A Deterministic Agentic Workflow for HS Tariff Classification: Multi-Dimensional Rule Reasoning with Interpretable Decisions
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Abstract (arXiv)
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 stages, and reflection and verification are retained as local mechanisms. This design yields interpretability by construction--each decision is decomposed into stage-wise structured outputs with verbatim citation of the chapter or section notes that bear on it. The architecture combines offline knowledge-engineering of the Chinese HS tariff with an online six-stage pipeline. Evaluated on HSCodeComp at the six-digit level, the workflow reaches 75.0% top-1 and 91.5% top-3 at four digits, and 64.2% top-1 and 78.3% top-3 at six digits with Qwen3.6-plus; an open-weight Qwen3.6-27B-FP8 backbone in non-thinking mode achieves 84.2% four-digit and 77.4% six-digit top-1 agreement with the frontier model. A two-stage manual audit of 226 six-digit disagreements suggests that a non-trivial fraction of HSCodeComp ground-truth labels may deviate from HS general rules; full adjudication records are released in the appendix as preliminary findings for community review.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning. Builds on 10 of its 11 references that are in the map. First posted on arXiv 14 May 2026.
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10 of 11 references.
References · 10
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
- 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,064
- 2020ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERTcited by 1,160
- 2023Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agentscited by 356
- 2024Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Toolscited by 170
- 2024LLM-Based Robust Product Classification in Commerce and Compliancecited by 5
- 2025HSCodeComp: A Realistic and Expert-level Benchmark for Deep Search Agents in Hierarchical Rule Applicationcited by 5
- 2024Benchmarking Harmonized Tariff Schedule Classification Modelscited by 3
- 2025ATLAS: Benchmarking and Adapting LLMs for Global Trade via Harmonized Tariff Code Classificationcited by 3
- 2023Explainable Product Classification for Customscited by 2
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- 2025LLM Driven Processes to Foster Explainable AI
- 2025Towards Responsible and Explainable AI Agents with Consensus-Driven Reasoning
- 2025Agentic Reasoning and Tool Integration for LLMs via Reinforcement Learning
- 2025A Multi-LLM-Agent-Based Framework for Economic and Public Policy Analysis
- 2025From Competition to Coordination: Market Making as a Scalable Framework for Safe and Aligned Multi-Agent LLM Systems
- 2025Aligning Large Language Model Agents with Rational and Moral Preferences: A Supervised Fine-Tuning Approach