Title: HSCodeComp: A Realistic and Expert-level Benchmark for Deep Search Agents in Hierarchical Rule Application
Primary area: datasets and benchmarks
Keywords: HSCode Benchmark, Deep Search Agent, Precise Multi-Turn Deep Reasoning, Hierarchical Rule Application

Abstract: Effective deep search agents must not only access open-domain and domain-specific knowledge but also apply complex rules—such as legal clauses, medical manuals and tariff rules. These rules often feature vague boundaries and implicit logic relationships, making precise application challenging for agents. However, this critical capability is largely overlooked by current agent benchmarks. To fill this gap, we introduce \textsc{HSCodeComp}, the first realistic, expert-level e-commerce benchmark designed to evaluate deep search agents in hierarchical rule application. In this task, the deep reasoning process of agents is guided by these rules to predict 10-digit Harmonized System Code (HSCode) of products with noisy but realistic descriptions. These codes, established by the World Customs Organization, are vital for global supply chain efficiency. Built from real-world data collected from large-scale e-commerce platforms, our proposed \textsc{HSCodeComp} comprises 632 product entries spanning diverse product categories, with these HSCodes annotated by several human experts. Extensive experimental results on several state-of-the-art LLMs, open-source, and closed-source agents reveal a huge performance gap: best agent achieves only 46.8\% 10-digit accuracy, far below human experts at 95.0\%. 
Besides, detailed analysis demonstrates the challenges of hierarchical rule application, and test-time scaling fails to improve performance further. Codes and the benchmark will be publicly released.

TL;DR: We introduce HSCODECOMP, the first realistic and expert-level benchmark designed to evaluate deep search agents in hierarchical rule application of e-commerce domain.