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Boundlessness Overtaking Benchmarks: The Crisis of Evaluating AI Scientists
As AI systems begin drafting full research reports, our long-standing evaluation mindset is hitting its limits. We are used to benchmarking models on massive data sets with well-defined, comparable metrics. But modern AI-generated science is now judged on only a small number of long, open-ended research outputs, making traditional notions of generalization hard to verify. In the absence of standard evaluation frameworks, researchers find themselves creating case-specific evaluation criteria. This blog is a wake-up call, a look at how quickly LLM-based scientific agents are outgrowing our inherited evaluation paradigms, and why we must rethink our long-held assumptions to build rigorous and standardized ways of assessing this new form of AI-driven scientific work.