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

2026-08-03

AI Summary

This work addresses the degradation of model performance in production environments caused by data drift by proposing KC-Agent, a continual learning agent grounded in a dual-process cognitive architecture. Integrating rapid pattern recognition (System 1) with deliberate incremental updates (System 2), KC-Agent leverages structured memory to reuse historically successful strategies and incorporates atomic changes with rollback mechanisms to ensure reliability. Empirical evaluation demonstrates that KC-Agent achieves an average accuracy of 76.8% across five datasets with an inference time of only 13.2 seconds, significantly outperforming baseline methods such as CodeAct and ToT. Furthermore, its knowledge integration mechanism yields a 91% speedup, and large language model consensus scoring rates its output quality at 8.33 out of 10.

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.

Problem

• data drift
• model updating
• production ML systems
• automated model improvement
• cognitive architecture

Innovation

• dual-process cognitive architecture
• data drift adaptation
• structured memory system
• atomic model updates
• knowledge consolidation