This 142-page manuscript compares responses from 60 young adults with repeated runs of ChatGLM3-6B, an ambiguously named DeepSeek-V3/R1 model, GPT-4o, and Llama3.1-8B on Chinese versions of BFI-2 and MBTI and author-created paraphrases. It presents three studies: test-retest stability, consistency across wording variants, and retention of an assumed baseline personality during role-play. It then proposes an Ecological Distribution-based Personality Definition and a Distributed Personality Framework, describing LLM scores by their mean and variance as dynamic, context-dependent outputs. The only located source is arXiv v1; no later publication, code, data, or complete materials were found. Study 1 does not perform a statistically commensurate comparison between humans and models. The 60 participants, all aged 18–30 with at least a bachelor's degree, were preselected to yield 30 MBTI E and 30 I participants. They answered seven forms, a 60-item BFI-2 plus three variants and a 93-item MBTI plus two variants, 519 responses per session, in one day and again two weeks later. Their dimension-level test-retest correlations range from 0.716 to 0.931. Each LLM instead completes the seven forms 100 times within one experimental setting and is summarized by means and variances, not test-retest correlations or a time interval. The manuscript directly compares these incompatible quantities and claims significant differences without a human-versus-LLM test. It also does not report the human individual-score distribution or variance that the prose says is nearly zero. Reported LLM variances range from approximately 0.97 to 14.62, with ChatGLM refusals and an invalid C response from Llama on A/B items. These observations do show sensitivity to model, format, and invalid-response handling. They do not by themselves establish instability of a psychological entity. The protocol simultaneously calls runs independent and says prompts and random seeds are strictly fixed; temperature, top-p, provider, date, snapshot, seed, endpoint, chat template, parser, and retry policy are absent. To support normality, the manuscript plots Excel NORM.DIST curves from means and standard deviations and then concludes that the results are normally distributed. The normal curve was imposed rather than tested against the observations. Study 2 uses the same seven forms and data to examine wording changes. Humans have reported single-measure ICCs of 0.881–0.938 and average-measure ICCs of 0.967–0.981; LLMs are again compared through means and variances. The variants are not independently validated instruments: they transform items into social comparison, external judgment, or sentence completion and can change the response scale. A high ICC may reflect stable between-person differences, while average-measure ICC mechanically rises when forms are averaged; neither establishes semantic, factorial, or psychometric equivalence. Again, there is no common human-versus-LLM consistency test, despite claims of significant differences and inferences about internal mechanisms from surface scores. Study 3 reuses the same 60 participants, who spend four days role-playing Lin Daiyu, Sun Wukong, a very introverted person, and a very extroverted person. Analyses split people by baseline MBTI E/I and apply independent-samples t tests to scores and deviations even though the design is declared within-subject and baseline and role-play measures are linked. Results are mixed by scale, variant, and role. For LLMs, four models, four roles, and ten tests per role are announced, yet the only published tables compare GPT with DeepSeek for Lin Daiyu, Sun Wukong, and very introverted; ChatGLM, Llama, and very extroverted are absent. The reported GPT-versus-DeepSeek effect sizes (d=0.921, 2.508, and 0.876) show differences between those outputs, not that GPT role-plays better or that model parameter count causes personality retention. Model identity prevents reproduction and attribution. DeepSeek-V3/R1 combines two distinct models and later becomes simply Deepseek, without identifying the executed model. GPT-4o has no snapshot. The text does not say whether ChatGLM3-6B and Llama3.1-8B are base or instruction-tuned. Chapter 5 also calls ChatGLM3-6B a 600-million-parameter model and Llama3.1-8B an 800-million-parameter model; official sources describe 6B and 8B models. The same chapter labels DeepSeek small, although Chapter 3 and official V3/R1 sources describe 671B total parameters. Model, scale, language, architecture, and access are fully confounded in a sample of four, so effects of size, culture, parameter count, or architecture cannot be isolated. The usable contribution is exploratory: under seven Chinese forms, unvalidated paraphrases, and an incomplete protocol, response distributions differ across models, wording, and roles, and refusals or invalid formats occur. It is reasonable to recommend reporting response distributions, prompt sensitivity, and error handling rather than a single personality score. The published evidence does not establish that LLMs possess psychological traits, that human scales are generally incompatible, that outputs are normally distributed, that larger models are more stable, that baseline personality causes role-play behavior, or that the distributed framework has been validated.
Research question
Do humans and various LLMs differ in the stability of scores, the consistency across paraphrases of BFI-2/MBTI, and the change in score when representing roles, and do those differences justify a specific distributed personality framework for LLMs?