A Benchmark for Omni-Modal Reasoning in Long Videos

Mohammed Irfan Kurpath; Jaseel Muhammad Kaithakkodan; Jinxing Zhou; Sahal Shaji Mullappilly; Mohammad Almansoori; Noor Ahsan; Beknur Kalmakhanbet; Sambal Shikhar; Rishabh Lalla; Jean Lahoud; Mariette Awad; Fahad Shahbaz Khan; Salman Khan; Rao Muhammad Anwer; Hisham Cholakkal

arXiv · 2512.16978

A Benchmark for Omni-Modal Reasoning in Long Videos

Mohammed Irfan Kurpath·Jaseel Muhammad Kaithakkodan·Jinxing Zhou·Sahal Shaji Mullappilly·Mohammad Almansoori·Noor Ahsan·Beknur Kalmakhanbet·Sambal Shikhar·Rishabh Lalla·Jean Lahoud·Mariette Awad·Fahad Shahbaz Khan·Salman Khan·Rao Muhammad Anwer·Hisham Cholakkal

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Abstract

Long-form omni-modal video understanding requires integrating vision, speech, and ambient audio with coherent long-context reasoning. Existing video benchmarks often trade off temporal scale, modality coverage, open-ended interaction, and interpretable scoring. To address this gap, we introduce LongShOTBench, a long video understanding benchmark designed around three coupled goals: holistic omni-modal integration, intent-driven open-ended interaction, and rubric-level diagnosis. It builds single- and multi-turn questions from real viewing scenarios, with systematic tasks probing visual, speech, ambient-audio, temporal, and cross-modal reasoning. Each item includes a reference answer and a weighted criterion-level rubric, letting evaluation identify which perceptual facts, temporal links, modality-grounding requirements, and reasoning steps are satisfied or missed. All samples are manually verified to improve grounding, clarity, and rubric reliability. We also introduce LongShOTAgent, a training-free omni-modal evidence-seeking agent coupling full-video preprocessing with targeted retrieval, query-adaptive segment refinement, and explicit claim verification over visual, speech, and non-speech audio evidence. Its iterative search-refine-verify loop exposes intermediate evidence and lets modality-specific specialists re-analyze relevant moments before answering. We evaluate 105 video-capable models spanning open-source omni-modal models, vision-language systems, audio LLMs, agentic pipelines and closed-source APIs. Current MLLMs remain far from saturating LongShOTBench, while our LongShOTAgent is the strongest training-free system, reaching 66.64% overall. By releasing the benchmark, leaderboard, and method, we provide a shared, interpretable testbed for advancing long-form omni-modal video reasoning. Code, data, and the leaderboard are available at https://longshot.cvmbzuai.com/.

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Mohammed Irfan Kurpath, Jaseel Muhammad Kaithakkodan, Jinxing Zhou, Sahal Shaji Mullappilly, Mohammad Almansoori, Noor Ahsan, Beknur Kalmakhanbet, Sambal Shikhar, Rishabh Lalla, Jean Lahoud, Mariette Awad, Fahad Shahbaz Khan, Salman Khan, Rao Muhammad Anwer, Hisham Cholakkal. 2026-06-16. A Benchmark for Omni-Modal Reasoning in Long Videos. https://arxiv.org/abs/2512.16978

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