Abstract: The Emperor Marcus Aurelius and the former slave Epictetus represent the social poles of the Roman Empire, yet both are cornerstones of late Stoic thought. This study employs digital humanities tools to investigate how their disparate life experiences and professional roles produced divergent philosophical "signatures" in their extant literature. By analyzing the Lemmatized Ancient Greek Texts (LAGT) corpus, we identify a distinct linguistic polarity: Marcus Aurelius demonstrates a significant preference for physical and cosmological terminology, reflecting a Stoicism centered on the providential order of the universe. Conversely, Epictetus’s lexicon shifts toward terms of ethical practice, pedagogy, and the transformation of the moral will. While Marcus Aurelius employs a more poetically diverse and intellectually wide-ranging vocabulary, Epictetus utilizes a more repetitive, concentrated technical vocabulary suited for the classroom. Despite these differences, a high degree of overlap reveals a "common core" of Stoic concepts shared by both authors, such as the nature of impressions and the primacy of the divine. These findings quantitatively highlight the adaptability of Stoicism, illustrating how a robust philosophical core was reframed to serve both the private reflections of a struggling ruler and the public exhortations of a committed teacher. Technical Context & Methodology This research integrates philology with a computational pipeline to analyze late Stoic literature. The following technical components are included in this repository: Computational Environment: All analyses were performed using Python 3.11. The pipeline utilizes Pandas and PyArrow for high-speed data processing, and the Classical Language Toolkit (CLTK) for part-of-speech tagging and grammatical filtering. Corpus Data: The primary linguistic data was extracted from the Lemmatized Ancient Greek Texts (LAGT) v4.1 dataset, which provides advanced lemmatization via the GLAUx treebank and GreCy models. Lexicographical Mapping: English definitions were integrated using the LSJ Dictionary (JSON v1.0.0). A custom normalization pipeline was used to standardize lemmata into Normalization Form Canonical Composition (NFC). Lexical Metrics: Vocabulary richness was assessed using Type-Token Ratio (TTR), Guiraud’s Index (R) to compensate for corpus size differences, and the percentage of hapax legomena (terms appearing only once). Generative AI Integration: A Gemma-3-27b-it model was utilized for the thematic classification and translation of 5,371 sentences. Sentences were tagged into the traditional Stoic tripartite division—Logic, Physics, or Ethics—based on the framework established by Pierre Hadot. Visualizations: The included scripts generate Lexical Volcano Plots (mapping total relative frequency against authorial skew) and Weighted Word Clouds that distinguish between author-specific signatures and the "Shared Stoic Core". Files included in this record: Supplementary File S1: Complete Python computational pipeline, README, and requirements.txt. Supplementary File S2: stoic_master_comparison.tsv containing comprehensive lemma frequencies and delta-RF values. Supplementary File S3: Statistical visualizations, including KDE overlap plots and delta-RF histograms. Supplementary File S4: Thematic analysis CSV containing 5,371 sentences with original Greek, English translations, and AI-generated thematic tags.