This preprint studies automated apparent-personality prediction in simulated asynchronous video interviews. It does not induce a personality in an LLM; it attempts to reproduce scores assigned to candidates by human observers. The pipeline uses OpenFace to extract intensities for 17 facial action units, finds keyframes through whole-frame pixel changes, and selects a trait-specific AU subset through simulated annealing guided by an LSTM's MSE. GPT-4o converts seven-frame AU windows into facial-movement descriptions and iteratively summarizes them. Meta-Llama-3.1-8B-Instruct then fuses that text with an automatic transcript; LoRA and a linear head are trained separately for Honesty-Humility, Extraversion, Agreeableness, and Conscientiousness. The study uses AVI-6 and reports 644 participants split by person into 450 training, 64 validation, and 130 test cases. The official dataset paper clarifies the labels: 12 raters with nine hours of training applied BARS measures, with eight independent ratings per participant and four items per trait; inter-rater reliability ranges from ICC(1,8)=0.61 to 0.83. These are aggregated observer judgments of behavior shown in an interview response, not HEXACO self-reports or independent measurements of stable traits. The method obtains MSEs of 0.1555, 0.1380, 0.1597, and 0.1077, averaging 0.1402; mean MAE is 0.2982 and Pearson correlations range from 0.4654 to 0.7258. Against the paper's Longformer text-only baseline, MSE improves by 14.33% to 48.83%. The most informative ablation is more mixed: adding facial descriptions to the same architecture improves H, X, and C but worsens Agreeableness relative to the no-AU variant, from MSE 0.1390 to 0.1597. It is not the strongest reported AVI-6 result either. The challenge publication reports average MSEs of 0.12284 and 0.13724 for its top two systems, although this is not a fully controlled comparison because the documents disagree without explanation between 644/646 participants and 450/452 training cases. The defensible contribution is architectural: on this split and with these generated features, fusion helps reproduce observer impressions for three of four traits relative to the direct ablation. It does not show that AUs reveal true personality or validate post-hoc labels such as sincerity, empathy, or control. The OpenFace-to-GPT-4o-to-summary-to-Llama chain creates several opportunities for information loss or invention; no human semantic-fidelity study is reported, and the authors observe hallucinated temporal dynamics with single-frame windows. Tables provide one estimate without intervals, seed distributions, or a naive baseline. Seed counts and values are missing, while GPT-4o uses service defaults without a seed. The simulated-annealing pseudocode also fails to update the state when a worse solution is accepted. No code, derived data, predictions, adapters, or reproducible environment is released. Most importantly, despite the hiring frame, the paper reports no error analysis by gender, age, race, skin tone, camera quality, or intersections, and no biometric-privacy, consent, adverse-impact, job-performance-validity, or real-deployment study. It should be read as a benchmark experiment on apparent personality, not evidence for evaluating or hiring candidates.
Research question
Does the prediction of apparent personality scores assigned by raters in AVI-6 improve when textual descriptions of facial action units generated by GPT-4o are fused with the transcripts using an LLM fine-tuned with LoRA?