AI RESEARCH
Teaching Language Models to Forecast Research Success Through Comparative Idea Evaluation
arXiv CS.CL
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ArXi:2605.21491v1 Announce Type: cross As language models accelerate scientific research by automating hypothesis generation and implementation, a new bottleneck emerges: evaluating and filtering hundreds of AI-generated ideas without exhaustive experimentation. We ask whether LMs can learn to forecast the empirical success of research ideas before any experiments are run. We study comparative empirical forecasting: given a benchmark-specific research goal and two candidate ideas, predict which will achieve better benchmark performance.