KAIST's Principia Builds and Refines Scientific Principles at Test Time for Discovery
kaist-ai · hf · 2026-10-27
KAIST researchers propose Principia, an LLM framework that forms comparative causal hypotheses from inputs and outcomes, synthesizes recurring relationships into inductive principles, and iteratively refines them at test time as new observations arrive—targeting the hypothesis-to-principle bottleneck of scientific discovery, validated on molecular and biological tasks.
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