Incremental sliding-window processing of evolving literature via CKM-Lite outperforms batch methods on predictive hit rate, hypothesis yield, and alignment while cutting token costs by 92%, with change-aware analysis revealing quality-coverage trade-offs.
CKM-Full produces more diverse hypotheses in 16 out of 20 analyzed topics, suggesting that the change-aware pipeline encourages exploration of a broader hypothesis space
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Continuous Knowledge Metabolism: Generating Scientific Hypotheses from Evolving Literature
Incremental sliding-window processing of evolving literature via CKM-Lite outperforms batch methods on predictive hit rate, hypothesis yield, and alignment while cutting token costs by 92%, with change-aware analysis revealing quality-coverage trade-offs.