Building systems that discover missing factors, improve autonomously, and share learnings across networks.
Watch: Learning and Anomaly Detection in 3D Space
Physics as the teacher. Reality as the test. Networks as the accelerant.
Find new causal factors and physical laws in real-world domains. The System detects residual errors, generates new hypotheses, and adds new causal edges to the graph.
Self-improving causal engines for spacecraft, robots, and manufacturing systems. Learn from operational reality via pure error signals to close the last mile of the sim-to-real gap.
Lean intelligence for robot fleets, R&D teams, space missions. Share foundational causal learnings without sharing raw data. Learn once and broadcast across the network
The mechanisms that enable systems to discover missing factors, improve autonomously, and share knowledge
LLM + domain literature generate initial causal graph. System starts with best available knowledge from scientific papers and validated datasets.
Sensors measure actual outcomes. Pure error signals (ε = |predicted - actual|) from reality. This is the ground truth.
Learning rate η inversely proportional to certainty Z. Agile where ignorant (low Z = high η), stable where certain (high Z = low η). Sample-efficient by design.
High certainty + high error = missing factor. Curiosity triggers autonomously. System knows when it's confidently encountering a knowledge gap.
Validated relationships (Z > 0.85) shared across network. Learn once, benefit everywhere. All partners improve from collective knowledge.
Loop repeats with operational data. Systems improve autonomously from reality. No retraining needed. Learning is built into the architecture.