The Kṛṣṇa Yajurveda survives in multiple recensions that share substantial ritual content, yet the degree and distribution of textual overlap across recensions have never been quantified systematically. This paper presents a computational analysis of text reuse across three recensions—the Maitrāyaṇī Saṃhitā (MS), the Kāṭhaka Saṃhitā (KS), and the Taittirīya Saṃhitā (TS)—for two ritual sections (Agnyupasthāna and Punarādhāna), using ICoMa (Intertextuality Collation Machine), a new web-based multi-algorithm collation tool. Five independent similarity algorithms consistently rank MS–KS as the most closely related pair, corroborating the philological consensus. Crucially, the two ritual sections exhibit strikingly different reuse profiles: Punarādhāna shows near-identical MS–KS overlap (up to 93.5%) with sharp divergence from TS, while Agnyupasthāna displays moderate, broadly distributed similarity across all three pairs. These contrasting patterns provide quantitative evidence that different ritual categories followed distinct paths of textual transmission within the Yajurvedic tradition. ICoMa and the experimental data are freely available.

This paper examines semantic similarity and intertextuality in selected texts from the Vedic Sanskrit corpus, specifically the Maitrāyaṇī Saṃhitā (MS) and Kāṭhaka-Saṃhitā (KS). Three computational methods are employed: Word2Vec for word embeddings, stylo package for stylometric analysis, and TRACER for text reuse detection. By comparing various sections of the texts at different granularities, patterns of similarity and structural alignment are uncovered, providing insights into textual relationships and chronology. Word embeddings capture semantic similarities, while stylometric analysis reveals clusters and components that differentiate the texts. TRACER identifies parallel passages, indicating probable instances of text reuse. The computational analysis corroborates previous philological studies, suggesting a shared period of composition between MS.1.9 and MS.1.7. This research highlights the potential of computational methods in studying ancient Sanskrit literature, complementing traditional approaches. The agreement among the methods strengthens the validity of the findings, and the visualizations offer a nuanced understanding of textual connections. The study demonstrates that smaller chunk sizes are more effective for detecting intertextual parallels, showcasing the power of these techniques in unraveling the complexities of ancient texts.