AI agents watching video don't need long-form generation, they need fast judgments. VideoJev pushes that idea to its logical extreme. Title: VideoJev: System One Model for Video Understanding and Interaction URL: It unifies temporal grounding (finding the right video segment) and video question-answering under one design: scoring candidates directly. Here are three things worth knowing. 🎯 Scoring instead of generating The output head of a pretrained MLLM is replaced with a candidate scorer, so instead of generating tokens one by one, the model directly scores time intervals and text options. Trained on identical data, this approach beats autoregressive fine-tuning by 1.3 points on grounding and 1.9 points on QA. ⚡ Two inference modes for smart speedups Cached inference encodes the video once, while Joint inference processes multiple queries together in a single forward pass. Even at ten queries per video, the added cost is just tens of milliseconds, and prediction quality actually improves. 📈 Speed and accuracy, both at once On temporal grounding it improves mIoU by up to 10.8 points while running 2.8-3.1x faster, and on video QA it matches accuracy while delivering over 100x speedups. Judging instantly rather than "thinking then answering" looks like a real step forward for making visual agents responsive in real time. #VideoAI# #AIAgents#