mlsanityData to 7 Oct 2026

Stop-Think-AutoRegress: Language Modeling with Latent Diffusion Planning

arXiv 2602.20528 ↗First posted 24 Feb 2026cs.CL cs.LGcited by 2

On the map: LLM reasoning → Diffusion reasoning and test-time scaling

also in 1 more
Abstract (arXiv)

The Stop-Think-AutoRegress Language Diffusion Model (STAR-LDM) integrates latent diffusion planning with autoregressive generation. Unlike conventional autoregressive language models limited to token-by-token decisions, STAR-LDM incorporates a "thinking" phase that pauses generation to refine a semantic plan through diffusion before continuing. This enables global planning in continuous space prior to committing to discrete tokens. Evaluations show STAR-LDM significantly outperforms similar-sized models on language understanding benchmarks and achieves >70% win rates in LLM-as-judge comparisons for narrative coherence and commonsense reasoning. The architecture also allows straightforward control through lightweight classifiers, enabling fine-grained steering of attributes without model retraining while maintaining better fluency-control trade-offs than specialized approaches.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Diffusion reasoning and test-time scaling, one of 17 directions in LLM reasoning. Builds on 68 of its 82 references that are in the map. First posted on arXiv 24 Feb 2026.

Builds on

68 of 82 references.

References · 68

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  4. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  5. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  6. 2022Training language models to follow instructions with human feedbackcited by 15,712
  7. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  8. 2024The Llama 3 Herd of Modelscited by 14,192
  9. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  10. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  11. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  12. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
38 more
  1. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  2. 2022Scalable Diffusion Models with Transformerscited by 5,931
  3. 2022Classifier-Free Diffusion Guidancecited by 5,717
  4. 2018Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challengecited by 4,710
  5. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
  6. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
  7. 2019HellaSwag: Can a Machine Really Finish Your Sentence?cited by 4,018
  8. 2019The Curious Case of Neural Text Degenerationcited by 2,881
  9. 2019PIQA: Reasoning about Physical Commonsense in Natural Languagecited by 2,818
  10. 2019Fine-Tuning Language Models from Human Preferencescited by 2,124
  11. 2018Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answeringcited by 2,114
  12. 2018CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledgecited by 1,793
  13. 2023Pythia: A Suite for Analyzing Large Language Models Across Training and Scalingcited by 1,734
  14. 2020GLU Variants Improve Transformercited by 1,724
  15. 2021Structured Denoising Diffusion Models in Discrete State-Spacescited by 1,665
  16. 2020Don't Stop Pretraining: Adapt Language Models to Domains and Taskscited by 1,548
  17. 2019Root Mean Square Layer Normalizationcited by 1,425
  18. 2020RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Modelscited by 1,235
  19. 2021Cascaded Diffusion Models for High Fidelity Image Generationcited by 1,155
  20. 2021Variational Diffusion Modelscited by 1,145
  21. 2024The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scalecited by 1,104
  22. 2022Diffusion-LM Improves Controllable Text Generationcited by 1,006
  23. 2022DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Modelscited by 924
  24. 2024Chameleon: Mixed-Modal Early-Fusion Foundation Modelscited by 904
  25. 2022STaR: Bootstrapping Reasoning With Reasoningcited by 895
  26. 2019Plug and Play Language Models: A Simple Approach to Controlled Text Generationcited by 793
  27. 2024Training Large Language Models to Reason in a Continuous Latent Spacecited by 763
  28. 2024Emu3: Next-Token Prediction is All You Needcited by 761
  29. 2019CTRL: A Conditional Transformer Language Model for Controllable Generationcited by 754
  30. 2024Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Modelcited by 468
  31. 2021MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontierscited by 466
  32. 2021Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Modelscited by 462
  33. 2022DiffuSeq: Sequence to Sequence Text Generation with Diffusion Modelscited by 439
  34. 2021DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Expertscited by 434
  35. 2016A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Storiescited by 413
  36. 2023Fine-Grained Human Feedback Gives Better Rewards for Language Model Trainingcited by 399
  37. 2020GeDi: Generative Discriminator Guided Sequence Generationcited by 377
  38. 2021FUDGE: Controlled Text Generation With Future Discriminatorscited by 374

50 most cited of 68 All 68 (JSON)

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026MetaState: Persistent Working Memory Enhances Reasoning in Discrete Diffusion Language Modelscited by 3
  2. 2026Token Time Continuous Diffusion for Language Modelingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Reasoning with Latent Tokens in Diffusion Language Models
  2. 2025Diffuse Thinking: Exploring Diffusion Language Models as Efficient Thought Proposers for Reasoning
  3. 2026Think First, Diffuse Fast: Improving Diffusion Language Model Reasoning via Autoregressive Plan Conditioning
  4. 2024Scaling Diffusion Language Models via Adaptation from Autoregressive Models
  5. 2026Diffusion Language Models: An Experimental Analysis
  6. 2025Planner and Executor: Collaboration between Discrete Diffusion And Autoregressive Models in Reasoning
  7. 2025A Survey on Diffusion Language Models
  8. 2026Early Decisions Matter: Proximity Bias and Initial Trajectory Shaping in Non-Autoregressive Diffusion Language Models