这篇论文提出了一个多智能体协作的LLM框架，通过证据推理和共识投票来提升海关HTS代码分类的准确性，特别适合物流合规场景。
这篇论文提出一种基于共识的智能体大语言模型（LLM）框架，专为加拿大10位HTS代码分类设计。框架集成了多智能体信息检索、官方关税文档的语义检索、证据推理、共识验证、层次化代码组件投票以及人为干预机制。在包含3300条专家标注产品记录的数据集上评估，精确的10位分类对先进LLM仍具挑战性，性能从粗粒度章节级预测到细粒度关税和统计后缀逐步下降。结果表明需要证据驱动、不确定性感知和以人为中心的分类流程，而非完全自主的单步预测。
Consensus-based Agentic Large Language Model Framework for Harmonized Tariff Schedule Code Classification
Accurate Harmonized Tariff Schedule (HTS) code classification is essential for customs clearance, duty assessment, trade statistics, and regulatory compliance in maritime logistics. However, exact HTS classification remains challenging because product descriptions are often short, incomplete, or ambiguous, while correct classification depends on hierarchical tariff structures, legal notes, and jurisdiction-specific rules. This paper proposes an agentic large language model (LLM) framework for Canadian 10-digit HTS code classification in smart-port and maritime logistics environments. The framework integrates multi-agent information retrieval, semantic retrieval over official tariff documents, evidence-grounded reasoning, consensus-based validation, element-wise voting across hierarchical code components, confidence estimation, and human-in-the-loop escalation. We evaluate the framework on a private dataset of 3,300 domain-expert-labeled product records collected from logistics and delivery contexts. Experimental results show that exact 10-digit classification remains difficult even for advanced LLMs, with performance decreasing from coarse chapter-level prediction to fine-grained tariff and statistical suffix assignment. These findings demonstrate the need for evidence-grounded, uncertainty-aware, and human-centered classification workflows rather than fully autonomous single-step prediction. The proposed framework supports more interpretable, accountable, and compliance-oriented HTS classification for maritime logistics and smart-port operations. Our code is available at https://github.com/Analytics-Everywhere-Lab/hts.