A Deterministic Agentic Workflow for HS Tariff Classification: Multi-Dimensional Rule Reasoning with Interpretable Decisions
Yu Zhang, Dongjiang Zhuang, Qu Zhou, Zheng Huang, Junhe Wu, Jing Cao, Kai Chen

TL;DR
This paper introduces a deterministic, interpretable workflow for HS tariff classification that effectively manages multi-dimensional rule reasoning, outperforming large language models in accuracy and transparency.
Contribution
It presents a fixed control flow architecture combining offline knowledge engineering with a six-stage pipeline, enhancing interpretability and accuracy in tariff classification tasks.
Findings
Achieves 75.0% top-1 accuracy at four digits and 64.2% at six digits with Qwen3.6-plus.
Open-weight Qwen3.6-27B-FP8 reaches 84.2% four-digit and 77.4% six-digit top-1 agreement.
Manual audit suggests some ground-truth labels may deviate from HS rules, indicating potential data issues.
Abstract
Harmonized System (HS) tariff classification is a high-stakes, expert-level task in which a free-form product description must be mapped to a specific six- or eight-digit code under the General Interpretive Rules (GIR), section notes, chapter notes, and Explanatory Notes. The difficulty lies not in knowledge volume but in *multi-dimensional rule reasoning*: a correct classification must satisfy competing priority rules along several axes simultaneously, including material, form, function, essential character, the part-versus-whole boundary, and specific listing versus residual headings. End-to-end prompting of large language models fails characteristically by resolving one axis while ignoring the priority constraints on the others. We present a *deterministic agentic workflow* in contrast to self-planning agents: the control flow is fixed, language model calls are confined to narrow…
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