mlsanityData to 7 Oct 2026

Prefix-Denoising Consistency: Test-Time Verification for Diffusion Language Models

arXiv 2608.25311 ↗First posted 26 Aug 2026cs.LGcited by 0

On the map: Diffusion models → Discrete and text diffusion

Abstract (arXiv)

Diffusion Language Models (DLMs) have recently become increasingly competitive with autoregressive (AR) models, and even outperform them on certain tasks. Unlike AR models, DLMs produce output through iterative denoising without a left-to-right order. To further improve the performance of DLMs, we introduce PDC (Prefix-Denoising Consistency), a test-time self-verification method for DLMs. PDC exploits a distinctive test-time signal in DLMs under prefix conditioned regeneration, correct trajectories are more stable and reproducible than incorrect ones. Concretely, given an initially generated sample, PDC splits the sentence at an intermediate position and regenerates the remaining tokens conditioned on the fixed prefix. Across mathematical reasoning and commonsense reasoning benchmarks, PDC consistently improves upon the initial sample, outperforms independent generations under a computational constrained comparison, and is robust to different unmasking strategies and parameter settings. These results highlight prefix-conditioned regeneration as an effective DLM-specific primitive for test-time verification.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Discrete and text diffusion, one of 14 directions in Diffusion models. Builds on 34 of its 37 references that are in the map. First posted on arXiv 26 Aug 2026.

Builds on

34 of 37 references.

Closest ancestors · 12

Same direction, newest first

  1. 2025Learning Unmasking Policies for Diffusion Language Models9 Dec
  2. 2025Dream 7B: Diffusion Large Language Models21 Aug
  3. 2025Time Is a Feature: Exploiting Temporal Dynamics in Diffusion Language Models12 Aug
  4. 2025Diffusion LLMs Can Do Faster-Than-AR Inference via Discrete Diffusion Forcing8 Aug
  5. 2025Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models12 Mar
  6. 2025Large Language Diffusion Models14 Feb
  7. 2024Scaling Diffusion Language Models via Adaptation from Autoregressive Models23 Oct
  8. 2024Simple and Effective Masked Diffusion Language Models11 Jun
  9. 2024Simplified and Generalized Masked Diffusion for Discrete Data6 Jun
  10. 2023Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution25 Oct
  11. 2023AR-Diffusion: Auto-Regressive Diffusion Model for Text Generation16 May
  12. 2022SSD-LM: Semi-autoregressive Simplex-based Diffusion Language Model for Text Generation and Modular Control31 Oct

Other references · 22

  1. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
  2. 2025Qwen3 Technical Reportcited by 8,195
  3. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  4. 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,203
  5. 2023Let's Verify Step by Stepcited by 3,598
  6. 2018CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledgecited by 1,793
  7. 2021Structured Denoising Diffusion Models in Discrete State-Spacescited by 1,665
  8. 2025gpt-oss-120b & gpt-oss-20b Model Cardcited by 1,258
  9. 2021Are NLP Models really able to Solve Simple Math Word Problems?cited by 861
  10. 2021Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategiescited by 736
  11. 2025Fast-dLLM: Training-free Acceleration of Diffusion LLM by Enabling KV Cache and Parallel Decodingcited by 404
  12. 2025LLaDA 1.5: Variance-Reduced Preference Optimization for Large Language Diffusion Modelscited by 273
10 more
  1. 2023Let's Sample Step by Step: Adaptive-Consistency for Efficient Reasoning and Coding with LLMscited by 111
  2. 2024Escape Sky-high Cost: Early-stopping Self-Consistency for Multi-step Reasoningcited by 105
  3. 2025Nemotron 3 Nano: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoningcited by 85
  4. 2025Accelerating Diffusion Large Language Models with SlowFast Sampling: The Three Golden Principlescited by 43
  5. 2025Learning to Parallel: Accelerating Diffusion Large Language Models via Learnable Parallel Decodingcited by 26
  6. 2026Nemotron-Labs-Diffusion: A Tri-Mode Language Model Unifying Autoregressive, Diffusion, and Self-Speculation Decodingcited by 19
  7. 2026DAWN: Dependency-Aware Fast Inference for Diffusion LLMscited by 12
  8. 2025The Sequential Edge: Inverse-Entropy Voting Beats Parallel Self-Consistency at Matched Computecited by 11
  9. 2026The Path of Least Resistance: Guiding LLM Reasoning Trajectories with Prefix Consensuscited by 3
  10. 2026Reliable Chain-of-Thought via Prefix Consistencycited by 3

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