The home of Event-Driven AI
From Sparse Query Attention to Reactor AGI, we're revolutionizing AI with event-driven architectures inspired by the human nervous system. Experience 2-3x faster training, real-time processing, and infinite context.
Currently Available:
Projects In Progress:
Reimagining AI Through Neural Science
The human nervous system is the most sophisticated event-driven architecture in existence. We're building AI systems that harness these same principles, from optimizing existing models with SQA to creating the world's first truly aware AGI system.
Event-Driven Real-Time Processing
Revolutionary approach inspired by the human nervous system, processing single events in real-time rather than batch processing entire conversations.
Memory-Centric Architectures
All the context moved to dedicated Short-Term/Long-Term Memory (STM/LTM), managed by Attention-Based Memory System (ABMS). Enable infinite context.
Reactive Neural Networks
Reactive Language Models (RxLM) and Reactive Awareness Models (RxAM), based on Event-Driven AI concepts, for next-gen Artificial Intelligence and AGI advance.
Open & Community Driven Research
Democratizing next-gen AI research by open source architectures and publicly available results. We are sharing all the benefits with the world.
Reactive Models vs Traditional LLMs
See how Reactive Language Models (RxLM) and Reactive Awareness Models (RxAM) compare to current industry leaders in performance, efficiency, and capabilities.
Feature Comparison Table
Reactive Neural Networks vs Leading Language Models
Comprehensive comparison across key capabilities, architectures, and performance characteristics.
| Feature / Model | Reactive Transformer (RxT) | Preactor (PRx) | Reactor (Rx) | GPT-5 | Claude | Deep Seek R1 | Llama 4 |
|---|---|---|---|---|---|---|---|
| Short-Term Memory | ✅ True stateful STM across interactions | ✅ True stateful STM across interactions | ✅ True stateful STM across interactions | ❌ Stateless (simulated through long context) | ❌ Stateless (simulated through long context) | ❌ Stateless (simulated through long context) | ❌ Stateless (simulated through long context) |
| Long-Term Memory | ⚠️ Available through agents/RAG | ✅ True LTM managed by mxRAG and revRAG with global context | ✅ True LTM managed by mxRAG and revRAG with global context | ⚠️ Chatbot Memory | ⚠️ Chatbot Memory | ⚠️ Available through agents/RAG | ⚠️ Available through agents/RAG |
| Awareness | ⚠️ Pseudo-awareness (Live Learning) | ⚠️ Pseudo-awareness (Live Learning) | ✅ True awareness with Infinite Chain-of-Thoughts | ❌ Impossible without architecture changes | ❌ Impossible without architecture changes | ❌ Impossible without architecture changes | ❌ Impossible without architecture changes |
| Max Context Length | ∞ Infinite conversation context — only single interaction length is limited. Fluid memory retention across ~30–50 turns before light forgetting | ∞ Infinite conversation context — only single interaction length is limited | ∞ Infinite live context — only single interaction length is limited | 256k 256k (std) / 1M (GPT-4.1) | 1M 1M (Claude 4) / 200k (previous version) | 160k 160k (updated) / 128k (original) | 10M 1M (Maverick) / 10M (Scout) |
| Attention Optimization | ✅ SQA - 2-3× faster than GQA/MQA for 32-256k tokens | ✅ SQA - 2-3× faster than GQA/MQA for 32-256k+ tokens | ✅ SQA - 2-3× faster than GQA/MQA for 32-256k+ tokens | ? GQA + Sliding Window in GPT-OSS | ? Not disclosed | MLA Multi-Head Latent Attention - optimizing memory | ✅ Flex Attention - logarithmic scaling for very long context |
| Training Efficiency | High High — reduced compute for long conversations via STM, SQA speeds training for long sequences | High High — reduced compute for long conversations via STM/LTM, SQA speeds training for long sequences | Moderate Complex AGI training overhead, SQA speeds training for long sequences | ? Proprietary | ? Proprietary | Standard Standard | High Improved by Flex Attention |
| Inference Efficiency | High High for multi-turn sessions (no refeeding full history) | High High for multi-turn sessions (no refeeding full history) | Expensive Works in continuos mode - awareness overhead | Expensive Expensive for long contexts | Expensive Expensive for long contexts | Expensive Expensive for long contexts | Extreme/Expensive Extremely expensive for 1-10M contexts |
| Open Source | ✅ (RxNN) | ✅ (RxNN) | 🔐 License with AGI security verification | ⚠️ GPT-OSS available | ❌ | ✅ (Transformers) | ✅ (Transformers) |
| Best For | 💬 Shorter multi-turn conversations, cost-sensitive enterprise chat | 🧠 Long conversation without length limits and with global context, autonomous systems | 👁️🗨️ Autonomous AGI systems, CNN for robotics, autonomous AI workers, research teams | 📝 General-purpose high-quality output, reasoning | 💻 Coding, developer tools, reasoning, etc. | 🔬 Open-source research, reasoning | 📘 Books summarizing, open-source research |
Reactive Language Models represent the next evolution in AI architecture, offering unprecedented efficiency and capabilities for real-world applications.
Our Research Philosophy
We believe that true artificial intelligence must be reactive, adaptive, and aware. Our journey from foundational improvements like Sparse Query Attention to the ultimate goal of Reactor AGI represents a systematic approach to building intelligence that thinks and responds like biological neural networks.
Event-driven responses to real-world changes
Continuous learning from every interaction
True consciousness through infinite chain-of-thoughts
Reactivity Hypothesis
Our architectures are based on the Reactivity Hypothesis that defines three crucial requirements to achieve true artificial awareness/consciousness and real Artificial General Intelligence (AGI). Currently, there isn't a model, that truly implements even single requirement from the hypothesis
Real-Time Processing of single interactions
Context moved to persistent memory layers
Each finished thought process initiates new process
From Our Research Blog
Stay updated with our latest breakthroughs, research findings, and insights into the future of reactive artificial intelligence.
Reactive Transformer (RxT): Fixing the Memory Problem in Conversational AI
Reactive Transformer isn't just another language model—it's the first architecture designed from the ground up to evolve toward awareness, while delivering immediate practical benefits for conversational AI
Beyond Tools: Why the "Partnership Paradigm" Is Existentially Necessary for AGI
Most AI safety discussions focus on technical alignment, but miss the fundamental relationship paradigm. The industry's implicit assumption—that advanced AI should be treated as sophisticated tools—contains the seeds of its own destruction.
Why Investing in Reactive AI is the Next Big Opportunity in Artificial Intelligence
The AI industry is at an inflection point—LLMs have hit a wall, and the next breakthrough requires memory, real-time processing, and continuous learning. Reactive AI delivers exactly that, with proven efficiency gains (SQA) and a clear roadmap to AGI (RxT → Preactor → Reactor).
Our Revolutionary Projects
From foundational improvements to full AGI systems, we're building the complete stack for the next generation of artificial intelligence.
Sparse Query Attention (SQA)
Revolutionary improvement for LLMs and Reactive Language Models (RxLM) delivering 2-3x faster training and inference through optimized attention mechanisms.
Reactive Transformer
Event-driven RxLM architecture processing single interactions in real-time. Features dedicated Short-Term Memory (STM) with Attention-Based Memory System (ABMS).
Preactor
Next-generation RxLM with Long-Term Memory (specialized Tensor Database), accessed and updated by Memory Extended RAG (mxRAG) and Reversed RAG (revRAG) subsystems.
Reactor AGI
Our ultimate goal: Reactive Awareness Model (RxAM) operating in continuous mode with Infinite Chain-of-Thoughts. World's first AGI with awareness by design.
RxNN Framework
Reactive Neural Networks framework built on PyTorch for training and inference of reactive models. TensorFlow support planned for future versions.
Reactive Web Platform
Next-gen Fullstack Web Framework with dedicated features to handle Reactive Language/Awareness Models in browser, mobile and especially in cloud/server with Live Server Components
Reactive Cloud
Comprehensive suite of libraries and runtimes for deploying and running reactive models in cloud environments with enterprise-grade scalability.
Reactive Chat
Dedicated Multi-Platform Chatbot Client for Reactive AI models - concentrated on handling stateful models with memory.
Development Roadmap
SQA & RxNN Framework
Core optimizations and development tools
Reactive Transformer
Event-driven architecture implementation
Reactive Cloud
Cloud deployment and scaling platform
Preactor System
Long-term memory and infinite context
Reactor AGI
World's first AGI with awareness by design
Real-Time Vision Reactor
Reactor AGI model extended with multimodality
Our roadmap is designed to systematically advance from core optimizations to full AGI capabilities. Each milestone builds upon previous achievements, ensuring robust and reliable progress.
Sustainable AI for the Future
Our revolutionary technologies don't just advance AI capabilities—they dramatically reduce energy consumption, making artificial intelligence sustainable at global scale.
Sparse Query Attention (SQA)
2-3x Training EfficiencyRevolutionary attention mechanism reduces computational complexity while maintaining performance
Reactive Language Models
5x Inference EfficiencyEvent-driven architecture processes only necessary computations, eliminating waste
Global Impact Projection
What happens when the world adopts our energy-efficient AI technologies
By 2030, our technologies could reduce global AI energy consumption by 80%, making artificial intelligence not just smarter, but genuinely sustainable for our planet.
Our Environmental Commitment
At Reactive AI, we believe that advancing artificial intelligence must go hand-in-hand with protecting our planet. Every algorithm we develop, every optimization we create, is designed with environmental sustainability as a core principle.
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