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arXiv Computer Vision By Shawn Liang, Sahil Shah, Chengwei Zhou, S P Sharan, Harsh Goel, Arnab Sanyal, Sandeep Chinchali, Gourav Datta

RT-NeuS: Towards Real-Time Neuro-Symbolic Video Understanding via Adaptive Temporal Verification

Read the original on arXiv Computer Vision →

RT-NeuS is a neuro‑symbolic framework for long‑form video question answering that retains the accuracy and formal guarantees of temporal‑logic‑guided methods while dramatically reducing inference latency. It achieves this by using coarse‑to‑fine adaptive sampling to focus on query‑relevant frames and batched proposition detection with KV‑cache reuse, enabling all propositions to be evaluated in a single forward pass. Experiments on LongVideoBench, Video‑MME, and MLVU show up to a 13× speed‑up on an NVIDIA H200 GPU while matching or surpassing prior neuro‑symbolic accuracy.

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 Computer Vision.

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