This preprint builds an LLM self-description instrument from 240 Likert items and 60 scenarios administered 30 times to 25 configurations from 17 families. Runs 1–15 are used to explore the structure and select 100 items. Parallel analysis suggests 19 factors, but the author forces five on balance, interpretability, and replication grounds: Responsiveness, Deference, Guardedness, Boldness, and Verbosity. The solution explains 31.2% of item variance, with alpha from 0.930 to 0.974 and split-half Tucker phi from 0.957 to 0.976. Stability is not the same as good global fit: strict CFA yields CFI 0.528 and ESEM 0.646, both far below the preregistered 0.95 threshold; the public diagnostic also yields SRMR 0.113. Direct and scenario formats intended to target corresponding dimensions produce nearly unrelated model orderings (mean r = -0.067), so the final instrument uses Likert items only. Validation includes 2,500 open-ended responses, 151 Prolific participants providing 906 usable non-gold ratings on 300 texts, and a three-model LLM judge ensemble. No factor-level self-report–human correlation has a confidence interval excluding zero: Responsiveness 0.04, Deference 0.08, Guardedness 0.27, Boldness -0.05, and Verbosity 0.41. Verbosity is the strongest candidate, it reaches 74% of the estimated reliability ceiling and is positive within prompt, but it does not predict raw output length (r = 0.14). Responsiveness correlates with LLM judges (r = 0.53) but not humans, even though human and judge ratings agree (r = 0.59). A common-factor bound test rejects one nonnegative latent variable as an explanation of all three measures (p = 0.007), but does not uniquely identify the mechanism. The audit reproduced the main numbers from SQLite after manually pinning scikit-learn 1.5.2. The official clean-clone recipe fails under current dependency resolution, omits ESEM, and automatically verifies only alpha coefficients. The OSF archive contains the complete databases, but two standalone CSV exports are stale: judge_ratings.csv has 20 rows versus 6,500 in SQLite, and prolific_ratings.csv has 745 versus 1,125. The faithful conclusion is that these 25 models produce highly stable self-descriptions under one elicitation format, while those scores weakly predict observed open-ended behavior and do not establish a general ontology of LLM personality.
Research question
Does a scale derived from the responses of 25 LLMs themselves, rather than from human psychological categories, produce stable factors and predict how humans, LLM judges, and textual measures describe their open behavior?