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Writing study: Protein Autoregressive Modeling via Multiscale Structure Generation

← ICML 2026 oral writing corpus

Yanru Qu, Cheng-Yen Hsieh, Zaixiang Zheng, Ge Liu, Quanquan Gu · Official ICML 2026 oral record

Figure, table and section ledger

Editorial contribution type: Architecture & representation (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
21 physical pages
Before references / appendix
≈ 8.81 page-equivalents
Main figures / tables
6 / 5
Attached appendix figures / tables
4 / 10
Attached appendix span
≈ 8.91 page-equivalents
Before references / appendix: ≈ 41.95%References / intervening material: ≈ 15.62%Attached appendix: ≈ 42.43%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 headingStarts on PDF pageEstimated spanShare of entire PDF
1. Introduction1≈ 1.46 pp.6.95%
2. Background and Related Work3≈ 0.54 pp.2.57%
3. Protein Autoregressive Modeling3≈ 2.3 pp.10.95%
4. Experiments5≈ 3.39 pp.16.14%
5. Discussion9≈ 0.34 pp.1.62%
Acknowledgments9≈ 0.05 pp.0.24%
Impact Statement9≈ 0.12 pp.0.57%
References9≈ 3.27 pp.15.57%
A. Implementation and Evaluation Details13≈ 1.72 pp.8.19%
B. Datasets14≈ 0.45 pp.2.14%
C. More Empirical Analysis15≈ 5.21 pp.24.81%
D. Other Related Work20≈ 0.23 pp.1.1%
E. The Use of Large Language Models20≈ 0.08 pp.0.38%
F. Discussion on Future Work20≈ 1.23 pp.5.86%
All detected figure and table locations

Argument and organization

  1. Problem and contribution

    Defines limitations of discretization and residue-wise ordering before proposing a coarse-to-fine order that preserves within-scale structural interactions.

  2. Argument sequence

    Multiscale representation comes before the autoregressive factorization and decoder objective; exposure bias is then addressed before evaluation.

  3. Evidence and verification

    Sampling analysis contrasts mixed SDE/ODE execution with both aggressive SDE step reduction and all-ODE alternatives, tying the speed claim to a designability control.

Problem, claim and contribution

Defines limitations of discretization and residue-wise ordering before proposing a coarse-to-fine order that preserves within-scale structural interactions.

Introduction and Section 3, pp. 1-4 · Main paper

Argument and section order

Multiscale representation comes before the autoregressive factorization and decoder objective; exposure bias is then addressed before evaluation.

Sections 3.1-3.3, pp. 4-5 · Main paper

Figures, captions and visual explanation

The method diagram and generated-scale grid share a coarse-to-fine reading direction. The motif figure uses a stable motif color across input/generated rows, while scaling plots retain metric direction arrows.

Figures 1-2, p. 2; Figures 4-5, p. 7; both PNGs inspected · Main paper

Tables, comparisons and evidence

Sampling analysis contrasts mixed SDE/ODE execution with both aggressive SDE step reduction and all-ODE alternatives, tying the speed claim to a designability control.

Section 4.3, pp. 7-8; Appendix C.1 and Table 8, p. 15 · Main paper

Appendix and reproducibility

Runtime tables state model sizes, conditioning, lengths, sample counts and steps; later subsections distinguish completed experiments from proposed multi-chain/molecular-dynamics extensions.

Table 8, p. 15; Appendix F, pp. 20-21 · Main paper

Clarity and writing conventions

A sculpting analogy introduces the scale order, followed immediately by explicit downsampling and conditional-probability definitions.

Introduction, p. 2; Sections 3.1-3.2, p. 4 · Main paper

What to reuse—and what not to infer

Transferable lesson: Reuse one representation hierarchy across the overview, formal factorization and qualitative output sequence.

Boundary: Plausible computational scaffolds and self-consistency metrics are not wet-lab validation; future biomolecular extensions are not demonstrated capabilities.

Apply this lesson using the argument and evidence worksheet.

Inspection record and sources

Checked
2026-10-07
Reading status
Writing-focused manuscript scan + selected visuals inspected
Main text
Writing-focused distributed manuscript scan, not exhaustive technical/proof validation. Sections 1-5, pp. 1-9: autoregressive-order motivation, multiscale construction, exposure-bias mitigation, generation/generalization and sampling analysis.
Appendix / supplement
Writing-focused appendix scan; code, data and external assets not executed. Appendices A-F, pp. 13-21: implementation, metrics/data, extended ablations, sampling/runtime comparisons, motif benchmarks, attention views and future directions.
Visually inspected PDF pages
2, 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.