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README.md

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-# Reactive AI Page
-Working on our own idea of Reactive Neural Networks (RxNN) - special kind of memory-augmented neural networks, that keeps state/memory between interactions/sequences instead of between tokens/elements in sequence and provides reactive communication patterns.
+# Reactive AI
+We are working on our own idea of Reactive Neural Networks (RxNN) - special kind of memory-augmented neural networks, that keeps state/memory
+between interactions/sequences instead of between tokens/elements in sequence and provides reactive communication patterns.
+
+Our primary architecture - **Reactor** - is planned as the first _**awareness AGI model**_, that's modelling awareness as an _Infinite Chain-of-Thoughts_,
+connected to _Short-Term and Long-Term Memory_ (_Attention-based Memory System_) and _Receptors/Effectors_ systems for real-time reactive processing.
+It will be able to constantly and autonomously learn from interactions in _Continouos Live Learning_ process.
+
+While the **Reactor** is the main goal, it's extremely hard to achieve, as it's definitely the most advanced neural network ensemble ever.
+
+That's why we designed simplified architectures, for incremental transformation from language/reasoning models to awareness model:
+- **Reactive Transformer** is introducing _Attention-based Memory System_ and adding _Short-Term Memory_ to Transformer language models
+- **Preactor** is adding _Long-Term Memory_ and ability to learn from interactions
+
+We are currently working on **Reactive Transformer Proof-of-Concept - RxT-Alpha**, that will be published soon
 
 More info soon
 
 ## RxNN Platform
-We are working on complete Reactive Neural Networks development framework - [RxNN github](https://github.com/RxAI-dev/RxNN)
+We are working on complete Reactive Neural Networks development framework - [RxNN github](https://github.com/RxAI-dev/RxNN)
+
+## Additional Research
+- **Sparse Query Attention** - the most cost-effective GQA variant, reducing training time/cost by ~10%. Research in progress