Efficiency Matters in Autonomous Research
The paper argues that autonomous research systems should be judged by how efficiently they search, not only by their final answer.
Yang and Cao propose using the area under the Pareto-frontier curve alongside final outcome quality to measure performance. They compare hill climbing, beam search, tree search, and evolutionary search across twelve systems-optimization tasks, finding no search structure is consistently most efficient. Their “fluid search” method dynamically allocates a fixed evaluation budget across multiple search processes and performs best overall, nearly matching a per-task oracle. ArXiv · AI/CL/LG's note
Yang and Cao propose using the area under the Pareto-frontier curve alongside final outcome quality to measure performance. They compare hill climbing, beam search, tree search, and evolutionary search across twelve systems-optimization tasks, finding no search structure is consistently most efficient. Their “fluid search” method dynamically allocates a fixed evaluation budget across multiple search processes and performs best overall, nearly matching a per-task oracle. ArXiv · AI/CL/LG's note
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