让AI自主思考、自我改进，动态适应真实世界变化。
Active Thinking Model: A Goal-Directed Self-Improving Framework for Real-World Adaptive Intelligence
- 构建可自定目标、自动生成任务的智能框架
- 无需外部监督即可从低效进化到高效行为
- 适合需要长期自主运行的智能系统设计
现实世界的人工智能系统需在动态、不确定且持续变化的环境中自主运行。然而，现有AI模型多依赖预设目标、静态训练数据和外部反馈，难以自主适应、反思与提升。本文提出主动思维模型（Active Thinking Model, ATM）——一种整合目标推理、动态任务生成与自省学习的统一认知框架。不同于被动执行固定流程的传统系统，ATM通过逻辑推理与环境指标主动评估自身表现，复用有效方法解决新问题，并基于持续自我改进循环为未知情境生成新策略。数学分析表明，ATM可在无外部监督下实现从次优到最优行为的自主演化，并在环境变化时保持有界追踪损失。
原文摘要 · Abstract (English)
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.
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