← Back to all news
arXiv AI By Anita Yang, Siu Lun Chau, Tomoya Wakayama, Krikamol Muandet, Masaki Adachi

Open-ended Scientific Discovery with Possibilistic Reasoning

Read the original on arXiv AI →

The paper introduces Abductive Autonomous Scientific Discovery (AASD), a framework that uses possibility theory to guide large language models in generating and testing hypotheses while preserving statistical validity. It defines abductive utility as a computable metric for discovery progress and presents possibility frontier search, the first algorithm for AASD, which maintains anytime validity and asymptotically achieves ε‑optimal abductive utility under suitable conditions. Experiments on both synthetic and real‑world scientific‑discovery tasks demonstrate strong performance of the proposed method.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv AI
Jun 30

Evidence-Informed LLM Beliefs for Continual Scientific Discovery

arXiv:2606. 29182v1 Announce Type: new Abstract: Open-ended scientific discovery with large language models (LLMs) increasingly operates as a long-horizon loop of hypothesis search and verification, where a reward signal guides which hypotheses to test next.

By Dhruv Agarwal, Reece Adamson, Andrew McCallum, Peter Clark, Ashish Sabharwal, Bodhisattwa Prasad Majumder
llmsrag
More like this →
arXiv Machine Learning
Sep 11

Measuring Progress in Reasoning Toward Mathematical Discovery with Automatic Verification

The paper introduces HorizonMath, a benchmark of 113 largely unsolved mathematical problems across eight domains, paired with an open-source framework for automated verification. It focuses on the generator‑verifier gap, targeting problems that are hard to discover but easy to verify computationally, thereby avoiding costly formal proof verification or manual review. Using this framework, the authors found six novel solutions—three each from GPT‑5.4 Pro and GPT‑5.6 Sol—demonstrating that current models can contribute to mathematical research, while most state‑of‑the‑art models score below 10%.

By Erik Y. Wang, Sumeet R. Motwani, James V. Roggeveen, Eliot Hodges, Dulhan Jayalath, Charles London, Kalyan Ramakrishnan, Jakob Foerster, Cheng Zhang, Flaviu Cipcigan, Philip Torr, Alessandro Abate
llmsbenchmarks
More like this →