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Imagination Helps Visual Reasoning, But Not Yet in Latent Space

You Li, Chi Chen, Yanghao Li, Fanhu Zeng, Kaiyu Huang, Jinan Xu, Maosong Sun

arXiv:2602.22766v2cs.CL

TL;DR

The mechanisms underlying latent-space visual reasoning remain unclear. This paper applies causal mediation analysis and proposes CapImagine, finding that explicit text-space imagination outperforms latent-space approaches across vision-centric benchmarks.

  • Problem

    Latent visual reasoning lacks rigorous investigation of its core design choices and mechanisms, limiting understanding of how it works.

  • Method

    The paper uses causal mediation analysis to examine latent tokens and reformulates training data into text-space visual imagination for CapImagine.

  • Results

    4.0% on HR-Bench-8K and 4.9% on MME-RealWorld-Lite surpassing Monet, while CapImagine outperforms latent-space approaches across multiple vision-centric benchmarks.

  • Takeaways & Limitations

    Latent tokens behave more like soft prompts or placeholders than active carriers of visual imagination or reasoning in current methods.

  • Takeaways & Limitations

    CapImagine has higher inference latency than latent-based methods and is presented primarily as a verification probe rather than an optimal solution.

Abstract

from arXiv · show

Latent visual reasoning aims to mimic human's imagination process by meditating through hidden states of Multimodal Large Language Models. While recognized as a promising paradigm for visual reasoning, the underlying mechanisms driving its effectiveness remain unclear. Motivated to demystify the true source of its efficacy, we investigate the validity of latent reasoning using Causal Mediation Analysis. We model the process as a causal chain: the input as the treatment, the latent tokens as the mediator, and the final answer as the outcome. Our findings uncover two critical disconnections: (a) Input-Latent Disconnect: dramatic perturbations on the input result in negligible changes to the latent tokens, suggesting that latent tokens do not effectively attend to the input sequence. (b) Latent-Answer Disconnect: perturbations on the latent tokens yield minimal impact on the final answer, indicating the limited causal effect latent tokens imposing on the outcome. Furthermore, extensive probing analysis reveals that latent tokens encode limited visual information and exhibit high similarity. Consequently, we challenge the necessity of latent reasoning and propose a straightforward alternative named CapImagine, which teaches the model to explicitly imagine using text. Experiments on vision-centric benchmarks show that CapImagine significantly outperforms complex latent-space baselines, highlighting the superior potential of visual reasoning through explicit imagination.

1. Introduction

The paper analyzes latent visual reasoning as a causal chain and finds that latent tokens are weakly connected to both inputs and final answers. It proposes CapImagine, a text-space imagination method that outperforms latent-space approaches on vision-centric benchmarks.

  • Background: Latent Visual Reasoning reasons through hidden-state latent tokens intended to encode rich visual semantics in a shared multimodal space.It enables visual imagination by deliberating in a high-dimensional latent space.
  • Causal analysis: The study models latent reasoning as the causal chain X → Z → Y, linking input X, latent tokens Z, and output Y.Causal Mediation Analysis is used to examine whether latent tokens mediate visual reasoning.
  • Findings: Perturbing entire input sequences produces highly homogeneous latent tokens across diverse inputs and tasks, indicating an input–latent causal disconnect.The homogeneity is measured using cosine similarity.
  • Findings: Systematic intervention and probing analyses find that latent tokens contribute little to the causal reasoning process across methods and benchmarks.These analyses motivate questioning whether latent-space deliberation is necessary for visual reasoning.
  • CapImagine: CapImagine converts Monet-SFT-125K into a text-space imagination format and surpasses Monet by 4.0% on HR-Bench-8K and 4.9% on MME-RealWorld-Lite.Evaluations on V*, HR-Bench, MME-RealWorld-Lite, and other vision-centric benchmarks show consistent improvements.

2. Related Work

Related work develops visual reasoning through tool-augmented perception, self-generated visual imagination, and latent-space reasoning. Latent supervision methods increasingly broaden reasoning formats and scenarios, but compression and direct supervision can dilute semantics or cause latent mode collapse.

  • Tool-Augmented Visual Reasoning: Tool-augmented visual reasoning actively manipulates visual content and differs in how intermediate visual observations are produced.Some methods use fixed tools such as zoom-in or image-drawing operations.
  • Visual Imagination: Visual imagination can arise from self-generation or latent-space reasoning, with unified multimodal models explicitly instantiating internal reasoning states.Latent visual reasoning was first introduced by Mirage, which addressed latent supervision by compressing visual features from intermediate reasoning images.
  • Latent Supervision: Subsequent latent-reasoning works use vision-encoder features as supervision and extend reasoning to broader perception scenarios, flexible latent formats, and improved supervisory-feature selection.These works largely follow the use of vision encoder features extracted as supervision signals.
  • Limitations: Mirage’s compression can dilute discriminative semantics, while supervising latents with entire visual-token sequences can cause latent mode collapse during inference.Monet introduces distillation that restricts gradient propagation exclusively to latent tokens to preserve informative semantics.

3. Analysis: Latent Tokens Hardly Helps

The analysis finds that latent tokens become highly homogeneous, respond weakly to input and token perturbations, and encode limited visual semantics. These results suggest that current latent reasoning contributes only marginally to final predictions.

  • Finding 1: Latent tokens are highly similar across instances and tasks, progressively collapsing into nearly identical representations as reasoning continues.This contrasts with text/image tokens and inner MLLM representations, which retain distinctive semantics across instances and tasks.
  • Finding 1: Across models, latent representations progressively degenerate as autoregressive reasoning proceeds, although collapse speed and residual distinctiveness vary.Monet degenerates more slowly, LVR retains some partial distinctiveness, and Mirage shows minimal distinctiveness throughout.
  • Finding 2: Drastic latent-token interventions produce only marginal answer changes across V*, HR-Bench-4K, and MME-RealWorld-Lite.Performance changes by +0.5% on V*, -1.0% on HR-Bench-4K, and -0.7% on MME-RealWorld-Lite.
  • Finding 2: Replacing Mirage’s latent tokens with Gaussian noise generally causes negligible answer changes, except for a strong stage-2 small-value intervention that triggers repetition.The reported dramatic decline is limited to the stage-2 variant under the small-value setting.
  • Finding 3: Using latent tokens alone yields weak performance below text-only guessing baselines, whereas providing the original image enables 76.67% accuracy for Monet and Qwen3-VL-32B.The result indicates that latent tokens encode limited visual semantics and are insufficient for accurate answer derivation.
  • Summary: Overall, homogeneous latent tokens contribute marginally to prediction and may behave more like soft prompts or placeholders than an active visual-reasoning pathway.The analysis also reports minimal encoded semantics and a possible implicit shortcut around latent visual reasoning.

4. CapImagine

CapImagine replaces latent visual thoughts with textual captions that explicitly represent image transformations within an interleaved reasoning chain. Its pipeline rewrites and globally refines training trajectories, filters flawed instances, and outperforms competing baselines across perception-centric and visual reasoning benchmarks.

  • Method: CapImagine converts semantic changes from intermediate images into textual captions, forcing explicit text-space imagination over the original image.The method aims to preserve information from interleaved multimodal data without relying on latent variables.
  • Method: The approach makes imagination occur as though intermediate images were present while reasoning remains in textual space.This replaces hidden-space operations with explicit textual descriptions integrated into the reasoning chain.
  • Data Rewriting: CapImagine rewrites Visual-CoT and Zebra-CoT examples by captioning highlighted image regions with Qwen3-VL-4B.The captions refocus the visual semantics of regions selected for zooming-based visual search.
  • Data Rewriting: Global MLLM refinement corrects inconsistencies and improves fluency so generated visual descriptions integrate smoothly into original reasoning trajectories.Directly inserting rewritten text can otherwise create rigid transitions and disrupt logical coherence.
  • Data Filtering: CapImagine filters training instances for reasoning correctness and question ambiguity, addressing conflicts between original answers and generated visual observations.The source passage identifies low-quality Visual-CoT data as undermining the rewriting strategy and describes automated MLLM assessment with manual confirmation.
  • Results: CapImagine consistently outperforms competing baselines across perception-centric and visual reasoning benchmarks.Table 1 reports the performance comparison and highlights the best results in bold.

5. Experiments

CapImagine consistently outperforms latent-space and tool-based baselines across fine-grained perception and abstract visual reasoning tasks, while ablations, causal analysis, and efficiency comparisons support text-driven imagination as an effective approach.

  • Benchmark Results: CapImagine improves over Monet by 3.44% on HR-Bench and 2.6% on V*, while effectively handling diverse queries on MME-RealWorld-Lite.The method also consistently outperforms the strong Monet baseline across perception-centric benchmarks.
  • Benchmark Results: CapImagine surpasses LVR and Monet by over 10 points on Jigsaw and multiview reasoning, and improves over Monet by 6.1% on TableVQA.These tasks evaluate global-structure reconstruction, cross-view spatial reasoning, and key-value comparison.
  • Ablation Studies: Replacing text-space imagination descriptions with a single <think_image> token causes a 3.13% drop on V* and consistent degradation across all benchmarks.The ablation uses identical fine-tuning settings and is denoted + w/o Rewriting.
  • Ablation Studies: Training without data filtering produces another continual performance decline, while Monet trained on CapImagine's identical 17K curated subset substantially underperforms CapImagine.The filtering ablation replaces intermediate images with <think_image> to remove training-inference misalignment.
  • Causal Analysis: Causal mediation analysis finds low inter-instance similarity, diverse consecutive hidden states, and sharp prediction drops when key imagination content is modified.These results indicate dependence between input and text imagination tokens and a pronounced effect of those tokens on final predictions.
  • Inference Efficiency: CapImagine achieves inference speed comparable to Monet and is nearly twice as fast as DeepEyes despite generating relatively long text-form imagination sequences.The comparison measures decoding time while requiring all models to generate complete answers.

6. Conclusion

The paper finds that latent-space visual reasoning is undermined by homogeneous, input-insensitive, weakly result-oriented, and semantically limited latent tokens. It therefore proposes text-space imagination, which achieves stronger causal effects and higher performance.

  • Findings: Causal mediation analysis shows that latent tokens are highly homogeneous and minimally sensitive to the input.These properties limit their ability to carry visual imagination and genuine reasoning.
  • Findings: Latent tokens are weakly result-oriented and semantically limited, failing to serve as effective carriers of visual imagination and genuine reasoning.
  • Proposed method: The paper proposes a text-space imagination method to address the limitations of latent-space reasoning.
  • Proposed method: The proposed text-space imagination method exhibits better causal effect and higher performance.

A. Examples for Derived Questions in Probing Analysis.

The probing analysis uses derived questions that preserve the original visual region while varying the queried attributes. These questions are paired with latent embeddings for final answer generation to assess semantic consistency.

  • Question Design: Derived questions target the same visual regions or objects as the original queries while varying the queried attributes.This design examines whether latent representations remain semantically consistent across attribute changes.
  • Probing Procedure: The probing procedure presents latent embeddings together with derived questions to the model for final answer generation.The derived questions therefore serve as inputs for evaluating latent representations during answer generation.

B. Detailed Results for Intervention on Z.

Table 4 examines how Monet’s performance varies under interventions on latent tokens. It compares setting all latent tokens to one tensor with several alternative intervention strategies.

  • Intervention Setup: Table 4 reports Monet’s performance variation under different latent interventions.The upper table applies a uniform intervention to all latent tokens, while the lower table evaluates multiple strategies.
  • Uniform Intervention: The upper table sets every latent token to the same tensor through do(Z).This intervention replaces all latent tokens with one shared tensor.
  • Intervention Strategies: The lower table evaluates Monet under various latent intervention strategies using τ, ϵ, µ, and Z_i.τ is a fixed tensor, ϵ is Gaussian noise with ϵ ∼N(0, σ2), µ is a small near-zero constant, and Z_i denotes the latent token at position i.

C. More Evaluation Results

CapImagine consistently outperforms strong text-space reasoning baselines across diverse benchmarks, including V*, HR-Bench, MME-Realworld-Lite, and BLINK. It also maintains an advantage over Monet in imagination-intensive and spatial reasoning tasks on STARE and Hyperphantasia.

  • Text-Space Reasoning Benchmarks: CapImagine outperforms PAPO, Vision-R1, R1-OneVision, and LLaVA-OneVision across V*, HR-Bench, MME-Realworld-Lite, and BLINK.These results are presented as validating the efficacy and necessity of text-space imagination across multiple datasets.
  • Complex Visual Reasoning: CapImagine consistently outperforms Monet on STARE and Hyperphantasia, which target imagination-intensive and spatial reasoning abilities.The advantage extends to complex scenarios involving spatial perception and counting, supporting the generalizability of text-space imagination.

D. Clarification on Our Position

This section clarifies that the paper diagnoses fundamental failures in current latent visual reasoning rather than offering a complete fix or rejecting latent reasoning generally. It identifies representation degeneration as requiring new training paradigms and provides causal-analysis tools and a text-space baseline for future work.

  • Position and scope: The paper’s primary position is to diagnose fundamental failures in current latent visual reasoning as a foundation for future improvements, not to provide a complete fix.The authors explicitly frame complete remediation as outside this work’s scope.
  • Position and scope: Causal analysis identifies representation degeneration as the root cause, while resolving it requires entirely new training paradigms to regularize the latent space.The authors characterize this resolution as valuable future work rather than a direct patch.
  • Position and scope: The paper does not reject latent reasoning generally; it analyzes why representative implementations have not yet achieved strong causal latent reasoning.The “NOT YET” scope targets current implementations rather than the paradigm as a whole.
  • Position and scope: The work equips the community with a systematic causal-analysis framework and an interpretable text-space baseline to guide future development.These resources are presented as diagnostic tools and necessary baselines.

E. Limitations and Future Work

The work identifies three limitations: CapImagine increases inference latency, functions mainly as a causal verification probe rather than an optimized solution, and uses natural language with lower granularity than high-dimensional representations.

  • Limitations: CapImagine has higher inference latency than latent-based methods because autoregressive decoding produces longer sequences.The passage identifies this as the first limitation.
  • Limitations: CapImagine primarily verifies the causality gap in current latent paradigms rather than serving as an optimal solution.The authors characterize the approach as a verification probe.
  • Limitations: Natural language is inherently less granular than the theoretical information capacity of high-dimensional representations.The passage presents this as another acknowledged limitation of the text-based approach.
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