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:2603. 22278v2 Announce Type: replace-cross Abstract: Many multimodal tasks, such as image captioning and visual question answering, require vision-language models (VLMs) to bind objects with their properties and spatial relations.
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...
arXiv:2608.23074v1 Announce Type: new Abstract: Vision-language models (VLMs) can answer spatial questions, yet the mechanisms connecting object grounding to spatial reasoning remain poorly understoo...
arXiv:2605.05593v2 Announce Type: replace Abstract: Despite the remarkable success of Multimodal Large Language Models (MLLMs) across diverse tasks, the internal mechanisms governing how they encode...
arXiv:2607. 03358v1 Announce Type: cross Abstract: We study how visual information is routed in vision-language models (VLMs).
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.
Vision-language models (VLMs) can answer spatial questions, yet the mechanisms connecting object grounding to spatial reasoning remain poorly understood. It is underexplored whether spatial reasoning...
arXiv:2606. 20077v1 Announce Type: cross Abstract: Visual tokens enter Large Language Models (LLMs) as raw, foreign signals.
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:2610.08401v1 Announce Type: cross Abstract: While recent vision-language models (VLMs) have shown outstanding performance across diverse applications, they tend to under-use visual information...
arXiv:2608. 12333v1 Announce Type: cross Abstract: Vision-language models must associate visual entities with textual attributes.
The paper introduces S‑Space, a linear subspace within multimodal language models where object‑token representations encode continuous spatial coordinates along horizontal, vertical, and distance axes. This subspace is intrinsic, readable under non‑spatial queries, and serves as an internal spatial workspace: perception maps object locations into it, reasoning transforms it for viewpoint changes, and verbal responses selectively access it. Diagnostic experiments using S‑Space reveal that while models can form spatial representations, they struggle with spatial transformations needed for reliable reasoning across viewpoint changes.