Learning Scientific Trajectories to Predict Future Biomedical Discoveries
arXiv:2610.38719v1 Announce Type: cross Abstract: AI co-scientists have expanded the use of large language models for generating scientific hypotheses and guiding experimental investigation. Here we investigate whether temporal patterns in the semantic representations of complete biomedical conclusions can guide hypothesis generation. We introduce ForeSCITE, a framework that identifies thematic clusters of published conclusions, estimates a direction separating earlier from more recent findings…