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

This architecture enables recursive knowledge extraction and transfer across tasks. By structuring learning feedback in layers, it optimizes generalization and accelerates adaptive model development. 本アーキテクチャは、タスク間における再帰的な知識抽出と転移を可能にします。学習フィードバックを階層構造で整理することで、汎化性能を高め、適応的なモデル構築を加速します。

meta-learning adaptive-models recursive-learning transfer-architecture layered-feedback

• Updated Jun 25, 2025

🔍 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