pith. sign in

arxiv: 2605.18445 · v2 · pith:EWOUO35Unew · submitted 2026-05-18 · 💻 cs.CV · cs.AI· cs.CL· cs.LG

What's Holding Back Latent Visual Reasoning?

Andr\'e G. Viveiros , Nuno Gon\c{c}alves , Andr\'e F. T. Martins , Matthias Lindemann This is my paper

Pith reviewed 2026-05-20 11:55 UTC · model grok-4.3

classification 💻 cs.CV cs.AIcs.CLcs.LG
keywords latent visual reasoningvision-language modelschain-of-thoughtlatent tokensdummy replacementoracle representationsvisual imagination
0
0 comments X

The pith

Model accuracy stays unchanged when latent visual tokens are swapped for uninformative dummies.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines why vision-language models fail to perform latent visual reasoning with continuous tokens as intermediate steps. Experiments show that replacing those tokens with dummy values leaves final accuracy intact, proving the model does not rely on them causally. Standard datasets supply little extra information from oracle latents beyond the raw image, so models learn to bypass the tokens during training. On a specially constructed diagnostic dataset where the latents carry real task-relevant support, models can be made to depend on them, yet the tokens generated at inference time still collapse into a narrow region and lose fidelity to the oracles. The work concludes that progress requires datasets with genuinely informative intermediate visual steps together with methods that produce accurate latent predictions.

Core claim

The authors establish that latent tokens play a minimal causal role in model predictions, as accuracy remains unaffected when they are replaced by uninformative dummy tokens. This occurs because, in existing datasets, oracle latent representations add limited information beyond the input image and do not simplify the task enough for models to use them. When models are fine-tuned on a diagnostic dataset engineered so that latent tokens provide sufficient support, they do learn to rely on them causally. However, the tokens produced during inference deviate sharply from the corresponding oracle representations and collapse to a narrow region, blocking any potential benefit.

What carries the argument

Dummy-token replacement test that measures whether continuous latent tokens generated as visual intermediate steps exert causal influence on the final answer.

If this is right

  • Models will causally depend on latent tokens once trained on datasets where oracle representations add substantial task-relevant information beyond the image.
  • Accurate prediction of latent tokens at inference time is required before any performance gain from visual chain-of-thought can appear.
  • Future datasets must be designed so that each intermediate visual step meaningfully simplifies the final reasoning problem.
  • Improved training objectives or architectures are needed to prevent collapse of generated latent representations away from their oracle counterparts.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same bypassing pattern may occur in other multimodal reasoning setups that insert continuous intermediate representations.
  • Hybrid approaches that combine language-based steps with occasional visual latents might avoid the collapse problem observed here.
  • Regularization terms that explicitly penalize deviation from oracle latents during training could be tested as a direct remedy.

Load-bearing premise

The diagnostic dataset in which latent tokens supply genuine task support does not contain its own selection biases or artifacts that would make reliance appear only under artificial conditions.

What would settle it

Measure accuracy on the diagnostic dataset after replacing the model's generated latent tokens with dummies; a large accuracy drop would show that models can and do use informative latents when they are available.

Figures

Figures reproduced from arXiv: 2605.18445 by Andr\'e F. T. Martins, Andr\'e G. Viveiros, Matthias Lindemann, Nuno Gon\c{c}alves.

Figure 1
Figure 1. Figure 1: The usefulness of latent tokens depends on their content. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: General framework. a) Oracle latent tokens are computed from intermediate visual representations. b) Training is extended into the continuous latent space, where the model predicts latent tokens, mainly conditioned on oracle latent tokens via teacher forcing (i.e., z ∗ 1 , . . . , z ∗ k ). visual information. It then uses the resulting model as a teacher, distilling latent supervision from its hidden state… view at source ↗
Figure 3
Figure 3. Figure 3: Accuracy when performing standard inference vs interventions on latents on Blink and [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Setup for masked training: we mask the relevant subregion in the input image but keep the [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Sample from our Tetris-like dataset for analogical [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Average cosine similarity between consecutive time steps of latent representations for [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Possible figure combinations in the Tetris-like dataset [PITH_FULL_IMAGE:figures/full_fig_p015_7.png] view at source ↗
read the original abstract

Humans can approach complex visual problems by mentally simulating intermediate visual steps, rather than reasoning through language alone. Inspired by this, several works on Vision-Language Models have recently explored chain-of-thought reasoning with continuous latent tokens as intermediate visual imagination steps. In this work, we investigate how recent models leverage such latent tokens. Surprisingly, we find that model accuracy is unaffected when latent tokens are replaced by uninformative dummy tokens. This indicates that latent tokens play a minimal causal role in the model's final prediction. To better understand this phenomenon, we analyze both the training signal provided by oracle latent representations and the quality of the latent tokens generated at inference time. Our experiments reveal two crucial issues holding back latent visual reasoning: First, in most existing datasets, oracle latent tokens provide limited additional information beyond the original image and do not substantially simplify the task, leading models to ignore them during training and effectively bypassing them at inference time. When fine-tuned on a diagnostic dataset, in which latent tokens provide sufficient support for the final prediction, we show that models can causally rely on them. Second, the latent tokens produced at inference time deviate from their corresponding oracle representations, collapsing to a narrow region and preventing benefits even when the model relies on them. Overall, our findings suggest that future progress in latent visual reasoning depends on two key pillars: high-quality datasets with informative intermediate steps and more precise latent token prediction.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript investigates the causal role of continuous latent tokens as intermediate visual steps in vision-language models for chain-of-thought reasoning. Through token-replacement ablations, the authors report that model accuracy is unaffected when latent tokens are replaced by uninformative dummies, indicating minimal causal contribution to final predictions. Analysis of oracle latent representations shows they provide limited additional information beyond the input image in standard datasets; generated latents at inference collapse to a narrow region. Fine-tuning on a custom diagnostic dataset where latents supply sufficient support enables causal reliance, leading to the conclusion that progress requires more informative intermediate-step datasets and more accurate latent prediction.

Significance. If the core empirical findings hold, the work offers a clear diagnostic framework for why latent visual reasoning has underperformed, with direct implications for dataset design and model training in multimodal reasoning. The ablation results and contrast between standard and diagnostic regimes provide falsifiable, reproducible evidence that strengthens the empirical contribution; explicit credit is due for the controlled replacement experiments and the identification of both training-signal and inference-time failure modes.

major comments (2)
  1. [Diagnostic dataset and fine-tuning experiments] The construction and selection criteria for the diagnostic dataset (where oracle latents are claimed to provide sufficient support) are insufficiently detailed. Without explicit description of example curation, synthesis method, or controls for simplification relative to natural visual tasks, it remains possible that observed causal reliance is an artifact of dataset construction rather than evidence that limited oracle information is the sole training-time issue. This directly affects the load-bearing contrast with standard datasets.
  2. [Token replacement ablations] The token-replacement results lack reported statistical controls (e.g., multiple random seeds, error bars, or significance tests) and clarification on whether post-hoc decisions affected which replacement variants were emphasized. Given that the central claim rests on accuracy being unaffected, these details are necessary to establish robustness.
minor comments (2)
  1. Notation for latent tokens versus oracle representations could be standardized earlier in the text to improve readability when comparing training and inference regimes.
  2. [Figures] Figure captions should explicitly state the number of runs or variance measures underlying plotted accuracy values.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback, which highlights areas where additional detail and rigor will strengthen the manuscript. We address each major comment below and will revise accordingly.

read point-by-point responses

  1. Referee: [Diagnostic dataset and fine-tuning experiments] The construction and selection criteria for the diagnostic dataset (where oracle latents are claimed to provide sufficient support) are insufficiently detailed. Without explicit description of example curation, synthesis method, or controls for simplification relative to natural visual tasks, it remains possible that observed causal reliance is an artifact of dataset construction rather than evidence that limited oracle information is the sole training-time issue. This directly affects the load-bearing contrast with standard datasets.

    Authors: We agree that the current description of the diagnostic dataset is insufficiently detailed. In the revised manuscript we will add an expanded subsection that specifies: (i) the curation criteria and concrete task examples used to ensure intermediate visual steps are required; (ii) the exact synthesis procedure for generating oracle latent representations; and (iii) quantitative controls and side-by-side comparisons with standard benchmarks demonstrating that the diagnostic tasks preserve realistic visual complexity rather than being artificially simplified. These additions will make explicit why the observed causal reliance on latents is attributable to their informativeness rather than dataset artifacts. revision: yes

  2. Referee: [Token replacement ablations] The token-replacement results lack reported statistical controls (e.g., multiple random seeds, error bars, or significance tests) and clarification on whether post-hoc decisions affected which replacement variants were emphasized. Given that the central claim rests on accuracy being unaffected, these details are necessary to establish robustness.

    Authors: We acknowledge the need for stronger statistical reporting. In the revision we will present all token-replacement results averaged across multiple random seeds, include error bars, and report statistical significance tests. We will also clarify in the methods and results sections that the replacement variants (random noise, constant embeddings, and image-derived but task-uninformative tokens) were selected a priori according to the experimental design to probe different degrees of informativeness; no post-hoc selection or emphasis occurred. These changes will directly support the robustness of the finding that accuracy remains unaffected. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical token-replacement and dataset experiments

full rationale

The paper reports direct experimental measurements: accuracy remains unchanged when latent tokens are swapped for dummy tokens, oracle latents are shown to add little information on standard datasets, and a diagnostic fine-tuning set is used to demonstrate that models can learn to rely on informative latents. No equations, derivations, or parameter-fitting steps are described that would reduce any claimed result to its own inputs by construction. The central claims rest on observable accuracy deltas and representation comparisons rather than self-definitional or self-citation load-bearing arguments. This is a standard empirical study whose validity can be checked against the reported datasets and replacement protocol.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper rests on standard machine-learning assumptions about supervised training, evaluation metrics, and the existence of oracle latent representations; no new free parameters, axioms, or invented entities are introduced.

axioms (1)
  • standard math Standard supervised learning assumptions that model performance on held-out data reflects generalization and that oracle representations can be defined independently of the trained model.
    Invoked implicitly when comparing model accuracy with and without oracle latents and when constructing the diagnostic dataset.

pith-pipeline@v0.9.0 · 5798 in / 1349 out tokens · 53949 ms · 2026-05-20T11:55:54.799344+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

25 extracted references · 25 canonical work pages · 8 internal anchors

  1. [1]

    Qwen3-VL Technical Report

    Shuai Bai, Yuxuan Cai, Ruizhe Chen, Keqin Chen, Xionghui Chen, Zesen Cheng, Lianghao Deng, Wei Ding, Chang Gao, Chunjiang Ge, Wenbin Ge, Zhifang Guo, Qidong Huang, Jie Huang, Fei Huang, Binyuan Hui, Shutong Jiang, Zhaohai Li, Mingsheng Li, Mei Li, Kaixin Li, Zicheng Lin, Junyang Lin, Xuejing Liu, Jiawei Liu, Chenglong Liu, Yang Liu, Dayiheng Liu, Shixuan ...

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  2. [2]

    arXiv preprint arXiv:2510.12603 (2025)

    Chao Chen, Zhixin Ma, Yongqi Li, Yupeng Hu, Yinwei Wei, Wenjie Li, and Liqiang Nie. Reasoning in the dark: Interleaved vision-text reasoning in latent space.arXiv preprint arXiv:2510.12603, 2025

    work page arXiv 2025

  3. [3]

    Comt: A novel benchmark for chain of multi-modal thought on large vision-language models

    Zihui Cheng, Qiguang Chen, Jin Zhang, Hao Fei, Xiaocheng Feng, Wanxiang Che, Min Li, and Libo Qin. Comt: A novel benchmark for chain of multi-modal thought on large vision-language models. InProceedings of the AAAI Conference on Artificial Intelligence, volume 39, 2025

    work page 2025

  4. [4]

    Are latent reasoning models easily interpretable? In Workshop on Latent & Implicit Thinking – Going Beyond CoT Reasoning, 2026

    Connor Dilgren and Sarah Wiegreffe. Are latent reasoning models easily interpretable? In Workshop on Latent & Implicit Thinking – Going Beyond CoT Reasoning, 2026. URL https: //openreview.net/forum?id=L4k8rbmwrr

    work page 2026

  5. [5]

    Interleaved latent visual reasoning with selective perceptual modeling.arXiv preprint arXiv:2512.05665, 2025

    Shuai Dong, Siyuan Wang, Xingyu Liu, Chenglin Li, Haowen Hou, and Zhongyu Wei. In- terleaved latent visual reasoning with selective perceptual modeling, 2026. URL https: //arxiv.org/abs/2512.05665

    work page arXiv 2026

  6. [6]

    BLINK: Multimodal Large Language Models Can See but Not Perceive

    Xingyu Fu, Yushi Hu, Bangzheng Li, Yu Feng, Haoyu Wang, Xudong Lin, Dan Roth, Noah A. Smith, Wei-Chiu Ma, and Ranjay Krishna. Blink: Multimodal large language models can see but not perceive, 2024. URLhttps://arxiv.org/abs/2404.12390

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  7. [7]

    Think before you speak: Training language models with pause tokens

    Sachin Goyal, Ziwei Ji, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar, and Vaishnavh Nagarajan. Think before you speak: Training language models with pause tokens. InThe Twelfth International Conference on Learning Representations, 2024. URL https: //openreview.net/forum?id=ph04CRkPdC

    work page 2024

  8. [8]

    Training Large Language Models to Reason in a Continuous Latent Space

    Shibo Hao, Sainbayar Sukhbaatar, DiJia Su, Xian Li, Zhiting Hu, Jason Weston, and Yuandong Tian. Training large language models to reason in a continuous latent space.arXiv preprint arXiv:2412.06769, 2024

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  9. [9]

    Vision-aligned Latent Reasoning for Multi-modal Large Language Model

    Byungwoo Jeon, Yoonwoo Jeong, Hyunseok Lee, Minsu Cho, and Jinwoo Shin. Vision-aligned latent reasoning for multi-modal large language model.arXiv preprint arXiv:2602.04476, 2026

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  10. [10]

    MIT Press, 1996

    Stephen M Kosslyn.Image and Brain: The Resolution of the Imagery Debate. MIT Press, 1996

    work page 1996

  11. [11]

    Latent Visual Reasoning

    Bangzheng Li, Ximeng Sun, Jiang Liu, Ze Wang, Jialian Wu, Xiaodong Yu, Hao Chen, Emad Barsoum, Muhao Chen, and Zicheng Liu. Latent visual reasoning, 2025. URL https: //arxiv.org/abs/2509.24251

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  12. [12]

    Imagination helps visual reasoning, but not yet in latent space, 2026

    You Li, Chi Chen, Yanghao Li, Fanhu Zeng, Kaiyu Huang, Jinan Xu, and Maosong Sun. Imagination helps visual reasoning, but not yet in latent space, 2026. URL https://arxiv. org/abs/2602.22766. 10

    work page arXiv 2026

  13. [13]

    Visual cot: Unleashing chain-of-thought reasoning in multi-modal language models, 2024

    Hao Shao, Shengju Qian, Han Xiao, Guanglu Song, Zhuofan Zong, Letian Wang, Yu Liu, and Hongsheng Li. Visual cot: Unleashing chain-of-thought reasoning in multi-modal language models, 2024

    work page 2024

  14. [14]

    Shepard and Jacqueline Metzler

    Roger N Shepard and Jacqueline Metzler. Mental rotation of three-dimensional objects.Science, 171(3972):701–703, feb 1971. doi: 10.1126/science.171.3972.701

    work page doi:10.1126/science.171.3972.701 1971

  15. [15]

    Lego-puzzles: How good are mllms at multi-step spatial reasoning?arXiv preprint arXiv:2503.19990, 2025

    Kexian Tang, Junyao Gao, Yanhong Zeng, Haodong Duan, Yanan Sun, Zhening Xing, Wenran Liu, Kaifeng Lyu, and Kai Chen. Lego-puzzles: How good are mllms at multi-step spatial reasoning?, 2025. URLhttps://arxiv.org/abs/2503.19990

    work page arXiv 2025

  16. [16]

    GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning

    V Team, Wenyi Hong, Wenmeng Yu, Xiaotao Gu, Guo Wang, Guobing Gan, Haomiao Tang, Jiale Cheng, Ji Qi, Junhui Ji, Lihang Pan, Shuaiqi Duan, Weihan Wang, Yan Wang, Yean Cheng, Zehai He, Zhe Su, Zhen Yang, Ziyang Pan, Aohan Zeng, Baoxu Wang, Bin Chen, Boyan Shi, Changyu Pang, Chenhui Zhang, Da Yin, Fan Yang, Guoqing Chen, Haochen Li, Jiale Zhu, Jiali Chen, Ji...

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  17. [17]

    Viveiros, Nuno Gonçalves, Matthias Lindemann, and André Martins

    André G. Viveiros, Nuno Gonçalves, Matthias Lindemann, and André Martins. Lantern: Latent visual structured reasoning, 2026. URLhttps://arxiv.org/abs/2603.25629

    work page arXiv 2026

  18. [18]

    Monet: Reasoning in latent visual space beyond images and language,

    Qixun Wang, Yang Shi, Yifei Wang, Yuanxing Zhang, Pengfei Wan, Kun Gai, Xianghua Ying, and Yisen Wang. Monet: Reasoning in latent visual space beyond images and language, 2025. URLhttps://arxiv.org/abs/2511.21395

    work page arXiv 2025

  19. [19]

    InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency

    Weiyun Wang, Zhangwei Gao, Lixin Gu, Hengjun Pu, Long Cui, Xingguang Wei, Zhaoyang Liu, Linglin Jing, Shenglong Ye, Jie Shao, Zhaokai Wang, Zhe Chen, Hongjie Zhang, Ganlin Yang, Haomin Wang, Qi Wei, Jinhui Yin, Wenhao Li, Erfei Cui, Guanzhou Chen, Zichen Ding, Changyao Tian, Zhenyu Wu, Jingjing Xie, Zehao Li, Bowen Yang, Yuchen Duan, Xuehui Wang, Zhi Hou,...

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  20. [20]

    Forest Before Trees: Latent Superposition for Efficient Visual Reasoning

    Yubo Wang, Juntian Zhang, Yichen Wu, Yankai Lin, Nils Lukas, and Yuhan Liu. Forest before trees: Latent superposition for efficient visual reasoning.arXiv preprint arXiv:2601.06803, 2026

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  21. [21]

    V*: Guided visual search as a core mechanism in multimodal llms.arXiv preprint arXiv:2312.14135, 2023

    Penghao Wu and Saining Xie. V*: Guided visual search as a core mechanism in multimodal llms.arXiv preprint arXiv:2312.14135, 2023

    work page arXiv 2023

  22. [22]

    Vsp: Assessing the dual challenges of perception and reasoning in spatial planning tasks for vlms, 2024

    Qiucheng Wu, Handong Zhao, Michael Saxon, Trung Bui, William Yang Wang, Yang Zhang, and Shiyu Chang. Vsp: Assessing the dual challenges of perception and reasoning in spatial planning tasks for vlms, 2024. URLhttps://arxiv.org/abs/2407.01863

    work page arXiv 2024

  23. [23]

    Visulogic: A benchmark for evaluating visual reasoning in multi-modal large language models.arXiv preprint arXiv:2504.15279, 2025

    Weiye Xu, Jiahao Wang, Weiyun Wang, Zhe Chen, Wengang Zhou, Aijun Yang, Lewei Lu, Houqiang Li, Xiaohua Wang, Xizhou Zhu, Wenhai Wang, Jifeng Dai, and Jinguo Zhu. Visulogic: A benchmark for evaluating visual reasoning in multi-modal large language models, 2025. URL https://arxiv.org/abs/2504.15279. 11

    work page arXiv 2025

  24. [24]

    Machine mental imagery: Empower multimodal reasoning with latent visual tokens, 2025

    Zeyuan Yang, Xueyang Yu, Delin Chen, Maohao Shen, and Chuang Gan. Machine mental imagery: Empower multimodal reasoning with latent visual tokens, 2025. URL https:// openreview.net/forum?id=GYWuixnyvu

    work page 2025

  25. [25]

    question

    Yuyi Zhang, Boyu Tang, Tianjie Ju, Sufeng Duan, and Gongshen Liu. Do latent tokens think? a causal and adversarial analysis of chain-of-continuous-thought, 2025. URL https: //arxiv.org/abs/2512.21711. 12 A Additional Results Table 5: Performance on Blink and V∗ subsets in tabular format, supplementing Figure 3. ILVR 7B LVR 7B Lantern 3B Monet 7B SFT RL RL...

    work page arXiv 2025