The AI Industry Has Hit a Scaling Wall.
The transformer architecture, while powerful, is a dead end. Its quadratic scaling (O(n²)) makes it exponentially expensive, and its fixed context window creates "AI amnesia," preventing true long-form reasoning. Its time to move past a paradigm that is fundamentally limited.
Our Flagship OMNI Suite
Infinite Context & Memory
Our flagship language model, OMNI-λ, will be able to process entire texts and conversations with constant memory usage, enabled by our stateful persistent memory architecture. This unlocks truly personal AI assistants that remember every interaction.
True Omni-Modal Unification
FSE is the only architecture designed to natively process language (OMNI-λ), vision (OMNI-Φ), and generation (OMNI-Δ) within a single, shared computational field, enabling holistic reasoning impossible for stitched-together systems.
A New Era of Efficiency
By replacing quadratic self-attention with linear field evolution (O(n)), we solve the scaling crisis. Our models are designed to be smaller, faster, and cheaper to run, making powerful AI accessible for on-device applications.
Beyond Language: A Universal Field Solver
FSE is not just a better AI architecture; it is a general-purpose computational platform. Its unique ability to model complex, continuous systems can unlock a new frontier of applications in industries that demand a deeper, physics-based understanding of their data, complementing and extending the capabilities of today's leading AI solutions.
Geophysical Modeling
Model complex physical systems like pyroclastic flows or seismic waves to predict mineral deposits and geological events, replacing slow, classical simulators with a high-speed, learnable surrogate.
Autonomous Systems
Process LiDAR, radar, and camera data into a single, unified 4D spatiotemporal field to learn traffic-flow dynamics and predict vehicle behavior with unprecedented accuracy and efficiency.
Computational Biology
Simulate protein folding and drug interactions by treating molecular concentrations as continuous fields, providing a powerful new engine for drug discovery and personalized medicine.
Technical Specifications
Computational Complexity
O(n) linear scaling vs O(n²) quadratic for transformers
Context Window
Infinite context with constant memory usage
Architecture Type
Continuous field-based computation using Float-Native State Elements
Patent Status
Patent pending neural architecture (Filed 2025)
Native Framework
FlowField™ - purpose-built computational framework for FSE
Multi-Modal Support
Native unified processing across text, vision, and generation
Evidence of a New Learning Paradigm
FSE models exhibit unique, self-stabilizing learning dynamics. Instead of brittle optimization, our models go through phases of grappling with chaos before finding a more profound, organized state. Below is live data from our OMNI-λ training run.
Consistent Convergence: A steady, downward trend in perplexity proves the model is successfully learning the fundamental patterns of language.
Emergent Organization: An upward trend in internal field consistency shows the architecture self-organizing as it learns.
Fundamental Stability: The model's core field magnitudes reach a stable plateau, proving the system is robust and not at risk of numerical explosion.
FSE vs Traditional Architectures
| Feature | FSE Architecture | Transformer Architecture |
|---|---|---|
| Computational Complexity | O(n) Linear | O(n²) Quadratic |
| Context Window | Infinite | Fixed (128K-2M tokens) |
| Memory Usage | Constant | Scales with context |
| Gradient Flow | Continuous optimization | Discrete backpropagation |
| State Persistence | Stateful continuous memory | Stateless per-sequence |
| Multi-Modal Processing | Native unified fields | Separate architectures stitched together |
Frequently Asked Questions
What is Float-Native State Elements (FSE)?
FSE is a patented neural architecture that uses continuous field dynamics instead of discrete operations, enabling O(n) linear scaling and infinite context processing through stateful persistent memory.
How does FSE achieve infinite context?
Through stateful persistent memory architecture that maintains constant memory usage regardless of context length, unlike transformers that scale quadratically with context size.
What makes FSE different from Neural ODEs?
FSE implements true continuous computation throughout the entire architecture, while Neural ODEs still use discrete operations with continuous depth. FSE uses continuous field evolution for all computational processes.
How does FSE handle multi-modal data?
FSE processes text, vision, and generation within a single unified computational field, enabling true multi-modal reasoning rather than stitching together separate architectures.
What is FlowField™?
FlowField™ is FSE's native computational framework, purpose-built for continuous field computation. Unlike approaches that adapt existing frameworks like TensorFlow or PyTorch, FlowField™ implements true continuous mathematics from the ground up, enabling genuine field-based neural computation without discrete approximations.
Can FSE integrate with existing transformer architectures?
Yes! Auralith is actively developing hybrid integration solutions that allow organizations to enhance their existing transformer models with FSE/FlowField™ capabilities. These solutions provide significant performance boosts while preserving current investments, offering a practical migration path to next-generation AI architecture.
When will FSE models be available?
We're currently training OMNI-λ and will be providing early access to select partners and researchers. Join our mailing list for updates on availability and early access opportunities.
Scientific Foundation
FSE builds upon established research in Neural ODEs, Physics-Informed Neural Networks, and continuous dynamical systems while introducing novel innovations in stateful field evolution and unified multi-modal processing.
- Neural Ordinary Differential Equations (Chen et al., 2018)
- Physics-Informed Neural Networks (Raissi et al., 2019)
- Geometric Deep Learning: Going Beyond Euclidean Data (Bronstein et al., 2017)
Be First to Experience the Future
Get exclusive early access to OMNI-λ and other groundbreaking FSE models. Join researchers, developers, and visionaries who are shaping the next era of intelligence.
The Architect
Pirassena Sabaratnam
Founder & Chief Architect
A seasoned entrepreneur guided by the principle of "seeking voids to fill via disruption," Pirassena Sabaratnam is the founder of Auralith and the inventor of the FSE architecture. After identifying the fundamental scaling and context limitations that have stalled traditional AI, he went back to first principles. After months of research, he developed a completely new, physics-based computational paradigm from scratch. His work on FSE is a testament to the power of intuitive, rapid, and foundational innovation in solving the industry's most challenging problems.
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