Pith. sign in

REVIEW 2 major objections 1 minor 1 cited by

Thinking Without Images: Internalizing Visual Manipulation with On-Policy Self-Distillation

T0 review · 2 major / 1 minor · reviewed 2026-06-27 · grok-4.3

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Models internalize the benefits of zooming into image regions by learning to imagine visual cues through on-policy self-distillation.

desk verdict The paper tries to internalize explicit image zooming via on-policy self-distillation from privileged annotations, but the abstract leaves the faithfulness of the resulting internal trajectories unproven. read the letter →

arxiv 2606.08719 v1 pith:PEB7KHZB submitted 2026-06-07 cs.CV

Yishuo Cai , Jiahui Liu , Yuanxin Liu , Haobo Deng , Linli Yao , Yuhao Zheng , Kun Ouyang , Zhimo Li
show 4 more authors
Ziyue Wang Xu Sun Haoli Bai Xiaohui Li
This is my paper · ORCID
classification cs.CV
keywords visualreasoningself-distillationon-policylearningimagezoominginternalimaginationcomputervisionfine-grained
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper establishes that the reasoning advantages of explicitly zooming into relevant image parts can be captured internally without tool calls at test time. It introduces Imagine-OPD, where a teacher using privileged zoomed views from annotations supervises the student's imagined reasoning paths. This self-distillation requires no external teacher or demo data. A sympathetic reader would care because it promises accurate fine-grained visual reasoning at lower inference cost than methods that repeatedly invoke image tools.

What carries the argument

Imagine-OPD, the on-policy self-distillation framework that transfers supervision from privileged zoomed evidence views to internal imagination trajectories.

What would settle it

A test where the Imagine-OPD model shows no improvement or worse performance than non-imagination baselines on fine-grained visual reasoning tasks, or where its internal predictions do not correlate with actual zoomed image contents.

Watch

Extended reading notes

Core claim

Imagine-OPD is an on-policy self-distillation framework in which a teacher plays the role of a 'Thinking with Images' reasoner during training: it receives privileged zoomed evidence views derived from annotated regions, and supervises the model's own imagination reasoning trajectories. The resulting model achieves the best average performance among compared models while significantly reducing inference overhead compared with 'Thinking with Images' methods.

Load-bearing premise

Supervision from privileged zoomed evidence views derived from annotated regions during training will produce faithful internal imagination trajectories that generalize without an external teacher or high-quality demonstrations.

Editorial extensions

If this is right

  • Best average performance on vision-centric benchmarks compared to other models.
  • Significantly lower inference overhead than methods that explicitly think with images.
  • Internalization works without external teachers or high-quality imagination demonstrations.
  • Imagined trajectories become faithful to task-relevant visual evidence.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Similar self-distillation could internalize other tool-using behaviors in agents beyond visual zooming.
  • Training with privileged information might enable better generalization in multimodal reasoning systems.
  • Inspection of imagined trajectories could reveal what the model has learned to focus on.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 1 minor

Summary. The paper introduces Imagine-OPD, an on-policy self-distillation framework to internalize the benefits of 'Thinking with Images' (explicit zooming into relevant regions) into 'Thinking with Imagination' (internal decision of where to look and what visual cues would be revealed). A teacher model receives privileged zoomed evidence views derived from annotated regions and supervises the student's imagination trajectories during training. The method is presented as not requiring an external teacher or high-quality demonstrations, and experiments on vision-centric benchmarks are claimed to show the best average performance among compared models while significantly reducing inference overhead relative to explicit tool-use methods.

Significance. If the results hold, the work would demonstrate a viable path to efficient visual reasoning by internalizing tool-use behaviors, lowering inference cost without sacrificing performance. The on-policy self-distillation setup that avoids external high-quality demonstrations is a positive design choice if the generalization from privileged training signals is shown to be robust.

major comments (2)
  1. [Method] The central claim that privileged supervision from annotated zoomed views produces faithful internal imagination trajectories that generalize at inference (without external teacher or annotations) is load-bearing but unsupported by any mechanism or analysis in the method description; the abstract provides no evidence that the student learns accurate cues rather than spurious correlations or regularization effects alone.
  2. [Experiments] The performance claim ('best average performance among compared models') cannot be evaluated because the manuscript text supplies no metrics, baselines, ablations, or statistical details; without these, it is impossible to determine whether gains are attributable to the self-distillation procedure.
minor comments (1)
  1. The abstract would be clearer if it named the specific vision-centric benchmarks and the magnitude of the inference-overhead reduction (e.g., tokens or latency).

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive feedback. We address each major comment below and indicate the revisions we will make to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Method] The central claim that privileged supervision from annotated zoomed views produces faithful internal imagination trajectories that generalize at inference (without external teacher or annotations) is load-bearing but unsupported by any mechanism or analysis in the method description; the abstract provides no evidence that the student learns accurate cues rather than spurious correlations or regularization effects alone.

    Authors: We agree that the method description would benefit from explicit analysis demonstrating that the self-distillation produces faithful trajectories rather than spurious correlations. The on-policy self-distillation mechanism transfers the teacher's privileged zoomed views to supervise the student's internal imagination process during training, enabling generalization without external tools at inference. In the revised manuscript we will add a dedicated analysis subsection with qualitative trajectory examples and an ablation isolating the distillation signal from generic regularization effects. revision: yes

  2. Referee: [Experiments] The performance claim ('best average performance among compared models') cannot be evaluated because the manuscript text supplies no metrics, baselines, ablations, or statistical details; without these, it is impossible to determine whether gains are attributable to the self-distillation procedure.

    Authors: We acknowledge that the submitted manuscript version does not present the full quantitative results, baselines, ablations, or statistical details in the main text. The experiments were conducted on multiple vision-centric benchmarks and include comparisons showing the claimed average performance and reduced overhead, but these details were omitted from the provided text. We will expand the experiments section with complete tables, ablations, and statistical reporting in the revision. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: method is a training procedure without equations or self-referential derivations

full rationale

The paper describes Imagine-OPD as an on-policy self-distillation training framework in which a teacher model uses privileged zoomed views (from annotations) to supervise student imagination trajectories. No equations, fitted parameters, uniqueness theorems, or derivation chains are presented in the abstract or described structure. The central claim is an empirical training outcome rather than a mathematical reduction that could collapse to its inputs by construction. No self-citations are invoked as load-bearing for any uniqueness or ansatz. This is a standard procedural ML method paper whose validity rests on experimental results, not on any internal definitional loop.

Assumptions & free parameters 0 free parameters · 0 assumptions · 0 invented entities

Abstract supplies no explicit free parameters, axioms, or invented entities; the approach relies on standard supervised distillation assumptions that are not enumerated.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Thinking Without Images: Internalizing Visual Manipulation with On-Policy Self-Distillation." pith.science (2026). https://pith.science/paper/PEB7KHZB

@misc{pith2026260608719,
  author       = {Pith},
  title        = {Pith review of: Thinking Without Images: Internalizing Visual Manipulation with On-Policy Self-Distillation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PEB7KHZB}},
  note         = {Machine review of arXiv:2606.08719}
}
read the original abstract

''Thinking with Images'' has emerged as an effective paradigm for fine-grained visual reasoning: by explicitly zooming into relevant regions and reasoning over crops, models can access local evidence that is difficult to recover from a single global image. However, this benefit comes with redundant tool invocations and longer inference traces. Moreover, when such behaviors are learned mainly from outcome reward, the resulting intermediate crops or visual cues can be noisy or fail to faithfully capture task-relevant visual evidence. In this work, we ask whether the reasoning benefits of ''Thinking with Images'' can be internalized through Thinking with Imagination: an internal process that decides where to look and imagines what visual cues closer inspection would reveal without actually invoking tools. We propose Imagine-OPD, an on-policy self-distillation framework in which a teacher plays the role of a ''Thinking with Images'' reasoner during training: it receives privileged zoomed evidence views derived from annotated regions, and supervises the model's own imagination reasoning trajectories. Imagine-OPD does not require an external teacher or high-quality imagination demonstrations. Experiments on vision-centric benchmarks show that Imagine-OPD achieves the best average performance among compared models while significantly reducing inference overhead compared with ''Thinking with Images'' methods.

Figures

Figures reproduced from arXiv: 2606.08719 by the authors.

Figure 1
Figure 1. From “Thinking with Images” to “Thinking with Imagination”. “Thinking with Images” interleaves text reasoning with explicit tool calls and cropped images, improving access to local evidence but incurring repeated tool interaction and visual encoding. Thinking with Imagination replaces these intermediate images with an internal imagination process that can carry forward the same fine-grained visual evidence without a… view at source ↗
Figure 2
Figure 2. A concrete training instance of Imagine-OPD. Given an original image and a question, the student model generates its own imagination trace. A teacher then evaluates the same trajectory with privileged cropped evidence views and answer, providing on-policy supervision through a token-level KL objective. a full-image student (Yuan et al., 2026). Imagine￾OPD shares the idea of privileged regional supervi￾sion, but uses… view at source ↗
Figure 3
Figure 3. Qualitative attention analysis. Imagine-OPD places more attention on the decisive visual evidence compared with the base model. 0.4 0.5 0.6 0.7 0.8 0.9 Inference Speed 62.5 65.0 67.5 70.0 72.5 75.0 77.5 Benchmark Accuracy Qwen2.5-VL-7B Qwen3-VL-4B Qwen3-VL-8B Pixel-Reasoner Thyme DeepEyes TreeVGR-7B Imagine-OPD-4B Imagine-OPD-8B Base Models Our Models Thinking with Images [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Trade-off between benchmark accuracy and [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SkillLens: Visual Skill Cards for Retrieval-Augmented GUI Action Prediction and On-Policy Distillation

    cs.AI 2026-08 conditional novelty 6.0 of 10

    Visual Skill Cards improve GUI action prediction for frozen and distilled agents by retrieving reusable, state-conditioned visual procedures.

Reference graph

Works this paper leans on

10 extracted references · cited by 1 Pith paper

  1. [3]

    11 Input Template

    If this is a multiple-choice question and your reasoning conflicts with the op- tions, select the single most plausible option once and finish immediately. 11 Input Template. <image><image>... Question. {question} Ground truth. {answer} Student Prompt.The prompt used for the student model is shown below. In contrast to the teacher, the model receives only...

  2. [4]

    When using imagine, describe both the mental operation and what becomes clearer after it

  3. [5]

    End with<answer>...</answer>

  4. [6]

    Input Template

    If this is a multiple-choice question and your reasoning conflicts with the op- tions, select the single most plausible option once and finish immediately. Input Template. <image> User’s Question. {question} Self-Bbox Proposal Prompt.For the self- proposed bounding-box ablation, we first ask the model to predict a single bounding box from the original ima...

  5. [7]

    If one object is sufficient, return a tight box around that object

  6. [8]

    If the answer depends on multiple ob- jects or their relationship, return one box covering all necessary evidence

  7. [9]

    Use integer pixel coordinates on the original image of size {width}x{height}

  8. [10]

    Do not answer the question

Show all 10 references
  1. [11]

    Return exactly one bbox in this format and nothing else: <bbox>[x1, y1, x2, y2]</bbox>

  2. [12]

    Do not say that the object is absent

    Always provide your best bbox guess even if the target is small or hard to see. Do not say that the object is absent. Question. {question} 12

Pith tools

Reviewed June 27, 2026 · model on record in the stance chip above.