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Writing study: Geometric Flow Grounding: A Unified Manifold Decoupling Framework for Dynamics Discovery and Verification

← ICML 2026 oral writing corpus

Chang Yu, Yuxuan Luo, Yixuan Du, Yuqing Zhou, Siyuan Li, Jingbo Zhou, Jiawei Jiang, Zhen Lei, Stan Z. Li · 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
27 physical pages
Before references / appendix
≈ 9.04 page-equivalents
Main figures / tables
6 / 6
Attached appendix figures / tables
2 / 5
Attached appendix span
≈ 14.91 page-equivalents
Before references / appendix: ≈ 33.48%References / intervening material: ≈ 11.3%Attached appendix: ≈ 55.22%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≈ 1.24 pp.4.59%
2. Preliminaries2≈ 0.56 pp.2.07%
3. Method3≈ 2.33 pp.8.63%
4. Experiments5≈ 3.14 pp.11.63%
5. Related Work8≈ 0.45 pp.1.67%
6. Conclusion and Future Work9≈ 0.39 pp.1.44%
Acknowledgements9≈ 0.1 pp.0.37%
Impact Statement9≈ 0.21 pp.0.78%
References10≈ 3.04 pp.11.26%
A. Theoretical Proofs13≈ 2.09 pp.7.74%
B. Datasets and Evaluation Metrics15≈ 2.49 pp.9.22%
C. Network Architectures and Training Details17≈ 5.42 pp.20.07%
D. Supplementary Experiments and Analysis23≈ 2.67 pp.9.89%
E. Detailed Case-specific Related Work25≈ 2.25 pp.8.33%
All detected figure and table locations

Argument and organization

  1. 1 Introduction; 2 Preliminaries

    Distinguishes smoothing drift from integration drift and defines learned geometry.

  2. 3 Method

    States tangent-space assumptions, presents a projection argument and implements dual state/dynamics streams.

  3. 4 Experiments; 5 Related Work; 6 Conclusion

    Maps the common formulation to ODE, RNA-velocity and video cases with domain-specific objectives and limitations.

Problem, claim and contribution

The unifying claim is geometric consistency with a learned decoder manifold, not access to the true manifold or a uniquely identified latent system.

Assumption 3.1, p.3; Limitation, p.9 · Main paper

Argument and section order

A proof sketch separates ideal orthogonal projection from the practical Jacobian-vector construction, and a domain-objective table explains what changes across applications.

Theorem 3.2 and Sections 3.3-3.4, pp.4-5 · Main paper

Figures, captions and visual explanation

The overview consistently separates blue state and green velocity codebooks, linked by a red JVP arrow; RNA plots use matched embeddings and highlighted regions beside distinct atom activations.

Figure 2, p.3; Figures 4-5, p.7 · Main paper

Tables, comparisons and evidence

Geometric coherence is distinguished from lineage-direction metrics, and the video table separately reports the standalone signal and its combination with an existing detector.

Table 3, p.6; Table 4, p.7 · Main paper

Appendix and reproducibility

The supplement gives the generator-specific train/validation/test protocol and a degradation experiment, making the scope of unseen-generator claims inspectable.

Appendix B.3, p.17; Appendix D.3, p.25 · Main paper

Clarity and writing conventions

The video discussion preserves the combined model’s lower F1 despite stronger recall/AUROC, whereas universal and hallucination-free language needs the later manifold assumptions.

Section 4.3, p.8; Limitation, p.9 · Main paper

What to reuse—and what not to infer

Transferable lesson: When proposing one framework across domains, show the common operator, the changing supervision and the distinct validation target for each case.

Boundary: Tangency to learned geometry is not guaranteed physical correctness; RNA streamlines and enrichment are not lineage experiments, and the complete video pipeline includes training and pretrained-model assumptions.

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-oriented scan of the main argument through physical p.9, with focused physical-page text inspection on 1, 3, 4, 5, 6, 7, 8, 9. Framing, section progression, mechanism/proof explanation, displays, comparison conditions and conclusions were inspected. This is not exhaustive technical reading, proof verification or reproduction.
Appendix / supplement
Appendix organization and main-text pointers were checked; focused text inspection of physical pages 13, 17, 25. Remaining appendix proofs and details were not exhaustively read. No separately linked supplement inspected.
Visually inspected PDF pages
3, 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.