Writing study: Generative Modeling via Drifting

NeurIPS overviewCorpus atlasBy contribution typeVisual galleryRules & templateWriting patternsPaper studies

← NeurIPS 2026 oral writing corpus

Mingyang Deng, He Li, Tianhong Li, Yilun Du, Kaiming He · Official NeurIPS 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: Training, objectives & optimization (primary).

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

28 physical pages

Before references / appendix

≈ 8.11 page-equivalents

Main figures / tables

4 / 7

Attached appendix figures / tables

11 / 4

Attached appendix span

≈ 16.96 page-equivalents

Before references / appendix: ≈ 28.96%References / intervening material: ≈ 10.46%Attached appendix: ≈ 60.57%Estimated layout envelopes, not text/artwork area. Unresolved appendix boundaries remain within the undivided post-body material.

Source-bound correction: Top-level heading and reference/appendix boundaries checked against source PDF text. Bibliography titles/author initials, numbered contribution/property bullets, diagram steps, equations and TOC entries are not substantive headings. Section spans are page-coordinate estimates, not ink area or prose-only length. This is an author manuscript mirror, not a verified accepted-version template. Source layout uses two-column reading order.

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	1	≈ 0.81 pp.	2.89%
2. Related Work	2	≈ 0.6 pp.	2.14%
3. Drifting Models for Generation	2	≈ 2.87 pp.	10.25%
4. Implementation for Image Generation	5	≈ 0.71 pp.	2.54%
5. Experiments	6	≈ 2.36 pp.	8.43%
6. Discussion and Conclusion	8	≈ 0.31 pp.	1.11%
Acknowledgements	9	≈ 0.07 pp.	0.25%
References	9	≈ 2.93 pp.	10.46%
A. Additional Implementation Details	12	≈ 4.67 pp.	16.68%
B. Additional Experimental Results	16	≈ 1.33 pp.	4.75%
C. Additional Derivations	18	≈ 10.96 pp.	39.14%

All detected figure and table locations

Main text

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

Attached appendix

• Table 8 · p. 13
• Table 9 · p. 16
• Table 10 · p. 16
• Table 11 · p. 17
• Figure 5 · p. 17
• Figure 6 · p. 17
• Figure 7 · p. 20
• Figure 8 · p. 21
• Figure 9 · p. 22
• Figure 10 · p. 23
• Figure 11 · p. 24
• Figure 12 · p. 25
• Figure 13 · p. 26
• Figure 14 · p. 27
• Figure 15 · p. 28

Argument and organization

1. Problem and contribution

   Moves generative evolution from iterative inference into training, making one-network-evaluation generation the motivating contrast.

2. Argument sequence

   Introduces a field, its equilibrium condition and an implementation before toy distributions and image/robotics evaluations.

3. Evidence and verification

   Tables separate sample allocation at fixed compute, feature-space choices, longer training and system-level comparisons with parameter and NFE columns.

Problem, claim and contribution

Moves generative evolution from iterative inference into training, making one-network-evaluation generation the motivating contrast.

Introduction; Figure 1 · Main paper

Argument and section order

Introduces a field, its equilibrium condition and an implementation before toy distributions and image/robotics evaluations.

Sections 3–5 · Main paper

Figures, captions and visual explanation

Figure 2 uses positive and negative sample clouds plus signed vectors; adjacent pseudocode exposes the stop-gradient target that the geometric picture omits.

Figure 2 and Algorithm 1, p. 4 · Main paper

Tables, comparisons and evidence

Tables separate sample allocation at fixed compute, feature-space choices, longer training and system-level comparisons with parameter and NFE columns.

Tables 2–5, p. 7 · Main paper

Appendix and reproducibility

Implementation, extra experiments and equilibrium reasoning are separated from uncurated samples and matched-class comparisons.

Appendices A–C; pp. 20–28 · Main paper

Clarity and writing conventions

The equilibrium discussion explicitly warns that a small drift does not imply matched distributions for arbitrary fields.

Section 3.3, p. 4 · Main paper

What to reuse—and what not to infer

Transferable lesson: Give the geometric intuition and executable update adjacent roles, then separate mechanism ablations from scaled system comparisons.

Boundary: The inspected two-column author version is not evidence of NeurIPS formatting rules. Feature encoders, CFG and compute remain comparison conditions.

Apply this lesson using the argument and evidence worksheet.

Inspection record and sources

Checked

2026-10-02

Reading status

Writing-focused manuscript scan + selected visuals inspected

Main text

Writing-oriented distributed manuscript scan of Sections 1–6, pp. 1–8; references pp. 9–11: abstract/framing, section sequence, substantive text windows, captions, evidence organization and conclusion; not exhaustive technical verification.

Appendix / supplement

Writing-oriented scan of Appendices A–C and qualitative sample pages, pp. 12–28; code and experiments were not executed.

Visually inspected PDF pages

4, 7

This is a writing-focused scan of the manuscript and the stated supplementary scope: framing, organization, visual explanation, evidence presentation and wording. It is not a line-by-line proof audit, reproduction of results or exhaustive scientific review.

• Public author arXiv manuscript 2602.04770v2; not verified identical to the accepted or camera-ready OpenReview revision · 28 pages