Writing study: Temporal Sparse Autoencoders: Leveraging the Sequential Nature of Language for Interpretability

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← ICLR 2026 oral writing corpus

Usha Bhalla, Alex Oesterling, Claudio Mayrink Verdun, Hima Lakkaraju, Flavio Calmon · Official ICLR 2026 oral record

Observed writing choices, not acceptance rules. This study examines the inspected manuscript version. It does not establish why the paper was accepted or that its presentation choices should be copied unchanged.

Figure, table and section ledger

Editorial contribution type: Architecture & representation design (primary) · Learning & optimization algorithms · Empirical analysis, diagnosis & auditing.

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

29 physical pages

Before references / appendix

≈ 10.52 page-equivalents

Main figures / tables

5 / 2

Attached appendix figures / tables

7 / 8

Attached appendix span

≈ 14.89 page-equivalents

Before references / appendix: ≈ 36.28%References / intervening material: ≈ 12.38%Attached appendix: ≈ 51.34%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 heading	Starts on PDF page	Estimated span	Share of entire PDF
1 INTRODUCTION	1	≈ 1.55 pp.	5.34%
2 RELATED WORK	3	≈ 0.65 pp.	2.24%
3 OUR FRAMEWORK: TEMPORAL SPARSE AUTOENCODERS	3	≈ 1.7 pp.	5.86%
4 EXPERIMENTAL EVALUATION	5	≈ 5.1 pp.	17.59%
5 DISCUSSION AND CONCLUSIONS	10	≈ 0.55 pp.	1.9%
6 ACKNOWLEDGEMENTS	11	≈ 0.14 pp.	0.48%
7 REPRODUCIBILITY STATEMENT	11	≈ 0.14 pp.	0.48%
8 ETHICS STATEMENT	11	≈ 0.14 pp.	0.48%
REFERENCES	11	≈ 3.59 pp.	12.38%
APPENDIX	15	≈ 0.29 pp.	1%
A LIMITATIONS AND FUTURE WORK	15	≈ 1.03 pp.	3.55%
B ADDITIONAL CASE STUDY DETAILS	16	≈ 7.67 pp.	26.45%
C ADDITIONAL RESULTS	24	≈ 5 pp.	17.24%
D ADDITIONAL EXAMPLES OF SYNTACTIC FEATURES IN BASELINE SAES	29	≈ 0.89 pp.	3.07%

All detected figure and table locations

Main text

• Figure 1 · p. 2
• Figure 2 · p. 5
• Figure 3 · p. 6
• Table 1 · p. 7
• Figure 4 · p. 8
• Figure 5 · p. 9
• Table 2 · p. 10

Attached appendix

• Table 3 · p. 17
• Table 4 · p. 18
• Table 5 · p. 19
• Table 6 · p. 20
• Figure 6 · p. 20
• Table 7 · p. 22
• Table 8 · p. 23
• Figure 7 · p. 24
• Table 9 · p. 24
• Figure 8 · p. 25
• Table 10 · p. 25
• Figure 9 · p. 26
• Figure 10 · p. 27
• Figure 11 · p. 27
• Figure 12 · p. 28

Argument and organization

1. Temporal-consistency motivation

   Use the contrast between local syntax and slowly varying semantics to motivate a structural prior.

2. Data-generating assumptions and T-SAEs

   Translate the prior into a high/low feature split, hierarchical reconstruction, and temporal contrastive loss.

3. Evaluation and applications

   Progress from probes and reconstruction to sequence visualizations, dataset inspection, steering, and ablations.

Problem, claim and contribution

The opening offers competing explanations for shallow SAE features, then argues for a testable method-design explanation rather than concluding that language models lack semantic information.

Section 1, pp. 1–2 · Main paper

Argument and section order

Explicit temporal and hierarchical assumptions precede the objective, exposing where linguistic intuition enters the model.

Section 3, pp. 3–4 · Main paper

Figures, captions and visual explanation

The teaser moves from a language-production cartoon to feature traces over spliced passages, connecting the conceptual claim to an immediately legible observable.

Figure 1, p. 2 · Main paper

Tables, comparisons and evidence

Separate semantic/context/syntax probes and high/low split analyses support specialization more directly than t-SNE alone; reconstruction and interpretability metrics show the trade-offs.

Figures 2–3, Table 1, pp. 5–7; Figure 7, p. 24 · Main paper

Appendix and reproducibility

The appendix contents map limitations, steering settings, additional datasets, and scaling; matching feature descriptions and judge prompts make evaluation assumptions visible.

Appendices A–C, pp. 15–28 · Main paper

Clarity and writing conventions

The paper notes judge noise and the greater specificity of baseline steering concepts, useful qualifications to broad interpretability claims.

Footnote 4, p. 7; Appendix B.2, p. 21 · Main paper

What to reuse—and what not to infer

Transferable lesson: Make a representation-learning prior visible in an example first, formalize it as assumptions, and test the desired property alongside reconstruction and downstream utility.

Boundary: Temporal proximity is a proxy, not proof of semantic identity or causal disentanglement. Probe splitting and unequal dense-feature dimensions need care; steering feature descriptions differ across methods. Appendix Table 10 contradicts the claim of fewer split features, and its speculative linearized-loss algebra should not be reused as a validated derivation.

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

Sections 1–5, pp. 1–10; statements and references pp. 11–14

Appendix / supplement

Appendices A–D, pp. 15–29

Visually inspected PDF pages

2, 7, 24

• ICLR 2026 official proceedings PDF, retrieved 2026-09-19 · 29 pages