LLaDA-o: An Effective and Length-Adaptive Omni Diffusion Model

• URL: http://arxiv.org/abs/2603.01068v1
• Date: Sun, 01 Mar 2026 12:05:06 GMT
• Title: LLaDA-o: An Effective and Length-Adaptive Omni Diffusion Model
• Authors: Zebin You, Xiaolu Zhang, Jun Zhou, Chongxuan Li, Ji-Rong Wen,
• Abstract summary: We present textbfLLaDA-o, an effective and length-adaptive omni diffusion model for multimodal understanding and generation.<n>Building on MoD, we introduce a data-centric length adaptation strategy that enables flexible-length decoding in multimodal settings.<n>Experiments show that LLaDA-o achieves state-of-the-art performance among omni-diffusion models on multimodal understanding and generation benchmarks.
• Score: 77.66516875262963
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present \textbf{LLaDA-o}, an effective and length-adaptive omni diffusion model for multimodal understanding and generation. LLaDA-o is built on a Mixture of Diffusion (MoD) framework that decouples discrete masked diffusion for text understanding and continuous diffusion for visual generation, while coupling them through a shared, simple, and efficient attention backbone that reduces redundant computation for fixed conditions. Building on MoD, we further introduce a data-centric length adaptation strategy that enables flexible-length decoding in multimodal settings without architectural changes. Extensive experiments show that LLaDA-o achieves state-of-the-art performance among omni-diffusion models on multimodal understanding and generation benchmarks, and reaches 87.04 on DPG-Bench for text-to-image generation, supporting the effectiveness of unified omni diffusion modeling. Code is available at https://github.com/ML-GSAI/LLaDA-o.

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