Writing study: EditVerse: Unifying Image and Video Editing and Generation with In-Context Learning

ICLR overviewCorpus atlasBy contribution typeVisual galleryRules & templateWriting patternsPaper studies

← ICLR 2026 oral writing corpus

Xuan Ju, Tianyu Wang, Yuqian Zhou, HE Zhang, Qing Liu, Cherry Zhao, Zhifei Zhang, Yijun Li, Yuanhao Cai, Shaoteng Liu, Daniil Pakhomov, Zhe Lin, Soo Ye Kim, Qiang Xu · Official ICLR 2026 oral record

Observed writing choices, not acceptance rules. This study examines the inspected manuscript version. It does not establish why the paper was accepted or that its presentation choices should be copied unchanged.

Figure, table and section ledger

Editorial contribution type: Architecture & representation design (primary) · Datasets, benchmarks & measurement resources.

Layout extraction, not a required paper template. Counts are distinct caption labels; a multi-panel figure counts once. Section spans are estimated from heading positions, including floats and whitespace. Method and limitations.

Canonical PDF

22 physical pages

Before references / appendix

≈ 10.16 page-equivalents

Main figures / tables

8 / 5

Attached appendix figures / tables

2 / 6

Attached appendix span

≈ 5.89 page-equivalents

Before references / appendix: ≈ 46.18%References / intervening material: ≈ 27.05%Attached appendix: ≈ 26.77%Estimated layout envelopes, not text/artwork area. Unresolved appendix boundaries remain within the undivided post-body material.

Section-by-section lengths and source pages

The title/abstract/front matter precede the first numbered heading. A span ends at the next detected top-level heading, not at the last sentence; end-page whitespace is included. References and statements are listed separately. These are not measurements of text-only or artwork area.

Top-level heading	Starts on PDF page	Estimated span	Share of entire PDF
1 INTRODUCTION	2	≈ 0.96 pp.	4.36%
2 RELATED WORK	3	≈ 0.81 pp.	3.68%
3 METHOD	4	≈ 1.64 pp.	7.45%
4 DATA PIPELINE	5	≈ 1.49 pp.	6.77%
5 EXPERIMENTS	7	≈ 3.47 pp.	15.77%
6 CONCLUSION	10	≈ 0.4 pp.	1.82%
REFERENCES	11	≈ 5.95 pp.	27.05%
A APPENDIX	17	≈ 5.89 pp.	26.77%

All detected figure and table locations

Main text

• Figure 1 · p. 1
• Figure 2 · p. 4
• Figure 3 · p. 4
• Table 1 · p. 6
• Figure 4 · p. 7
• Table 2 · p. 8
• Figure 5 · p. 8
• Figure 6 · p. 9
• Table 3 · p. 9
• Figure 7 · p. 9
• Figure 8 · p. 10
• Table 4 · p. 10
• Table 5 · p. 10

Attached appendix

• Table 6 · p. 18
• Table 7 · p. 19
• Table 8 · p. 19
• Table 9 · p. 20
• Figure 9 · p. 20
• Table 10 · p. 21
• Figure 10 · p. 22
• Table 11 · p. 22

Argument and organization

1. Unification problem and related work

   Separate architecture limitations from video-edit data scarcity.

2. Method and data pipeline

   Explain interleaved tokens, four-dimensional RoPE, flow matching, and task-specific synthetic-data production.

3. Benchmark, comparisons, transfer and ablations

   Evaluate the unified editor and separately test data sources and positional design.

Problem, claim and contribution

The paper presents architecture and data as coupled obstacles, then gives each its own method section instead of attributing all improvement to one module.

Introduction, pp. 2–3; Sections 3–4 · Main paper

Argument and section order

A dedicated data-pipeline section names specialist generators and filtering stages; the appendix adds counts and mixture ratios rather than leaving the data contribution abstract.

Section 4, pp. 5–7; Table 10, p. 21 · Main paper

Figures, captions and visual explanation

A compact token legend is reused by the architecture and interleaved-input examples, making conditioning and generated segments recognizable across diagrams.

Figures 2–3, p. 4 · Main paper

Tables, comparisons and evidence

Table 2 groups baselines by access pattern and separates the commercial model; its caption acknowledges the editing-faithfulness versus generation-quality trade-off.

Table 2, p. 8 · Main paper

Appendix and reproducibility

The appendix retains copyright attribution, judge procedure, human-study design, failure examples, and resource curves, making practical limitations inspectable.

Appendix A.1–A.5, pp. 17–22 · Main paper

Clarity and writing conventions

The headline unification claim is supported by task examples, but arbitrary-duration flexibility and emergence should remain bounded by tested lengths, data, and examples.

Sections 5.4 and 6, pp. 9–10; Table 11, p. 22 · Main paper

What to reuse—and what not to infer

Transferable lesson: When unification is the contribution, use a shared visual grammar for all input/output modes and independently ablate architecture choices and training mixtures.

Boundary: Private training data prevents exact public reproduction. EditVerse does not beat the commercial comparator on every metric, and image editing remains below several specialists. Table 4's metric headings are inconsistent with neighboring tables; the temporal encoder naming/citation also needs verification. Full attention makes long videos expensive.

Apply this lesson using the argument and evidence worksheet.

Inspection record and sources

Checked

2026-09-19

Reading status

Full text + selected visuals inspected

Main text

Sections 1–6 and figures, pp. 1–10; references pp. 11–16

Appendix / supplement

Appendix A.1–A.5, pp. 17–22

Visually inspected PDF pages

4, 8, 19

• ICLR 2026 official proceedings PDF, retrieved 2026-09-19 · 22 pages