Consensus-based Agentic Large Language Model Framework for Harmonized Tariff Schedule Code Classification
Researchers have developed a new agentic large language model (LLM) framework designed to improve the classification of Harmonized Tariff Schedule (HTS) codes, which are crucial for international trade and customs. The framework incorporates multi-agent retrieval, semantic search of tariff documents, and a consensus-based validation system with element-wise voting and confidence estimation. While experimental results on a dataset of 3,300 product records indicate that precise 10-digit classification remains challenging for LLMs, the proposed system offers a more interpretable and accountable approach for maritime logistics and smart-port operations. AI
IMPACT This framework offers a more interpretable and accountable approach to HTS classification, potentially improving efficiency in customs and logistics operations.