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Writing study: Information Shapes Koopman Representation

← ICLR 2026 oral writing corpus

Xiaoyuan Cheng, Wenxuan Yuan, Yiming Yang, Yuanzhao Zhang, Sibo Cheng, Yi He, Zhuo Sun · Official ICLR 2026 oral record

Figure, table and section ledger

Editorial contribution type: Learning & optimization algorithms (primary) · Theory & formal foundations.

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
50 physical pages
Before references / appendix
≈ 10.28 page-equivalents
Main figures / tables
6 / 2
Attached appendix figures / tables
16 / 10
Attached appendix span
≈ 35.79 page-equivalents
Before references / appendix: ≈ 20.56%References / intervening material: ≈ 7.86%Attached appendix: ≈ 71.58%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≈ 1.6 pp.3.2%
2 PRELIMINARIES3≈ 0.56 pp.1.12%
3 METHOD3≈ 3.75 pp.7.5%
4 EXPERIMENTS7≈ 3.22 pp.6.44%
5 CONCLUSION10≈ 0.28 pp.0.56%
ACKNOWLEDGMENTS11≈ 0.17 pp.0.34%
REFERENCES11≈ 3.93 pp.7.86%
A NOTATION15≈ 0.89 pp.1.78%
B A GENTLE INTRODUCTION TO APPENDIX16≈ 1 pp.2%
C MORE LITERATURE REVIEW RELATED TO KOOPMAN REPRESENTATION17≈ 0.45 pp.0.9%
D LIMITATIONS AND FUTURE DIRECTIONS17≈ 0.55 pp.1.1%
E KEY TECHNICAL DEFINITIONS AND RELATED PROPERTIES18≈ 2 pp.4%
F THEORETICAL FRAMEWORK20≈ 12 pp.24%
G PRACTICAL DETAILS, IMPLEMENTATION AND EXPERIMENTAL DETAILS32≈ 18.89 pp.37.78%
All detected figure and table locations

Argument and organization

  1. Three representation desiderata and five theoretical questions

    Connects temporal coherence, structural consistency and predictive sufficiency to a sequence of propositions.

  2. Practical objective and proposition-led experiments

    Labels each loss term by purpose and organizes empirical analysis as questions referring back to propositions.

Problem, claim and contribution

Table 1 translates the familiar information-bottleneck formulation into a dynamical prediction setting before introducing additional Koopman constraints.

Table 1 and introduction, page 2. · Main paper

Argument and section order

The five-proposition progression provides a clear argument map: information loss, prediction discrepancy, spectral decomposition, concentration and anti-collapse. Appendix B restates this as five reader questions.

Sections 3.1–3.3, pages 4–7; Appendix B, page 16. · Main paper

Figures, captions and visual explanation

Figure 1 combines an encoder–linear-operator–decoder diagram, geometric information decomposition and water-filling cartoons; matching labels help link mechanism and regularization.

Figure 1, page 5. · Main paper

Tables, comparisons and evidence

Table 2 groups metrics by task and horizon, marking best and second-best cells and retaining cases where PFNN wins. Figures 2–3 supply geometric evidence complementary to forecasting scores.

Table 2 and Figures 2–3, pages 8–9. · Main paper

Appendix and reproducibility

Separate stochastic and deterministic algorithms, task-specific transition structures, model tables and baseline links make the general formulation inspectable as distinct implementations.

Algorithms 1–2 and Tables 5, 10–12, pages 32–38 and 49–50. · Main paper

Clarity and writing conventions

Question-led experimental interpretation is a useful organizing device, but wording that an empirical table verifies a mathematical bound exceeds what the comparison alone demonstrates.

Section 4, first result-analysis question, page 8. · Main paper

What to reuse—and what not to infer

Transferable lesson: Make each theoretical motivation correspond to a labeled objective term and a distinct empirical diagnostic, then provide implementation-specific algorithms in the appendix.

Boundary: Do not reuse the theory as an independently validated guarantee. Fact F.1 claims an entropy inequality from data processing for a stochastic encoder, which is not generally valid; the proof's encoded-state conditional independences also require reconciliation with the generative chain in Equation 2. Gaussian latent assumptions, metric-label inconsistencies and non-winning control results require explicit qualification.

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
Pages 1–10, Sections 1–5; acknowledgments, reproducibility and references on pages 11–14.
Appendix / supplement
Pages 15–50, Appendices A–G: notation, roadmap, literature, limitations, definitions, all six proof subsections, AE/VAE algorithms, task settings, additional results and architectures.
Visually inspected PDF pages
5, 8