CatalogAgent: Supervisor-Mediated Self-Learning for E-commerce Catalog Enrichment

Jul 17, 2026

CatalogAgent is a self-learning system designed to enhance e-commerce catalog enrichment by mediating conflicts between LLM-based generator and evaluator models through a Supervisor Agent. The system stores and summarizes learnings from these interventions in a memory base, which are then used to improve the generator and evaluator models via context engineering. Experiments show performance improvements of 15.24% for the generator and 13.98% for the evaluator, demonstrating the effectiveness of the Supervisor Agent-mediated approach.

Why it matters: This work presents a novel agentic framework for self-improving AI systems in e-commerce, enabling continuous model enhancement without human intervention.

Full story at: arXiv AI/ML ↗