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This system models adaptive reflection across multiple cognitive layers, enabling agents to restructure their reasoning through a meta-level feedback process. It enhances self-regulation and decision robustness in dynamic environments. 本システムは、複数の認知層にわたる適応的反射をモデル化し、エージェントがメタレベルのフィードバックを通じて推論を再構築できるようにします。これにより、動的環境下での自己調整力と意思決定の堅牢性が向上します。

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Meta-Cognitive Learning Protocol / メタ認知型学習プロトコル

This theory proposes a structured protocol that enables systems to monitor, evaluate, and modify their own learning strategies based on meta-cognitive awareness. It is designed for advanced AI models that require strategic control over their learning processes.

本理論は、メタ認知的な自己認識に基づき、システムが自らの学習戦略を監視・評価・修正できる構造化されたプロトコルを提案します。これは、学習過程を戦略的に制御する高度なAIモデルを対象に設計されています。

Specialized Applications

Applicable in adaptive AI tutoring systems, intelligent coaching agents, and autonomous decision-making environments where learning strategy control is essential.

General Applications

Can be implemented in personal development tools, habit-tracking apps, and cognitive enhancement platforms where users benefit from self-reflective learning feedback.

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This system models adaptive reflection across multiple cognitive layers, enabling agents to restructure their reasoning through a meta-level feedback process. It enhances self-regulation and decision robustness in dynamic environments. 本システムは、複数の認知層にわたる適応的反射をモデル化し、エージェントがメタレベルのフィードバックを通じて推論を再構築できるようにします。これにより、動的環境下での自己調整力と意思決定の堅牢性が向上します。

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