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As AI has become better at assisting with research, I've noticed a real uptick in the breadth of experiments in AI safety papers.
Current AI automation makes it easy to copy and paste someone else's technique to help support your own results. But what hasn't increased is an understanding of which experimental results are highly correlated and which ones provide genuinely differentiated evidence for a conclusion.
Determining quantitatively how to aggregate individual experimental results - each of which only moderately support a hypothesis - into a stronger claim should be a focus of the field over the next six months.
(This capability will also be necessary for the rigorous, quantitative safety cases that will be required in the future.)