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REVIEW 4 major objections 6 minor 8 cited by

HumanEdit: A High-Quality Human-Rewarded Dataset for Instruction-based Image Editing

T0 review · 4 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read HumanEdit claims that a human-rewarded, four-stage annotation pipeline can produce instruction-editing pairs aligned with human preferences, and delivers a 5,751-pair dataset plus a benchmark to support that claim.

desk verdict A genuinely useful released dataset for instruction-based editing, but the 'high-quality' claim rests on unverified DALL-E 2 alignment — especially shaky for Counting and Relation — so the benchmark scores should be read with caution. read the letter →

arxiv 2412.04280 v2 pith:NC2USPTS submitted 2024-12-05 cs.CV cs.GR

Jinbin Bai , Wei Chow , Ling Yang , Xiangtai Li , Juncheng Li , Hanwang Zhang , Shuicheng Yan This is my paper · ORCID
classification cs.CVcs.GR
keywords instruction-basedimageeditinghuman-annotateddatasethumanpreferencealignmentbenchmarkdiffusionmodelsmasksDALL-E2sixeditcategories
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 introduces HumanEdit, a curated dataset for instruction-guided image editing whose pairs were authored by human annotators and then reviewed by administrators, rather than assembled automatically. The claim is that this human-rewarded pipeline yields source-target pairs that better match what real users ask for, addressing a fidelity gap in large auto-generated editing datasets. The dataset spans six instruction categories (Action, Add, Counting, Relation, Remove, Replace), comes with masks for every image, and includes a subset detailed enough for mask-free editing. The paper also benchmarks several existing editing models on the dataset, reporting where they succeed and where they fall short. If the claim holds, HumanEdit gives the field a reliable training and evaluation resource for making instruction-following image editors behave more like human editors.

What carries the argument

The four-stage annotation pipeline is the load-bearing mechanism: after a tutorial and quiz select annotators, images are curated from high-resolution sources, annotators create instructions and use DALL-E 2 with masks to generate edited images, and administrators perform a two-tier quality review that returns or discards submissions. Roughly 20,000 annotated images were reduced to 5,751 retained pairs. The six-category taxonomy (Action, Add, Counting, Relation, Remove, Replace) is the organizing device that turns the collection into a benchmark capable of reporting per-task strengths and weaknesses.

What would settle it

A count audit of the Counting subset: automatically or manually count the relevant objects in the ground-truth target images and compare against the numbers stated in the instructions; if a substantial share of pairs violates the stated count, the fidelity claim collapses. A complementary test is a human-preference study in which fresh annotators judge whether each target image satisfies its instruction, with the pass rate reported.

Watch

Extended reading notes

Core claim

HumanEdit is a 5,751-pair dataset for instruction-guided image editing in which every pair was hand-built: annotators wrote the edit instruction, drew the mask, and used DALL-E 2 to generate the edited image, and administrators then accepted, returned for re-annotation, or discarded each submission. The central claim is that roughly 2,500 hours of human effort across four stages make the dataset better aligned with human preferences than prior large-scale editing datasets built largely from language models and synthesis pipelines. A further contribution is the six-way taxonomy of editing tasks (Action, Add, Counting, Relation, Remove, Replace), which the authors argue supports fine-grained evaluation, and the release of a benchmark with mask-free and mask-provided baselines showing, for example, that most methods perform better on Add than on Remove. The dataset also provides masks for every image while keeping a mask-free subset, and it draws on high-resolution images from diverse sources rather than a single dataset.

Load-bearing premise

The reliability of the dataset rests on the assumption that DALL-E 2's edited images, after human review, actually do what the instruction says; if the model's known failures in counting and spatial-relation edits slip through the reviewers' filter, the dataset's ground truth can be systematically wrong.

Editorial extensions

If this is right

  • Models fine-tuned on HumanEdit should produce edits that follow user instructions more faithfully than models trained only on auto-generated editing data, as measured by human preference.
  • The six-part taxonomy makes per-task reporting possible; the benchmark numbers indicate Relation and Action edits are the hardest for current methods, pointing to where training data and architectures must improve.
  • The provided masks enable mask-conditioned training, while the mask-free subset allows evaluation of whether purely instruction-driven localization can replace explicit masks.
  • The HI-EDIT benchmark gives future work a fixed, human-verified test bed, making results across editing models comparable.
  • The mask-versus-mask-free split lets researchers measure how much spatial supervision is actually needed for reliable instruction editing.

Reading between the lines

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

  • At 5,751 pairs, the dataset's practical value may be more as an evaluation benchmark than as a large-scale training corpus; scaling the pipeline to millions of pairs would be costly, though the quality-controlled subset could be used to filter or validate larger auto-generated collections.
  • Because DALL-E 2 is weakest at Counting and Relation edits, the retained pairs in those categories may over-represent easy instances, which would make benchmark scores on those categories optimistic relative to real-world difficulty.
  • The fact that only 46.5 percent of instructions are detailed enough for mask-free editing suggests natural user instructions are often spatially ambiguous, signaling a need for research on instruction-driven region grounding.
  • The human-rewarded annotation scheme could transfer to other instruction-following generation tasks, such as video or 3D editing, where alignment with human preference is currently a bottleneck.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The paper introduces HumanEdit, a dataset of 5,751 instruction-based image-editing pairs constructed on real high-resolution images, with masks for every pair, six instruction categories (Action, Add, Counting, Relation, Remove, Replace), and a 400-pair core subset. The authors describe a four-stage annotation pipeline in which human annotators create instructions and use DALL-E 2 to produce edited targets, followed by administrator review that they term human-rewarded. The paper reports dataset statistics, compares with prior editing datasets, and benchmarks eight methods in mask-free and mask-provided settings. The central claim is that HumanEdit is a high-quality, human-rewarded dataset that supports both masked and mask-free editing and serves as a versatile benchmark.

Significance. If the quality claim can be substantiated, HumanEdit would be a useful community resource: it provides real-image editing pairs, detailed masks, a six-category taxonomy for fine-grained evaluation, and an open benchmark with multiple baselines. The paper's strengths include the detailed four-stage pipeline description, the explicit documentation of failure cases in Appendix C, the release of the dataset, and the breadth of reported statistics and baseline comparisons. The significance, however, rests on the validity of the DALL-E 2 targets as ground truth, and that validity is not yet independently established.

major comments (4)
  1. [Section 2, Stage 4 and Appendix C] The central 'high-quality' claim depends on the assumption that each retained DALL-E 2 edited image is a faithful realization of its instruction. Appendix C explicitly documents DALL-E 2's limited editing capabilities for counting and relational edits, including cases where the model removes rather than adds objects (Fig. 48) and where instructed relations could not be achieved despite dozens of trials (Figs. 46, 49). Because the Counting subset (698 pairs) and Relation subset (410 pairs) in Table 1 are produced by the same model and filtered only by internal administrator review, the paper does not demonstrate that the retained targets satisfy their instructions. I request inter-annotator agreement on a random sample, an independent instruction-target alignment check, or category-wise retention statistics from the 20,000 annotated images to the final 5,751. This is load-bearing because Tables 3-5 use these targets as ground truth for evaluation.
  2. [Section 3 and Figure 6(a)] The proportion of the dataset that supports mask-free editing is reported inconsistently: the abstract says 'a subset', the text in Section 3 says '46.5% of the data supporting editing without masks', and Figure 6(a) reports 'no need for mask 53.1%' with 'need mask 46.9%'. Since the benchmark includes mask-free settings (Tables 3 and 5), the exact split and the criterion used to determine it must be clarified. Without this, the mask-free benchmark results are not reproducible.
  3. [Section 4, Tables 3-5] The benchmark evaluates models by comparing their outputs to DALL-E 2 targets using L1, L2, CLIP-I, DINO, and CLIP-T. If some retained targets contain the artifacts or misalignments documented in Appendix C, then these scores partly measure fidelity to imperfect targets rather than editing quality. I recommend adding a human evaluation on a sample of model outputs, at least on HumanEdit-core, or reporting per-instance instruction-target alignment scores to validate the benchmark conclusions.
  4. [Section 2, Stage 4] The 'human-rewarded' mechanism is described only as 'annotators with good performance receive higher rewards, while those with poor performance are removed from the annotator teams.' No details are given on the reward scheme, the scoring rubric used by administrators, or the number of administrators and their agreement. Since this quality-control procedure is the primary evidence for the dataset's quality claim, it should be quantified, for example by reporting the number of administrators per submission and the rate of returned versus discarded submissions.
minor comments (6)
  1. [Table 1] The header contains a typo: 'Rmove' should be 'Remove'.
  2. [Figure 2] The label 'Toturial' is a typo and should be 'Tutorial'.
  3. [Section 3] The sentence 'MagicBrush has only 46.6 input images above 1000' appears to mean 46.6% of images, not 46.6 images; please correct the typo.
  4. [Introduction] The Introduction mentions 'Vendi Score calculations' but does not define or cite the Vendi Score; please add a definition and reference.
  5. [Appendix C.1] The sentence 'with the resulting images mostly [Podell et al., 2023, Ge et al., 2024b] showing a reduction in the number of objects' contains stray citation markers that interrupt the prose; these should be removed or moved to the end of the sentence.
  6. [Table 2] The column 'Real-world Scenario' is defined as 'whether images edited by users in the real world are included', but HumanEdit is marked with a checkmark even though its instructions are created by annotators rather than collected from real user editing requests. The column definition or the table entry should be clarified to avoid overclaiming.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: HumanEdit is a dataset-and-benchmark paper with no fitted parameters or derivations that reduce to their own inputs.

full rationale

The paper's contribution is a curated dataset and an evaluation benchmark. There is no claimed derivation chain in which a prediction is computed from fitted parameters: the ground-truth edited images are produced by DALL-E 2 and accepted or rejected by human annotators and administrators, which is an external filtering process rather than a quantity derived from the paper's own equations. The benchmark tables report standard metrics (L1, L2, CLIP-I, DINO, CLIP-T) for independently published baselines on the new data; those baselines are not used to define the dataset's ground truth, so no result is forced by construction. The inclusion of Meissonic, a baseline authored by overlapping researchers, is a self-citation, but it is used as one of several comparison systems and does not carry the paper's central claim; the dataset's value would stand or fall independently of that baseline. Appendix C candidly documents DALL-E 2 failures, including counting and relational edits, and states that such failures were excluded from the released data. This is a transparency statement about data quality, not a circular step: even if the retained pairs inherit some DALL-E 2 artifacts, that would be a validity or correctness risk, not a reduction of the benchmark's conclusions to its inputs. The six-category taxonomy is a labeling scheme applied to instructions, and the 'mask-free versus masked' distinction is a dataset property, not a prediction derived from prior outputs. Accordingly, no step in the paper exhibits self-definition, fitted-input-called-prediction, load-bearing self-citation, imported uniqueness, ansatz-smuggling, or renaming of a known result.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

No free parameters are fitted in this dataset paper. The central claims rest on domain assumptions about the reliability of DALL-E 2 outputs, human quality review, the instruction taxonomy, and the representativeness of the image source. These assumptions are reasonable but not quantitatively validated.

assumptions (4)
  • domain assumption DALL-E 2 generated edited images, after human filtering, are valid ground-truth targets for the given editing instructions.
    Section 2 Stage 3 uses DALL-E 2 to generate edited images; Appendix C documents DALL-E 2 limitations, but the retained 5,751 pairs assume the generated targets are faithful enough.
  • domain assumption Human annotator and administrator review reliably identifies 'high-quality' edits.
    Section 2 Stage 4 relies on human judgment for quality control; no inter-annotator agreement or quantitative quality metric is reported.
  • ad hoc to paper The six instruction categories (Action, Add, Counting, Relation, Remove, Replace) cover the diverse space of real-world editing instructions.
    Section 1 and Table 1 define this taxonomy; no external validation that these categories are exhaustive or natural.
  • domain assumption Unsplash images provide a representative broad set of real-world scenes.
    Section 3: images are mostly from Unsplash; selection is not random and may not represent the distribution of user images.

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Cite this review

Pith. "Pith review of HumanEdit: A High-Quality Human-Rewarded Dataset for Instruction-based Image Editing." pith.science (2026). https://pith.science/paper/NC2USPTS

@misc{pith2026241204280,
  author       = {Pith},
  title        = {Pith review of: HumanEdit: A High-Quality Human-Rewarded Dataset for Instruction-based Image Editing},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NC2USPTS}},
  note         = {Machine review of arXiv:2412.04280}
}
abstract

We present HumanEdit, a high-quality, human-rewarded dataset specifically designed for instruction-guided image editing, enabling precise and diverse image manipulations through open-form language instructions. Previous large-scale editing datasets often incorporate minimal human feedback, leading to challenges in aligning datasets with human preferences. HumanEdit bridges this gap by employing human annotators to construct data pairs and administrators to provide feedback. With meticulously curation, HumanEdit comprises 5,751 images and requires more than 2,500 hours of human effort across four stages, ensuring both accuracy and reliability for a wide range of image editing tasks. The dataset includes six distinct types of editing instructions: Action, Add, Counting, Relation, Remove, and Replace, encompassing a broad spectrum of real-world scenarios. All images in the dataset are accompanied by masks, and for a subset of the data, we ensure that the instructions are sufficiently detailed to support mask-free editing. Furthermore, HumanEdit offers comprehensive diversity and high-resolution $1024 \times 1024$ content sourced from various domains, setting a new versatile benchmark for instructional image editing datasets. With the aim of advancing future research and establishing evaluation benchmarks in the field of image editing, we release HumanEdit at https://huggingface.co/datasets/BryanW/HumanEdit.

Figures

Figures reproduced from arXiv: 2412.04280 by the authors.

Figure 1
Figure 1. Data examples of instruction-guided image editing in HumanEdit. Our dataset en￾compasses six distinct editing categories. In the images, gray shapes represent masks, which are provided for every photograph. Moreover, approximately half of the dataset includes instructions that are sufficiently detailed to enable editing without masks. It is important to note that, for conciseness, masks are depicted directly on the … view at source ↗
Figure 2
Figure 2. Overview of data collection process. are problematic, the submission is returned to stage three for re-annotation. Submissions with poor editing quality are discarded. We refer to this process as human-rewarded, as annotators with good performance receive higher rewards, while those with poor performance are removed from the annotator teams. Examples of failure cases excluded from HumanEditcan be found in Appendix C… view at source ↗
Figure 3
Figure 3. More examples of instruction-guided image editing in HumanEdit. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (53 more)
Figure 4
Figure 4. Figure 4: (a) The distribution chart of the first 30 objects in the editing instructions for HumanEdit. [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: The river chart of HumanEdit-full. The first node of the river represents the type of edit, the second node corresponds to the verb extracted from the instruction, and the final node corresponds to the noun in the instruction. To maintain clarity, we only selected the …
Figure 6
Figure 6. Figure 6: (a) The distribution of images for which HumanEdit requires masking, where [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: Qualitative comparisons between mask-provided baselines. The first three rows show the original images, corresponding masks, and ground truth edited images from DALL-E 2. The subsequent four rows present results generated by Blended Latent Diffusion SDXL, GLIDE, aMUSEd…
Figure 8
Figure 8. Figure 8: Qualitative comparisons between mask-provided baselines. The first three rows show the original images, corresponding masks, and ground truth edited images from DALL-E 2. The subsequent four rows present results generated by Blended Latent Diffusion SDXL, GLIDE, aMUSEd…
Figure 9
Figure 9. Figure 9: An Overview of Keywords in HumanEdit-core Edit Instructions: The inner circle represents the verb in the edit instruction, while the outer circle illustrates the noun following the verb in each instruction. remove change reduce replace add turn show move raise put plac…
Figure 10
Figure 10. Figure 10: An Overview of Keywords in HumanEdit-full Edit Instructions: The inner circle represents the verb in the edit instruction, while the outer circle highlights the noun associated with the verb in each instruction. 13 [PITH_FULL_IMAGE:figures/full_fig_p013_10.png]
Figure 11
Figure 11. Figure 11: The river chart of HumanEdit-core. The first node of the river represents the type of edit, the second node corresponds to the verb extracted from the instruction, and the final node corresponds to the noun in the instruction. To maintain clarity, we only selected the…
Figure 12
Figure 12. Figure 12: Case of Object Removal [PITH_FULL_IMAGE:figures/full_fig_p014_12.png]
Figure 13
Figure 13. Figure 13: Object Replacement Example I. Editing instruction: Let windows become square. Edited image description: Above the bathtub is a square window. Original Mask Output [PITH_FULL_IMAGE:figures/full_fig_p015_13.png]
Figure 14
Figure 14. Figure 14: Object Replacement Example II. (1.3) Object Addition. As shown in [PITH_FULL_IMAGE:figures/full_fig_p015_14.png]
Figure 15
Figure 15. Figure 15: Case of Object Addition. 15 [PITH_FULL_IMAGE:figures/full_fig_p015_15.png]
Figure 16
Figure 16. Figure 16: Case of Object Counting Change. (2) Action Change. As shown in [PITH_FULL_IMAGE:figures/full_fig_p016_16.png]
Figure 17
Figure 17. Figure 17: Case of Action Change. (3) Relation Change. As shown in [PITH_FULL_IMAGE:figures/full_fig_p016_17.png]
Figure 18
Figure 18. Figure 18: Case of Relation Change. 16 [PITH_FULL_IMAGE:figures/full_fig_p016_18.png]
Figure 19
Figure 19. Figure 19: An Example of Prompt Word Selection Target Image Caption: Four parrots are perched on a girl’s shoulders, arms, and head [PITH_FULL_IMAGE:figures/full_fig_p017_19.png]
Figure 20
Figure 20. Figure 20: An Example of Prompt Word Selection (2) Image Resolution. After uploading the image, click ’crop’ first, then click ’Edit image’ to proceed with editing [PITH_FULL_IMAGE:figures/full_fig_p017_20.png]
Figure 21
Figure 21. Figure 21: Performing a Crop Operation on the DALL-E 2 Platform. [PITH_FULL_IMAGE:figures/full_fig_p017_21.png]
Figure 22
Figure 22. Figure 22: Performing an Editing Operation on the DALL-E 2 Platform. [PITH_FULL_IMAGE:figures/full_fig_p018_22.png]
Figure 23
Figure 23. Figure 23: An Illustration of Avoiding Edits in Irrelevant Areas. [PITH_FULL_IMAGE:figures/full_fig_p018_23.png]
Figure 24
Figure 24. Figure 24: An Illustration of Avoiding Edits in Irrelevant Areas. [PITH_FULL_IMAGE:figures/full_fig_p019_24.png]
Figure 25
Figure 25. Figure 25: An Illustration of Avoiding Edits in Irrelevant Areas. [PITH_FULL_IMAGE:figures/full_fig_p019_25.png]
Figure 26
Figure 26. Figure 26: An Illustration of Avoiding Edits in Irrelevant Areas. [PITH_FULL_IMAGE:figures/full_fig_p020_26.png]
Figure 27
Figure 27. Figure 27: , the fingers are distorted and do not resemble a normally outstretched hand [PITH_FULL_IMAGE:figures/full_fig_p020_27.png]
Figure 28
Figure 28. Figure 28: A Case for Ensuring Edit Quality. The car door in [PITH_FULL_IMAGE:figures/full_fig_p021_28.png]
Figure 29
Figure 29. Figure 29: A Case for Ensuring Edit Quality. (5) Success Rate. DALL-E 2 has a relatively low success rate. If multiple regenerations or instruction modifications do not yield satisfactory results, it may be best to abandon the task. The exact number of attempts before abandonmen…
Figure 30
Figure 30. Figure 30: An Illustration of Consistency in Style Before and After Editing. [PITH_FULL_IMAGE:figures/full_fig_p022_30.png]
Figure 31
Figure 31. Figure 31: Examples of valid and invalid images. The first image is valid, while the following three [PITH_FULL_IMAGE:figures/full_fig_p022_31.png]
Figure 32
Figure 32. Figure 32: Log in to the DALL·E 2 platform and click "Try DALL-E" to upload an image. [PITH_FULL_IMAGE:figures/full_fig_p022_32.png]
Figure 33
Figure 33. Figure 33: After uploading the image, a cropping page will be displayed. [PITH_FULL_IMAGE:figures/full_fig_p023_33.png]
Figure 34
Figure 34. Figure 34: Click the "Edit" button to enter the editing window. [PITH_FULL_IMAGE:figures/full_fig_p023_34.png]
Figure 35
Figure 35. Figure 35: Drag the editing points to select the area to be edited. [PITH_FULL_IMAGE:figures/full_fig_p024_35.png]
Figure 36
Figure 36. Figure 36: Input the editing instructions in the text bo. [PITH_FULL_IMAGE:figures/full_fig_p024_36.png]
Figure 37
Figure 37. Figure 37: Generate edited images. If the generated result is of poor quality (e.g., none of the images meet the requirements), you can click the “regenerate” button to try again [PITH_FULL_IMAGE:figures/full_fig_p025_37.png]
Figure 38
Figure 38. Figure 38: Regenerate edited images. However, please avoid generating the same instruction more than three times. Instead, try modifying the instruction to make it more precise. For example, change the expected image description to “A cute little girl with her arms outstretched,…
Figure 39
Figure 39. Figure 39: Regenerated images are still not satisfactory and may require revised instructions. [PITH_FULL_IMAGE:figures/full_fig_p025_39.png]
Figure 40
Figure 40. Figure 40: Download and finish the editing process. [PITH_FULL_IMAGE:figures/full_fig_p026_40.png]
Figure 41
Figure 41. Figure 41: Defective Image Example. (6) Submission of results. Finally, you need to submit the following materials as a group to our platform [PITH_FULL_IMAGE:figures/full_fig_p026_41.png]
Figure 42
Figure 42. Figure 42: Submission Example. 26 [PITH_FULL_IMAGE:figures/full_fig_p026_42.png]
Figure 43
Figure 43. Figure 43: An Illustration of the Mismatch Between Editing Results and Instructions. [PITH_FULL_IMAGE:figures/full_fig_p027_43.png]
Figure 44
Figure 44. Figure 44: An Illustration of the Mismatch Between Editing Results and Instructions. [PITH_FULL_IMAGE:figures/full_fig_p027_44.png]
Figure 45
Figure 45. Figure 45: An Illustration of the Mismatch Between Editing Results and Instructions. [PITH_FULL_IMAGE:figures/full_fig_p027_45.png]
Figure 46
Figure 46. Figure 46: An Illustration of the Limited Editing Capabilities for Specific Types. [PITH_FULL_IMAGE:figures/full_fig_p028_46.png]
Figure 47
Figure 47. Figure 47: An Illustration of the Limited Editing Capabilities for Specific Types. [PITH_FULL_IMAGE:figures/full_fig_p028_47.png]
Figure 48
Figure 48. Figure 48: An Illustration of the Limited Editing Capabilities for Specific Types. [PITH_FULL_IMAGE:figures/full_fig_p028_48.png]
Figure 49
Figure 49. Figure 49: An Illustration of the Limited Editing Capabilities for Specific Types. [PITH_FULL_IMAGE:figures/full_fig_p028_49.png]
Figure 50
Figure 50. Figure 50: An Illustration of the Limited Editing Capabilities for Specific Types. [PITH_FULL_IMAGE:figures/full_fig_p029_50.png]
Figure 51
Figure 51. Figure 51: An Illustration of the Limited Editing Capabilities for Specific Types. [PITH_FULL_IMAGE:figures/full_fig_p029_51.png]
Figure 52
Figure 52. Figure 52: An Illustration of the Limited Editing Capabilities for Specific Types. [PITH_FULL_IMAGE:figures/full_fig_p029_52.png]
Figure 53
Figure 53. Figure 53: An example of object distortion. 29 [PITH_FULL_IMAGE:figures/full_fig_p029_53.png]
Figure 54
Figure 54. Figure 54: The discrepancy between the instruction and the generated image. [PITH_FULL_IMAGE:figures/full_fig_p030_54.png]
Figure 55
Figure 55. Figure 55: An example of subtle editing effects [PITH_FULL_IMAGE:figures/full_fig_p030_55.png]
Figure 56
Figure 56. Figure 56: An example of inconsistent editing. 30 [PITH_FULL_IMAGE:figures/full_fig_p030_56.png]

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Forward citations

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Reviewed August 11, 2026 · model on record in the stance chip above.