Shayan Ali Akbar

Optimizing Large Language Models (LLMs) for production AI agent deployment demands substantial computational resources and specialized human expertise (e.g., prompt engineering). Self-evolution offers a promising solution by enabling agents to autonomously enhance capabilities through structured feedback, improving performance without expensive manual optimization. However, most existing self-evolving agents rely on costly frontier LLMs, limiting scalability.

Taxonomies organize knowledge into hierarchical structures that support effective information seeking behaviors. However, developing taxonomies in fast-evolving domains like e-commerce remains a labor-intensive process. In this paper, we present an interactive system that assists users in expanding taxonomies through automated knowledge discovery from large text corpora.