Anastasiya Belyaeva

Gheorghe Comanici

Marc Coram

Hao Cui

Jake Garrison

Renee Johnston Anton Kast

Cory Y. McLean

Peter C. Norgaard

Zahra Shamsi

David Smalling

James Thompson

Subhashini Venugopalan

Brian P Williams

Chujun He

Sarah Martinson

Martyna Plomecka

Lai Wei

Yuchen Zhou

Qian-Ze Zhu … (voir 21 de plus)

Matthew Abraham

Erica Brand

Anna Bulanova

Chris Co

Scott Ellsworth

Grace Joseph

Malcolm Kane

Ryan K. Krueger

Johan Kartiwa

D. Liebling

Jan-Matthis Lueckmann

Paul Raccuglia

Xuefei Wang

Katherine Chou

James Manyika

Yossi Matias

J.C. Platt

Lizzie Dorfman

Shibl Mourad

Michael P. Brenner

The cycle of scientific discovery is frequently bottlenecked by the slow, manual creation of software to support computational experiments. … (voir plus)To address this, we present an AI system that creates expert-level scientific software whose goal is to maximize a quality metric. The system uses a Large Language Model (LLM) and Tree Search (TS) to systematically improve the quality metric and intelligently navigate the large space of possible solutions. The system achieves expert-level results when it explores and integrates complex research ideas from external sources. The effectiveness of tree search is demonstrated across a wide range of benchmarks. In bioinformatics, it discovered 40 novel methods for single-cell data analysis that outperformed the top human-developed methods on a public leaderboard. In epidemiology, it generated 14 models that outperformed the CDC ensemble and all other individual models for forecasting COVID-19 hospitalizations. Our method also produced state-of-the-art software for geospatial analysis, neural activity prediction in zebrafish, time series forecasting and numerical solution of integrals. By devising and implementing novel solutions to diverse tasks, the system represents a significant step towards accelerating scientific progress.

2025-09-08

ArXiv (prépublication)

An AI system to help scientists write expert-level empirical software

Eser Aygün

Gheorghe Comanici

Marc Coram

Hao Cui

Jake Garrison

Renee Johnston Anton Kast

Cory Y. McLean

Peter C. Norgaard

Zahra Shamsi

David Smalling

James Thompson

Subhashini Venugopalan

Brian P Williams

Chujun He

Sarah Martinson

Martyna Plomecka

Lai Wei

Yuchen Zhou

Qian-Ze Zhu … (voir 21 de plus)

Matthew Abraham

Erica Brand

Anna Bulanova

Chris Co

Scott Ellsworth

Grace Joseph

Malcolm Kane

Ryan K. Krueger

Johan Kartiwa

D. Liebling

Jan-Matthis Lueckmann

Paul Raccuglia

Xuefei Wang

Katherine Chou

James Manyika

Yossi Matias

J.C. Platt

Lizzie Dorfman

Shibl Mourad

Michael P. Brenner

The cycle of scientific discovery is frequently bottlenecked by the slow, manual creation of software to support computational experiments. … (voir plus)To address this, we present an AI system that creates expert-level scientific software whose goal is to maximize a quality metric. The system uses a Large Language Model (LLM) and Tree Search (TS) to systematically improve the quality metric and intelligently navigate the large space of possible solutions. The system achieves expert-level results when it explores and integrates complex research ideas from external sources. The effectiveness of tree search is demonstrated across a wide range of benchmarks. In bioinformatics, it discovered 40 novel methods for single-cell data analysis that outperformed the top human-developed methods on a public leaderboard. In epidemiology, it generated 14 models that outperformed the CDC ensemble and all other individual models for forecasting COVID-19 hospitalizations. Our method also produced state-of-the-art software for geospatial analysis, neural activity prediction in zebrafish, time series forecasting and numerical solution of integrals. By devising and implementing novel solutions to diverse tasks, the system represents a significant step towards accelerating scientific progress.

2025-09-08

ArXiv (prépublication)

An AI system to help scientists write expert-level empirical software

Eser Aygün

Gheorghe Comanici

Marc Coram

Hao Cui

Jake Garrison

Renee Johnston Anton Kast

Cory Y. McLean

Peter C. Norgaard

Zahra Shamsi

David Smalling

James Thompson

Subhashini Venugopalan

Brian P Williams

Chujun He

Sarah Martinson

Martyna Plomecka

Lai Wei

Yuchen Zhou

Qian-Ze Zhu … (voir 21 de plus)

Matthew Abraham

Erica Brand

Anna Bulanova

Chris Co

Scott Ellsworth

Grace Joseph

Malcolm Kane

Ryan K. Krueger

Johan Kartiwa

D. Liebling

Jan-Matthis Lueckmann

Paul Raccuglia

Xuefei Wang

Katherine Chou

James Manyika

Yossi Matias

J.C. Platt

Lizzie Dorfman

Shibl Mourad

Michael P. Brenner

The cycle of scientific discovery is frequently bottlenecked by the slow, manual creation of software to support computational experiments. … (voir plus)To address this, we present an AI system that creates expert-level scientific software whose goal is to maximize a quality metric. The system uses a Large Language Model (LLM) and Tree Search (TS) to systematically improve the quality metric and intelligently navigate the large space of possible solutions. The system achieves expert-level results when it explores and integrates complex research ideas from external sources. The effectiveness of tree search is demonstrated across a wide range of benchmarks. In bioinformatics, it discovered 40 novel methods for single-cell data analysis that outperformed the top human-developed methods on a public leaderboard. In epidemiology, it generated 14 models that outperformed the CDC ensemble and all other individual models for forecasting COVID-19 hospitalizations. Our method also produced state-of-the-art software for geospatial analysis, neural activity prediction in zebrafish, time series forecasting and numerical solution of integrals. By devising and implementing novel solutions to diverse tasks, the system represents a significant step towards accelerating scientific progress.

2025-09-08

ArXiv (prépublication)

An AI system to help scientists write expert-level empirical software

Eser Aygün

Gheorghe Comanici

Marc Coram

Hao Cui

Jake Garrison

Renee Johnston Anton Kast

Cory Y. McLean

Peter C. Norgaard

Zahra Shamsi

David Smalling

James Thompson

Subhashini Venugopalan

Brian P Williams

Chujun He

Sarah Martinson

Martyna Plomecka

Lai Wei

Yuchen Zhou

Qian-Ze Zhu … (voir 21 de plus)

Matthew Abraham

Erica Brand

Anna Bulanova

Chris Co

Scott Ellsworth

Grace Joseph

Malcolm Kane

Ryan K. Krueger

Johan Kartiwa

Daniel J. Liebling

Jan-Matthis Lueckmann

Paul Raccuglia

Xuefei Wang

Katherine Chou

James Manyika

Yossi Matias

J.C. Platt

Lizzie Dorfman

Shibl Mourad

Michael P. Brenner

The cycle of scientific discovery is frequently bottlenecked by the slow, manual creation of software to support computational experiments. … (voir plus)To address this, we present an AI system that creates expert-level scientific software whose goal is to maximize a quality metric. The system uses a Large Language Model (LLM) and Tree Search (TS) to systematically improve the quality metric and intelligently navigate the large space of possible solutions. The system achieves expert-level results when it explores and integrates complex research ideas from external sources. The effectiveness of tree search is demonstrated across a wide range of benchmarks. In bioinformatics, it discovered 40 novel methods for single-cell data analysis that outperformed the top human-developed methods on a public leaderboard. In epidemiology, it generated 14 models that outperformed the CDC ensemble and all other individual models for forecasting COVID-19 hospitalizations. Our method also produced state-of-the-art software for geospatial analysis, neural activity prediction in zebrafish, time series forecasting and numerical solution of integrals. By devising and implementing novel solutions to diverse tasks, the system represents a significant step towards accelerating scientific progress.

2025-09-08

ArXiv (prépublication)

doi.org