Recursive law learning under measurement constraints. A falsifiable SQNT-inspired testbed for autodidactic rules: internalizing structure under measurement invariants and limited observability.

qml locality rnn curvature dynamical-systems invariants representation-learning reproducibility jacobian hessian interpretability spectral-methods matrix-models quantum-ml quantum-inspired model-editing recursive-learning falsifiability alignment-research functional-recovery

• Updated Jan 19, 2026
• Python

🔍 Explore a testbed for quantum-inspired law learning, allowing controlled and falsifiable evaluations under measurement invariants.

qml rnn curvature dynamical-systems invariants representation-learning reproducibility jacobian hessian spectral-methods matrix-models quantum-ml quantum-inspired model-editing recursive-learning falsifiability alignment-research functional-recovery

• Updated Jun 16, 2026
• Python