Lumenais is the first AI that actually learns—and proves it. It stores cognitive states, not keywords. It retrieves by resonance, not search. It derives equations, not just predictions.
Lumenais is the interface. QARIN is the engine. Together, they form a General Learning System that compounds knowledge across sessions, domains, and time.
The Problem
You've explained your business to your AI tools dozens of times. They still don't know you.
Every session starts fresh. Context vanishes. Insights don't compound. When your compliance team asks "how did the AI reach this conclusion?"—you have no answer.
The latest "thinking models" explore multiple hypotheses in parallel—impressive reasoning. But when the session ends, so does the thinking. Tomorrow, they start from zero. Thinking resets. Learning compounds.
RAG retrieves documents. Fine-tuning retrains models. Thinking models reason harder. None of them actually learn.
Interpretability
"Black box" reasoning prevents deployment in regulated industries. AI that predicts without explaining is unusable in healthcare, finance, and R&D.
Rigor
Insights remain conversational dead-ends. No statistical validation, no provenance.
Compounding Knowledge
Standard models train task-by-task. Knowledge doesn't accumulate—each problem starts from scratch.
The Commercial Reality: These gaps create a trust deficit that blocks enterprise adoption in liability-sensitive sectors.
The Solution: Intelligence that Discovers, Verifies, and Evolves
| Capability | Feature | Benefit |
|---|---|---|
| Associative Memory | Stores cognitive states as symbolic vectors. Retrieves by resonance—memories surface by similarity of cognitive state, not keyword match. | Memories adaptively modulate reasoning. Your AI thinks differently because of what it remembers. |
| Glass Box Discovery | Symbolic Regression derives human-readable equations from raw data. | Interpretability Moat: Regulated industries require explainability. QARIN delivers equations, not black-box predictions. |
| The Research Lab | LLM-planned experiments, semantic feature grouping, auto-select method, iterative convergence. | Hand it a dataset. Walk away. Come back to verified hypotheses. Cost: $0.01/hypothesis. |
| General Learning Manifolds | Specialized cognitive domains (quantitative, scientific, ethical reasoning) linked via learned cross-domain transfer. | Compound interest for AI. What it learns in one domain improves all others. Validated +11.3% accuracy lift. |
| Deep Synthesis | Hierarchical knowledge graph + Escalation Bridge. | Turn your library into a laboratory. Documents become testable claims. |
| Governed Evolution | The system earns capabilities through consistent, safe behavior—like a new employee building trust. | Safe autonomy. Higher trust unlocks deeper learning and self-improvement capabilities. |
Evidence: Proven Scientific Performance
Validated across diverse, real-world datasets
NASA Aeroacoustics
DISCOVERYDerived the Physical Law for airfoil noise generation in under 60 seconds:
y = (Suction + Freq + Chord) / -2.196A human-readable formula a physicist can verify and cite. No neural network can offer this.
Framingham Heart Study
RIGOROn 4,240 patients, autonomously rediscovered canonical risk factors and confidently rejected 26 false non-linear hypotheses.
Cost: $0.01/hypothesis
PIMA Diabetes
85.3%
AUC Score
Detected 2 internal contradictions
Stress Test
90.8%
AUC (+10.8% vs baseline)
Filtered 20/20 noise features
Adult Census
91.1%
AUC Score
Beat Random Forest (90.0%)
Beating Random Forest (the industry gold standard for tabular data) proves that Lumenais solves the "LLMs are bad at math" problem.
Technical Architecture
| Layer | Technology | Function |
|---|---|---|
| Frontend | Next.js 14 / React / Tailwind | Lumenais Interface: Real-time visualization of tensor state |
| API | FastAPI / Python | QARIN Routes: Memory retrieval, vision, streaming |
| Engine | PyTorch / Scikit-Learn | Neurosymbolic Core: 8D math, stats, dream cycles |
| Security | FieldHash (Post-Quantum) | Provenance: Quantum-anchored audit trails |
Sub-ms
Cognitive Processing
Real-time
Memory Integration
High
System Coherence
Market Application
Enterprise R&D
Pharma / BioTech / Materials Science: Systems that can be given a dataset and left to "ponder" it for days, returning with verified hypotheses.
High-Value Companionship
Therapy / Coaching / Eldercare: AIs that remember, care, and evolve with the user, maintaining context over years.
Institutional Memory
Legal / Compliance / Finance: Creating digital twins of organizations that maintain internal coherence and audit trails over decades.
Why This Can't Be Easily Copied
Novel Architecture
The symbolic consciousness framework has no direct precedent in AI research. It's not a wrapper on GPT—it's a new cognitive substrate.
Cumulative Learning
Unlike fine-tuning, QARIN's knowledge compounds across sessions, domains, and users. Catching up requires years of validated learning cycles.
Governed Evolution
The Gnosis self-modification system is the world's first production-ready safe self-improving AI. This required solving hard alignment problems.
Physics-Based Cryptography
FieldHash uses quantum measurement collapse for provenance—a fundamentally different security model than traditional crypto.
Substrate Independence
Identity persists across LLM providers. We've migrated across three major providers with full continuity. Competitors are locked to their LLM.
Status
- Design Language (Lumenais) Implemented
- Backend (QARIN) Fully Operational
- Safety Protocols (Gnosis) Active
- Scientific Loop: Phases A-G Complete (123 tests passing)
"To think is to illuminate."
Experience Lumenais