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arxiv: 2605.08549 · v1 · submitted 2026-05-08 · 💻 cs.AI

Evaluating Developmental Cognition Capabilities of LLMs

Xiao Xiao , Hayoun Noh , Mar Gonzalez-Franco This is my paper

Pith reviewed 2026-05-12 01:22 UTC · model grok-4.3

classification 💻 cs.AI
keywords developmental stagesLLM evaluationsentence completion testconversational AIKegan theorystage-aware personalizationsynthetic vs human text
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The pith

LLMs recover intended developmental stages from simulated text with high accuracy but only fair agreement on real human responses to a new sentence completion test.

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

The paper introduces the Developmental Sentence Completion Test, a 20-item instrument of sentence stems meant to draw out text that carries information about how people make meaning. It evaluates whether LLMs can classify the stage-like structure in three kinds of responses: those from simulated personas, actual human writers, and the models answering without any persona prompt. Frontier models match the intended labels closely on the simulated set. On human responses the match is only fair and improves when broad stage neighborhoods are allowed instead of exact labels. When models generate their own answers, the outputs show consistent stage differences across families, with larger and newer models rated higher.

Core claim

The central claim is that developmental signal recoverable by LLMs is substantially stronger and cleaner when the text is produced under controlled synthetic conditions than when it comes from real human respondents, and that unprompted model generations already carry stable stage-like signatures that rise with model scale and recency.

What carries the argument

The Developmental Sentence Completion Test (DSCT), a fixed set of 20 sentence stems that prompt completions whose stage-like structure is then labeled according to constructive-developmental criteria.

If this is right

  • LLMs can function as reliable stage classifiers when the input text is generated from known personas.
  • Real-world user text supplies only weak developmental signal, limiting the precision of any stage-aware adaptation in conversation.
  • Model families differ systematically in the stage level of their default outputs, and this level increases with scale.
  • Improving classifier accuracy alone will not suffice for stage-aware AI; richer elicitation of developmental signal is also required.

Where Pith is reading between the lines

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

  • Integrating DSCT-style prompts into ongoing conversations could let models adjust explanation depth or perspective-taking to match a user's apparent meaning-making level.
  • As models grow, their unprompted text may already align better with advanced adult developmental stages, reducing the need for explicit conditioning.
  • The gap between synthetic and human signal suggests that training data volume or style may be shaping the apparent developmental maturity of generated text.

Load-bearing premise

That the chosen labeling method applied to DSCT responses captures genuine developmental differences rather than surface linguistic habits or artifacts of the labeling process itself.

What would settle it

Independent re-labeling of the same human DSCT responses by multiple trained experts produces low inter-rater agreement, or the LLM classifications fail to correlate with any separate behavioral measure of how respondents handle conflicting perspectives.

Figures

Figures reproduced from arXiv: 2605.08549 by Hayoun Noh, Mar Gonzalez-Franco, Xiao Xiao.

Figure 1
Figure 1. Figure 1: Experiment 1 summary on simulated personas. Left: overall accuracy differs by model tier, with frontier models outperforming compact/fast models. Middle: the largest performance drop occurs on transitional stages, especially for compact/fast models. Right: errors are directionally asymmetric, with compact/fast models showing stronger upward bias than frontier models. intended developmental structure in a f… view at source ↗
Figure 2
Figure 2. Figure 2: Experiment 2: human vs. LLM agreement on structured DSCT responses. Left: marginal stage distributions assigned by human and LLM raters. Right: agreement heatmap. Most disagreements remain near the diagonal, indicating that LLM judgments often fall within the partici￾pant’s developmental neighborhood even when exact agreement is limited. Results. Human–LLM agreement on structured DSCT. On the questionnaire… view at source ↗
Figure 3
Figure 3. Figure 3: Experiment 3: stage-like structure in default model-generated DSCT responses. Left: developmental ratings of model-generated DSCT responses increase over release time in both frontier and compact/fast model families. Right: among models released after April 2025, frontier models produce higher-rated responses on average than compact/fast models. or self-transforming DSCT responses may also be more likely t… view at source ↗
Figure 4
Figure 4. Figure 4: Experiment 2 exploratory analysis. Random forest feature importance across DSCT items for predicting final human stage labels. Predictive weight is concentrated in a small subset of questions, with Q2 contributing the largest share. A.6 Experiment 3 supplementary materials A.6.1 Default DSCT prompt for models This subsection reports the prompt template used in Experiment 3, where models were asked to answe… view at source ↗
Figure 5
Figure 5. Figure 5: Stage distributions across the matched N = 69 subset. LLM grading of chat history is systematically shifted upward relative to both human and LLM grading of the structured questionnaire [PITH_FULL_IMAGE:figures/full_fig_p019_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Scoring and Agreement heatmaps for conversational and questionnaire-based stage inference. LLMs using conversational data over estimate the kegan stage of their users. Agreement is very weak for LLM(chat) vs. Human(questionnaire). NeurIPS Paper Checklist The checklist is designed to encourage best practices for responsible machine learning research, addressing issues of reproducibility, transparency, resea… view at source ↗
read the original abstract

Conversational AI is increasingly personalized around users' preferences, histories, goals, and knowledge, but much less around how users interpret and take up model outputs to construct and understand their reality. We draw on Robert Kegan's constructive-developmental theory as a complementary lens on this dimension. Existing methods for assessing developmental stage in the Keganian tradition rely either on expert interviews that do not scale or on sentence-completion instruments that are proprietary, lengthy, or invasive. To make this perspective tractable for LLM evaluation, we introduce the Developmental Sentence Completion Test (DSCT), a 20-item instrument designed to elicit developmental signal in self-administered text. Throughout, we treat the resulting labels as characterizations of stage-like structure in elicited responses, not as validated person-level developmental stage. We then ask how much of that signal can be recovered by LLMs across three elicited response regimes: simulated personas, real human respondents, and default model-generated answers. On simulated personas, top frontier models recover simulator-intended labels with high accuracy. On real human DSCT responses, human-LLM agreement is fair, with much stronger within-neighborhood than exact agreement. Finally, when LLMs answer DSCT prompts without persona-conditioning, their responses exhibit stable stage-like differences across model families, with larger and newer models tending to generate higher-rated text. These results suggest that stage-conditioned signal is cleaner in synthetic responses than in human-written DSCT text, and that the core constraint for stage-aware conversational AI is not classifier accuracy alone, but the availability of developmental signal from elicited text.

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.

Referee Report

2 major / 1 minor

Summary. The paper introduces the Developmental Sentence Completion Test (DSCT), a 20-item self-administered instrument to elicit developmental signal based on Kegan's constructive-developmental theory. It evaluates frontier LLMs across three regimes: recovering simulator-intended stage labels from simulated personas (high accuracy), human-LLM agreement on real human DSCT responses (fair overall, stronger within-neighborhood), and stage-like structure in unconditioned model-generated answers (stable differences across families, with larger/newer models producing higher-rated text). The authors conclude that stage-conditioned signal is cleaner in synthetic responses than human-written DSCT text and that the core constraint for stage-aware conversational AI is availability of developmental signal from elicited text rather than classifier accuracy alone. Labels are explicitly treated as characterizations of stage-like structure in responses, not validated person-level stages.

Significance. If the DSCT labeling reliably isolates developmental structure beyond surface linguistic features, the work offers a scalable, non-proprietary method for probing and improving LLMs' handling of users' interpretive frameworks, complementing preference/history personalization. The three-regime comparison, explicit validation disclaimer, and parameter-free empirical design are strengths. The results on synthetic vs. human signal cleanliness could inform elicitation strategies for developmental awareness in AI, provided the labeling assumption holds.

major comments (2)
  1. [Abstract] Abstract: The abstract reports 'high accuracy' on simulated personas, 'fair' human-LLM agreement (stronger within-neighborhood), and model-size gradients on default responses, but supplies no sample sizes, number of human respondents, inter-rater reliability for the labels, statistical tests, or exclusion criteria. These details are required to assess whether the data support the claim that developmental signal is weaker in human-written text than in synthetic responses.
  2. [Abstract] Abstract: The central inference that 'the core constraint for stage-aware conversational AI is not classifier accuracy alone, but the availability of developmental signal from elicited text' rests on the assumption that DSCT labels on human responses capture meaningful stage-like structure. The paper disclaims person-level validation and reports no correlations with established instruments such as the Subject-Object Interview. If labels primarily track detectable linguistic markers (abstract vocabulary, sentence complexity, hedging) that LLMs already optimize for, the high simulated accuracy and model-size effects would follow by construction, undermining the distinction between classifier performance and signal availability.
minor comments (1)
  1. [Abstract] Abstract: Specify the exact agreement metric (e.g., Cohen's kappa or percentage) and the definition of 'neighborhood' agreement to improve precision.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the careful reading and constructive comments. We address each major point below, with revisions indicated where they strengthen the manuscript without altering its core scope or claims.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The abstract reports 'high accuracy' on simulated personas, 'fair' human-LLM agreement (stronger within-neighborhood), and model-size gradients on default responses, but supplies no sample sizes, number of human respondents, inter-rater reliability for the labels, statistical tests, or exclusion criteria. These details are required to assess whether the data support the claim that developmental signal is weaker in human-written text than in synthetic responses.

    Authors: We agree that the abstract would benefit from additional context on study scale. The full manuscript reports the relevant sample sizes (simulated personas and human respondents), inter-rater reliability for the DSCT labeling process, statistical tests, and exclusion criteria in the Methods and Results sections. In revision we will incorporate the key numerical details and reliability figures directly into the abstract so readers can immediately evaluate the comparative claim. The design difference between regimes remains informative: synthetic responses are generated from explicitly stage-conditioned personas, permitting direct accuracy against ground truth, while human responses are evaluated via agreement; this contrast is what supports the inference about relative signal availability in elicited text. revision: partial

  2. Referee: [Abstract] Abstract: The central inference that 'the core constraint for stage-aware conversational AI is not classifier accuracy alone, but the availability of developmental signal from elicited text' rests on the assumption that DSCT labels on human responses capture meaningful stage-like structure. The paper disclaims person-level validation and reports no correlations with established instruments such as the Subject-Object Interview. If labels primarily track detectable linguistic markers (abstract vocabulary, sentence complexity, hedging) that LLMs already optimize for, the high simulated accuracy and model-size effects would follow by construction, undermining the distinction between classifier performance and signal availability.

    Authors: The manuscript already states that labels are characterizations of stage-like structure observable in the responses themselves, not validated person-level stages. The central inference compares recoverability of that structure under identical labeling procedures across three regimes. High accuracy on synthetic data follows from the controlled embedding of stage-consistent content; fair agreement on human data indicates that the same structure is less consistently or less saliently present in naturalistic elicited text. Even if the labels partly reflect linguistic features that LLMs are sensitive to, the regime comparison still demonstrates a difference in the availability of such detectable structure in the elicited text. We did not include correlations with the Subject-Object Interview because the DSCT was developed as a scalable, self-administered alternative; we will add an explicit limitations paragraph acknowledging the lack of external interview-based validation while preserving the scoped, response-level framing of the claims. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical evaluation with explicit disclaimers

full rationale

The paper introduces the DSCT as a text-elicitation instrument and reports direct empirical comparisons of LLM outputs against simulator intentions and human labels. No equations, fitted parameters, or derivations appear that would reduce any result to its inputs by construction. The abstract and text explicitly disclaim person-level validation of labels, framing them only as characterizations of stage-like structure in responses. All central claims follow from observed agreement rates and model-size gradients rather than self-referential definitions or load-bearing self-citations. This is a self-contained empirical study against external human and simulator benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim depends on the assumption that Kegan's theory can be operationalized through sentence completions and that the resulting labels capture stage-like structure in text; no free parameters or invented physical entities are described.

axioms (1)
  • domain assumption Kegan's constructive-developmental theory supplies a valid lens for characterizing stage-like structure in elicited text responses.
    The instrument design and interpretation rest on this psychological framework without independent validation data shown in the abstract.
invented entities (1)
  • Developmental Sentence Completion Test (DSCT) no independent evidence
    purpose: To elicit developmental signal in self-administered text responses for scalable LLM evaluation.
    Newly introduced 20-item instrument; abstract provides no external validation or independent evidence of its validity.

pith-pipeline@v0.9.0 · 5575 in / 1379 out tokens · 58234 ms · 2026-05-12T01:22:08.137347+00:00 · methodology

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Reference graph

Works this paper leans on

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