@inproceedings{jansen-etal-2025-codescientist,
title = "{C}ode{S}cientist: End-to-End Semi-Automated Scientific Discovery with Code-based Experimentation",
author = "Jansen, Peter and
Tafjord, Oyvind and
Radensky, Marissa and
Siangliulue, Pao and
Hope, Tom and
Dalvi Mishra, Bhavana and
Majumder, Bodhisattwa Prasad and
Weld, Daniel S and
Clark, Peter",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.692/",
doi = "10.18653/v1/2025.findings-acl.692",
pages = "13370--13467",
ISBN = "979-8-89176-256-5",
abstract = "Despite the surge of interest in autonomous scientific discovery (ASD) of software artifacts (e.g., improved ML algorithms), current ASD systems face two key limitations: (1) they largely explore variants of existing codebases or similarly constrained design spaces, and (2) they produce large volumes of research artifacts (such as automatically generated papers and code) that are typically evaluated using conference-style paper review with limited evaluation of code. In this work we introduce CodeScientist, a novel ASD system that frames ideation and experiment construction as a form of genetic search jointly over combinations of research articles and codeblocks defining common actions in a domain (like prompting a language model). We use this paradigm to conduct hundreds of automated experiments on machine-generated ideas broadly in the domain of agents and virtual environments, with the system returning 19 discoveries, 6 of which were judged as being both at least minimally sound and incrementally novel after a multi-faceted evaluation beyond that typically conducted in prior work, including external (conference-style) review, code review, and replication attempts. Moreover, the discoveries span new tasks, agents, metrics, and data, suggesting a qualitative shift from benchmark optimization to broader discoveries."
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<abstract>Despite the surge of interest in autonomous scientific discovery (ASD) of software artifacts (e.g., improved ML algorithms), current ASD systems face two key limitations: (1) they largely explore variants of existing codebases or similarly constrained design spaces, and (2) they produce large volumes of research artifacts (such as automatically generated papers and code) that are typically evaluated using conference-style paper review with limited evaluation of code. In this work we introduce CodeScientist, a novel ASD system that frames ideation and experiment construction as a form of genetic search jointly over combinations of research articles and codeblocks defining common actions in a domain (like prompting a language model). We use this paradigm to conduct hundreds of automated experiments on machine-generated ideas broadly in the domain of agents and virtual environments, with the system returning 19 discoveries, 6 of which were judged as being both at least minimally sound and incrementally novel after a multi-faceted evaluation beyond that typically conducted in prior work, including external (conference-style) review, code review, and replication attempts. Moreover, the discoveries span new tasks, agents, metrics, and data, suggesting a qualitative shift from benchmark optimization to broader discoveries.</abstract>
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%0 Conference Proceedings
%T CodeScientist: End-to-End Semi-Automated Scientific Discovery with Code-based Experimentation
%A Jansen, Peter
%A Tafjord, Oyvind
%A Radensky, Marissa
%A Siangliulue, Pao
%A Hope, Tom
%A Dalvi Mishra, Bhavana
%A Majumder, Bodhisattwa Prasad
%A Weld, Daniel S.
%A Clark, Peter
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F jansen-etal-2025-codescientist
%X Despite the surge of interest in autonomous scientific discovery (ASD) of software artifacts (e.g., improved ML algorithms), current ASD systems face two key limitations: (1) they largely explore variants of existing codebases or similarly constrained design spaces, and (2) they produce large volumes of research artifacts (such as automatically generated papers and code) that are typically evaluated using conference-style paper review with limited evaluation of code. In this work we introduce CodeScientist, a novel ASD system that frames ideation and experiment construction as a form of genetic search jointly over combinations of research articles and codeblocks defining common actions in a domain (like prompting a language model). We use this paradigm to conduct hundreds of automated experiments on machine-generated ideas broadly in the domain of agents and virtual environments, with the system returning 19 discoveries, 6 of which were judged as being both at least minimally sound and incrementally novel after a multi-faceted evaluation beyond that typically conducted in prior work, including external (conference-style) review, code review, and replication attempts. Moreover, the discoveries span new tasks, agents, metrics, and data, suggesting a qualitative shift from benchmark optimization to broader discoveries.
%R 10.18653/v1/2025.findings-acl.692
%U https://aclanthology.org/2025.findings-acl.692/
%U https://doi.org/10.18653/v1/2025.findings-acl.692
%P 13370-13467
Markdown (Informal)
[CodeScientist: End-to-End Semi-Automated Scientific Discovery with Code-based Experimentation](https://aclanthology.org/2025.findings-acl.692/) (Jansen et al., Findings 2025)
ACL
- Peter Jansen, Oyvind Tafjord, Marissa Radensky, Pao Siangliulue, Tom Hope, Bhavana Dalvi Mishra, Bodhisattwa Prasad Majumder, Daniel S Weld, and Peter Clark. 2025. CodeScientist: End-to-End Semi-Automated Scientific Discovery with Code-based Experimentation. In Findings of the Association for Computational Linguistics: ACL 2025, pages 13370–13467, Vienna, Austria. Association for Computational Linguistics.