VideoSeek Targets Long-Horizon Video Understanding With Tool-Guided Evidence Search

Researchers behind VideoSeek introduced a long-horizon video agent designed to find answer-critical evidence without exhaustively parsing every frame of a long video.

A tool-guided approach to long videos

The paper, “VideoSeek: Long-Horizon Video Agent with Tool-Guided Seeking,” is listed by the Computer Vision Foundation’s CVPR 2026 Open Access site. According to the CVF page, VideoSeek uses a think-act-observe loop and a set of video tools to decide where to look in a video when answering questions.

That design targets a practical problem in video understanding: long videos can contain large amounts of irrelevant footage, while the evidence needed to answer a question may appear only briefly. The arXiv abstract for the same paper describes VideoSeek as a long-horizon video agent that uses video logic flow to seek relevant evidence rather than exhaustively parsing video frames.

The official GitHub repository for jylins/videoseek describes the project as the implementation for the CVPR 2026 paper and lists affiliations with AMD and the University of Rochester. The repository also documents several tools used by the system, including overview, skim, and focus tools. Those names indicate a workflow in which the model can first form a broad understanding of a video, then narrow attention to potentially relevant intervals, and finally inspect specific segments more closely.

Reported benchmark gains

The CVF Open Access page reports that VideoSeek improves performance on LVBench by 10.2 points while using 93% fewer frames than its base model. The paper’s framing suggests that the improvement comes from selecting evidence more selectively, rather than increasing the amount of video processed.

That distinction matters because many video-language systems face a trade-off between accuracy and computational cost. Processing more frames can help capture important moments, but it also increases memory and compute requirements. A system that can identify relevant portions of a video with fewer frames may be useful for long-form question answering, surveillance review, educational video search, and other settings where evidence is sparse across time.

Why it matters

VideoSeek fits into a broader research direction in which multimodal models are given explicit tools and iterative decision-making loops. Instead of treating a video as a fixed input to be sampled uniformly, the system can decide what to inspect next based on what it has already observed.

The sources do not establish how VideoSeek performs outside the reported benchmarks, nor whether the same efficiency gains hold across different domains or video distributions. The GitHub repository provides implementation materials, but broader adoption will depend on reproducibility, hardware requirements, and how well the approach integrates with existing video-language models.

For now, the key claim from the CVF and arXiv materials is narrower: VideoSeek proposes a tool-guided method for long-horizon video question answering, and the authors report stronger LVBench results while using substantially fewer frames than the base model.