Writing study: Visual symbolic mechanisms: Emergent symbol processing in Vision Language Models

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← ICLR 2026 oral writing corpus

Rim Assouel, Declan Campbell, Yoshua Bengio, Taylor Webb · 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: Empirical analysis, diagnosis & auditing (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

32 physical pages

Before references / appendix

≈ 10.32 page-equivalents

Main figures / tables

7 / 0

Attached appendix figures / tables

40 / 1

Attached appendix span

≈ 18.89 page-equivalents

Before references / appendix: ≈ 32.25%References / intervening material: ≈ 8.72%Attached appendix: ≈ 59.03%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	≈ 1.12 pp.	3.5%
2 RELATED WORK	2	≈ 0.82 pp.	2.56%
3 SYMBOLIC MECHANISMS FOR VISUAL BINDING IN VLMS	3	≈ 2.97 pp.	9.28%
4 RESULTS	6	≈ 3.85 pp.	12.03%
5 CONCLUSION AND FUTURE WORK	10	≈ 0.77 pp.	2.41%
6 REPRODUCIBILITY STATEMENT	11	≈ 0.11 pp.	0.34%
7 ACKNOWLEDGMENTS	11	≈ 0.11 pp.	0.34%
REFERENCES	11	≈ 2.78 pp.	8.69%
A APPENDIX	14	≈ 15 pp.	46.88%
B GENERALIZATION OF POSITION IDS TO REAL-WORLD SETTINGS	29	≈ 1.41 pp.	4.41%
C INVOLVEMENT OF VISUAL SYMBOLIC MECHANISMS IN COUNTING	30	≈ 1 pp.	3.13%
D BINDING ERROR INTERVENTIONS	31	≈ 1.49 pp.	4.66%

All detected figure and table locations

Main text

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

Attached appendix

• Figure 8 · p. 14
• Figure 9 · p. 15
• Figure 10 · p. 17
• Figure 11 · p. 17
• Figure 12 · p. 18
• Figure 13 · p. 18
• Figure 14 · p. 19
• Figure 15 · p. 19
• Figure 16 · p. 19
• Figure 17 · p. 20
• Figure 18 · p. 20
• Figure 19 · p. 20
• Figure 20 · p. 21
• Figure 21 · p. 21
• Figure 22 · p. 21
• Figure 23 · p. 22
• Figure 24 · p. 22
• Figure 25 · p. 22
• Figure 26 · p. 23
• Figure 27 · p. 24
• Figure 28 · p. 24
• Figure 29 · p. 24
• Figure 30 · p. 25
• Figure 31 · p. 25
• Figure 32 · p. 25
• Figure 33 · p. 26
• Figure 34 · p. 26
• Figure 35 · p. 26
• Figure 36 · p. 26
• Figure 37 · p. 26
• Figure 38 · p. 27
• Figure 39 · p. 27
• Figure 40 · p. 27
• Figure 41 · p. 28
• Figure 42 · p. 28
• Figure 43 · p. 28
• Figure 44 · p. 28
• Figure 45 · p. 29
• Figure 46 · p. 30
• Figure 47 · p. 30
• Table 1 · p. 31

Argument and organization

1. Binding problem → three-stage hypothesis

   Uses a simple feature-swap example to define the missing explanation.

2. Representation → causal mediation → interventions

   Triangulates a mechanism rather than relying on one visualization.

Problem, claim and contribution

The red-square/blue-circle example defines binding concretely before connecting the question to VLM failures and earlier language-model binding IDs.

Introduction, pages 1–2 · Main paper

Argument and section order

The evidence progresses from representation to designed patching conditions, then tests relative position, image complexity, localization, task transfer, and errors in separate question-led subsections.

Sections 3.3–4.5, pages 4–9 · Main paper

Figures, captions and visual explanation

Figure 1 maintains blue/red/green stage colors across the circuit, layerwise RSA, and attention-head map; PCA provides a complementary view instead of carrying the causal claim alone.

Figure 1, page 4 · Main paper

Tables, comparisons and evidence

The intervention panel shows all tested model families, but the text discloses that the reported values use the best head-count and strength settings from a sweep.

Figure 3, pages 7–8; Appendix A.5.5, pages 27–28 · Main paper

Appendix and reproducibility

Worked clean/modified-context diagrams explain each patching experiment, while separate appendix sections extend the claim to COCO and counting and provide a compact error-repair table.

Appendix A.3, pages 17–18; Appendices B–D, pages 29–32 · Main paper

Clarity and writing conventions

The conclusion discusses architectural inductive biases as an unresolved source of emergence; that qualification is more precise than treating the discovered pattern as a universal symbolic architecture.

Conclusion, page 10 · Main paper

What to reuse—and what not to infer

Transferable lesson: For a mechanistic claim, align one circuit diagram with several distinct evidence types and organize follow-up experiments as explicit questions about the proposed mechanism.

Boundary: Best-sweep intervention success is not an untuned deployment result. Controlled grids and limited naturalistic tests do not establish a universal circuit for arbitrary scenes; relative-position evidence varies across model families.

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 1–7; references on pages 11–13

Appendix / supplement

Pages 14–32, Appendices A–D: dataset generation, RSA/CMA, parameter sweeps, COCO, counting, and error interventions

Visually inspected PDF pages

4, 7, 31

• ICLR 2026 final proceedings · 32 pages