Writing study: Energy-Based Transformers are Scalable Learners and Thinkers

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← ICLR 2026 oral writing corpus

Alexi Gladstone, Ganesh Nanduru, Md Mofijul Islam, Peixuan Han, Hyeonjeong Ha, Aman Chadha, Yilun Du, Heng Ji, Jundong Li, Tariq Iqbal · 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) · 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

44 physical pages

Before references / appendix

≈ 10.45 page-equivalents

Main figures / tables

8 / 5

Attached appendix figures / tables

9 / 5

Attached appendix span

≈ 25.7 page-equivalents

Before references / appendix: ≈ 23.75%References / intervening material: ≈ 17.84%Attached appendix: ≈ 58.41%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	≈ 2.05 pp.	4.66%
2 ENERGY-BASED TRANSFORMERS (EBT) INTUITION	3	≈ 1.01 pp.	2.3%
3 ENERGY-BASED TRANSFORMERS (EBT) APPROACH	4	≈ 2.02 pp.	4.59%
4 EXPERIMENTATION AND RESULTS	6	≈ 3.77 pp.	8.57%
5 LIMITATIONS AND CONCLUSION	10	≈ 0.54 pp.	1.23%
6 ACKNOWLEDGEMENTS	11	≈ 0.34 pp.	0.77%
REFERENCES	11	≈ 7.85 pp.	17.84%
A FUTURE WORKS AND BROADER IMPACT	19	≈ 2.17 pp.	4.93%
B ADDITIONAL EXPERIMENTATION	21	≈ 3.25 pp.	7.39%
C ADDITIONAL EBT DETAILS	24	≈ 4.1 pp.	9.32%
D EXPERIMENTATION DETAILS	28	≈ 4.9 pp.	11.14%
E RELATED WORKS	33	≈ 3.07 pp.	6.98%
F ADDITIONAL COGNITIVE FACETS	36	≈ 0.58 pp.	1.32%
G COUNTERARGUMENTS	37	≈ 0.98 pp.	2.23%
H ENERGY-BASED MODELS (EBMS) INTRODUCTION	38	≈ 2.16 pp.	4.91%
I ENERGY-BASED TRANSFORMERS (EBTS) TUTORIAL	40	≈ 4.49 pp.	10.2%

All detected figure and table locations

Main text

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

Attached appendix

• Figure B.1 · p. 22
• Figure B.2 · p. 22
• Figure B.3 · p. 23
• Figure B.4 · p. 24
• Figure B.5 · p. 24
• Figure B.6 · p. 25
• Figure C.1 · p. 26
• Table D.1 · p. 30
• Table D.2 · p. 30
• Table D.3 · p. 31
• Table D.4 · p. 32
• Table D.5 · p. 33
• Figure E.1 · p. 35
• Figure H.1 · p. 39

Argument and organization

1. Cognitive motivation, energy intuition, training/inference algorithms

   Builds an accessible optimization-based prediction story before technical details.

2. Learning scaling versus inference scaling

   Separates pretraining fit from additional computation used to refine predictions.

Argument and section order

Side-by-side training and inference pseudocode makes clear that training differentiates through prediction updates, whereas inference updates predictions with fixed parameters.

Algorithms 1–2, page 5. · Main paper

Figures, captions and visual explanation

Figure 2 reuses the same context–candidate–energy loop for tokens and video frames, making the shared mechanism visible while preserving modality-specific outputs.

Figure 2, page 3. · Main paper

Tables, comparisons and evidence

Table 3 states the unequal FLOP budget despite matched data and parameters, and Table 4 exposes absolute linear-probe accuracy rather than only its impressive relative gain.

Tables 3–4, pages 8–9. · Main paper

Raw scaling losses and hardware throughput/memory reveal that a steeper fitted scaling trend is not present-day training efficiency.

Tables D.1 and D.5, pages 30 and 33. · Main paper

Appendix and reproducibility

The appendix is a layered reader service: additional experiments, implementation, compute accounting, counterarguments, a beginner introduction and a practical tuning tutorial.

Appendix roadmap, page 19; Appendices C–I. · Main paper

Clarity and writing conventions

The human-thinking analogy is memorable, but claims about understanding and uncertainty exceed what perplexity, low-accuracy linear probes and example energies alone establish. The appendix explicitly narrows the cognitive analogy.

Sections 2 and 4; Appendix G.1.1, pages 37–38. · Main paper

What to reuse—and what not to infer

Transferable lesson: Use one visual mechanism across modalities, separate training and inference algorithms, and report absolute metrics plus actual computation alongside scaling slopes.

Boundary: This is not evidence of human-like reasoning, calibrated uncertainty or large-foundation-model superiority. The 99% claim concerns forward-pass counts, not matched wall-clock cost; NLP results largely concern perplexity. S1/S2 descriptions in Appendix D.5 conflict with C.2 and I.2–I.3, so implementation details need code reconciliation.

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 1–11, Sections 1–6, including limitations and reproducibility statement; references through page 18.

Appendix / supplement

Pages 19–44, Appendices A–I, including failure cases, causal implementation, compute accounting, counterarguments and training tutorial.

Visually inspected PDF pages

3, 9

• Official ICLR 2026 proceedings conference PDF; PDF creation metadata: Sun Mar 1 19:01:20 2026 EST · 44 pages