Consensus-based Agentic Large Language Model Framework for Harmonized Tariff Schedule Code Classification

• URL: http://arxiv.org/abs/2606.16987v1
• Date: Mon, 15 Jun 2026 17:24:07 GMT
• Title: Consensus-based Agentic Large Language Model Framework for Harmonized Tariff Schedule Code Classification
• Abstract summary: This paper proposes an agentic large language model (LLM) framework for Canadian 10-digit HTS code classification.<n>We evaluate the framework on a private dataset of 3,300 domain-expert-labeled product records collected from logistics and delivery contexts.
• Score: 1.0521109824605632
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

Related papers

• Formal Verification of Agentic Systems over Operational Data [59.98246281888422]
  Agentic systems driven by large language models (LLMs) are increasingly deployed in real-world where they act on persistent operational data.<n>Existing approaches do not provide such system-level guarantees.<n>We study here the verification of agentic systems comprising a single LLM and a tool orchestration harness over relational operational data.
  arXiv  Detail & Related papers  (2026-08-04T13:01:30Z)
• LLMEval-Logic: A Solver-Verified Chinese Benchmark for Logical Reasoning of LLMs with Adversarial Hardening [69.1037790901185]
  We present LLMEval-Logic, a Chinese logical reasoning benchmark built from realistic situational scenarios.<n>Its pipeline forward-authors and expert-audits natural-language items together with their reference formalizations, verifies annotated answers with Z3, constructs expert rubrics for natural-to-formal grading, and hardens selected items through a closed-loop adversarial workflow.
  arXiv  Detail & Related papers  (2026-05-19T09:40:29Z)
• ClaHF: A Human Feedback-inspired Reinforcement Learning Framework for Improving Classification Tasks [11.138973570127206]
  We propose ClaHF, a human feedback-inspired reinforcement learning framework for text classification.<n>ClaHF integrates preference modeling and RL optimization into the classification pipeline without requiring additional human annotations.<n>Results demonstrate that ClaHF consistently improves both classification performance and confidence calibration across diverse language models.
  arXiv  Detail & Related papers  (2026-05-17T14:00:01Z)
• GeoDecider: A Coarse-to-Fine Agentic Workflow for Explainable Lithology Classification [52.5232577739543]
  Lithology classification aims to infer subsurface rock types from well-logging signals.<n>Existing methods still treat lithology classification as a single-pass classification task.<n>We propose GeoDecider, a coarse-to-fine agentic workflow that enables accurate and explainable lithology classification.
  arXiv  Detail & Related papers  (2026-05-05T05:42:51Z)
• Job Skill Extraction via LLM-Centric Multi-Module Framework [51.37063712967151]
  We present SRICL, an LLM-centric framework that combines semantic retrieval, in-context learning, and supervised fine-tuning.<n>On six public span-labeled corpora of job-ad sentences, SRICL achieves substantial STRICT-F1 improvements over GPT-3.5 prompting baselines and sharply reduces invalid tags and hallucinated spans.
  arXiv  Detail & Related papers  (2026-04-23T10:46:07Z)
• ORCHID: Orchestrated Retrieval-Augmented Classification with Human-in-the-Loop Intelligent Decision-Making for High-Risk Property [6.643427585499247]
  ORCHID is a modular agentic system for HRP classification.<n>It pairs retrieval-augmented generation (RAG) with human oversight to produce policy-based outputs that can be audited.<n>The demonstration shows single item submission, grounded citations, SME feedback capture, and exportable audit artifacts.
  arXiv  Detail & Related papers  (2025-11-07T03:48:05Z)
• Issue-Oriented Agent-Based Framework for Automated Review Comment Generation [15.04868140672973]
  RevAgent is a novel agent-based issue-oriented framework for code review comments.<n>It decomposes the task into three stages: Generation, Discrimination, and Training.<n>It significantly outperforms state-of-the-art PLM- and LLM-based baselines.
  arXiv  Detail & Related papers  (2025-11-01T11:44:11Z)
• SCAN: Structured Capability Assessment and Navigation for LLMs [54.54085382131134]
  textbfSCAN (Structured Capability Assessment and Navigation) is a practical framework that enables detailed characterization of Large Language Models.<n>SCAN incorporates four key components:.<n>TaxBuilder, which extracts capability-indicating tags from queries to construct a hierarchical taxonomy;.<n>RealMix, a query synthesis and filtering mechanism that ensures sufficient evaluation data for each capability tag;.<n>A PC$2$-based (Pre-Comparison-derived Criteria) LLM-as-a-Judge approach achieves significantly higher accuracy compared to classic LLM-as-a-Judge method
  arXiv  Detail & Related papers  (2025-05-10T16:52:40Z)
• A Hierarchical Neural Framework for Classification and its Explanation in Large Unstructured Legal Documents [0.5812284760539713]
  We define this problem as "scarce annotated legal documents" We propose a deep-learning-based classification framework which we call MESc. We also propose an explanation extraction algorithm named ORSE.
  arXiv  Detail & Related papers  (2023-09-19T12:18:28Z)
• CoPHE: A Count-Preserving Hierarchical Evaluation Metric in Large-Scale Multi-Label Text Classification [70.554573538777]
  We argue for hierarchical evaluation of the predictions of neural LMTC models. We describe a structural issue in the representation of the structured label space in prior art. We propose a set of metrics for hierarchical evaluation using the depth-based representation.
  arXiv  Detail & Related papers  (2021-09-10T13:09:12Z)
• Overview of the TREC 2019 Fair Ranking Track [65.15263872493799]
  The goal of the TREC Fair Ranking track was to develop a benchmark for evaluating retrieval systems in terms of fairness to different content providers. This paper presents an overview of the track, including the task definition, descriptions of the data and the annotation process.
  arXiv  Detail & Related papers  (2020-03-25T21:34:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.