation with high correlation.

By Adam Hollowell
llmsrag
More like this →
arXiv AI
6d ago

Beyond Bag-of-Words: Diagnosing Compositional Binding Failures in Vision-Language Models

The paper introduces Auto-Comp, a fully automated, concept-driven pipeline that generates photorealistic compositional benchmarks for vision‑language models. Auto‑Comp creates paired Minimal and Contextual samples for each concept, enabling isolation of core binding abilities from visio‑linguistic complexity. Evaluations across 25 models reveal consistent failures in attribute and relational binding, with context helping relational tasks but hindering attribute tasks due to visual clutter.

By Cristian Sbrolli, Toshihiko Yamasaki, Matteo Matteucci
llmsmultimodalbenchmarks
More like this →