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    <abstract>Diffusion vision-language models generate answers through iterative refinement, exposing intermediate answer trajectories that can be inspected and controlled at inference time. However, this controllability creates a reasoning-need mismatch, where a universal generation length is applied to questions with different reasoning demands. Visually closed questions may be harmed by continued refinement after a stable answer has formed, whereas reasoning-sensitive questions may be harmed by premature commitment. We formulate this problem as reasoning-budget mismatch and study it in LLaDA-V. Rather than choosing a universal generation length, our training-free controller routes each example to early commitment, baseline preservation, or reasoning-supportive decoding using trajectory signals from answer closure, commitment evidence, and representation revision pressure, without using ground-truth answers. Across answer-focused, mixed-reasoning, and CoT-sensitive benchmarks, routed control improves robustness over fixed long decoding, pure short decoding, and single-rule interventions. The gains are not explained by shorter outputs alone. Answer-closed examples often benefit from commitment, whereas CoT-sensitive examples require preserving or supporting intermediate reasoning. Taken together, these results suggest diffusion VLM decoding should route inference-time control by the state suggested by the observed trajectory instead of relying on a universal decoding length.</abstract>
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%0 Conference Proceedings
%T Routing by Reasoning Need: Trajectory-Aware Decoding Control for Diffusion Vision-Language Models
%A Liu, Yixiang
%A Xu, Zhongxing
%A Wang, Zhonghua
%A Tang, Xiaoying
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F liu-etal-2026-routing-reasoning
%X Diffusion vision-language models generate answers through iterative refinement, exposing intermediate answer trajectories that can be inspected and controlled at inference time. However, this controllability creates a reasoning-need mismatch, where a universal generation length is applied to questions with different reasoning demands. Visually closed questions may be harmed by continued refinement after a stable answer has formed, whereas reasoning-sensitive questions may be harmed by premature commitment. We formulate this problem as reasoning-budget mismatch and study it in LLaDA-V. Rather than choosing a universal generation length, our training-free controller routes each example to early commitment, baseline preservation, or reasoning-supportive decoding using trajectory signals from answer closure, commitment evidence, and representation revision pressure, without using ground-truth answers. Across answer-focused, mixed-reasoning, and CoT-sensitive benchmarks, routed control improves robustness over fixed long decoding, pure short decoding, and single-rule interventions. The gains are not explained by shorter outputs alone. Answer-closed examples often benefit from commitment, whereas CoT-sensitive examples require preserving or supporting intermediate reasoning. Taken together, these results suggest diffusion VLM decoding should route inference-time control by the state suggested by the observed trajectory instead of relying on a universal decoding length.
%R 10.18653/2026.findings-emnlp.995
%U https://aclanthology.org/2026.findings-emnlp.995/
%U https://doi.org/10.18653/2026.findings-emnlp.995
%P 21005-21021

Markdown (Informal)

[Routing by Reasoning Need: Trajectory-Aware Decoding Control for Diffusion Vision-Language Models](https://aclanthology.org/2026.findings-emnlp.995/) (Liu et al., Findings 2026)

• Routing by Reasoning Need: Trajectory-Aware Decoding Control for Diffusion Vision-Language Models (Liu et al., Findings 2026)

ACL

• Yixiang Liu, Zhongxing Xu, Zhonghua Wang, and Xiaoying Tang. 2026. Routing by Reasoning Need: Trajectory-Aware Decoding Control for Diffusion Vision-Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 21005–21021, Budapest, Hungary. Association for Computational Linguistics.