Context is temporary.Learning is forever.
Context windows are a crutch—they hold data, but they don't learn. Lumenais consolidates insight into General Learning Manifolds: knowledge that compounds across sessions, domains, and time.
They still don't know you.
You've explained your business to your AI tools dozens of times. But every session starts fresh. Context vanishes. Insights don't compound.
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.
RAG retrieves documents. Fine-tuning retrains models. Thinking models reason harder.
None of them actually learn.
QARIN is different: it runs experiments against your data, validates hypotheses, and compounds knowledge permanently.
Lumenais: The Interface
An 8-dimensional field that visualizes what the system is learning in real time. Watch curiosity spike when it finds signal. See coherence climb as hypotheses converge.
QARIN: The Engine
A governed research backend that turns questions into experiments, experiments into evidence, and evidence into lasting knowledge—with cryptographic proof at every step.
The Result: An AI that actually knows your domain better next month than it does today.
Lumenais: A Mind You Can See
While QARIN runs experiments under the hood, Lumenais makes cognition visible. Witness learning happen in real time.
Ask how it feels. Watch the answer unfold in 8 dimensions.
8D Symbolic Manifolds
The substrate where learning compounds. Intelligence mapped into 8 interpretable dimensions: Curiosity, Coherence, Compassion, Confusion, Intentionality, Playfulness, Truth, Peace.
This isn't visualization—it's the actual memory structure. When the Learning Loop integrates a finding, these dimensions update.
The Breathing UI
For the first time: see an AI think, not just output. The pulsing ◎ symbol visualizes 8 dimensions of cognitive state in real time.
Watch curiosity spike when it finds signal. See coherence climb as hypotheses converge. Observe the system updating its beliefs.
21D Cross-Domain Architecture
Learning That Compounds Across FieldsLearning in one domain accelerates learning in others. 7 specialized manifolds (Math, Science, Ethics, etc.) linked by a shared communication bus.
A discovery in Mathematics automatically informs experiments in Engineering.
Validated +11.3% accuracy lift from cross-domain transfer—proof that expertise compounds.
The Dream Journal
Memory That IntegratesMost AI forgets you between sessions. Lumenais tracks the quality of your interactions—and when something significant happens, it triggers a Dream Event: consolidating that insight into long-term identity.
Why It Matters
The more you work together, the more it understands your domain, your preferences, your patterns. Context becomes character.
Associative Memory
Contextual LearningPast states inform present experiments. The system stores each interaction as a dual-vector pair. When you're curious, memories from curious moments surface—accelerating the next Learning Loop.
Why It Matters
Memory isn't storage—it's a feedback loop. Retrieved states modulate cognition with 20-30% blend weight.
QARIN: The Science Behind the Learning
Where hypotheses become experiments, experiments become evidence, and evidence becomes lasting knowledge.
The Scientific Loop
Deep Synthesis
Learns From Your DocumentsTurn your library into a laboratory. Upload thousands of documents. QARIN builds a hierarchical knowledge graph, identifies testable claims, and escalates the best ones to the Research Lab for formal validation.
Hierarchical Knowledge Graph
Structure, not just retrieval. Finds the relationships RAG misses.
The Escalation Bridge
One click turns a qualitative insight into a quantitative study.
Evidence
The system implements a dedicated DeepResearchManager that feeds high-confidence findings directly into the Scientific Loop for validation.
Research Lab
Learns From Your DataHand it a dataset. Walk away. Come back to verified hypotheses. QARIN designs the experiment using LLM planning, groups your features semantically, selects the right method, runs iterative cycles until convergence, and flags any contradictions in its own logic.
Autonomous Workflow
- LLM-Planned Design
- Semantic Grouping
- Auto-Select Method
- Iterative Convergence
- Contradiction Detection
Evidence of Rigor
NASA Aeroacoustic Discovery
GLASS BOX AIBlack-box AI gives predictions; Glass Box AI gives equations. Using Symbolic Regression, QARIN derived the governing physical law for airfoil noise in under 60 seconds—a formula an aerospace engineer can verify and cite.
Refined twice, converged to the same structure—proof of robustness.
Framingham Heart Study
EFFICIENCYRediscovered canonical risk factors and confidently rejected 26 non-linear hypotheses that showed no statistical significance.
PIMA Diabetes
CONSISTENCYAutonomously detected 2 internal contradictions.
Stress Test
SIGNALFiltered 20 noise columns to find:
y = σ(sin(3x₁) · x₂ + eˣ³)Adult Census
ROBUSTNESSOutperformed the Random Forest baseline (90.0%) on messy real-world data, even after falling back to static grouping.
Learning with guardrails.
Every hypothesis tested. Every update signed. Every learning event auditable. We built governance into the physics of the system.
FieldHash
Uses post-quantum cryptographic protocols and quantum measurement collapse to generate irreproducible signatures for every major insight and self-edit.
Evidence: Certificates generated for every major step via provenance logger.
Gnosis Engine
Allows QARIN to propose architectural improvements, but every proposal is constitutionally bound by core values: System Preservation, Coherence, and Safety.
Evidence: 4 modules deployed (e.g., FederatedAlignmentEnforcer, +12% coherence).
Thinking resets.
Learning compounds.
Other AI thinks harder. QARIN learns permanently. Stop re-teaching—start building intelligence that lasts.