New arXiv paper formalizes open-ended LLM scientific discovery with possibilistic reasoning
RexDouglass · x · 2026-10-26
arXiv paper "Open-ended Scientific Discovery with Possibilistic Reasoning" (Yang, Chau, Wakayama, Muandet, Adachi) tackles a core challenge in autonomous LLM-driven science: hypotheses must be generated and tested adaptively as evidence accumulates while preserving statistical validity. The authors formalize the problem as Abductive Autonomous Scientific Discovery (AASD) using possibility theory, handle the case where the best discovered hypothesis may still be from a bad lot, and even allow background knowledge like physical laws to be revised. They introduce abductive utility, a computable measure of discovery progress, and possibility frontier search — the first AASD algorithm, which maintains anytime validity and asymptotically achieves ε-optimal abductive utility. Experiments on synthetic and real-world discovery tasks show strong performance.
More from Research
- Research agenda for multi-agent swarms: 7 open alignment problems — ZhijingJin · 2026-10-26
- Mathematician skips the trauma logs, launches a course on computer-assisted problem solving — zakkohane · 2026-10-26
- Doctor argues tinnitus, untreatable for 50 years, is exactly where AI medicine should break through — LaCaipirinha · 2026-10-26
- CS professor: 3D loss landscape visualizations mislead, fueling old local-minima fears — CSProfKGD · 2026-10-26
- Stanford's Marin 535B open model is now over 80% through pretraining — stanfordnlp · 2026-10-26
- 24 speech recognizers encode phonemes at very different depths: 21% vs 49% — stanfordnlp · 2026-10-26