Multi-Stream Residual Computation in Diffusion Language Models

Key points

• Yet their Transformer backbones typically carry visible evidence and evolving masked predictions through a single residual stream.1
• We equip masked diffusion models with multiple residual streams that persist through depth and adaptive routing between them, then investigate how they support these complementary roles.1
• State interventions explain the benefit of the additional stream: it preserves early token representations for later reuse while evidence accumulates at masked positions.1
• This mechanism recurs at a larger model scale, showing how persistent streams support context preservation alongside prediction refinement.1

Sources (1)

• [1]Multi-Stream Residual Computation in Diffusion Language Models
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 01:50 PM

  Diffusion language models reconstruct masked tokens in parallel, offering an alternative to autoregressive generation.

  Yet their Transformer backbones typically carry visible evidence and evolving masked predictions through a single residual stream.

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