arXiv:2607.21799v1 Announce Type: new 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 pressure.Comparing 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.
Toward User-Conditioned Evaluation of Personal LLM Agents under Temporal Interventions