mlsanityData to 14 Oct 2026

Unveiling the Potential of Diffusion Large Language Model in Controllable Generation

arXiv 2507.04504 ↗First posted 6 Jul 2025cs.CLcited by 15

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

Controllable generation is a fundamental task in NLP with many applications, providing a basis for function calling to agentic communication. However, even state-of-the-art autoregressive Large Language Models (LLMs) today exhibit unreliability when required to generate structured output. Inspired by the current new diffusion-based large language models (dLLM), we realize that the architectural difference, especially the global information-sharing mechanism for language modeling, may be the key to unlock next-level controllable generation. To explore the possibility, we propose Self-adaptive Schema Scaffolding (S3), a novel framework that enables dLLM to stably generate reliable structured outputs (e.g., JSON) by utilizing its innate reverse reasoning capability and global context awareness. S3 initiates a schematic template directly in the output context as a starting state for dLLM, offering a more robust and general method than intricate prompt optimization. Experiments demonstrate that our method substantially unlocks the dLLM's potential in controllable generation in terms of structure adherence, content fidelity, and faithfulness. These results establish new perspectives and practical pathways for deploying language models in controllable generation tasks.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 29 of its 32 references that are in the map. First posted on arXiv 6 Jul 2025.

Builds on

29 of 32 references.

References · 29

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2022Training language models to follow instructions with human feedbackcited by 15,971
  3. 2020Measuring Massive Multitask Language Understandingcited by 7,963
  4. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,330
  5. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,780
  6. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,761
  7. 2023WebArena: A Realistic Web Environment for Building Autonomous Agentscited by 2,006
  8. 2021Structured Denoising Diffusion Models in Discrete State-Spacescited by 1,719
  9. 2025Large Language Diffusion Modelscited by 912
  10. 2025Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Modelscited by 409
  11. 2025LLaDA 1.5: Variance-Reduced Preference Optimization for Large Language Diffusion Modelscited by 285
  12. 2024Scaling Diffusion Language Models via Adaptation from Autoregressive Modelscited by 274
17 more
  1. 2025Mercury: Ultra-Fast Language Models Based on Diffusioncited by 235
  2. 2023Grammar-Constrained Decoding for Structured NLP Tasks without Finetuningcited by 197
  3. 2023Multimodal Large Language Models: A Surveycited by 195
  4. 2025LLaDA-V: Large Language Diffusion Models with Visual Instruction Tuningcited by 148
  5. 2025A Comprehensive Survey on Long Context Language Modelingcited by 117
  6. 2025Accelerating Diffusion LLMs via Adaptive Parallel Decodingcited by 112
  7. 2025Discrete Diffusion in Large Language and Multimodal Models: A Surveycited by 61
  8. 2025Edit Flows: Flow Matching with Edit Operationscited by 55
  9. 2024Automata-based constraints for language model decodingcited by 51
  10. 2023Diffusion Models for Non-autoregressive Text Generation: A Surveycited by 44
  11. 2025Type-Constrained Code Generation with Language Modelscited by 35
  12. 2025Mapping the Minds of LLMs: A Graph-Based Analysis of Reasoning LLMcited by 21
  13. 2016Neural Text Generation from Structured Data with Application to the Biography Domaincited by 20
  14. 2025SLOT: Structuring the Output of Large Language Modelscited by 18
  15. 2025CtrlDiff: Boosting Large Diffusion Language Models with Dynamic Block Prediction and Controllable Generationcited by 17
  16. 2024DRS: Deep Question Reformulation With Structured Outputcited by 6
  17. 2025AppealCase: A Dataset and Benchmark for Civil Case Appeal Scenarioscited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 5; 2026: 10.

15 papers.

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  1. 2025dVLM-AD: Enhance Diffusion Vision-Language-Model for Driving via Controllable Reasoningcited by 14
  2. 2025DiffuGuard: How Intrinsic Safety is Lost and Found in Diffusion Large Language Modelscited by 12
  3. 2025Activation Steering for Masked Diffusion Language Modelscited by 9
  4. 2025Decoding Large Language Diffusion Models with Foreseeing Movementcited by 6
  5. 2026Gated Differentiable Working Memory for Long-Context Language Modelingcited by 6
  6. 2026VideoStir: Understanding Long Videos via Spatio-Temporally Structured and Intent-Aware RAGcited by 6
  7. 2025Orchestrating Dual-Boundaries: An Arithmetic Intensity Inspired Acceleration Framework for Diffusion Language Modelscited by 5
  8. 2026Improving Variable-Length Generation in Diffusion Language Models via Length Regularizationcited by 4
  9. 2026Diffusion LMs Can Approximate Optimal Infilling Lengths Implicitlycited by 2
  10. 2026VDLM: Variable Diffusion LMs via Robust Latent-to-Text Renderingcited by 1
  11. 2026Differences in Text Generated by Diffusion and Autoregressive Language Modelscited by 1
  12. 2026MaskForge: Structure-Aware Adaptive Attacks for Jailbreaking Diffusion Large Language Modelscited by 1
3 more
  1. 2026When Confidence Misleads: Suffix Anchoring and Anchor-Proximity Confidence Modulation for Diffusion Language Modelscited by 0
  2. 2026FastGuide: Accelerating Reward Guidance for Diffusion Large Language Modelscited by 0
  3. 2026Anchors Enhance Controllability and Reasoning of Diffusion Language Modelscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Agents of Diffusion: Enhancing Diffusion Language Models with Multi-Agent Reinforcement Learning for Structured Data Generation (Extended Version)
  2. 2025On Powerful Ways to Generate: Autoregression, Diffusion, and Beyond
  3. 2026The Bitter Lesson of Diffusion Language Models for Agentic Workflows: A Comprehensive Reality Check
  4. 2026Diffusion Language Models: An Experimental Analysis
  5. 2026The Flexibility Trap: Rethinking the Value of Arbitrary Order in Diffusion Language Models
  6. 2026Hierarchical Continuous Diffusion Language Models
  7. 2025Unlocking the Potential of Diffusion Language Models through Template Infilling
  8. 2026Efficient Self-Evaluation for Diffusion Language Models via Sequence Regeneration