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
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 | ≈ 1.46 pp. | 6.95% |
| 2. Background and Related Work | 3 | ≈ 0.54 pp. | 2.57% |
| 3. Protein Autoregressive Modeling | 3 | ≈ 2.3 pp. | 10.95% |
| 4. Experiments | 5 | ≈ 3.39 pp. | 16.14% |
| 5. Discussion | 9 | ≈ 0.34 pp. | 1.62% |
| Acknowledgments | 9 | ≈ 0.05 pp. | 0.24% |
| Impact Statement | 9 | ≈ 0.12 pp. | 0.57% |
| References | 9 | ≈ 3.27 pp. | 15.57% |
| A. Implementation and Evaluation Details | 13 | ≈ 1.72 pp. | 8.19% |
| B. Datasets | 14 | ≈ 0.45 pp. | 2.14% |
| C. More Empirical Analysis | 15 | ≈ 5.21 pp. | 24.81% |
| D. Other Related Work | 20 | ≈ 0.23 pp. | 1.1% |
| E. The Use of Large Language Models | 20 | ≈ 0.08 pp. | 0.38% |
| F. Discussion on Future Work | 20 | ≈ 1.23 pp. | 5.86% |
All detected figure and table locations
Main text
Argument and organization
- Problem and contribution
Defines limitations of discretization and residue-wise ordering before proposing a coarse-to-fine order that preserves within-scale structural interactions.
- Argument sequence
Multiscale representation comes before the autoregressive factorization and decoder objective; exposure bias is then addressed before evaluation.
- 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.