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

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

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。