This paper discusses challenges in evaluating multi-agent scientific AI systems and proposes strategies like contamination-resistant tasks and multi-turn testing, demonstrated via a novel research ideas dataset and quantum science interviews.
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Toward Evaluation Frameworks for Multi-Agent Scientific AI Systems
This paper discusses challenges in evaluating multi-agent scientific AI systems and proposes strategies like contamination-resistant tasks and multi-turn testing, demonstrated via a novel research ideas dataset and quantum science interviews.