The need for verification in AI-driven scientific Discovery
File(s)
Accepted version
Author(s)
Cornelio, Cristina
Ito, Takuya
Cory-Wright, Ryan
Dash, Sanjeeb
Horesh, Lior
Type
Journal Article
Abstract
Artificial intelligence (AI) is transforming the practice of science. Machine learning and large language models (LLMs) can generate hypotheses at a scale and speed far exceeding traditional methods, offering the potential to accelerate discovery across diverse fields. However, the abundance of hypotheses introduces a critical challenge: without scalable and reliable mechanisms for verification, scientific progress risks being hindered rather than being advanced. In this article, we trace the historical development of scientific discovery, examine how AI is reshaping established practices for scientific discovery, and review the principal approaches, ranging from data-driven methods and knowledge-aware neural architectures to symbolic reasoning frameworks and LLM agents. While these systems can uncover patterns and propose candidate laws, their scientific value ultimately depends on rigorous and transparent verification, which we argue must be the cornerstone of AI-assisted discovery.
Date Acceptance
2025-12-08
Citation
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
ISSN
1364-503X
Publisher
The Royal Society
Journal / Book Title
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
Copyright Statement
Subject to copyright This paper is embargoed until publication. Once published the author’s accepted manuscript will be made available under a CC-BY License in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy).
License URL
Identifier
https://arxiv.org/abs/2509.01398
Publication Status
Accepted
Date Publish Online
2026-03-27