KC-Agent：用于高效机器学习模型改进的双进程认知架构
KC-Agent: A Dual-Process Cognitive Architecture for Efficient ML Model Improvement
AI总结：
KC-Agent是一种双进程认知架构，结合快速模式识别与审慎增量更新，在五组数据集评估中，以最优效率和领先性能优于现有认知架构，可高效应对真实世界数据漂移场景。
AI中文摘要：
数据漂移对生产环境中的机器学习系统构成重大挑战，需要持续更新模型以维持性能。我们提出KC-Agent，一种用于自动化机器学习模型改进的双进程认知架构，将快速模式识别（系统1）与审慎的增量更新（系统2）相结合。我们的方法实现了结构化记忆系统，使系统1能够利用系统2先前发现的成功解决方案，在无需昂贵重新计算的情况下实现高效的基于模式的响应。KC-Agent包含原子变更原则和回滚功能，以确保生产环境中可靠、可验证的更新。我们在五个数据集上评估了我们的方法，包括具有真实时间退化的NASA涡扇真实世界数据和具有受控漂移场景的合成数据集。KC-Agent达到了最先进的性能（准确率76.8%），同时保持了最优效率（执行时间13.2秒），优于现有认知架构：CodeAct（+2.4%）、思维树（Tree of Thoughts，+3.6%）、ReAct（+8.0%）和Reflexion（+8.9%）。一组最先进大型语言模型（LLM）的共识评估确认其具有卓越的战略效能（智能性评分8.33/10），显著优于基线智能体。知识整合机制比慢变体实现了91%的加速，同时保持更高的准确率。我们的方法展示了认知启发的自动化机器学习改进系统的理论基础和实践可行性，该系统能够处理复杂的真实世界数据漂移场景。
英文摘要：
Data drift poses significant challenges for machine learning systems in production, requiring continuous model updates to maintain performance. We present KC-Agent, a dual-process cognitive architecture for automated ML model improvement that combines fast pattern recognition (System 1) with deliberate incremental updates (System 2). Our approach implements structured memory systems enabling System 1 to leverage successful solutions previously discovered by System 2, achieving efficient pattern-based responses without costly re-computation. KC-Agent incorporates atomic change principles and rollback capabilities to ensure reliable, verifiable updates in production environments. We evaluate our method on five datasets including real-world NASA turbofan data with authentic temporal degradation and synthetic datasets with controlled drift scenarios. KC-Agent achieves state-of-the-art performance (76.8% accuracy) while maintaining optimal efficiency (13.2s execution time), outperforming established cognitive architectures: CodeAct (+2.4%), Tree of Thoughts (+3.6%), ReAct (+8.0%), and Reflexion (+8.9%). Consensus evaluation by a panel of state-of-the-art LLMs confirms superior strategic efficacy (8.33/10 Smartness score), significantly outperforming baseline agents. The knowledge consolidation mechanism delivers 91% speedup over the slow variant while maintaining higher accuracy. Our approach demonstrates both theoretical foundations and practical viability for cognitive-inspired automated ML improvement systems capable of handling complex real-world data drift scenarios.
