Diachronic word embeddings have become the modern standard for tracking semantic change, yet they have been largely validated on modern, high-resource, and well-segmented languages.
This paper tests whether the paradigm transfers to Sanskrit, an ancient, low-resource language whose phonological fusion (sandhi), morphological inflection, compounding, and polysemy pose a unique challenge.
I assemble a 2.7M-token corpus spanning four canonical periods, recover word boundaries with a neural byte-level sandhi splitter and lemmatizer, and train per-period embeddings across configurations.
To evaluate the system, I curate a validation set from historical scholarship and test recovery directionally with anchor displacement.
Of 21 testable shifts, 19 move in the philologically attested direction (sign test, p=0.00011).
I further show which configuration the language forces and comment on opportunities for improvement.