Writing study: Steering the Herd: A Framework for LLM-based Control of Social Learning

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← ICLR 2026 oral writing corpus

Raghu Arghal, Kevin He, Shirin Saeedi Bidokhti, Saswati Sarkar · 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: Theory & formal foundations (primary) · 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

38 physical pages

Before references / appendix

≈ 10.19 page-equivalents

Main figures / tables

2 / 0

Attached appendix figures / tables

3 / 1

Attached appendix span

≈ 23.9 page-equivalents

Before references / appendix: ≈ 26.82%References / intervening material: ≈ 10.29%Attached appendix: ≈ 62.89%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	≈ 1.4 pp.	3.68%
2 RELATED WORK	3	≈ 1.16 pp.	3.05%
3 SOCIAL LEARNING MODEL	4	≈ 2.04 pp.	5.37%
4 OPTIMAL ALTRUISTIC POLICIES	6	≈ 0.92 pp.	2.42%
5 OPTIMAL BIASED POLICIES	7	≈ 1.19 pp.	3.13%
6 EVALUATION VIA LLM-BASED SIMULATION	8	≈ 2 pp.	5.26%
7 CONCLUSION	10	≈ 0.68 pp.	1.79%
ACKNOWLEDGMENTS	11	≈ 0.09 pp.	0.24%
REFERENCES	11	≈ 3.91 pp.	10.29%
A APPLICATIONS ELABORATED	15	≈ 0.62 pp.	1.63%
B SOCIAL LEARNING DYNAMICS	15	≈ 2.67 pp.	7.03%
C PROOFS	18	≈ 15.04 pp.	39.58%
D EXTENSION TO HETEROGENEOUS AGENT PREFERENCES	33	≈ 0.86 pp.	2.26%
E EXPERIMENTS	34	≈ 4.71 pp.	12.39%

All detected figure and table locations

Main text

• Figure 1 · p. 8
• Figure 2 · p. 9

Attached appendix

• Figure 3 · p. 22
• Table 1 · p. 34
• Figure 4 · p. 37
• Figure 5 · p. 38

Argument and organization

1. Concrete mediation example, explicit information model and optimal policies

   Moves from recommendation scenarios to binary-state Bayesian social learning, then distinguishes altruistic and biased planners.

2. LLM simulation as a comparison with analytical behavior

   Separates deviations from Bayesian updating, planner-policy structure and welfare consequences.

Problem, claim and contribution

The restaurant example explains why an informative message affects both the current decision and future observers. This makes an information externality understandable before introducing value functions.

Introduction, pages 1–2. · Main paper

Argument and section order

Theorems are followed by prose describing policy regimes; the biased case explicitly acknowledges that a supremum may require epsilon-optimal rather than attained policies.

Sections 4–5, pages 6–8. · Main paper

Figures, captions and visual explanation

Figure 1 distinguishes planner, agent and oracle with colored roles and routes precision, messages and actions through a shared history. Its companion plot contrasts LLM and Bayesian belief changes.

Figure 1, page 8. · Main paper

Tables, comparisons and evidence

Figure 2 separates policy shape, deviation distribution and welfare/expenditure, using line style for objectives and color for planner type. Captions state the fixed adverse-world setting.

Figure 2, page 9. · Main paper

Appendix and reproducibility

The proof appendix includes an annotated three-tree construction. Experiment details disclose synthetic car facts, profile generation, exact prompts and an additional oracle-precision check.

Figure 3, page 22; Appendix E, pages 34–38. · Main paper

Clarity and writing conventions

Remark 2 lists restrictive information and transparency assumptions directly in the main text, while the conclusion acknowledges that LLM-agent simulation has not replaced human validation.

Remark 2, page 6; conclusion, page 10. · Main paper

What to reuse—and what not to infer

Transferable lesson: When connecting a formal model to LLM simulations, visualize the information available to each role and report systematic departures from the theoretical agent assumptions.

Boundary: These are synthetic EXAONE simulations, not measured human welfare effects. An LLM rating a message's intended precision is not the same as empirically verifying a binary channel's accuracy. Also reconcile the discounted theoretical objective with the displayed planner prompt and the appendix's discount-factor value of 1 before reproduction.

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 1–10, Sections 1–7; acknowledgments and references on pages 11–14.

Appendix / supplement

Pages 15–38, Appendices A–E: applications, complete belief-update derivation and policy proofs, heterogeneous-agent extension, experimental prompts, oracle checks and trajectories.

Visually inspected PDF pages

8, 9

• ICLR 2026 official proceedings PDF, retrieved 2026-09-19 · 38 pages