# The need for verification in artificial intelligence-driven scientific discovery

Review · Cristina Cornelio, Takuya Ito, Ryan Cory-Wright, Sanjeeb Dash, Lior Horesh C

- Archive id: doi:10.1098/rsta.2024.0591
- Published: 2026-04-09
- Venue: Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences
- DOI: https://doi.org/10.1098/rsta.2024.0591
- PMID: 41952583
- Citations: 3
- Source URL: https://doi.org/10.1098/rsta.2024.0591
- PDF: https://royalsocietypublishing.org/rsta/article-pdf/doi/10.1098/rsta.2024.0591/6131801/rsta.2024.0591.pdf
- Provenance: openalex

## Abstract

Artificial intelligence (AI) is transforming the practice of science. Machine learning (ML) 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 advanced. In this article, we trace the historical development of scientific discovery, examine how AI is reshaping established practices for 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. This article is part of the discussion meeting issue 'Symbolic regression in the physical sciences'.

_Abstract reproduced under CC-BY (publisher license, via OpenAlex)._

Topics: [Scaling Laws & Compute-Optimal Training](https://space-kitty.com/topics/scaling-laws)

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