Reaction-Diffusion AI: An Emergent Language Model Inspired by Bhartrhari's Sphoṭa Theory and Turing's Computational Principles

This paper presents an interdisciplinary framework that reinterprets the ancient Indic concepts of Sphoṭa, Apoha, and Śabda Advaita in the context of modern reaction–diffusion dynamics, neural heterogeneities, and probabilistic inference. Drawing upon seminal works such as Bhartrhari’s Vākyapadīya, Pāṇini’s linguistic theories, and Buddhist Apoha, as well as Western philosophical and computational foundations from Wittgenstein and Turing, we propose a novel reaction–diffusion model for language generation.

Unlike conventional transformer-based architectures that rely on pretrained embeddings, our model autonomously generates language through a learnable diffusion process that mimics the “bursting forth” of meaning and the holistic emergence of linguistic content. The mathematical foundation of our approach is grounded in discrete approximations to reaction–diffusion partial differential equations—drawing inspiration from Turing’s work on morphogenesis—and is augmented by probabilistic cue integration mechanisms similar to the category adjustment model.

Additionally, the model incorporates neural heterogeneities and gap junction dynamics to emulate brain-like connectivity. Experimental evaluations on WikiText-2 demonstrate that our model achieves competitive perplexity and text generation quality, while advanced multivariate analyses reveal that its hidden activations exhibit measurable correlation with human EEG signals. These findings offer promising new directions for developing truly human-like language systems and integrating neurobiological principles with artificial intelligence.