Agent-NASCD: Efficient and Reliable Agentic Architecture Search for Cognitive Diagnosis

Cognitive diagnosis (CD) aims to estimate students’ proficiency in knowledge concepts from response logs. However, existing evolutionary and one-shot neural architecture search (NAS) methods for designing CD interaction functions still incur substantial candidate evaluation costs. Besides, directly prompting a language model can produce invalid programs or unstable search directions. To address these issues, this paper proposes Agent-NASCD, a single-agent framework that treats CD architecture search as sequential evidence-conditioned decision making. Specifically, CD-oriented tools support program construction and mutation, a model optimization graph (MOG) records candidate lineage and evaluation results, and constrained proxy/full evaluation screens candidates at different training fidelities. Experiments on three real-world datasets show competitive predictive performance of the selected architectures and support the contributions of the MOG and diagnostic feedback to candidate-training efficiency.