Building systems that discover missing factors, improve autonomously, and share learnings across networks.
Physics as the teacher. Reality as the test. Networks as the accelerant.
Find new causal factors and physical laws in real-world domains. System ingests literature, tracks validation, detects gaps, generates hypotheses.
Self-improving systems for spacecraft, robots, satellites, data centers. Learn from operational reality via pure error signals. Adapt autonomously, quantify uncertainty.
Lean intelligence for robot fleets, R&D teams, scientific collaborations. 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.