Hscodecomp Eval

hscodecomp-eval

HSCodeComp: A Realistic and Expert-level Benchmark for Deep Search Agents in Hierarchical Rule Application — Yang et al. (2025) (arXiv:2510.19631, 2025)

What this evaluates

This benchmark evaluates deep search agents' ability to perform multi-hop reasoning across hierarchical tariff rules to predict 10-digit Harmonized System Codes (HSCode) from noisy product descriptions and images. It probes rule-based reasoning, agentic knowledge utilization, and handling of vague or implicit classification logic.

Datasets

• HSCodeComp — total ?; splits: test (-1)

Metrics

• 10-digit accuracy (primary) — range: percent
  • Calculated as the percentage of test instances where the model's predicted 10-digit HSCode exactly matches the ground truth code.

Input / output format

Input: Textual product description and optionally product images.

Output: A single 10-digit HSCode enclosed in \boxed{...} format.

Scoring recipe

def compute_accuracy(predictions, golds):
    correct = 0
    for pred, gold in zip(predictions, golds):
        pred_code = pred.replace('\\boxed{', '').replace('}', '').strip()
        if pred_code == gold.strip():
            correct += 1
    return (correct / len(golds)) * 100

Common pitfalls

• Including hierarchical decision rules (DR) in the prompt actually decreases agent accuracy, contrary to intuition.
• Full webpage visiting tools overwhelm agents with irrelevant content, reducing accuracy compared to using search engine snippets.
• Closed-source agents were evaluated on a small manual subset (49 examples), which may not be directly comparable to the full automated test set.

Evidence (verbatim from paper)

All systems produce standardized outputs: a single HSCode in \boxed{...} format. ... The best baseline, SmolAgent (GPT-5 VLM version), achieves only 46.83% 10-digit accuracy, which remains substantially below the 95% accuracy achieved by experienced human experts.

Citation

@misc{yang2025hscodecomp,
  title={HSCodeComp: A Realistic and Expert-level Benchmark for Deep Search Agents in Hierarchical Rule Application},
  author={Yang et al. (2025)},
  year={2025},
  note={arXiv:2510.19631}
}

• arXiv: 2510.19631