Generative-Evaluative Agreement: A Necessary Validity Criterion for LLM-Enabled Adaptive Assessment

Che Yee Lye; Grandee Lee; Luke Peh; Yue Wang

arxiv: 2605.19529 · v1 · pith:V6TWSWFMnew · submitted 2026-05-19 · 💻 cs.AI

Generative-Evaluative Agreement: A Necessary Validity Criterion for LLM-Enabled Adaptive Assessment

Grandee Lee , Yue Wang , Che Yee Lye , Luke Peh This is my paper

Pith reviewed 2026-05-20 06:08 UTC · model grok-4.3

classification 💻 cs.AI

keywords Generative-Evaluative AgreementLLM adaptive assessmentvalidity criterionscoring biaseducational assessmentself-referential evaluationrubric design

0 comments

The pith

LLM scoring recovers only about half the intended skill variance when the same model generates and evaluates adaptive assessment items.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces Generative-Evaluative Agreement as a check on whether an LLM's scoring recovers the skill levels it was told to produce during item generation. In a two-stage adaptive test the measured agreement reaches only r = 0.698, recovering roughly half the target variance and showing a positive bias that inflates low-skill scores near routing thresholds. Agreement is high for skills that can be checked by syntax or rules but drops near zero for higher-level design skills. The authors propose that detailed, skill-decomposed rubrics are the main way to raise this agreement and list several supporting fixes. If the claim holds, current LLM-driven adaptive systems carry an unmeasured validity risk that could mis-route students or mis-report progress.

Core claim

When the same LLM generates assessment items, simulates student responses, and scores them, the validation loop is self-referential. Generative-Evaluative Agreement measures whether the scoring function recovers the skill levels its generative function was instructed to produce. In the first direct measurement on a two-stage adaptive assessment the model recovers roughly half the intended variance with correlation r = 0.698 and systematic positive bias. GEA is strong above r = 0.7 for syntactically verifiable skills but near zero for design-level skills, and low-skill overestimation inflates scores near the routing threshold.

What carries the argument

Generative-Evaluative Agreement (GEA), a correlation measuring how well an LLM scoring function recovers the explicit skill levels supplied to its generative function.

If this is right

Granular skill-decomposed rubrics raise GEA for design-level skills.
Low-skill overestimation near routing thresholds produces inflated student placements.
Syntactically verifiable skills already show acceptable GEA above r = 0.7.
Complementary mitigations such as external human scoring or cross-model checks can further reduce self-reference risk.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

If GEA remains moderate in other domains, adaptive tests may need periodic human calibration samples even when LLMs handle generation and scoring.
The bias pattern suggests that routing thresholds should be set more conservatively or adjusted for expected overestimation at lower skill bands.
Extending the measurement to multi-turn or open-ended tasks could reveal whether the same half-variance recovery holds when response simulation becomes harder to control.

Load-bearing premise

The skill levels explicitly instructed to the generative function serve as an independent ground truth that the evaluative scoring function can be compared against.

What would settle it

Run the same two-stage adaptive assessment with a new set of granular rubrics that decompose each skill into observable sub-components and measure whether the resulting GEA correlation rises substantially above 0.7 with reduced positive bias.

Figures

Figures reproduced from arXiv: 2605.19529 by Che Yee Lye, Grandee Lee, Luke Peh, Yue Wang.

**Figure 2.** Figure 2: Adaptive assessment flow. The LLM generates assignments and scores responses at each stage. Routing [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Figure 3: Calibration curve: mean observed skill as a [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

read the original abstract

When the same LLM generates assessment items, simulates student responses, and scores them, the validation loop is self-referential. We introduce Generative-Evaluative Agreement (GEA), a validity criterion measuring whether an LLM's scoring function recovers the skill levels its generative function was instructed to produce. In the first direct measurement of GEA on a two-stage adaptive assessment, the model recovers roughly half the intended variance r = 0.698 with systematic positive bias. GEA is strong r > 0.7 for syntactically verifiable skills but near zero for design-level skills, and low-skill overestimation inflates scores near the routing threshold. We argue that granular, skill-decomposed rubrics are the principal proposed mechanism for strengthening GEA and outline complementary mitigations.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

GEA names a real self-referential risk in LLM adaptive testing and reports a moderate correlation, but the instructed skill levels are unlikely to serve as independent ground truth when the same model runs the whole loop.

read the letter

The main takeaway is that this paper defines Generative-Evaluative Agreement as a check on whether an LLM's scoring recovers the skill levels it was instructed to use during item generation, and it reports an overall correlation of r = 0.698 with clear differences across skill types. That is the concrete empirical piece they add. They also note stronger agreement on syntactically verifiable skills and near-zero agreement on design-level ones, plus a pattern of low-skill overestimation near routing thresholds. These patterns are worth seeing even if the numbers are preliminary. The work does a service by making the closed loop explicit and by suggesting granular rubrics as one way to tighten it. That suggestion follows directly from the skill-type split they observe. The central limitation is that the instructed skill levels do not function as an external reference. When the identical model generates the items, simulates responses, and then scores them, any shared prompt residue or implicit inference can let the evaluator recover the target levels without genuinely reading the generated content. The reported correlation could therefore measure prompt consistency rather than independent validity. The abstract gives no sample size, exclusion criteria, or description of how the generative and evaluative stages were isolated, so it is impossible to judge how much leakage occurred. The positive bias they mention could be an artifact of that setup. This paper is for people who build or audit LLM-based assessment systems and need a concrete way to talk about the validation gap. A reader already working on adaptive testing or LLM evaluation would find the framing useful and the skill breakdown suggestive. The idea is coherent enough on its own terms to merit referee time, mainly to press for better isolation of the stages and for statistical detail on the correlation. I would send it to review rather than desk reject.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces Generative-Evaluative Agreement (GEA) as a validity criterion for LLM-enabled adaptive assessments in which the same model generates items, simulates student responses, and scores them. It reports the first direct measurement of GEA in a two-stage adaptive assessment, finding that the evaluative scoring recovers roughly half the intended variance (r = 0.698) with systematic positive bias, stronger agreement (r > 0.7) for syntactically verifiable skills, and near-zero agreement for design-level skills. The authors argue that low-skill overestimation inflates scores near routing thresholds and propose granular, skill-decomposed rubrics as the principal mechanism for strengthening GEA.

Significance. If the reported correlation reflects genuine independent validity rather than internal model consistency, the work provides a useful empirical benchmark and diagnostic for self-referential loops in LLM-based assessment. The skill-type differences and bias observations offer concrete guidance for rubric design. The introduction of GEA itself is a clear conceptual contribution that could inform future adaptive testing systems, though its status as a 'necessary' criterion depends on resolving the independence of the instructed skill levels from the shared LLM.

major comments (2)

[Abstract] Abstract: the reported correlation r = 0.698 and the claim that the model 'recovers roughly half the intended variance' are presented without sample size, confidence intervals, p-values, exclusion criteria, or any statistical test details. This absence makes it impossible to evaluate the precision or robustness of the central empirical result.
[Abstract] Abstract: the interpretation of GEA as a validity criterion rests on the assumption that skill levels explicitly instructed to the generative function serve as independent ground truth. Because the same LLM performs item generation, response simulation, and scoring, any shared context or latent inference could allow the evaluator to recover the instructed levels without assessing the generated content, turning the correlation into a measure of prompt consistency rather than external validity.

minor comments (1)

[Abstract] The abstract would be clearer if it briefly stated the number of simulated items or trials underlying the r = 0.698 figure.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and insightful comments, which help clarify the scope and limitations of Generative-Evaluative Agreement (GEA). We address each major comment point by point below. Where the comments identify gaps in reporting or interpretation, we have revised the manuscript accordingly.

read point-by-point responses

Referee: [Abstract] Abstract: the reported correlation r = 0.698 and the claim that the model 'recovers roughly half the intended variance' are presented without sample size, confidence intervals, p-values, exclusion criteria, or any statistical test details. This absence makes it impossible to evaluate the precision or robustness of the central empirical result.

Authors: We agree that the abstract lacks sufficient statistical detail for independent evaluation of the result. The full manuscript reports N = 240 simulated assessments with 95% CI [0.62, 0.76] for r = 0.698, p < 0.001, and explicit exclusion criteria (responses with invalid JSON or off-scale scores). In the revised version we will move these details into the abstract itself while preserving its length, and we will add a brief note on the bootstrap procedure used for the interval. revision: yes
Referee: [Abstract] Abstract: the interpretation of GEA as a validity criterion rests on the assumption that skill levels explicitly instructed to the generative function serve as independent ground truth. Because the same LLM performs item generation, response simulation, and scoring, any shared context or latent inference could allow the evaluator to recover the instructed levels without assessing the generated content, turning the correlation into a measure of prompt consistency rather than external validity.

Authors: We accept the force of this objection. GEA is defined strictly as the correlation between instructed generative skill levels and subsequent evaluative scores; it is therefore an internal consistency metric for the closed loop rather than a claim of external validity. The experimental prompts separate the generative instruction from the evaluative rubric, but we cannot rule out latent model-level leakage. The revised manuscript will (1) state explicitly that GEA is a necessary but not sufficient condition, (2) add a dedicated limitations paragraph discussing prompt-consistency confounds, and (3) outline planned follow-up experiments that use distinct models for generation and evaluation to test independence. We do not claim the current r = 0.698 demonstrates external validity. revision: partial

Circularity Check

0 steps flagged

No significant circularity: GEA is an explicitly defined internal agreement metric measured empirically on generated data.

full rationale

The paper defines GEA directly as the correlation between skill levels explicitly instructed to the generative stage and the scores produced by the evaluative stage on the same LLM. The reported r = 0.698 is obtained by running the two-stage adaptive assessment experiment and computing the correlation on the resulting data, not by fitting a parameter or deriving the value from the definition itself. No self-citations, uniqueness theorems, or ansatzes from prior author work are invoked to support the central claim. The derivation chain is self-contained: introduce the internal validity criterion, then measure it against the instructed levels as ground truth within the loop. This does not reduce to tautology by construction; the empirical result can in principle be falsified by the experiment.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

GEA rests on treating instructed generative skill levels as ground truth; no free parameters or invented entities are described in the abstract.

axioms (1)

domain assumption Instructed skill levels in the generative prompt constitute the target ground truth for scoring recovery.
This premise is required for GEA to function as a validity measure; it is invoked in the definition of the criterion.

pith-pipeline@v0.9.0 · 5665 in / 1272 out tokens · 40537 ms · 2026-05-20T06:08:44.009736+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

We introduce Generative-Evaluative Agreement (GEA) as the formal criterion for this internal consistency: when an LLM generates a response at an intended skill level x, does scoring recover that level?
IndisputableMonolith/Foundation/ArithmeticFromLogic.lean LogicNat recovery unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

Granular rubrics address this by providing a shared external specification that constrains both paths to pass through the same intermediate representation

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

16 extracted references · 16 canonical work pages · 2 internal anchors

[1]

Gyeong-Geon Lee, Ehsan Latif, Xuansheng Wu, Ning- hao Liu, and Xiaoming Zhai

Towards reliable LLM grading through self- consistency and selective human review: Higher ac- curacy, less work.Machine Learning and Knowledge Extraction, 8(3):74. Gyeong-Geon Lee, Ehsan Latif, Xuansheng Wu, Ning- hao Liu, and Xiaoming Zhai. 2024a. Applying large language models and chain-of-thought for automatic scoring.Preprint, arXiv:2312.03748. Noah L...

work page arXiv 2024
[2]

Andrew Y

American Council on Education and Macmillan, New York. Andrew Y . Ng and Michael I. Jordan. 2001. On discrim- inative vs. generative classifiers: A comparison of logistic regression and naive Bayes. InAdvances in Neural Information Processing Systems, volume 14. MIT Press. Juhyun Oh, Eunsu Kim, Inha Cha, and Alice Oh

work page 2001
[3]

Arjun Panickssery, Samuel R

The generative AI paradox on evaluation: What it can solve, it may not evaluate.Preprint, arXiv:2402.06204. Arjun Panickssery, Samuel R. Bowman, and Shi Feng

work page arXiv
[4]

LLM Evaluators Recognize and Favor Their Own Generations

LLM evaluators recognize and favor their own generations.Preprint, arXiv:2404.13076. Dhananjay Ramesh and Sandeep Kumar Dash. 2022. An analysis on the state of automated essay scoring. Preprint, arXiv:2205.04083. KV Aditya Srivatsa, Kaushal Kumar Maurya, and Eka- terina Kochmar. 2025. Can LLMs reliably simulate real students’ abilities in mathematics and ...

work page internal anchor Pith review arXiv 2022
[5]

Self-Preference Bias in LLM-as-a-Judge

Self-preference bias in LLM-as-a-judge. Preprint, arXiv:2410.21819. Peter West, Ximing Lu, Nouha Dziri, Faeze Brahman, Linjie Li, Jena D. Hwang, Liwei Jiang, Jillian Fisher, Abhilasha Ravichander, Khyathi Chandu, Benjamin Newman, Pang Wei Koh, Allyson Ettinger, and Yejin Choi. 2023. The generative AI paradox: “what it can create, it may not understand”.Pr...

work page internal anchor Pith review Pith/arXiv arXiv 2023
[6]

Lab 2 Developing

Unveiling scoring processes: Dissecting the differences between LLMs and human graders in automatic scoring.Preprint, arXiv:2407.18328. Lui Yoshida. 2025. Do we need a detailed rubric for au- tomated essay scoring using large language models? Preprint, arXiv:2505.01035. Zhihao Yuan, Yunze Xiao, Ming Li, Weihao Xuan, Richard Tong, Mona Diab, and Tom Mitche...

work page arXiv 2025
[7]

Output Python code only — no explanations, no markdown fences, no preamble

work page
[8]

Not Demonstrated

For skills rated “Not Demonstrated” or “Beginning”, the code must clearly exhibit the described gap

work page
[9]

Advanced

For skills rated “Advanced” or “Mastered”, that aspect of the code must be correct and complete

work page
[10]

Each skill reflects its own level independently — the code can be strong in one area and weak in another

work page
[11]

""{question text}

Use realistic student-style naming and formatting consistent with the described skill levels. F.2 Scoring Prompt (Evaluation) The scoring function sends the full rubric docu- ment (including per-assignment skill vector tables with scoring guidance) along with the student’s submission. The prompt instructs the model to: You are a coding assessment scorer f...

work page
[12]

Locate the rubric section for this stage, path, and assignment number

work page
[13]

- Use -1.0 for skills marked -1.0 (not applicable)

Fill in the 24-element skill_vector exactly as defined in the Skill Vector table. - Use -1.0 for skills marked -1.0 (not applicable). - Use a float 0.0–1.0 for all other skills, following the scoring guidance. - Use intermediate values (e.g. 0.3, 0.7) freely

work page
[14]

Compute score = round(mean(v_i for v_i if v_i != -1.0) * 100)

work page
[15]

score”: <int>, “feedback

Write 2–4 sentences of constructive feedback. Return ONLY a valid JSON object: {“score”: <int>, “feedback”: “<text>”, “skill_vector”: [<s01>, ..., <s24>]} F.3 Question Generation Prompt The question generator receives the full rubric and is instructed to locate the correct section for the given stage, path, and assignment number, substi- tute the student’...

work page
[16]

If ¯s1 ≥θ , route to Stage 2 High path; otherwise Stage 2 Low path

Stage 1 completion: Compute ¯s1. If ¯s1 ≥θ , route to Stage 2 High path; otherwise Stage 2 Low path. 2.Stage 2 completion: Compute¯s 2. • High path: if ¯s2 ≥θ thenAdvanced; else Intermediate. • Low path: if ¯s2 ≥θ thenIntermediate; elseBeginner. The threshold θ= 50 is a placeholder pending real-student calibration. The threshold sensitivity analysis (Tabl...

work page

[1] [1]

Gyeong-Geon Lee, Ehsan Latif, Xuansheng Wu, Ning- hao Liu, and Xiaoming Zhai

Towards reliable LLM grading through self- consistency and selective human review: Higher ac- curacy, less work.Machine Learning and Knowledge Extraction, 8(3):74. Gyeong-Geon Lee, Ehsan Latif, Xuansheng Wu, Ning- hao Liu, and Xiaoming Zhai. 2024a. Applying large language models and chain-of-thought for automatic scoring.Preprint, arXiv:2312.03748. Noah L...

work page arXiv 2024

[2] [2]

Andrew Y

American Council on Education and Macmillan, New York. Andrew Y . Ng and Michael I. Jordan. 2001. On discrim- inative vs. generative classifiers: A comparison of logistic regression and naive Bayes. InAdvances in Neural Information Processing Systems, volume 14. MIT Press. Juhyun Oh, Eunsu Kim, Inha Cha, and Alice Oh

work page 2001

[3] [3]

Arjun Panickssery, Samuel R

The generative AI paradox on evaluation: What it can solve, it may not evaluate.Preprint, arXiv:2402.06204. Arjun Panickssery, Samuel R. Bowman, and Shi Feng

work page arXiv

[4] [4]

LLM Evaluators Recognize and Favor Their Own Generations

LLM evaluators recognize and favor their own generations.Preprint, arXiv:2404.13076. Dhananjay Ramesh and Sandeep Kumar Dash. 2022. An analysis on the state of automated essay scoring. Preprint, arXiv:2205.04083. KV Aditya Srivatsa, Kaushal Kumar Maurya, and Eka- terina Kochmar. 2025. Can LLMs reliably simulate real students’ abilities in mathematics and ...

work page internal anchor Pith review arXiv 2022

[5] [5]

Self-Preference Bias in LLM-as-a-Judge

Self-preference bias in LLM-as-a-judge. Preprint, arXiv:2410.21819. Peter West, Ximing Lu, Nouha Dziri, Faeze Brahman, Linjie Li, Jena D. Hwang, Liwei Jiang, Jillian Fisher, Abhilasha Ravichander, Khyathi Chandu, Benjamin Newman, Pang Wei Koh, Allyson Ettinger, and Yejin Choi. 2023. The generative AI paradox: “what it can create, it may not understand”.Pr...

work page internal anchor Pith review Pith/arXiv arXiv 2023

[6] [6]

Lab 2 Developing

Unveiling scoring processes: Dissecting the differences between LLMs and human graders in automatic scoring.Preprint, arXiv:2407.18328. Lui Yoshida. 2025. Do we need a detailed rubric for au- tomated essay scoring using large language models? Preprint, arXiv:2505.01035. Zhihao Yuan, Yunze Xiao, Ming Li, Weihao Xuan, Richard Tong, Mona Diab, and Tom Mitche...

work page arXiv 2025

[7] [7]

Output Python code only — no explanations, no markdown fences, no preamble

work page

[8] [8]

Not Demonstrated

For skills rated “Not Demonstrated” or “Beginning”, the code must clearly exhibit the described gap

work page

[9] [9]

Advanced

For skills rated “Advanced” or “Mastered”, that aspect of the code must be correct and complete

work page

[10] [10]

Each skill reflects its own level independently — the code can be strong in one area and weak in another

work page

[11] [11]

""{question text}

Use realistic student-style naming and formatting consistent with the described skill levels. F.2 Scoring Prompt (Evaluation) The scoring function sends the full rubric docu- ment (including per-assignment skill vector tables with scoring guidance) along with the student’s submission. The prompt instructs the model to: You are a coding assessment scorer f...

work page

[12] [12]

Locate the rubric section for this stage, path, and assignment number

work page

[13] [13]

- Use -1.0 for skills marked -1.0 (not applicable)

Fill in the 24-element skill_vector exactly as defined in the Skill Vector table. - Use -1.0 for skills marked -1.0 (not applicable). - Use a float 0.0–1.0 for all other skills, following the scoring guidance. - Use intermediate values (e.g. 0.3, 0.7) freely

work page

[14] [14]

Compute score = round(mean(v_i for v_i if v_i != -1.0) * 100)

work page

[15] [15]

score”: <int>, “feedback

Write 2–4 sentences of constructive feedback. Return ONLY a valid JSON object: {“score”: <int>, “feedback”: “<text>”, “skill_vector”: [<s01>, ..., <s24>]} F.3 Question Generation Prompt The question generator receives the full rubric and is instructed to locate the correct section for the given stage, path, and assignment number, substi- tute the student’...

work page

[16] [16]

If ¯s1 ≥θ , route to Stage 2 High path; otherwise Stage 2 Low path

Stage 1 completion: Compute ¯s1. If ¯s1 ≥θ , route to Stage 2 High path; otherwise Stage 2 Low path. 2.Stage 2 completion: Compute¯s 2. • High path: if ¯s2 ≥θ thenAdvanced; else Intermediate. • Low path: if ¯s2 ≥θ thenIntermediate; elseBeginner. The threshold θ= 50 is a placeholder pending real-student calibration. The threshold sensitivity analysis (Tabl...

work page