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arxiv: 2512.13609 · v2 · submitted 2025-12-15 · 💻 cs.CV · cs.LG

Recognition: 2 theorem links

· Lean Theorem

Do-Undo Bench: Reversibility for Action Understanding in Image Generation

Shweta Mahajan , Shreya Kadambi , Hoang Le , Rajeev Yasarla , Apratim Bhattacharyya , Munawar Hayat , Fatih Porikli
Authors on Pith no claims yet

Pith reviewed 2026-05-16 22:07 UTC · model grok-4.3

classification 💻 cs.CV cs.LG
keywords action understandingreversibilityimage generationvision-language modelsbenchmarkscene transformationcause and effect
0
0 comments X

The pith

Requiring image generators to apply and then undo real actions tests genuine cause-and-effect understanding.

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

The paper introduces the Do-Undo task and benchmark to evaluate vision-language models on real-world action understanding in image generation. Models must first produce the scene after an action occurs and then generate the exact reverse transformation back to the original image. This forward-reverse requirement is intended to force models to demonstrate cause-and-effect reasoning instead of relying on stylistic or semantic edits. The authors curate a collection of reversible real-world actions and show that existing models perform poorly at maintaining consistency across the pair of transformations. The benchmark is positioned as a new evaluation tool to drive progress in action-aware multimodal generation systems.

Core claim

The Do-Undo task requires models to simulate the visual outcome of a real-world action on an initial image and then produce the precise reversal that restores the scene to its starting state, thereby testing action understanding through explicit reversibility rather than one-directional prompt-based edits.

What carries the argument

The Do-Undo task, a paired forward-reverse generation requirement that forces models to demonstrate understanding of reversible real-world dynamics through image sequences.

If this is right

  • Models that succeed at Do-Undo will show improved modeling of real-world physical dynamics in generated scenes.
  • Current vision-language models exhibit clear difficulties preserving consistency when both applying and reversing actions.
  • The benchmark supplies a systematic way to measure action grounding beyond superficial semantic or stylistic image changes.
  • Progress on Do-Undo supports development of multimodal systems that must reason about real-world dynamics.

Where Pith is reading between the lines

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

  • Similar reversibility requirements could be applied to video generation to expose gaps in temporal consistency.
  • Training with Do-Undo pairs might improve generalization to physical prediction tasks that do not involve image editing.
  • Adding more complex multi-step actions to the benchmark would test whether the reversibility principle scales.

Load-bearing premise

That the curated high-quality set of reversible real-world actions isolates genuine cause-and-effect understanding rather than other visual or linguistic cues.

What would settle it

Finding a model that succeeds on the Do-Undo benchmark yet fails on independent physical-reasoning tests that do not involve reversible image pairs would indicate the benchmark does not fully isolate cause-and-effect comprehension.

Figures

Figures reproduced from arXiv: 2512.13609 by Apratim Bhattacharyya, Fatih Porikli, Hoang Le, Munawar Hayat, Rajeev Yasarla, Shreya Kadambi, Shweta Mahajan.

Figure 1
Figure 1. Figure 1: Action-conditioned image generation on the Do-Undo dataset highlights a key limitation of current vision-language models: their inability to reverse previously executed actions. Models trained with Do-Undo dataset show improved understand￾ing of physical actions and their effects on scene dynamics. ulations, they should be able to generate the final state with￾out observing a continuous sequence as in vide… view at source ↗
Figure 2
Figure 2. Figure 2: Do-Undo data curation pipeline. Starting with the EpicKitchens dataset [9], we select visually high quality samples which have reversible actions; the action annotations and the images are used to expand the prompts with additional visual context. videos with their captions starting from input image (which may or may not contain the target objects on which action is being performed) to an image showing act… view at source ↗
Figure 3
Figure 3. Figure 3: Dataset statistics of our Do-Undo training set. We generate data by mining reversible tasks from EpicKitchens [9] dataset. (left) We show the distribution of top-20 actions in the training data. (right) We analysize the diversity in the unique objects handled when performing each action. 0 10 20 30 40 50 Number of test samples rotate place-in remove-from place-on remove grab place put turn-off move turn-on… view at source ↗
Figure 4
Figure 4. Figure 4: Dataset statistics of our Do-Undo test set. (left) We show the distribution of actions in the test data. (right) The test set includes long and short prompts to account for the sensitivity of different VLMs to prompt length. forming actions in a real-world setup, we rely on the Epic Kitchens video dataset [9]. EpicKitchens consists of 100 video episodes with subsequences comprising video frames from start … view at source ↗
Figure 5
Figure 5. Figure 5: Do-Undo with BAGEL. Architecture of our approach with BAGEL [10] as the VLM. and prompt to generate images. By training a VLM on our Do-Undo dataset our goal is to induce physics aware gener￾ation grounded in actions by enforcing consistency between the forward and the reverse actions. In our work, we consider BAGEL [10] as the underly￾ing VLM. It is based on the large language model decoder￾only transform… view at source ↗
Figure 6
Figure 6. Figure 6 [PITH_FULL_IMAGE:figures/full_fig_p006_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Quantitative results on Do-Undo benchmark. We evaluate baseline BAGEL with the model trained on Do-Undo training data (Ours) and with the consistency loss (Ours+consistency). Incorporating Do-Undo training improves action-following while preserving image faithfulness on both action and start frame across tasks such as close, open, move, place, and place-in. standing on the instruction finetuning set from B… view at source ↗
Figure 8
Figure 8. Figure 8: Generalization to object-action interaction for Do-Undo task on rare annotations. Our Do-Undo model preserves hand￾object interaction and correctly emulates grasping actions, even for rare annotations. state. (ii) Similarly, DINO-F measures the similarity be￾tween the generated forward image ˆIF and the ground-truth IF. (iii) We measure the semantics of the generated forward and reverse images by measuring… view at source ↗
Figure 9
Figure 9. Figure 9: Hallucination of objects during action conditioned generation. (top):A person’s left hand opens the lower white freezer door of a refrigerator, revealing its interior. (bottom): Pick up yellow rectangular chopping board with both hands from a speckled countertop. model with our data generates content with high semantic similarity in the reverse image. On analyzing the DINO￾R scores closely in Figs. 7a and … view at source ↗
Figure 10
Figure 10. Figure 10: Training data statistics. Frequency of joint noun ac￾tion pairs in the training do-undo dataset. Clearly, even though pick-up as a lone action dominates the actions, the diversity of the action-noun pairs shows a balanced representation of the training data. A. Training Details To show the benefits of our Do-Undo training dataset for physics-aware generative image editing, we build upon the unified image … view at source ↗
Figure 11
Figure 11. Figure 11: MLLM as a judge. Qualitative results and the per sample scores from the MLLM as a judge for zero-shot evaluation on Gemini, BAGEL, and our approach. DoUndo (c) generally performs the action accurately almost retaining object identity as seen in (1) & (3) however at times the object consistency is missed. 13 [PITH_FULL_IMAGE:figures/full_fig_p013_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Qualitative analysis of similarity metrics. Qualitative analysis of the optical flow for the forward image (OF-F), optical flow for the reverse image (OF-R) and the end point error (EPE) metric. 14 [PITH_FULL_IMAGE:figures/full_fig_p014_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Qualitative results on out-of-distribution data. Qualitative comparison BAGEL [10] with Do-Undo (Ours) i.e. BAGEL trained with our training data; and Do-Undo(c) with our training data and the consistency loss on out-of-distribution actions. 15 [PITH_FULL_IMAGE:figures/full_fig_p015_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Qualitative results. Qualitative comparison BAGEL [10] with Do-Undo (Ours) i.e. BAGEL trained with our training data; and Do-Undo(c) with our training data and the consistency loss for physics-aware reversible generation with our prompt expansion. Row (1) DoUndo (c) adds a new slice of bread. Row (2) Do-Undo (c) is the only model that accurately generates ”place” action however, details about the refriger… view at source ↗
read the original abstract

We introduce the Do-Undo task and benchmark to address a critical gap in vision-language models: understanding and generating plausible scene transformations driven by real-world actions. Unlike prior work that relies on prompt-based image generation and editing to perform action-conditioned image manipulation, our training hypothesis requires models to simulate the outcome of a real-world action and then reverse it to the original state. This forward-reverse requirement tests genuine cause-and-effect understanding rather than stylistic or semantic edits. We curate a high-quality benchmark of reversible actions from real-world scenarios to enable robust action grounding. Our experiments reveal that current models struggle with action reversibility, highlighting the need to evaluate action understanding. Do-Undo provides an intuitive testbed for evaluating and advancing action-aware generation in multimodal systems that must reason about real-world dynamics.

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 / 1 minor

Summary. The paper introduces the Do-Undo task and benchmark to evaluate vision-language models on reversible real-world actions in image generation. It claims that requiring models to simulate an action outcome and then reverse the scene to the original state tests genuine cause-and-effect understanding, unlike prior prompt-based editing approaches. The work curates a benchmark of such actions and reports that current models struggle, highlighting the need for better action-aware generation.

Significance. If the benchmark construction successfully isolates causal simulation from visual shortcuts, this could become a useful testbed for assessing and improving multimodal models' reasoning about physical dynamics, addressing a noted gap in existing evaluation methods for action-conditioned generation.

major comments (2)
  1. [Benchmark construction] The description of benchmark curation (referenced in the abstract as 'high-quality benchmark of reversible actions from real-world scenarios') provides no explicit criteria or process for selecting pairs that cannot be solved via visual pattern matching, symmetry detection, or common diffusion priors on invertible edits such as object addition/removal. This directly affects whether the forward-reverse requirement actually enforces action simulation and cause-and-effect understanding.
  2. [Experiments] No quantitative results, model evaluation protocols, data statistics, or baseline comparisons are reported, despite the abstract stating that 'our experiments reveal that current models struggle.' This makes it impossible to assess the magnitude or reliability of the claimed limitations.
minor comments (1)
  1. [Abstract] The abstract refers to 'our training hypothesis' without clarifying whether this is a training objective or an evaluation hypothesis; this notation should be clarified for readers.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript introducing the Do-Undo Bench. We appreciate the emphasis on ensuring the benchmark truly isolates causal action understanding. Below we address each major comment and describe the revisions we will make to the manuscript.

read point-by-point responses

  1. Referee: [Benchmark construction] The description of benchmark curation (referenced in the abstract as 'high-quality benchmark of reversible actions from real-world scenarios') provides no explicit criteria or process for selecting pairs that cannot be solved via visual pattern matching, symmetry detection, or common diffusion priors on invertible edits such as object addition/removal. This directly affects whether the forward-reverse requirement actually enforces action simulation and cause-and-effect understanding.

    Authors: We agree that the current manuscript provides insufficient detail on curation criteria. In the revised version we will expand the Benchmark Construction section with an explicit protocol: (1) sourcing candidate reversible actions from real-world video datasets (e.g., Something-Something and Epic-Kitchens), (2) defining selection criteria that require non-trivial physical state changes (e.g., pouring, folding, or mixing) whose reversal cannot be achieved by symmetry detection or simple object addition/removal, (3) automated filtering followed by human verification to exclude pairs solvable via visual shortcuts or diffusion priors, and (4) reporting inter-annotator agreement and category statistics. These additions will directly demonstrate how the forward-reverse requirement enforces causal simulation. revision: yes

  2. Referee: [Experiments] No quantitative results, model evaluation protocols, data statistics, or baseline comparisons are reported, despite the abstract stating that 'our experiments reveal that current models struggle.' This makes it impossible to assess the magnitude or reliability of the claimed limitations.

    Authors: We acknowledge that the submitted manuscript summarizes experimental findings only in the abstract and does not include full quantitative results. In the revision we will add a dedicated Experiments section containing: dataset statistics (number of pairs, action categories, train/test splits), evaluation protocols (forward and reverse generation metrics including perceptual similarity, action-success verification via VLM judges and human raters), quantitative results across multiple models (e.g., Stable Diffusion variants, InstructPix2Pix, and multimodal LLMs), and baseline comparisons against prompt-only editing methods. This will provide concrete evidence for the reported limitations. revision: yes

Circularity Check

0 steps flagged

No circularity: benchmark proposal with no derivations or self-referential reductions

full rationale

The paper proposes the Do-Undo task and benchmark for evaluating action reversibility in vision-language models. It contains no equations, fitted parameters, predictions, or derivation chains that could reduce to inputs by construction. The central hypothesis (forward-reverse simulation for cause-and-effect understanding) is stated as a design choice for the benchmark rather than derived from prior results or self-citations. No self-citation load-bearing steps, uniqueness theorems, or ansatzes appear. The work is self-contained as an empirical benchmark introduction whose validity rests on external model evaluations rather than internal definitional loops.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the assumption that reversible real-world actions can be curated to isolate cause-and-effect reasoning. No free parameters or invented physical entities are introduced.

axioms (1)
  • domain assumption Reversible actions from real-world scenarios can be curated into a benchmark that tests genuine cause-and-effect understanding rather than stylistic edits.
    Invoked in the abstract when stating the training hypothesis and benchmark purpose.
invented entities (1)
  • Do-Undo task no independent evidence
    purpose: To evaluate action understanding via forward simulation and reversal in image generation.
    Newly defined evaluation protocol introduced in the paper.

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Reference graph

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