Meaning-Oriented Language Model.
A next-generation training architecture that transcends words to deeply grasp context and conceptual relationships.
The Meaning Layer
Unlike traditional models, this study adds a 'meaning layer' rather than just teaching text. It forms an ID-referenced concept network with structured artifacts like events, claims, and evidence. The model learns to see a sentence not as a string of words, but as a semantic graph.
Cultural Context
In Turkish and local scenarios, details like place, behavior, and tone are decisive. By enriching concept cards with local references, the model ensures its responses are not just fluent, but culturally consistent, traceable, and capable of saying 'I am not sure'.
Data Efficiency
This is a research experiment: measuring how modeling 'meaning' schematically and training it with text affects data efficiency and generalization. We aim for more with less by embedding a semantic architecture within the text.
Current Status
Our goal is to develop a model infrastructure that can learn language not just as text, but with layers of 'meaning and context'.
Last Update: T-Minus 2 Days
Active Development
- Establishing data backbone: Creating an auditable and organized data flow.
- Enriching semantic layers: Building a structure for clear conceptual representation.
- Validating pilot runs: Testing the stability of the production-training chain.
What's Next?
“We will scale data volume by integrating larger text sources and move into long-term training phases.”
Our approach is not about more data, but more meaning within the data.
TTS Model Research
Advanced architectural research on natural and emotional speech synthesis.