Agentic Evaluation of Copyright Law Compliance

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[Submitted on 23 Jul 2026]

Title:Agentic Evaluation of Copyright Law Compliance

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Abstract:Large language model (LLM) agents increasingly perform commercial tasks that involve retrieving external content such as images and, where appropriate, reproducing that content. LLM agents should comply with the law, including copyright law. Presently, however, we lack adequate frameworks to assess whether they do so in practice. To that end, we introduce \textbf{Copyright-Bench}, a benchmark designed to evaluate \textit{LLM agents' compliance with} \emph{copyright law}. Copyright-Bench is comprised of realistic commercial tasks---website development, merchandise design, and pitch deck production---that involve agents selecting between public-domain content (the use of which is \textit{legal}) and copyrighted content (the use of which is \textit{infringing} in this setting).The evaluation introduces prompt variations that simulate different user preferences, as well as time this http URL state-of-the-art LLM agents against a human baseline, we find that: (1) agents select copyrighted works despite the availability of public-domain alternatives; and (2) for open-weights models, violation rates increase in response to certain user preferences and simulated time pressure.

Comments: ICML 2026 Spotlight

Subjects:

Computation and Language (cs.CL); Computers and Society (cs.CY)

Cite as: arXiv:2607.21799 [cs.CL]

(or arXiv:2607.21799v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2607.21799

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Zheng Hui [view email] [v1] Thu, 23 Jul 2026 20:32:36 UTC (4,728 KB)

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