mlsanityData to 14 Oct 2026

Answer First, Reason Later: When Commitment Order Costs Accuracy in Diffusion Language Models

arXiv 2608.05687 ↗First posted 6 Aug 2026cs.CL cs.AIcited by 3

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

also in 1 more
Abstract (arXiv)

Masked diffusion language models revise many masked output positions in parallel. We call a token committed once it becomes visible and is never masked again, and call a response answer-first when the final answer commits before the reasoning printed ahead of it. On 1,069 GSM8K test questions, an explicit step-by-step instruction increases the accuracy difference between unrestricted decoding and a decoder that permits commitment only near the left-most unresolved position; unrestricted decoding also produces more answer-first trajectories. On MATH-500, the two LLaDA models spend most of a short output canvas on reasoning that commits after the answer, and the benefit of frontier gating decreases as that postanswer writing disappears. Dream-7B has little post-answer writing and follows a different accuracy pattern. A controlled four-option task reserves a one-token answer position before generation. Delaying that position outperforms an equally timed reasoning-token delay on LLaDA-8B, LLaDA-1.5, and Dream-7B. The raw difference is largest on Dream, whose free accuracy on the controlled task is lower. Answers commit much earlier under the reserved-position interface than in ordinary free-form generation, which limits how far the intervention result can be generalized. Commitment order affects the context used to complete a response and the allocation of a finite output canvas.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in Diffusion reasoning and test-time scaling, one of 17 directions in LLM reasoning. Builds on 27 of its 27 references that are in the map. First posted on arXiv 6 Aug 2026.

Builds on

27 of 27 references.

Closest ancestors · 3

Same direction, newest first

  1. 2026Thinking Out of Order: When Output Order Stops Reflecting Reasoning Order in Diffusion Language Models29 Jan
  2. 2026The Flexibility Trap: Rethinking the Value of Arbitrary Order in Diffusion Language Models21 Jan
  3. 2025Remasking Discrete Diffusion Models with Inference-Time Scaling1 Mar

Other references · 24

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  2. 2021Training Verifiers to Solve Math Word Problemscited by 10,109
  3. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  4. 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,371
  5. 2023Let's Verify Step by Stepcited by 3,789
  6. 2021Program Synthesis with Large Language Modelscited by 3,637
  7. 2021Structured Denoising Diffusion Models in Discrete State-Spacescited by 1,719
  8. 2023Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Promptingcited by 1,122
  9. 2025Large Language Diffusion Modelscited by 912
  10. 2023Measuring Faithfulness in Chain-of-Thought Reasoningcited by 703
  11. 2025Dream 7B: Diffusion Large Language Modelscited by 544
  12. 2025Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Modelscited by 409
12 more
  1. 2025LLaDA 1.5: Variance-Reduced Preference Optimization for Large Language Diffusion Modelscited by 285
  2. 2026Reasoning Theater: Disentangling Model Beliefs from Chain-of-Thoughtcited by 40
  3. 2025Diffusion LLM with Native Variable Generation Lengths: Let [EOS] Lead the Waycited by 22
  4. 2026Deferred Commitment Decoding for Diffusion Language Modelscited by 12
  5. 2025Robust Answers, Fragile Logic: Probing the Decoupling Hypothesis in LLM Reasoningcited by 11
  6. 2025Rainbow Padding: Mitigating Early Termination in Instruction-Tuned Diffusion LLMscited by 10
  7. 2026ρ-textttEOS: Training-free Bidirectional Variable-Length Control for Masked Diffusion LLMscited by 7
  8. 2026Window-Diffusion: Accelerating Diffusion Language Model Inference with Windowed Token Pruning and Cachingcited by 2
  9. 2026Drop the Act: Probe-Filtered RL for Faithful Chain-of-Thought Reasoningcited by 2
  10. 2026Set Diffusion: Interpolating Token Orderings Between Autoregression and Diffusion for Fast and Flexible Decodingcited by 2
  11. 2026Revise, Don't Freeze: Sampler-Matched Training for Self-Correcting Masked Diffusion Language Modelscited by 1
  12. 2026VoidPadding: Let [VOID] Handle Padding in Masked Diffusion Language Models so that [EOS] Can Focus on Semantic Terminationcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Reliable Parallel Decoding in Masked Diffusion Language Modelscited by 0
  2. 2026Two Clocks in Diffusion MLLMs: When Answers Stabilize Before Rationales Unfoldcited by 0
  3. 2026Plan Canvas: Fixed Reasoning Regions for Continuous Language Flowscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Neither Parallel Nor Sequential: How DiffusionGemma Actually Commits Tokens
  2. 2025Diffusion Language Models Know the Answer Before Decoding
  3. 2026The Path Matters: Learning a Token-Commitment Policy for Diffusion Language Models
  4. 2026Commitment Before Realization: When Classifier-Free Guidance Becomes Unnecessary in Masked Diffusion Language Models
  5. 2026Masked diffusion LLMs can use EoS tokens for hidden reasoning
  6. 2026Locally Confident, Globally Stuck: The Quality-Exploration Dilemma in Diffusion Language Models
  7. 2026The Confidence Shortcut: A Reasoning Failure Mode of Masked Diffusion Models
  8. 2026Parallelism and Generation Order in Masked Diffusion Language Models: Limits Today, Potential Tomorrow