Constraint-Aware Hierarchical Search for Regulation-Driven Fine-Grained Classification

2026-07-12

AI Summary

This work addresses fine-grained hierarchical classification in regulatory-intensive domains such as customs tariff codes and export controls, where predictions must strictly adhere to hierarchical structures and rule-based boundaries. Existing approaches struggle to simultaneously ensure hierarchical validity, rule consistency, and boundary-aware reasoning. To bridge this gap, the paper formalizes, for the first time, a regulation-driven hierarchical classification task and introduces a constraint-aware hierarchical search framework. The method parses regulatory documents into a searchable tree and, at each step, retrieves only legally permissible candidate nodes, guiding path decisions through structured fields and evidential text snippets. Evaluated on four expert-validated datasets, the approach significantly outperforms baselines in average accuracy—particularly excelling in distinguishing adjacent categories and handling boundary cases—while producing auditable and traceable decision paths.

Abstract

Tasks such as customs tariff classification, export control categorization, and standards-based equipment coding require assigning an input instance to a fine-grained class under an explicit regulatory hierarchy. Unlike standard text classification, the correct label in these tasks is not determined by semantic similarity alone, but by rule-defined boundaries, threshold conditions, exclusion clauses, definitions, and local exceptions. As a result, two highly similar inputs may require different labels, while a retrieved passage that appears relevant may still be inapplicable under the governing rules. Existing flat classifiers, hierarchical text classification methods, and retrieval-augmented LLM systems are not designed to jointly enforce hierarchical validity, rule consistency, and fine-grained boundary reasoning. In this paper, we formulate this setting as regulation-driven fine-grained hierarchical classification, where an external instance must be assigned to a fine-grained class through a valid path in a regulatory hierarchy and supported by auditable evidence. We construct four benchmark datasets from representative regulation-intensive scenarios and validate the annotations through an expert-in-the-loop process. We further propose a constraint-aware hierarchical search framework that converts regulatory documents into a searchable tree, retrieves only valid local candidate nodes, and uses structured regulatory fields with evidence snippets to guide each next-hop decision. Experiments show that our method achieves the best mean accuracy on all four datasets and provides interpretable decision paths, with the largest gains on cases involving fine-grained neighboring categories and rule-based boundary conditions.

Problem

• regulation-driven classification
• fine-grained classification
• hierarchical text classification
• constraint-aware reasoning
• rule-based categorization

Innovation

• constraint-aware hierarchical search
• regulation-driven classification
• fine-grained hierarchical classification
• rule-based boundary reasoning
• auditable evidence