This paper reports the empirical capstone of the Devavāṇī-Derived Interpretable Network (DDIN) research program, focusing on the emergence of language-scale semantic organization in neuromorphic architectures. We identify and resolve the Sparsity Barrier, a regime where low input density relative to reservoir dimensionality prevents attractor fusion, through the mechanism of Semantic Pressure . By scaling the architecture to the full 2,000-root Pāṇinian Dhātupāṭha corpus using 1024-neuron Adaptive Exponential Leaky Integrate-and-Fire (AdEx) reservoirs stabilized by per-neuron BCM homeostasis, we achieve an Adjusted Rand Index (ARI) of 0.9758 . Key findings include: Phase Transition : Identification of a first-order phase transition in semantic attractor resolution at a critical density of o bj ec tO bj ec t ρ ≈ 2.0 . Zero-Shot Inference : Demonstration of zero-shot attractor assignment for novel, fabricated roots driven entirely by the 23-dimensional acoustic channel under maximal prior uncertainty. Biophysical Stability : Resolution of the Epileptiform Synchrony Limit (ESL) through homeostatic regulation, enabling continuous asynchronous spiking across language-scale datasets. The work establishes that phonosemantic organization in spiking reservoirs is not a linear artifact of dataset size but a structural emergence governed by representational density and articulatory grounding.
Dr. Amit Kumar (Fri,) studied this question.