KC-Agent: A Dual-Process Cognitive Architecture for Efficient ML Model Improvement
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-
Record details
Published: 3 August 2026
Source: arXiv cs.AI
Category: Research
Topics: Agents & autonomy
Retrieved: 4 August 2026
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
SkillTrace: Traversing a Query-Skill Graph for Composable LLM Agents
arXiv cs.AI · 3 August 2026
Can AI Agents Simulate A/B Test Outcomes? A Validation Framework for Agentic Experimentation
arXiv cs.AI · 3 August 2026
Faster-WAM: Do World Action Models Need Deep Action Modules?
arXiv cs.AI · 3 August 2026
Cooperative Coevolution for Resource-Constrained Agentic LLM Post-Training
arXiv cs.AI · 3 August 2026
Trajectories That Segment Themselves: Agent-Declared Boundaries as a Training Unit
arXiv · 3 August 2026
MechGeo: Autoformalizing and Proving Euclidean Geometry in Lean 4
arXiv cs.AI · 3 August 2026
How to cite this record
ethics.ai (3 August 2026), “KC-Agent: A Dual-Process Cognitive Architecture for Efficient ML Model Improvement,” evidence record 16103, https://ethics.ai/record/16103 (originally published by arXiv cs.AI).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.