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Video-Oasis: Rethinking Evaluation of Video Understanding
Abstract
Video-Oasis diagnostics reveal that half of existing video benchmarks can be solved without visual input, exposing significant capability gaps in current video understanding models.
The inherent complexity of video understanding makes it difficult to determine whether Video-LLM benchmark performance stems from visual perception, linguistic reasoning, or knowledge priors. While many benchmarks have emerged to assess high-level reasoning, shared criteria for evaluating video understanding remain largely overlooked. Instead of introducing yet another benchmark, we take a step back to re-examine the criteria for evaluating video understanding. In this work, we introduce Video-Oasis, a sustainable diagnostic suite for systematically auditing existing video understanding benchmarks. This audit reveals that 55\% of existing benchmark samples are solvable without visual input or temporal context. After filtering these shortcuts, the remaining video-native challenges expose a substantial capability gap: state-of-the-art models perform only marginally above random guessing. Building on these findings, we use the distilled challenges as a testbed to investigate which algorithmic design choices contribute to robust video understanding. We hope our work provides a practical foundation for constructing rigorous video benchmarks and evaluating future Video-LLMs. Code is available at https://github.com/sejong-rcv/Video-Oasis.
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TL;DR. Video-Oasis rethinks the current benchmark landscape by examining whether proliferating video benchmarks truly satisfy shared criteria for genuine video understanding.
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Cite arxiv.org/abs/2603.29616 in a model README.md to link it from this page.
Cite arxiv.org/abs/2603.29616 in a dataset README.md to link it from this page.
Cite arxiv.org/abs/2603.29616 in a Space README.md to link it from this page.
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