arXiv AI By Yijie Hu, Wei Li, Junjie Zhou, Zhiqi Jiang, Yi Jin, Licong Xu, Xinkai Sun, Kai Duan, Lisha Zhang

PatentAgentBench: Benchmarking LLM Agents with Professional Patent Task Suites

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PatentAgentBench is a new benchmark that tests large language model agents on 510 real-world patent tasks across eight suites, including novelty search, freedom‑to‑operate, design FTO, invalidity search, claim charting, patent drafting, office‑action response, and patent translation. The benchmark uses curated patent records and expert‑reviewed annotations to evaluate task‑specific metrics, requiring agents to search patent collections without pre‑supplied candidate pools and to produce high‑quality drafting and response work. Seven LLMs were evaluated on four agent harnesses, revealing that current systems still struggle with novelty identification, risk assessment, and understanding practitioner intent throughout patent workflows.

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