Update README.md

README.md

@@ -35,6 +35,9 @@ Our primary architecture - **Reactor** - is planned as the first _**awareness AG
 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.
 
+> Reactor architecture details were analysed by 30 state-of-the-art LLM/Reasoning models that rated it's potential
+> to reach the AGI as ~4.35/5
+
 ## 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.
 
@@ -44,7 +47,7 @@ That's why we designed simplified architectures, for incremental transformation
 
 ## RxLM vs LLM advantages
 Processing single interactions in real-time by **Reactive Language Models** leads to **revolutional** improvements in inference speed/cost:
-- LLM inference costs are increasing exponentially with conversation length (accumulated for each next message), because of full dialog history processing
+- LLM inference costs are increasing quadratically with conversation length (accumulated for each next message), because of full dialog history processing
 - RxLM inference costs are linear, depending only on single interaction tokens (not accumulated) - each next interaction is `number of steps` times cheaper than for LLM
 - same for inference speed - LLM has to process full history, while RxLM only single message (only first interaction could be slower because of encoder/memory attention overhead)