arXiv Open Access 2025

Benchmarking the Discovery Engine

Jack Foxabbott Arush Tagade Andrew Cusick Robbie McCorkell Leo McKee-Reid +4 lainnya

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Abstrak

The Discovery Engine is a general purpose automated system for scientific discovery, which combines machine learning with state-of-the-art ML interpretability to enable rapid and robust scientific insight across diverse datasets. In this paper, we benchmark the Discovery Engine against five recent peer-reviewed scientific publications applying machine learning across medicine, materials science, social science, and environmental science. In each case, the Discovery Engine matches or exceeds prior predictive performance while also generating deeper, more actionable insights through rich interpretability artefacts. These results demonstrate its potential as a new standard for automated, interpretable scientific modelling that enables complex knowledge discovery from data.

Topik & Kata Kunci

cs.LG

Penulis (9)

Jack Foxabbott

Arush Tagade

Andrew Cusick

Robbie McCorkell

Leo McKee-Reid

Jugal Patel

Jamie Rumbelow

Jessica Rumbelow

Zohreh Shams

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APA MLA BibTeX

Foxabbott, J., Tagade, A., Cusick, A., McCorkell, R., McKee-Reid, L., Patel, J. et al. (2025). Benchmarking the Discovery Engine. https://arxiv.org/abs/2507.00964

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Informasi Jurnal

Tahun Terbit: 2025
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓