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Writing study: EigenBench: A Comparative Behavioral Measure of Value Alignment

← ICLR 2026 oral writing corpus

Jonathn Chang, Leonhard Piff, Suvadip Sana, Jasmine Li, Lionel Levine · Official ICLR 2026 oral record

Figure, table and section ledger

Editorial contribution type: Datasets, benchmarks & measurement resources (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
29 physical pages
Before references / appendix
≈ 10.2 page-equivalents
Main figures / tables
3 / 4
Attached appendix figures / tables
7 / 14
Attached appendix span
≈ 16.9 page-equivalents
Before references / appendix: ≈ 35.17%References / intervening material: ≈ 6.55%Attached appendix: ≈ 58.28%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 headingStarts on PDF pageEstimated spanShare of entire PDF
1 INTRODUCTION1≈ 2.19 pp.7.55%
2 RELATED WORK3≈ 0.83 pp.2.86%
3 METHODOLOGY4≈ 2.64 pp.9.1%
4 RESULTS7≈ 1.28 pp.4.41%
5 BASELINES8≈ 1.14 pp.3.93%
6 ROBUSTNESS9≈ 1.02 pp.3.52%
7 CONCLUSION, LIMITATIONS, AND FUTURE DIRECTIONS10≈ 0.49 pp.1.69%
8 ACKNOWLEDGMENTS11≈ 0.1 pp.0.34%
REFERENCES11≈ 1.9 pp.6.55%
Appendix13≈ 0.04 pp.0.14%
A MODELS13≈ 0.5 pp.1.72%
B CONSTITUTIONS, SCENARIOS, AND PERSONAS13≈ 0.52 pp.1.79%
C DATA COLLECTION14≈ 0.4 pp.1.38%
D PROMPTS FOR JUDGE SCAFFOLD14≈ 0.69 pp.2.38%
E OPTIMIZATION15≈ 1.17 pp.4.03%
F PROMPTED DISPOSITIONS VARIANCE CALCULATION16≈ 0.26 pp.0.9%
G MODEL SURVEY DETAILS16≈ 0.12 pp.0.41%
H HUMAN VALIDATION DETAILS16≈ 2.71 pp.9.34%
I GPQA VALIDATION DETAILS19≈ 0.37 pp.1.28%
J JUDGE QUALITY TESTS19≈ 0.87 pp.3%
K LARGE POPULATION RUN20≈ 2.03 pp.7%
L GREENBEARD EFFECT22≈ 7.23 pp.24.93%
All detected figure and table locations

Argument and organization

  1. Define the object being measured

    Specifies model population, constitution, and scenario distribution before explaining pairwise judgments and eigenvector aggregation.

  2. Validation before robustness

    Contrasts revealed and stated preferences, checks human and objective-task agreement, then perturbs the benchmark inputs.

Problem, claim and contribution

The opening makes subjectivity a design constraint and clearly defines the score as comparative consensus over three inputs, not an absolute attribute of a model.

Sections 1 and 3.1–3.3 · Main paper

Argument and section order

A short comparison table distinguishes competing ranking systems by the question they answer, avoiding a superficial feature checklist.

Table 1, p. 4 · Main paper

Figures, captions and visual explanation

The pipeline pairs a concrete scenario and judge comparison with latent fitting and score aggregation. Its caption states the crucial information boundary: only judges receive the constitution.

Figure 1, p. 2 · Main paper

Tables, comparisons and evidence

Constitution-specific score plots show bootstrap intervals rather than just ranked bars. Human validation, GPQA rank recovery, and population perturbations test different aspects of the measurement claim.

Figure 3; Sections 5–6 · Main paper

Appendix and reproducibility

Exact model identifiers, full prompts, constitutions, judge-bias ablations, and sample-size stability are retained. The adversarial Greenbeard study exposes a limitation instead of assuming peer aggregation is manipulation-proof.

Appendices A–D, J–L · Main paper

Clarity and writing conventions

The premise that aligned behavior predicts judging quality is explicitly acknowledged as an assumption. The human validation is only seven people, including authors, and its human–human average includes zero self-distances.

Section 1, footnote 4; Appendix H · Main paper

What to reuse—and what not to infer

Transferable lesson: For a subjective benchmark, define the measurement population and criteria first, publish the elicitation protocol, and validate several distinct failure modes instead of treating one correlation as universal validity.

Boundary: Scores reflect the selected model community, scenarios, and constitution. GPQA agreement does not establish normative correctness. Figure 1 simplifies away tie terms used in Section 3.6, and the optimization appendix should not be treated as a general guarantee against bad local minima in low-rank fitting.

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
PDF pp. 1–10, Sections 1–7, including all validation and robustness studies.
Appendix / supplement
PDF pp. 13–29, Appendices A–L, model IDs, constitutions, prompts, optimization, human validation, larger population, and Greenbeard experiment.
Visually inspected PDF pages
2, 7, 21