arXiv:2606. 09547v1 Announce Type: cross Abstract: Learning everyday skills, like cooking a dish, relies increasingly on instructional media such as online videos.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2606. 00616v1 Announce Type: cross Abstract: Recent Vision-Language Models (VLMs) struggle with grounded reasoning, temporal consistency, and context aware planning in videos.
arXiv:2603. 10652v3 Announce Type: replace-cross Abstract: In real-world deployment, vision-language models often encounter disturbances such as weather, occlusion, and camera motion.
arXiv:2609.34581v2 Announce Type: replace Abstract: Temporal video grounding is a key capability of advanced Multimodal Large Language Models (MLLMs) for the thorough understanding of video events, w...
When should an intelligent assistant speak up without being asked? Continuous egocentric video offers rich, evolving context that enables a new form of assistance: one that is proactive rather than merely reactive.
Video-IFBench is a new benchmark designed to evaluate how well multimodal large language models (MLLMs) follow user-specified instructions in video understanding tasks. It introduces an instruction taxonomy with four templates—single-task, multi-task, selection, and nested—covering 32 task types and 39 constraint categories that span semantic and format requirements. The benchmark was built using a semi-automatic pipeline that combines MLLMs, programmatic processing, and human verification, producing 1.5K samples, and a large-scale evaluation of over 20 recent MLLMs shows that instruction following remains difficult, especially for complex constraints and conditional structures.
arXiv:2607. 11523v1 Announce Type: cross Abstract: When should an intelligent assistant speak up without being asked?