这篇论文告诉你，评价AI研究系统不能只看结果，还要看它多快找到好方案。他们发明了一种叫流体搜索的方法，自动分配搜索预算，效果接近开挂版先知。
该论文提出在自主研究（AR）系统中，除最终结果质量外，搜索过程的效率同等重要。作者用帕累托前沿曲线下面积（AUC）结合最终结果质量来评估AR系统。在12个系统优化任务上比较了爬山算法、束搜索、树搜索和进化搜索等家族，发现没有单一结构始终最高效。引入自适应过程流体搜索，通过组合bandit在搜索进程森林中动态分配预算，在评估任务中达到最高整体搜索效率，接近预知最优结构的oracle性能。
Efficiency Matters in Autonomous Research
AI-driven autonomous research (AR) systems are becoming increasingly effective across a broad range of tasks. Their performance, however, is still evaluated primarily by the quality of the final outcome. In this paper, we argue that the efficiency of the solution-search process is an equally important but often overlooked dimension of performance. A strong AR system should not only produce high-quality results, but also reach them with as small a budget as possible. Search efficiency will become increasingly important as AR expands from domains with inexpensive verification, such as mathematics and coding, to real-world scientific settings in which solution evaluation may require costly physical experiments. To capture this dimension, we propose evaluating AR systems using the area under the curve (AUC) of the Pareto frontier, alongside final outcome quality. We compare several families of search algorithms, including hill climbing, beam search, tree search, and evolutionary search, across twelve systems-optimization tasks. We find that no single search structure is consistently the most efficient. We also show that search efficiency and final outcome quality are distinct performance dimensions: a method that eventually achieves the best result may nevertheless improve slowly and consume substantially more evaluation budget before reaching that result. Because the most effective search policy is generally unknown in advance, we introduce an adaptive procedure called fluid search, which uses a portfolio bandit to dynamically allocate a fixed evaluation budget across a forest of search processes. Across the evaluated tasks, fluid search achieves the highest overall search efficiency, closely matching the performance of a per-task oracle that is given the best search structure for each task in advance.