A P\={a}ninian Foundation for Indic Language Processing

A new proposal argues that the ancient Sanskrit grammar of Pāṇini can serve as a unified computational foundation for processing modern Indic languages, a family spoken by more than a billion people but served by fragmented natural language processing tools. The work, posted to arXiv on June 23, 2026, contends that the field’s practice of building separate analyzers and datasets for each language ignores a deep structural regularity [1]. Over more than two millennia of convergence around Sanskrit, Indic languages came to share a morphosyntactic architecture formalized in Pāṇini’s grammar, the Astādhyāyī, which the authors say cuts across genealogical lines [1]. The paper proposes a four-part benchmark suite to make this shared architecture explicit and measurable for practical applications [1]. It also raises a question for interpretability research: whether neural models trained on these languages come to represent Pāṇini’s categories on their own [1]. The proposal arrives as the machine-learning community increasingly links research papers to executable demos. Since November 2022, arXiv has integrated with Hugging Face Spaces, allowing authors and the community to attach open-source demos directly to a paper’s abstract page [5][6]. Demos are built using tools such as Gradio and Streamlit and can be linked by including a paper’s URL in a Space’s README file or by associating a model on the Hugging Face Hub with the paper [7]. The integration aims to increase reproducibility and let a wider audience inspect model behavior without writing code [5]. Large language models remain the dominant paradigm for natural language processing tasks such as text generation, trained with self-supervised learning on vast text corpora [9]. Recent entrants have demonstrated that competitive performance can be achieved at sharply lower cost. DeepSeek, a Chinese AI firm founded in 2023, reported training its V3 model for roughly US$6 million, compared with an estimated US$100 million for OpenAI’s GPT-4 in 2023, using about one-tenth the computing power of Meta’s comparable Llama 3.1 model [8]. DeepSeek’s models are released under open-source licenses, though training data is not openly licensed [8]. Retrieval-augmented generation, introduced in a 2020 paper by researchers at Meta AI, has become another key technique for grounding language model outputs in external knowledge [10]. The lead author of that paper, Douwe Kiela, later served as Head of Research at Hugging Face and is now a research scientist director at Google DeepMind [10]. The Pāṇinian proposal does not introduce a new model but instead argues for a shared metalanguage bedrock that could merge many sparse Indic language resources into a single high-resource framework [1]. Whether the research community adopts such a linguistically grounded benchmark suite remains an open question, but the arXiv and Hugging Face infrastructure already provides a pathway for releasing accompanying demos and models that could make the approach testable at scale [5][7].