From Content to Knowledge: Lightning Fast Long-Video Understanding with Neural Knowledge Representations

• URL: http://arxiv.org/abs/2606.11913v1
• Date: Wed, 10 Jun 2026 10:43:35 GMT
• Title: From Content to Knowledge: Lightning Fast Long-Video Understanding with Neural Knowledge Representations
• Abstract summary: We propose a new paradigm for long video understanding by treating a long video as a Neural Knowledge Representation (NKR)<n>NKR represents video contents neither as a stream of tokens nor pre-organized databases, but as an individual small portion of network weights attached to the VLM backbone.<n>At inference, the lightweight NKR is mounted onto a frozen Vision-Language Model (VLM), enabling direct, query-based understanding without reloading or re-encoding the original video.
• Score: 61.234896830825754
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a new paradigm for long video understanding by treating a long video as a Neural Knowledge Representation (NKR). NKR represents video contents neither as a stream of tokens nor pre-organized databases, but as an individual small portion of network weights attached to the VLM backbone. The NKR weights are optimized to encapsulate the video's semantic content via a novel Agentic Knowledge Distillation (AKD) process, where an agent automatically synthesizes dense descriptions and question-answer pairs to distill the video's knowledge into the NKR. While AKD serves as a comprehensive, one-time encoding phase, the resulting NKR transforms the video into a portable, reusable asset. At inference, the lightweight NKR is mounted onto a frozen Vision-Language Model (VLM), enabling direct, query-based understanding without reloading or re-encoding the original video. This approach decouples video length from inference cost, offering high amortized efficiency for multi-turn video understanding. Experiments on the LVBench benchmark show our method achieves performance comparable to state-of-the-art approaches while reducing end-to-end latency by over two orders of magnitude, opening new possibilities for interactive long-video understanding.

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