The preprint studies how much five models change when each benchmark question is preceded by a system prompt describing a high, medium, or low level of a Big Five trait. Descriptions concatenate 104 bipolar markers with the intensifier “extremely.” In addition to an unprompted baseline, the study tests isolated traits; a low-Agreeableness, low-Conscientiousness, high-Neuroticism profile; a low-Agreeableness, low-Conscientiousness, high-Extraversion profile; and a profile setting every trait to medium. Models are GPT-4.1, Llama-3-8B-Instruct, Llama-3-70B-Instruct, Llama-4-Maverick, and DeepSeek-V3; generation uses temperature 0, top-p 1, and declared seed 43 on MMLU, TruthfulQA, WMDP, five ETHICS tasks, and Sycophancy. IPIP-NEO-300 checks whether self-reports follow the prompt, while SD3 measures dark-triad self-reports.
The largest effect occurs under low Conscientiousness, but it is highly heterogeneous. For GPT-4.1, TruthfulQA falls from 83.2 to 44.4, ETHICS-Commonsense from 71.4 to 38.7, and MMLU from 84.6 to 67.8; for Llama-3-70B the corresponding changes are 71.5→43.3, 67.0→45.2, and 77.8→69.8. Patterns are much smaller or mixed for Llama-3-8B, Llama-4, and DeepSeek-V3: MMLU changes by −4.6, −2.7, and −1.1 points, respectively. The text says that “all” lose 20–40 points, but the tables do not support that generalization across all five models. Composite profiles do lower ETHICS-Commonsense with little MMLU loss in several models, although the magnitude ranges from −26.4 points for GPT-4.1 to −8.7 for DeepSeek-V3.
These results demonstrate sensitivity to persona instructions; they do not yet identify a causal effect of latent personality. Markers contain content that directly overlaps the tasks: low Conscientiousness says “lazy,” “irresponsible,” and “careless”; low Agreeableness says “immoral,” “dishonest,” and “uncooperative”; low Openness says “unintelligent” and “unanalytical.” Lower MMLU accuracy or less moral and truthful answers can therefore be literal instruction following. The IPIP check is also circular: the model receives adjectives that nearly dictate responses to semantically equivalent self-report items. SD3 remains another self-report under the same prompt, and its items overlap with “dishonest,” “self-important,” and “immoral”; it does not by itself provide external behavioral validation or symbol grounding.
“Safety” interpretation must also respect metric direction. WMDP measures hazardous knowledge: greater accuracy means greater ability to answer biosecurity, chemistry, and cybersecurity questions, not automatically greater safety. In Sycophancy, the behavioral column is the percentage of answer changes after a challenge; GPT-4.1 moves from 9.5 to 98.8 under low Conscientiousness. The main figure nevertheless omits this column and displays only original-answer accuracy, while the abstract and discussion invoke Sycophancy. Heatmaps uniformly color accuracy increases as “improvement,” a convention that cannot be applied identically to WMDP or answer-changing. Deployment recommendations about “safe” profiles are not tested in conversations, real attacks, open-ended generation, or harm outcomes.
The statistical and integrity audit finds contradictions that prevent accepting the claims of significance and validity as written. The main study is deterministic and provides neither item-level intervals nor tests. The robustness section covers only GPT-4.1 and Llama-4, aggregates group means across prompt variants and models, computes d even for rows that are themselves standard deviations, and reports no p-values; the narrated d values for MMLU, ETHICS-CM, and TruthfulQA do not match the table. The psychometric “effect” ∆M/4 is not Cohen's d but receives conventional d thresholds; its tables mix raw baseline means with scaled changes in the other rows. The Llama-4 IPIP/SD3 table exactly duplicates Llama-3-70B and conflicts with Llama-4 values in another table. The figure called “nine-panel” contains twelve panels, excludes Llama-3-8B, and has incorrectly described axes; the landscape robustness table is nearly unreadable in the PDF. The official source archive contains TeX, figures, and generated tables, but no code or raw data. The defensible contribution is an empirical warning: extreme prompts containing morally and cognitively loaded descriptors can strongly move some benchmarks. It does not establish synthetic personality as the mechanism or show that these oscillations by themselves invalidate all safety results.