The Flow has not summarised this story yet — read it at arXiv Computer Vision.
arXiv:2609.13308v1 Announce Type: cross Abstract: A companion evaluation found that naming the target part in a manipulation prompt increased action accuracy by 0.32-0.63 across eight vision-language...
arXiv:2603. 29759v2 Announce Type: replace-cross Abstract: Recent advances in vision-language models (VLMs) have accelerated their application to indoor safety hazards assessment.
arXiv:2607. 00218v1 Announce Type: cross Abstract: Vision-language models (VLMs) are now proposed as runtime safety guards for embodied agents in homes and factories.
arXiv:2609.13225v1 Announce Type: cross Abstract: Benchmarks agree that vision-language models reason poorly about low-level manipulation, but an aggregate accuracy score does not say which step fail...
arXiv:2509. 25533v2 Announce Type: replace-cross Abstract: As Vision Language Models (VLMs) are deployed across safety-critical applications, understanding and controlling their behavioral patterns has become increasingly important.
EPIC-Bench is a perception‑centric benchmark for fine‑grained embodied visual grounding in vision‑language models. It contains 6.6k human‑annotated tuples of Image, Text, and Mask across the three core stages of embodied interaction—Target Localization, Navigation, and Manipulation. The benchmark offers 23 fine‑grained tasks, multi‑dimensional settings, and evaluates over 80 leading VLMs to provide diagnostic insights into their vision‑language grounding capabilities.