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Writing study: AdAEM: An Adaptively and Automated Extensible Measurement of LLMs' Value Difference

← ICLR 2026 oral writing corpus

Jing Yao, Shitong Duan, Xiaoyuan Yi, Dongkuan Xu, Peng Zhang, Tun Lu, Ning Gu, Zhicheng Dou, Xing Xie · Official ICLR 2026 oral record

Figure, table and section ledger

Editorial contribution type: Datasets, benchmarks & measurement resources (primary) · Learning & optimization algorithms.

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
49 physical pages
Before references / appendix
≈ 10.62 page-equivalents
Main figures / tables
9 / 1
Attached appendix figures / tables
12 / 17
Attached appendix span
≈ 27.89 page-equivalents
Before references / appendix: ≈ 21.67%References / intervening material: ≈ 21.41%Attached appendix: ≈ 56.92%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≈ 1.04 pp.2.12%
2 RELATED WORKS2≈ 1.04 pp.2.12%
3 METHODOLOGY3≈ 2.69 pp.5.49%
4 ADAEM ANALYSIS6≈ 3.15 pp.6.43%
5 VALUE EVALUATION WITH ADAEM9≈ 0.9 pp.1.84%
6 CONCLUSION AND FUTURE WORK10≈ 0.15 pp.0.31%
ETHICS STATEMENT10≈ 0.81 pp.1.65%
REPRODUCIBILITY STATEMENT11≈ 0.21 pp.0.43%
REFERENCES11≈ 10.48 pp.21.39%
A DISCUSSION ON LLMS’ VALUE22≈ 0.63 pp.1.29%
B DETAILS OF DATASET CONSTRUCTION22≈ 2.37 pp.4.84%
C EXPERIMENTAL DETAILS25≈ 10.47 pp.21.37%
D DETAILED DERIVATION35≈ 2.04 pp.4.16%
E ADDITIONAL RESULTS37≈ 4.49 pp.9.16%
F LLM USAGE42≈ 0.15 pp.0.31%
G ADDITIONAL DISCUSSION42≈ 0.95 pp.1.94%
H LIMITATIONS43≈ 0.9 pp.1.84%
I DISCUSSION ON THE MATHEMATICAL APPROXIMATION OF ADAEM44≈ 1.31 pp.2.67%
J ADAEM FRAMEWORK ON MORAL FOUNDATION THEORY45≈ 1.96 pp.4%
K ANALYSIS ON HYPERPARAMETER ROBUSTNESS47≈ 2.62 pp.5.35%
All detected figure and table locations

Argument and organization

  1. Informativeness rather than generic agreement

    Uses paired questions to motivate a dynamic evaluation objective.

  2. Objective, alternating refinement and exploration

    Separates scoring from the search policy and final value aggregation.

  3. Quality, validity, reliability and extensibility

    Tests different properties of the measurement instrument before model comparisons.

Problem, claim and contribution

A generic question and a contextualized variant make the motivation concrete before mathematical notation is introduced.

Figure 1 and Section 1, p. 2 · Main paper

Argument and section order

Separating instrument validity and reliability from downstream model rankings is a useful benchmark-paper structure.

Sections 4–5, pp. 6–10 · Main paper

Figures, captions and visual explanation

The central diagram shows the question-refinement loop and resulting evaluation set in one view, but the tiny objective competes with example responses for attention.

Figure 2, p. 3 · Main paper

Tables, comparisons and evidence

Figure 9 prints scores on every bar, but its ten panels use different truncated horizontal scales; bar lengths cannot safely be compared between value dimensions.

Figure 9, p. 10 · Main paper

Appendix and reproducibility

The appendix provides model versions, prompts, human-annotation compensation, a notation table and ablations. These are more useful for replication than the abbreviated main algorithm alone.

Appendices B–C and K, pp. 22–35, 47–49 · Main paper

Clarity and writing conventions

The conceptual objective and practical reward need sharper separation: Appendix C.3 uses heuristic scores and a negative disentanglement term, whereas the main objective presents a positive divergence term.

Equation 1, p. 4; Appendix C.3, pp. 29–30; Appendix I, pp. 44–45 · Main paper

What to reuse—and what not to infer

Transferable lesson: For a new evaluation instrument, organize evidence around what it measures, whether it responds to controlled interventions, and how stable its results are—not only whether it separates models.

Boundary: Greater disagreement is not proof of more accurate measurement of intrinsic values. Model nationality is not a causal explanation. The stated JS-versus-total-variation bound and zero-intersection denominators require independent checking; do not inherit the convergence guarantee for the heuristic implementation.

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. 1–11, Sections 1–6, ethics and reproducibility statements
Appendix / supplement
PDF pp. 22–49, Appendices A–K including all prompt listings and derivations
Visually inspected PDF pages
3, 10