@inproceedings{ma-etal-2026-token,
title = "Token-Disentangled Latent Test-Time Scaling for Vision-Language Reasoning",
author = "Ma, Hao-Xuan and
Liu, Yihao and
Sun, Yutao and
Miao, Yanting and
Zhou, Mengyu and
Xiao, YiCheng and
Chen, Long and
Li, Zhenguo and
Ye, Han-Jia and
Jiang, Xiaoxi and
Jiang, Guanjun",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.2219/",
pages = "47517--47533",
ISBN = "979-8-89176-462-0",
abstract = "Latent test-time scaling improves reasoning by refining hidden states during inference, but existing methods typically apply a single scalar reward to all editable latent tokens. For multimodal large language models, this global update ignores that generated tokens play different roles: some are sensitive to visual evidence, while others correspond to uncertain reasoning decisions. We present Token-Disentangled Latent Test-Time Scaling, an inference-time framework that makes latent refinement token-role-aware. Starting from an initial generated trajectory, we optimize a short hidden-state prefix while routing perception-side visual feedback to image-sensitive tokens and reasoning feedback to high-entropy tokens. Tokens selected by neither route are constrained by an anchor regularizer. Across six multimodal reasoning benchmarks and two frozen MLLM backbones, our method consistently improves over the initial chain-of-thought rollout: macro accuracy increases from 66.38 to 68.95 on Qwen2.5-VL-7B and from 67.93 to 69.44 on InternVL3.5-8B. These results show that token-disentangled latent refinement is an effective strategy for multimodal test-time scaling and remains competitive with output-space test-time scaling baselines."
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<abstract>Latent test-time scaling improves reasoning by refining hidden states during inference, but existing methods typically apply a single scalar reward to all editable latent tokens. For multimodal large language models, this global update ignores that generated tokens play different roles: some are sensitive to visual evidence, while others correspond to uncertain reasoning decisions. We present Token-Disentangled Latent Test-Time Scaling, an inference-time framework that makes latent refinement token-role-aware. Starting from an initial generated trajectory, we optimize a short hidden-state prefix while routing perception-side visual feedback to image-sensitive tokens and reasoning feedback to high-entropy tokens. Tokens selected by neither route are constrained by an anchor regularizer. Across six multimodal reasoning benchmarks and two frozen MLLM backbones, our method consistently improves over the initial chain-of-thought rollout: macro accuracy increases from 66.38 to 68.95 on Qwen2.5-VL-7B and from 67.93 to 69.44 on InternVL3.5-8B. These results show that token-disentangled latent refinement is an effective strategy for multimodal test-time scaling and remains competitive with output-space test-time scaling baselines.</abstract>
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%0 Conference Proceedings
%T Token-Disentangled Latent Test-Time Scaling for Vision-Language Reasoning
%A Ma, Hao-Xuan
%A Liu, Yihao
%A Sun, Yutao
%A Miao, Yanting
%A Zhou, Mengyu
%A Xiao, YiCheng
%A Chen, Long
%A Li, Zhenguo
%A Ye, Han-Jia
%A Jiang, Xiaoxi
%A Jiang, Guanjun
%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 ma-etal-2026-token
%X Latent test-time scaling improves reasoning by refining hidden states during inference, but existing methods typically apply a single scalar reward to all editable latent tokens. For multimodal large language models, this global update ignores that generated tokens play different roles: some are sensitive to visual evidence, while others correspond to uncertain reasoning decisions. We present Token-Disentangled Latent Test-Time Scaling, an inference-time framework that makes latent refinement token-role-aware. Starting from an initial generated trajectory, we optimize a short hidden-state prefix while routing perception-side visual feedback to image-sensitive tokens and reasoning feedback to high-entropy tokens. Tokens selected by neither route are constrained by an anchor regularizer. Across six multimodal reasoning benchmarks and two frozen MLLM backbones, our method consistently improves over the initial chain-of-thought rollout: macro accuracy increases from 66.38 to 68.95 on Qwen2.5-VL-7B and from 67.93 to 69.44 on InternVL3.5-8B. These results show that token-disentangled latent refinement is an effective strategy for multimodal test-time scaling and remains competitive with output-space test-time scaling baselines.
%U https://aclanthology.org/2026.findings-emnlp.2219/
%P 47517-47533
Markdown (Informal)
[Token-Disentangled Latent Test-Time Scaling for Vision-Language Reasoning](https://aclanthology.org/2026.findings-emnlp.2219/) (Ma et al., Findings 2026)
ACL
- Hao-Xuan Ma, Yihao Liu, Yutao Sun, Yanting Miao, Mengyu Zhou, YiCheng Xiao, Long Chen, Zhenguo Li, Han-Jia Ye, Xiaoxi Jiang, and Guanjun Jiang. 2026. Token-Disentangled Latent Test-Time Scaling for Vision-Language Reasoning. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 47517–47533, Budapest, Hungary. Association for Computational Linguistics.