ARCHIVES

1. P. Kiparsky. On the architecture of Pan.ini’s grammar. In G. Huet, A. Kulkarni, and P. Scharf, editors, Sanskrit Computational Linguistics, volume 5402 of Lecture Notes in Computer Science, pages 33 94. Springer, 2009.
2. G. Huet. Sanskrit heritage engine: A computational platform for Sanskrit processing. Technical report, INRIA, 2009.
3. Anonymous. Is Sanskrit the most tokene cient language? A quantitative study using GPT, Gemini, and SentencePiece. arXiv preprint arXiv:2601.06142, 2026.
4. A. Lundin et al. MorphBPE: A morphologyaware tokenizer bridging linguistic complexity for e cient LLM training across morphologies. arXiv preprint, 2025.
5. ArthaLabs. Panini tokenizer: Grammarrst Sanskrit tokenization. Hugging Face, 2025. https://huggingface.co/ArthaLabs/ panini-tokenizer.
6. J. Sandhan et al. SanskritShala: A neural Sanskrit NLP toolkit with web-based interface for pedagogical and annotation purposes. In Proceedings of ACL 2023 System Demonstrations, 2023.
7. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, . Kaiser, and I. Polosukhin. Attention is all you need. In Advances in Neural Information Processing Systems 30 (NeurIPS 2017), 2017.
8. H. Wang et al. TokenFormer: Rethinking transformer scaling with tokenized model parameters. arXiv preprint arXiv:2410.23168, 2024.
9. L. C. Lamb, A. d’Avila Garcez, M. Gori, M. Prates, P. Avelar, and M. Vardi. Graph neural networks meet neural-symbolic computing: A survey and perspective. arXiv preprint arXiv:2003.00330, 2020.
10. R. Singh et al. Sanskrit LLM: Proving the need for a Panini sutra rule-based AI model by benchmarking the capabilities of existing LLMs. In Springer, 2025.
11. J. Sandhan, R. Singha, N. Rao, S. Samanta, L. Behera, and P. Goyal. TransLIST: A transformer-based linguistically informed Sanskrit tokenizer. In Findings of EMNLP 2022, pages 6902 6912, 2022.