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| title: README | |
| emoji: 🦀 | |
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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. | |
| 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) | |
| ## Additional Research | |
| - **Sparse Query Attention** - the most cost-effective GQA variant, reducing training time/cost by ~3-10% with similar performance. Research in progress |