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
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 | ≈ 1.6 pp. | 3.2% |
| 2 PRELIMINARIES | 3 | ≈ 0.56 pp. | 1.12% |
| 3 METHOD | 3 | ≈ 3.75 pp. | 7.5% |
| 4 EXPERIMENTS | 7 | ≈ 3.22 pp. | 6.44% |
| 5 CONCLUSION | 10 | ≈ 0.28 pp. | 0.56% |
| ACKNOWLEDGMENTS | 11 | ≈ 0.17 pp. | 0.34% |
| REFERENCES | 11 | ≈ 3.93 pp. | 7.86% |
| A NOTATION | 15 | ≈ 0.89 pp. | 1.78% |
| B A GENTLE INTRODUCTION TO APPENDIX | 16 | ≈ 1 pp. | 2% |
| C MORE LITERATURE REVIEW RELATED TO KOOPMAN REPRESENTATION | 17 | ≈ 0.45 pp. | 0.9% |
| D LIMITATIONS AND FUTURE DIRECTIONS | 17 | ≈ 0.55 pp. | 1.1% |
| E KEY TECHNICAL DEFINITIONS AND RELATED PROPERTIES | 18 | ≈ 2 pp. | 4% |
| F THEORETICAL FRAMEWORK | 20 | ≈ 12 pp. | 24% |
| G PRACTICAL DETAILS, IMPLEMENTATION AND EXPERIMENTAL DETAILS | 32 | ≈ 18.89 pp. | 37.78% |
All detected figure and table locations
Main text
Attached appendix
- Table 3 · p. 15
- Figure 7 · p. 18
- Figure 8 · p. 20
- Figure 9 · p. 26
- Table 4 · p. 28
- Figure 10 · p. 36
- Figure 11 · p. 37
- Figure 12 · p. 37
- Table 5 · p. 38
- Figure 13 · p. 39
- Figure 14 · p. 39
- Figure 15 · p. 40
- Table 6 · p. 41
- Table 7 · p. 41
- Figure 16 · p. 42
- Figure 17 · p. 43
- Figure 18 · p. 44
- Figure 19 · p. 45
- Figure 20 · p. 46
- Figure 21 · p. 47
- Table 8 · p. 48
- Table 9 · p. 48
- Figure 22 · p. 48
- Table 10 · p. 49
- Table 11 · p. 49
- Table 12 · p. 49
Argument and organization
- Three representation desiderata and five theoretical questions
Connects temporal coherence, structural consistency and predictive sufficiency to a sequence of propositions.
- 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