SVFSearch: A Multimodal Knowledge-Intensive Benchmark for Short-Video Frame Search in the Gaming Vertical Domain
SVFSearch is a new benchmark designed for short-video frame search specifically in the gaming domain. It includes a comprehensive set of test and training examples to evaluate multimodal large language models. The benchmark aims to address challenges in visual grounding and retrieval quality, highlighting gaps in current model performance.
- ▪SVFSearch contains 5,000 test examples and 4,198 training examples focused on paused game scenes.
- ▪The benchmark provides a controlled environment for evaluation, avoiding reliance on web search APIs.
- ▪Results show a significant performance gap between model-only answering and practical agentic search.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.17946 |
| Publication time | Tue, 19 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-19T04:04:57.272Z |
| Last seen | 2026-05-19T04:04:57.272Z |
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Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Artificial Intelligence arXiv:2605.17946 (cs) [Submitted on 18 May 2026] Title:SVFSearch: A Multimodal Knowledge-Intensive Benchmark for Short-Video Frame Search in the Gaming Vertical Domain Authors:Lingtao Mao, Huangyu Dai, Xinyu Sun, Zihan Liang, Ben Chen, Chenyi Lei, Wenwu Ou View a PDF of the paper titled SVFSearch: A Multimodal Knowledge-Intensive Benchmark for Short-Video Frame Search in the Gaming Vertical Domain, by Lingtao Mao and 6 other authors View PDF HTML (experimental) Abstract:Multimodal large language models are increasingly used as agent backbones that understand multimodal inputs, plan retrieval actions, invoke external tools, and reason over retrieved information.
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