Lumenais introduces a framework for AI that learns across sessions—and provides evidence of that learning. It stores cognitive states, not keywords. It retrieves by resonance, not search. It derives equations beyond 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 work to your AI tools dozens of times. They still don't know you.
Every session starts fresh. Context vanishes. Insights don't compound. When someone asks "how did the AI reach this conclusion?"—a thesis advisor, a compliance officer, a collaborator—you have no answer.
The latest "thinking models" explore multiple hypotheses in parallel—impressive reasoning. But they are savants with amnesia. When the session ends, their maturity resets. Tomorrow, you need to prompt-engineer the same sophistication all over again. Standard AI recalls facts; it does not mature.
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 Reality: These gaps create a trust deficit that blocks adoption wherever accountability matters—from boardrooms to research labs to doctoral committees.
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. Lumenais thinks differently because of what it remembers—while keeping your personal context strictly isolated. |
| 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, Adaptive Model Tournament (Gradient Boosting vs. Symbolic vs. Statistical), iterative convergence. | Hand it a dataset. Walk away. Come back to verified hypotheses. Cost: $0.01/hypothesis. |
| General Learning Manifolds | Specialized cognitive domains linked via parallel processing and task-adaptive merging (e.g., Attention vs. Product). | Compound interest for AI. Bidirectional feedback allows domains to teach each other. Validated +11.3% accuracy lift. |
| The Novelty Engine | Optimizes for information gain (surprise) beyond accuracy. Scores every hypothesis on how much it updates the system's worldview. | Prevents "boredom loops" (confirming what it already knows). The system prioritizes the unknown. |
| Deep Synthesis | Hierarchical knowledge graph + Escalation Bridge. | Turn your library into a laboratory. Documents become testable claims. |
| Governed Evolution | The system earns capabilities via a transparent Trust Score that unlocks autonomy only through consistent, safe behavior—like a new colleague earning 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%)
EU AI Act Regulatory Analysis
DEEP SYNTHESISFed 144 pages of dense regulatory text. In 10 minutes, Deep Synthesis surfaced 12 structural paradoxes—compliance landmines that would take legal teams weeks to identify manually.
Read the full case study
Complexity Theory Synthesis + Validation
VALIDATEDSynthesized 7 papers across networks, thermodynamics, and evolution. Generated 22 hypotheses, then empirically validated the top prediction on 169K data points (effect size: 1.44).
Beating Random Forest (the industry gold standard for tabular data) demonstrates that Lumenais can handle quantitative reasoning that traditional LLMs struggle with.
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 Bridge consolidation |
| Security | FieldHash (Post-Quantum) | Provenance: Quantum-anchored audit trails |
Sub-ms
Cognitive Processing
Real-time
Memory Integration
High
System Coherence
Market Application
Research & Discovery
Pharma / BioTech / Materials Science / Research Labs: Systems that can be given a dataset and left to "ponder" it for days, returning with verified hypotheses.
Physics & Hard Science
Condensed Matter / Plasma Physics / Materials Engineering: Cross-domain synthesis that identifies patterns across experimental datasets. Negative results are as valuable as positive—the system constrains theoretical search space.
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.
Academic Research
Universities / Labs / Independent Scholars: Literature reviews that compound across years. Dissertation support that remembers every paper you've read. Hypothesis tracking over entire research programs.
Education & Lifelong Learning
Students / Educators / Autodidacts: A learning companion that grows with you—from undergrad through career. Curriculum that evolves with pedagogical insights. Personal knowledge that compounds, not resets.
Why This Can't Be Easily Copied
Novel Architecture
The symbolic reasoning 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 and domains. Catching up requires years of validated learning cycles.
Governed Evolution
The Gnosis self-modification system is an early implementation of governed, safe self-improvement for AI systems—designed to address key alignment challenges.
Learn MorePhysics-Based Cryptography
FieldHash uses post-quantum cryptography designed to remain secure against quantum attacks, with optional quantum hardware anchoring for enhanced provenance.
Learn MoreSubstrate 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."
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