SleuthVLM: Do We Really Need Audio-in-Video for Omni Video Understanding?
SleuthVLM is an agent harness built on a vision-language model that never processes audio directly.
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- Primary sourceHugging Face
- Discovery linkHugging Face
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SleuthVLM is an agent harness built on a vision-language model that never processes audio directly. Speech is transcribed into text, and the VLM autonomously examines the video without a predefined workflow. On four omni benchmarks with videos up to 90 minutes, the authors report 75.0% average accuracy, 2.6 points above Gemini 3.8 Flash, and 77.8% on Video-MME-v2. A 27B open-source VLM variant reportedly beats all open-source omni models.
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SleuthVLM reports 75.0% average accuracy across four omni benchmarks with videos up to 90 minutes.
Source: Hugging Face ↗Stored source-based brief · key point 1 · AINewsIQ paraphraseThe authors report 77.8% on the Video-MME-v2 leaderboard.
Source: Hugging Face ↗Stored source-based brief · key point 2 · AINewsIQ paraphraseThe system uses transcribed speech and never hears audio directly.
Source: Hugging Face ↗Stored source-based brief · key point 3 · AINewsIQ paraphraseThe authors conclude agentic ability, not auditory input, is the bottleneck for omni understanding.
Source: Hugging Face ↗Stored source-based brief · key point 4 · AINewsIQ paraphraseLimits from the stored brief
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Why this may matter
The work challenges the assumption that omni models need direct audio input and scale, suggesting agentic reasoning over transcribed speech may be a stronger direction for long-video understanding.
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