Two Clocks in Diffusion MLLMs: When Answers Stabilize Before Rationales Unfold
On the map: Diffusion models → Discrete and text diffusion
Abstract (arXiv)
An answer candidate in a masked diffusion MLLM can stabilize while its rationale is still unfolding. We distinguish retrospective stabilization of the logged candidate from token commitment, and examine these two clocks relative to rationale generation. Analyzing our results across three visual question-answering benchmarks, we find that 89.4-98.1% of the rationale-side canvas remains unwritten at stabilization in single-block, EOS-suppressed LaViDa runs. On V*Bench, reducing block length from 128 to 8 changes this fraction from 89.4% to 1.7%, together with answer coverage and the eligible observation window. Under EOS-enabled prompting, direct instructions improve Nemotron's overall accuracy by 15.0 and 19.5 percentage points on M3CoT and ScienceQA, but reduce LaViDa/V*Bench accuracy by 11.0 points. A symmetric decomposition associates the larger absolute component of each change with coverage rather than conditional accuracy. Matched-canvas image ablations measure visual sensitivity alongside answer stabilization, separating the two temporal readouts. Together, these measurements distinguish answer stabilization, rationale unfolding, and visual sensitivity, and identify coverage as the larger component of the prompting differences.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Discrete and text diffusion, one of 14 directions in Diffusion models. Builds on 14 of its 17 references that are in the map. First posted on arXiv 1 Oct 2026.
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14 of 17 references.
Closest ancestors · 3
Same direction, newest first
- 2026Answer First, Reason Later: When Commitment Order Costs Accuracy in Diffusion Language Models6 Aug
- 2025Diffusion Language Models Know the Answer Before Decoding27 Aug
- 2024Simple and Effective Masked Diffusion Language Models11 Jun
Other references · 11
- 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
- 2022Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answeringcited by 1,919
- 2023Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Promptingcited by 1,122
- 2023Measuring Faithfulness in Chain-of-Thought Reasoningcited by 703
- 2023V*: Guided Visual Search as a Core Mechanism in Multimodal LLMscited by 482
- 2024Multi-Modal Hallucination Control by Visual Information Groundingcited by 208
- 2024M3CoT: A Novel Benchmark for Multi-Domain Multi-step Multi-modal Chain-of-Thoughtcited by 121
- 2025LaViDa: A Large Diffusion Language Model for Multimodal Understandingcited by 84
- 2026Nemotron-Labs-Diffusion: A Tri-Mode Language Model Unifying Autoregressive, Diffusion, and Self-Speculation Decodingcited by 24
- 2026Thinking Diffusion: Penalize and Guide Visual-Grounded Reasoning in Diffusion Multimodal Language Modelscited by 3
- 2026ST-Veto: Spatio-Temporal Token Veto for Diffusion MLLMs via Taylor Prediction and Visual Groundingcited by 1
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