arXiv AI
Sep 16

Same Answer, Different Representations: Hidden instability in VLMs

arXiv:2602.06652v2 Announce Type: replace Abstract: The robustness of Vision Language Models (VLMs) is commonly assessed through output-level invariance, implicitly assuming that stable predictions r...

By Farooq Ahmad Wani, Alessandro Suglia, Rohit Saxena, Aryo Pradipta Gema, Wai-Chung Kwan, Fazl Barez, Maria Sofia Bucarelli, Fabrizio Silvestri, Pasquale Minervini
arXiv Computation and Language
Sep 24

PRISM-VLM: A Multi-Axis Discriminative Benchmark for Compact Vision-Language Models

PRISM‑VLM is a new benchmark for compact vision‑language models that evaluates each item across seven axes—task quality, behavioral robustness, and capability bottlenecks—rather than collapsing performance into a single accuracy score. It aggregates these axes into a single PScore while also providing per‑axis profiles, revealing differences that single‑axis benchmarks miss, such as sycophancy. The benchmark draws on items from fifteen public datasets and will be released with its full pipeline, prompts, and annotations.

By Sanghee Park, Kee-Eung Kim
arXiv AI
Aug 18

NumerosityVLM: A Cognitively Inspired Benchmark for Interpreting Numerosity Representations in Vision-Language Models

arXiv:2608. 15425v1 Announce Type: cross Abstract: Vision-language models (VLMs) achieve strong performance on high-level multimodal tasks, yet numerosity perception, a cognitive ability that emerges in human infants before language acquisition, remains poorly understood in current models, as existing counting benchmarks entangle numerosity with correlated visual factors.

By Yiming Fu, Fangjun Li, Xiujin Liu, Ruidong Ma, Hang Yu, Zhichen Lu, Kanwei He, Alessandro Di Nuovo, Angelo Cangelosi, Zhegong Shangguan
Hugging Face Trending Papers
Jul 30

Scaling Vision-Language Models Is Not Enough to Mitigate Bias

Vision-Language Models (VLMs) such as CLIP are now foundational to multimodal systems, yet their robustness to spurious correlations remains poorly understood at scale. We present the first large-scale empirical study of 194 publicly available VLMs, including 16 model families, covering a wide range of model sizes, 24 training datasets, and three evaluation benchmarks, namely ImageNet (overall performance), CelebA (typical single-attribute bias), and UrbanCars (complex multi-attribute biases).