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
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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.
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