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.
The Flow has not summarised this story yet — read it at arXiv AI.
arXiv:2606. 20077v1 Announce Type: cross Abstract: Visual tokens enter Large Language Models (LLMs) as raw, foreign signals.
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.
arXiv:2610.32360v1 Announce Type: new Abstract: Vision-language models (VLMs) process images and text jointly, yet robustness evaluations often perturb these inputs separately or assess only predefin...
arXiv:2606. 17389v1 Announce Type: cross Abstract: Multimodal Foundation Models are increasingly used as reasoning agents, making reliability, knowing when a model may hallucinate, critical.
arXiv:2606. 23763v1 Announce Type: cross Abstract: Recent work typically assesses vision--language consistency using attention distributions of answer-side tokens.
arXiv:2610.29241v1 Announce Type: new Abstract: In this paper, we investigate why vision-language models (VLMs) produce errors by decomposing them into two sources: visual-representation failures, in...