Active Thinking Model: A Goal-Directed Self-Improving Framework for Real-World Adaptive Intelligence
This addresses the need for adaptive intelligence in real-world AI applications, though it appears incremental as it builds on existing cognitive frameworks.
The paper tackles the problem of AI systems lacking autonomy in dynamic environments by proposing the Active Thinking Model (ATM), a framework that integrates goal reasoning and self-reflective learning, with theoretical results showing it can evolve to optimal behavior and maintain bounded regret.
Real-world artificial intelligence (AI) systems are increasingly required to operate autonomously in dynamic, uncertain, and continuously changing environments. However, most existing AI models rely on predefined objectives, static training data, and externally supplied feedback, which restrict their ability to adapt, reflect, and improve independently. In this paper, we propose the Active Thinking Model (ATM)- a unified cognitive framework that integrates goal reasoning, dynamic task generation, and self-reflective learning into an adaptive architecture. Unlike conventional systems that passively execute fixed procedures, ATM actively evaluates its performance through logical reasoning and environmental indicators, reuses effective methods to solve new problems, and generates novel strategies for unseen situations via a continuous self-improvement loop. A mathematically grounded theoretical analysis demonstrates that ATM can autonomously evolve from suboptimal to optimal behavior without external supervision and maintain bounded tracking regret under changing environmental conditions.