# A P=aninian Foundation for Indic Language Processing

arXiv: https://arxiv.org/abs/2606.24172 · HTML: https://mlsanity.com/paper/2606.24172/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 58 references that are in the map.
- First posted on arXiv 23 Jun 2026.

## Builds on (most cited first, 13 of 13)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Emerging Cross-lingual Structure in Pretrained Language Models](https://mlsanity.com/paper/1911.01464/) (2019)
- [Large Language Models Share Representations of Latent Grammatical Concepts Across Typologically Diverse Languages](https://mlsanity.com/paper/2501.06346/) (2025)
- [When Is Multilinguality a Curse? Language Modeling for 250 High- and Low-Resource Languages](https://mlsanity.com/paper/2311.09205/) (2023)
- [INDIC QA BENCHMARK: A Multilingual Benchmark to Evaluate Question Answering capability of LLMs for Indic Languages](https://mlsanity.com/paper/2407.13522/) (2024)
- [One Model is All You Need: ByT5-Sanskrit, a Unified Model for Sanskrit NLP Tasks](https://mlsanity.com/paper/2409.13920/) (2024)
- [Tokenization Matters: Improving Zero-Shot NER for Indic Languages](https://mlsanity.com/paper/2504.16977/) (2025)
- [MorphTok: Morphologically Grounded Tokenization for Indian Languages](https://mlsanity.com/paper/2504.10335/) (2025)
- [Cross-lingual Transfer in Programming Languages: An Extensive Empirical Study](https://mlsanity.com/paper/2310.16937/) (2023)
- [LEVOS: Leveraging Vocabulary Overlap with Sanskrit to Generate Technical Lexicons in Indian Languages](https://mlsanity.com/paper/2407.06331/) (2024)
- [Cross-Linguistic Transfer in Multilingual NLP: The Role of Language Families and Morphology](https://mlsanity.com/paper/2505.13908/) (2025)
- [Sanskrit Segmentation Revisited](https://mlsanity.com/paper/2005.06383/) (2020)
- [A Benchmark Corpus and Neural Approach for Sanskrit Derivative Nouns Analysis](https://mlsanity.com/paper/2010.12937/) (2020)

## Abstract (arXiv)

More than a billion people communicate in Indic languages, yet the natural language processing infrastructure serving them remains fragmented and underdeveloped. The cause is structural: the field organizes its tools and benchmarks around individual languages or small subsets of genealogical language families, building separate analyzers, parsers, and datasets for each language and starting over for the next. This overlooks a deep regularity. Through more than two millennia of convergence around Sanskrit, Indic languages came to share a morphosyntactic architecture formalized in P=anini's grammar, the Ast=adhy=ay=i. This cuts across genealogical lines, uniting languages through a common framework. We argue that this P=aninian framework supplies a unifying computational architecture the field has lacked, and that benchmarks grounded explicitly in it would make Indic language systems more accurate, more data-efficient, and more transferable, effectively merging many apparently disparate and sparse Indic language resources into a single high-resource metalanguage bedrock. We propose a four-part benchmark suite to render this shared architecture explicit, measurable, and ready to be leveraged for practical applications. Moreover, we underscore the question it raises for interpretability research: whether neural models trained on these languages come to represent P=anini's categories on their own.