mlsanityData to 14 Oct 2026

Diffusion-MF: Approximate Structured Diffusion for Sequence Labelling

arXiv 2606.18856 ↗First posted 17 Jun 2026cs.CL cs.LGcited by 0

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Abstract (arXiv)

We introduce Diffusion-MF, a discrete diffu- sion sequence labeller that places a linear-chain conditional random field (LCRF) inside the denoising loop. Unlike prior diffusion labellers, it performs structured inference at every step; parallel Mean-Field makes this efficient. Across multilingual POS, CoNLL-2003 NER, and joint Chinese Segmentation and POS, Diffusion-MF achieves the best primary result across all experimental settings.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 39 references that are in the map. First posted on arXiv 17 Jun 2026.

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References · 17

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  3. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  4. 2021Structured Denoising Diffusion Models in Discrete State-Spacescited by 1,719
  5. 2022Diffusion-LM Improves Controllable Text Generationcited by 1,027
  6. 2021Vector Quantized Diffusion Model for Text-to-Image Synthesiscited by 774
  7. 2021Argmax Flows and Multinomial Diffusion: Learning Categorical Distributionscited by 574
  8. 2016Neural Architectures for Named Entity Recognitioncited by 550
  9. 2022DiffuSeq: Sequence to Sequence Text Generation with Diffusion Modelscited by 450
  10. 2022Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioningcited by 373
  11. 2015Conditional Random Fields as Recurrent Neural Networkscited by 214
  12. 2022Score-based Continuous-time Discrete Diffusion Modelscited by 169
5 more
  1. 2014Deep Unfolding: Model-Based Inspiration of Novel Deep Architecturescited by 77
  2. 2013Loopy Belief Propagation for Approximate Inference: An Empirical Studycited by 76
  3. 2020Efficient Second-Order TreeCRF for Neural Dependency Parsingcited by 48
  4. 2020AIN: Fast and Accurate Sequence Labeling with Approximate Inference Networkcited by 3
  5. 2025Bregman Conditional Random Fields: Sequence Labeling with Parallelizable Inference Algorithmscited by 1

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