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 title: README
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+short_description: Reactive AI - Reactive Neural Networks and Event-Driven AI
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 # 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.
+We are working on our own ideas of Reactive Neural Networks (RxNN) and Event-Driven AI, advancing from language models to AGI awareness models.
 
+## Reactive Neural Networks and Event-Driven AI
+Reactive Neural Networks (RxNN) are memory-augmented neural networks with higher levels of recurrence (inter-sequence vs. intra-sequence in RNNs),
+focused on processing single interactions with access to previous interactions via memory layers. We call this _**event-driven real-time processing**_
+to distinguish it from classical _data-driven processing_ of the full conversation history in each interaction. This difference is crucial in case
+of AGI and awareness - the key feature of humans awareness, is that we remember what we were doing 10 mins ago, without recalling the whole-day history - we
+are working in real-time - just like event-driven _Reactive Neural Networks_.
+
+In Event-Driven AI models are processing the data in reaction to environment or internal events, and are emitting other response events as a result.
+Processing of input and output events by the model is called the interaction. Event or an interaction could occur in any point in continous time. Models
+have to be stateful and remember the data between the interactions.
+
+_**Strong Reactive Neural Networks**_ like **Reactor** could emit and listen to its internal events, while the _**Weak Reactive Neural Networks**_ are
+working only on environment events.
+
+## Reactor AGI
 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.
 
+## Reactive Language Models (RxLM)
 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
+### RxT-Alpha Open Research
+We are currently working on **Reactive Transformer Proof-of-Concept - RxT-Alpha**, especially on the new reinforcement learning stage - **Memory Reinforcement Learning**,
+that's required for our reactive models, between the _Supervised Fine-Tuning_ and _Reinforcement Learning from Human Feedback for reactive models (RxRLHF)_. The research
+is open, we are publishing the results of all separate steps, just after finishing them.
+
+The Proof-of-Concept includes 3 small scale models based on **Reactive Transformer** architecture:
+- RxT-Alpha-Micro (~11M params) - pre-training and fine-tuning finished, MRL in progress - training based on small synthetic datasets
+- RxT-Alpha-Mini (~70M params) - pre-training in progress - training on real data
+- RxT-Alpha (~530M/0.5B params) - pre-training in progress - training on real data
+
+All the models have theoretically infinite context, limited only for single interaction (message + response), but in practice it's limited by short-term memory
+capacity (it will be improved in Preactor). Limits are:
+- RxT-Alpha-Micro - 256 tokens for single interaction, 6 * 256 for STM size (768kb), expected length of a smooth conversation min. ~4k tokens
+- RxT-Alpha-Mini - 1024 tokens for single interaction, 8 * 1024 for STM size (8mb), expected length of a smooth conversation min. ~16k tokens
+- RxT-Alpha - 2048 tokens for single interaction, 12 * 2048 for STM size (50mb), expected length of a smooth conversation min. ~32k tokens
 
-More info soon
 
 ## RxNN Platform
 We are working on complete Reactive Neural Networks development framework - [RxNN github](https://github.com/RxAI-dev/RxNN)