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Kondratyuk et al.: autoregressive multimodal video/audio generation

Contribution grade

Multimodal sequence-generation systems · Human-designed research method or resource

Reference score80,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Trained a decoder-only transformer on mixed multimodal generation objectives over video, image, text and audio representations, then adapted the pretrained model to conditioned video-generation tasks.

Scientific scope

Trained a decoder-only transformer on mixed multimodal generation objectives over video, image, text and audio representations, then adapted the pretrained model to conditioned video-generation tasks. Autoregressive transformers and pretrained tokenizers are inherited. Acquired delta is unified multimodal training/task integration, not generic next-token modeling. Zero-shot generation in tested settings is not arbitrary-domain or long-horizon consistency.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Autoregressive transformers and pretrained tokenizers are inherited. Acquired delta is unified multimodal training/task integration, not generic next-token modeling. Zero-shot generation in tested settings is not arbitrary-domain or long-horizon consistency.

Contributors

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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