Update README.md

README.md

@@ -35,7 +35,7 @@ A scalable family of brain–AI architectures designed for real-time EEG interpr
   The **Professional line**, built for clinical and research-grade use.  
   These variants share the same naming logic but are trained on **medical datasets** for high-precision diagnostic performance and regulatory compliance.
 
-### **Vbai Family — Open-Source Imaging and Brain Models**
+### **Vbai Family — Imaging and Brain Models**
 A collection of open-access models for neurological imaging, analysis, and 3D brain modeling.
 
 - **Vbai-x.x (e.g., Vbai-2.5, Vbai-3.0)**  
@@ -48,11 +48,40 @@ A collection of open-access models for neurological imaging, analysis, and 3D br
   Each version (v1, v2, etc.) represents incremental improvements in precision, processing speed, and multimodal fusion.  
   These models can directly process 3D brain MRI inputs to detect structural and degenerative anomalies in real time.
 
+### **Tbai Family — Compact Neuro-Linguistic Models**
+A family of lightweight transformer-based models designed to bridge natural language and neural data interpretation.  
+Tbai models function similarly to small-scale tokenizers like **T5**, optimized for concise reasoning and domain-specific understanding.
+
+- **Tbai-HF-v.x (e.g., Tbai-HF-v1, Tbai-HF-v2)**  
+  Models trained for **neuroimaging-based interpretation**.  
+  Capable of generating analytical or descriptive text directly from **brain imaging data** such as MRI or CT scans.  
+  Each version enhances domain adaptation, linguistic fluency, and cross-modal comprehension between visual brain patterns and textual insight.
+
+- **Tbai-NAI-v.x (e.g., Tbai-NAI-v1, Tbai-NAI-v2)**  
+  Models specialized for **EEG-based cognitive commentary**.  
+  Designed to interpret brainwave signals and translate them into meaningful linguistic explanations.  
+  Subsequent versions expand accuracy in recognizing temporal EEG events, emotional states, and neurological anomalies.
+
+### **Lbai Family — Large-Scale Cognitive Reasoning Models**
+A suite of large language models (LLMs) trained on **medical, neuroscientific, and cognitive** corpora to perform advanced reasoning and diagnostic dialogue.
+
+- **Lbai-x-params_billion (e.g., Lbai-1-7B, Lbai-2-7B, Lbai-1-30B)**  
+  Each model’s identifier specifies its parameter scale in billions.  
+  These models emulate expert-level reasoning, trained to **think and respond like medical professionals**, with a strong focus on neuroscience, neurophysiology, and clinical linguistics.  
+  Lbai models aim to achieve near-complete domain comprehension across **brain science, medicine, and cognitive AI research**.
+
 ---
 
 #### **Summary**
-The **bai** and **Vbai** families represent two complementary branches of the same neuro-AI ecosystem — the former focused on **real-time EEG cognition**, and the latter on **advanced neuroimaging and 3D brain analysis**.  
-Both are designed to scale functionally, evolve through continuous versioning, and integrate seamlessly into broader cognitive AI frameworks.
+The **bai**, **Vbai**, **Tbai**, and **Lbai** families form a cohesive ecosystem within the **Neurazum Neuro-AI architecture**.  
+
+- **bai** models specialize in **EEG signal intelligence**, handling real-time neural data for cognitive and diagnostic applications.  
+- **Vbai** models extend this capability into **neuroimaging**, providing open-source frameworks for 2D and 3D brain analysis.  
+- **Tbai** models serve as the **linguistic interpreters**, translating neural activity and brain imagery into natural language insights.  
+- **Lbai** models deliver **deep reasoning and expert cognition**, acting as medical-grade large language models trained for neuroscience and diagnostics.  
+
+Together, they define a unified hierarchy of **neural understanding**, where data from brain signals and imaging seamlessly integrates with language and reasoning.  
+This layered system enables scalable, interpretable, and clinically meaningful intelligence across all domains of brain–AI interaction.
 
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