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

Same Answer, Different Representations: Hidden instability in VLMs

Read the original on arXiv AI →

The Flow has not summarised this story yet — read it at arXiv AI.

arXiv Computation and Language
Oct 12

When VLMs Destroy What They Know: Spatial Representations Fall Below Lexical Priors

The paper introduces EICS, a metric for assessing whether Vision‑Language Models (VLMs) consistently encode concepts under controlled one‑concept interventions, and CASP‑VL, a benchmark of causally controlled spatial perturbations derived from Visual Genome scene graphs. Experiments across multiple VLM architectures reveal that spatial relations are encoded far less faithfully than lexical information, even when models perform well on compositional benchmarks. The study shows that relational information is retained at the predicate token level but disappears when sentences are aggregated into a global representation, highlighting a systematic failure invisible to output‑based evaluation.

By Shubh Garg, Chen Shani