KC-Agent: A Dual-Process Cognitive Architecture for Efficient ML Model Improvement

• URL: http://arxiv.org/abs/2608.02351v1
• Date: Mon, 03 Aug 2026 15:03:35 GMT
• Title: KC-Agent: A Dual-Process Cognitive Architecture for Efficient ML Model Improvement
• Abstract summary: Data drift poses significant challenges for machine learning systems in production.<n>We present KC-Agent, a dual-process cognitive architecture for automated ML model improvement.<n>We evaluate our method on five datasets including real-world NASA turbofan data.
• Score: 2.716039597401137
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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