Continuous Knowledge Metabolism: Generating Scientific Hypotheses from Evolving Literature

Identifying promising research directions in fast-moving subareas is one of the most cognitively expensive tasks in modern AI research. Existing LLM-driven scientific-discovery systems are typically limited to one-shot prompting on static literature snapshots and validated only against contemporary judges (human reviewers, agent peer review, wet-lab assays, or self-evaluation), leaving open whether they anticipate future trends. We present Continuous Knowledge Metabolism (CKM), an AI workflow for hypothesis generation with three key capabilities: (i) continuous literature metabolism via sliding windows that maintain an evolving knowledge state; (ii) predictive evaluation, grading hypotheses against papers published after the generation window; and (iii) practitioner-grade failure detection that diagnoses workflow failure modes from its outputs. On a 50-topic ML benchmark, CKM-Lite produces at least one validated hypothesis on 72% of topics (36/50), more than doubling a one-shot baseline (30%) at ~\$3 per topic and 91% lower token cost. Validated hypotheses precede their matched papers by a mean of 404 days (n=55 hits across 36 topics; median 399, range 66–757). Broadly, predictive validation against future literature offers a falsifiable, low-cost alternative to contemporary-judge protocols and can be applied wherever a corpus has dated publication records.