Principia builds scientific principles from test-time hypotheses
Original title: Principia: Test-Time Hypotheses to Principle Construction for Scientific Discovery
The LLM framework beats knowledge, source, and experience baselines on molecular, protein, gene, and property tasks.
Principia is an LLM framework that forms comparative causal hypotheses from related inputs and outcomes, then synthesizes recurring relationships into inductive principles during test-time discovery. New observations revise those hypotheses and reconstruct principles. Across molecular optimization, protein engineering, gene perturbation, and molecular property prediction, the authors say it outperforms baselines that rely on internal knowledge, external sources, or experience summaries, and that generated principles are consistent with the literature.
Unconfirmed: Outperformance and literature consistency are author-reported and not independently checked here.
- Principles are refined continuously as new observations arrive.
- Benchmarks span molecules, proteins, gene perturbation, and property prediction.
- Code is released at the authors' GitHub repository.
- Authors describe generated principles as plausible and literature-consistent.
From the original article
1 min read at huggingface.co
Scientific discovery requires identifying the scientific principles underlying observed phenomena to guide further investigation. As these empirical principles are often unknown in complex discovery processes, they are constructed via a hypotheses-to-principle pipeline to obtain recurring causalities from progressively accumulated observations during test-time discovery.
By Dongki Kim, Sangwoo Park, Jinheon Baek · huggingface.co
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