ATLAS: Benchmarking and Adapting LLMs for Global Trade via Harmonized
  Tariff Code Classification
Authors: name: Pritish Yuvraj, name: Siva Devarakonda

Accurate classification of products under the Harmonized Tariff Schedule
(HTS) is a critical bottleneck in global trade, yet it has received little
attention from the machine learning community. Misclassification can halt
shipments entirely, with major postal operators suspending deliveries to the
U.S. due to incomplete customs documentation. We introduce the first benchmark
for HTS code classification, derived from the U.S. Customs Rulings Online
Search System (CROSS). Evaluating leading LLMs, we find that our fine-tuned
Atlas model (LLaMA-3.3-70B) achieves 40 percent fully correct 10-digit
classifications and 57.5 percent correct 6-digit classifications, improvements
of 15 points over GPT-5-Thinking and 27.5 points over Gemini-2.5-Pro-Thinking.
Beyond accuracy, Atlas is roughly five times cheaper than GPT-5-Thinking and
eight times cheaper than Gemini-2.5-Pro-Thinking, and can be self-hosted to
guarantee data privacy in high-stakes trade and compliance workflows. While
Atlas sets a strong baseline, the benchmark remains highly challenging, with
only 40 percent 10-digit accuracy. By releasing both dataset and model, we aim
to position HTS classification as a new community benchmark task and invite
future work in retrieval, reasoning, and alignment.