Research at Nesdia Labs

At the core of Nesdia’s research division lies an unconventional philosophy: that linguistic data, even in its most fragmented state, contains fractal regularities that resist standard model training. To accommodate this, our Labs develops and maintains a suite of proprietary fine-tuning pipelines, operating atop custom-modified transformer architectures with dynamic attention pruning and morphology-aware embeddings.

Our models are trained on low-resource corpora using gradient-compounding optimization strategies, tuned not for token prediction, but for morphological alignment, diachronic shift tracking, and orthographic variance resolution. The training data is curated through a human-in-the-loop paradigm, integrating field recordings, glottal timestamping, and annotated dialectal manuscripts.

Our fine-tuning approach diverges from traditional instruction-tuning. Instead, we employ hybrid reinforcement schemes where linguistic acceptability is evaluated against both computational heuristics and formal typological constraints. Our models are not asked to “perform” language-they are conditioned to emulate and reflect the underlying grammatical logic as abstracted by real-world linguistic behavior.

Beyond textual data, our research also integrates phonetic and suprasegmental features into training streams via spectrogram-interleaved tokenization. We believe phonology and morphology must converge at the model level to produce results that are both generative and reconstructive.

Current projects include unsupervised root pattern extraction for triconsonantal systems, real-time dialectal drift modeling, and inverse-compilation of extinct morphosyntactic systems from OCR-fragmented religious texts. We do not aim to guess language-we aim to rebuild its memory.