Toward Evaluation Frameworks for Multi-Agent Scientific AI Systems

arXiv cs.AI / 3/31/2026

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Key Points

The article explores key obstacles in benchmarking scientific multi-agent AI systems, such as separating true reasoning from information retrieval and handling tool-mediated behaviors.
It highlights threats to evaluation validity, including data/model contamination and the lack of trustworthy ground truth for genuinely novel research tasks.
The authors propose strategies like building contamination-resistant task sets and creating scalable families of problems to better measure generalization.
They argue that evaluations should rely on multi-turn interactions that mirror real scientific workflows, especially as tool use and continuously updating knowledge bases complicate replication.
As an early feasibility test, the paper demonstrates constructing a dataset of novel research ideas to assess out-of-sample performance and uses interviews with quantum researchers/engineers to inform realistic evaluation expectations.

Abstract

We analyze the challenges of benchmarking scientific (multi)-agentic systems, including the difficulty of distinguishing reasoning from retrieval, the risks of data/model contamination, the lack of reliable ground truth for novel research problems, the complications introduced by tool use, and the replication challenges due to the continuously changing/updating knowledge base. We discuss strategies for constructing contamination-resistant problems, generating scalable families of tasks, and the need for evaluating systems through multi-turn interactions that better reflect real scientific practice. As an early feasibility test, we demonstrate how to construct a dataset of novel research ideas to test the out-of-sample performance of our system. We also discuss the results of interviews with several researchers and engineers working in quantum science. Through those interviews, we examine how scientists expect to interact with AI systems and how these expectations should shape evaluation methods.