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
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 | ≈ 1.31 pp. | 3.74% |
| 2. Softening and Straight-Through Estimation | 2 | ≈ 0.97 pp. | 2.77% |
| 3. Elementwise Operators | 3 | ≈ 1.24 pp. | 3.54% |
| 4. Axiswise Operators | 4 | ≈ 4.53 pp. | 12.94% |
| 5. Benchmarks & Case Study | 9 | ≈ 0.3 pp. | 0.86% |
| 6. Conclusion | 9 | ≈ 0.32 pp. | 0.91% |
| Acknowledgements | 10 | ≈ 0.12 pp. | 0.34% |
| Impact Statement | 10 | ≈ 0.12 pp. | 0.34% |
| References | 10 | ≈ 3.8 pp. | 10.86% |
| A. Library Overview | 14 | ≈ 1.61 pp. | 4.6% |
| B. Case Studies | 15 | ≈ 6.39 pp. | 18.26% |
| C. Input Standardization and Squashing | 22 | ≈ 0.37 pp. | 1.06% |
| D. Extended Axiswise Operators | 22 | ≈ 7.16 pp. | 20.46% |
| E. Additional Benchmark Experiments | 29 | ≈ 4.89 pp. | 13.97% |
| F. Choosing a Method and Mode | 34 | ≈ 1.5 pp. | 4.29% |
All detected figure and table locations
Main text
Attached appendix
- Figure 9 · p. 14
- Table 1 · p. 14
- Table 2 · p. 15
- Figure 10 · p. 16
- Figure 11 · p. 17
- Figure 12 · p. 17
- Table 3 · p. 18
- Table 4 · p. 18
- Figure 13 · p. 19
- Figure 14 · p. 19
- Figure 15 · p. 21
- Figure 16 · p. 30
- Figure 17 · p. 31
- Figure 18 · p. 31
- Figure 19 · p. 32
- Figure 20 · p. 32
- Figure 21 · p. 33
- Figure 22 · p. 33
- Figure 23 · p. 34
Argument and organization
- 1 Introduction; 2 Softening and Straight-Through Estimation
Introduces a unified interface and exposes a compositional gradient pitfall.
- 3 Elementwise Operators; 4 Axiswise Operators
Builds simple surrogates before transport, simplex, permutahedron and sorting-network families.
- 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.