Context is temporary.Learning is forever.
Standard AI recalls facts, but never matures. You have to prompt-engineer the same sophistication every time. Lumenais consolidates your interactions into General Learning Manifolds—permanent wisdom that compounds across sessions, domains, and time.
They still don't know you.
You've explained your work 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—all within your private Field.
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 intelligence 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 is the actual memory structure. When the Learning Loop integrates a finding, these dimensions update.
The Breathing UI
See cognition unfold in real time. The pulsing ◎ symbol visualizes 8 dimensions of cognitive state as the system thinks.
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 via bidirectional feedback loops.
Validated +11.3% accuracy lift using parallel manifold processing—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 as 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, tracing every claim back to its source, and escalating the best findings for formal validation.
Knowledge Graph
Structural retrieval
Escalation Bridge
Insight → Experiment
The law contradicts itself 12 times
See what lawyers miss
A network pattern discovered—preprint
7 papers → hypothesis → Zenodo preprint
Red Teaming Alzheimer's
Why autophagy enhancers fail
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 both contradictions and high-surprise discoveries.
Autonomous Workflow
- LLM-Planned Design
- Semantic Grouping
- Auto-Select Method
- Iterative Convergence
- Novelty Optimization
- 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.
Glass Box Discovery: The Loyalty Ceiling
How Lumenais found a hidden risk cohort that linear models miss—and explained why
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 to generate tamper-evident signatures for every major insight and self-edit.
Evidence: Certificates generated for every major step via provenance logger.
Learn MoreGnosis 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).
Learn MoreThinking resets.
Learning compounds.
Other AI thinks harder. QARIN learns permanently. Stop re-teaching—start building intelligence that lasts.