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Audio-Visual Flamingo: Open Audio-Visual Intelligence for Long and Complex Videos

Research Multimodal & Generative

Merged summary

TL;DR - AV-Flamingo is a fully open, state-of-the-art audio-visual large language model built for joint understanding and reasoning over audio, images, and long-form videos, targeting the underserved regime of long and complex real-world content.

  • Introduces Audio-Visual-Skills, a large-scale dataset of real-world videos with ~7M caption and QA training instances emphasizing temporal, compositional, and cross-modal reasoning.
  • Uses a three-stage curriculum that progresses from short-range perception to long-horizon multi-event reasoning.
  • Proposes Temporal Audio-Visual Interleaved Chain-of-Thought, grounding intermediate reasoning steps to timestamps for better temporal alignment and interpretability.
  • Reports outperforming similarly sized open models across 15+ benchmarks and remaining competitive with (sometimes surpassing) much larger open-weight and closed models, especially on long/complex tasks.

Sources (1)

Audio-Visual Flamingo: Open Audio-Visual Intelligence for Long and Complex Videos

arXiv eess.AS Sreyan Ghosh, Arushi Goel, Kaousheik Jayakumar, Lasha Koroshinadze, Nishit Anand, Siddharth Gururani, Hanrong Ye, Pritam Biswas, Yuanhang Su, Ehsan Hosseini-Asl, Sang-gil Lee, Zhifeng Kong, Jaehyeon Kim, Sungwon Kim, S Sakshi, Ramani Duraiswami, Dinesh Manocha, Andrew Tao, Mohammad Shoeybi, Bryan Catanzaro, Ming-Yu Liu, Wei Ping 2026-07-17 arXiv:2607.16107

TL;DR - AV-Flamingo is a fully open, state-of-the-art audio-visual large language model built for joint understanding and reasoning over audio, images, and long-form videos, targeting the underserved regime of long and complex real-world content.

  • Introduces Audio-Visual-Skills, a large-scale dataset of real-world videos with ~7M caption and QA training instances emphasizing temporal, compositional, and cross-modal reasoning.
  • Uses a three-stage curriculum that progresses from short-range perception to long-horizon multi-event reasoning.
  • Proposes Temporal Audio-Visual Interleaved Chain-of-Thought, grounding intermediate reasoning steps to timestamps for better temporal alignment and interpretability.
  • Reports outperforming similarly sized open models across 15+ benchmarks and remaining competitive with (sometimes surpassing) much larger open-weight and closed models, especially on long/complex tasks.
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