AI Video Papers2026
Converts pretrained multimodal LLMs into a System One decision interface that outputs structured, software-friendly decisions for video (temporal spans or multiple-choice answers); uses a shared scoring head and cached/joint inference to raise temporal-grounding mIoU and drastically speed up repeated queries.
Reframes city-scale 3D visual grounding as active spatial search: a VLM agent interacts with a multi-view BEV environment to query landmarks, zoom, render oblique views, and commit world-aligned bounding boxes. Trains a tool-use policy with reinforcement learning on CityRefer, improving Hit@1, IoU, and inference speed over ranking baselines.