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Writing study: SoftJAX & SoftTorch: Empowering Automatic Differentiation Libraries with Informative Gradients

← ICML 2026 oral writing corpus

Anselm Paulus, Andreas René Geist, Vı́t Musil, Sebastian Hoffmann, Georg Martius · Official ICML 2026 oral record

Figure, table and section ledger

Editorial contribution type: Efficiency, inference & systems (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
35 physical pages
Before references / appendix
≈ 9.29 page-equivalents
Main figures / tables
8 / 0
Attached appendix figures / tables
15 / 4
Attached appendix span
≈ 21.91 page-equivalents
Before references / appendix: ≈ 26.54%References / intervening material: ≈ 10.86%Attached appendix: ≈ 62.6%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≈ 1.31 pp.3.74%
2. Softening and Straight-Through Estimation2≈ 0.97 pp.2.77%
3. Elementwise Operators3≈ 1.24 pp.3.54%
4. Axiswise Operators4≈ 4.53 pp.12.94%
5. Benchmarks & Case Study9≈ 0.3 pp.0.86%
6. Conclusion9≈ 0.32 pp.0.91%
Acknowledgements10≈ 0.12 pp.0.34%
Impact Statement10≈ 0.12 pp.0.34%
References10≈ 3.8 pp.10.86%
A. Library Overview14≈ 1.61 pp.4.6%
B. Case Studies15≈ 6.39 pp.18.26%
C. Input Standardization and Squashing22≈ 0.37 pp.1.06%
D. Extended Axiswise Operators22≈ 7.16 pp.20.46%
E. Additional Benchmark Experiments29≈ 4.89 pp.13.97%
F. Choosing a Method and Mode34≈ 1.5 pp.4.29%
All detected figure and table locations

Argument and organization

  1. 1 Introduction; 2 Softening and Straight-Through Estimation

    Introduces a unified interface and exposes a compositional gradient pitfall.

  2. 3 Elementwise Operators; 4 Axiswise Operators

    Builds simple surrogates before transport, simplex, permutahedron and sorting-network families.

  3. 5 Benchmarks & Case Study; 6 Conclusion

    Compares practical cost and points to operator coverage and application demonstrations.

Problem, claim and contribution

Fragmented relaxation methods motivate a library abstraction with shared softness controls, rather than an unsupported claim that all discrete programs become optimizable.

Introduction, pp.1-2 · Main paper

Argument and section order

The STE product-rule counterexample comes before the operator catalog, so users encounter a failure of naive composition before the convenience API.

Section 2, p.3 · Main paper

Figures, captions and visual explanation

Three vector fields make the zero-gradient pitfall visible beside the corrective decorator; the benchmark page separates runtime and peak memory with one shared method legend.

Figures 3-4, p.3; Figure 8, p.9 · Main paper

Tables, comparisons and evidence

The headline sort benchmark states a full forward-backward pass, smooth mode, input size and RTX 3060 hardware; the fastest method and most memory-efficient method differ.

Section 5 and Figure 8, p.9 · Main paper

Appendix and reproducibility

The library map explicitly marks unavailable Torch counterparts, while additional benchmarks separate runtime, JIT compilation and memory by regularization mode.

Appendix A, p.14; Appendix E, pp.29,31 · Main paper

Clarity and writing conventions

Soft indices and soft Booleans are explained as changed output domains, and the text distinguishes row-stochastic from bistochastic matrices rather than hiding semantic differences behind drop-in terminology.

Sections 2 and 4.2, pp.2,7-8 · Main paper

What to reuse—and what not to infer

Transferable lesson: Pair an easy API with an early counterexample, then document semantic changes and cost tradeoffs at the operator-family level.

Boundary: Informative surrogate gradients do not establish general optimization convergence or hard-program correctness; the highlighted GPU benchmark concerns SoftJAX sort rather than uniform performance across both libraries.

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, 2, 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 14, 20, 29, 31. Remaining appendix proofs and details were not exhaustively read. No separately linked supplement inspected.
Visually inspected PDF pages
3, 9

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.