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TOPIC · ENTITY #5693

diffusion language models (DLMs)

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3

RESEARCH · 1 SOURCE · arXiv cs.AI

DELA: controllable draft-level edits for diffusion language models (arXiv:2610.32096v1)

The paper introduces DELA (Diffusion Editing Language Adapter), a plug-in controller that enables explicit draft-level editing for frozen diffusion language models via four actions (Insert, Delete, Remask, Stop). DELA uses a two-phase decoding scheme—fast append-only drafting that preserves KV-cache reuse followed by global refinement—and a staged curriculum to train the controller; experiments on mathematical reasoning and code generation report up to 5.5 percentage points improvement over direct diffusion decoding.

6.0

RESEARCH · 1 SOURCE · arXiv cs.AI

Seq-Drifting: one-step text generation by drifting over token sequences (arXiv:2610.12879v1)

The paper proposes Seq-Drifting, a framework that learns a direct map from Gaussian noise to full token sequences in a single forward pass by building a position-wise drift field with attraction (to context-plausible token representations) and repulsion (to prevent sample collapse and repetition). The method achieves a one-step generative perplexity of 36.01 on LM1B, reports competitive unconditional and conditional generation, and is evaluated on translation, summarization, and reasoning; code is available at the authors' GitHub.

6.0

RESEARCH · 1 SOURCE · arXiv cs.AI

CaLR: Causal Latent Revision for robust diffusion reasoning (arXiv:2609.20981v1)

The arXiv paper (2609.20981v1) introduces CaLR (Causal Latent Revision), a framework that reformulates reasoning as constrained latent optimization. CaLR uses a causal topology matrix from an expert model and implicit differentiation to perform gradient-guided "thought revision," enforcing logical consistency during parallel generation; the authors report SOTA results for diffusion language models on complex benchmarks and improved robustness on constrained tasks such as Sudoku.

7.0