@inproceedings{li-etal-2025-frontierscience,
title = "{F}rontier{S}cience Bench: Evaluating {AI} Research Capabilities in {LLM}s",
author = "Li, Matthew and
Torres-Garcia, Santiago and
Halder, Shayan and
Kuppa, Phani and
O{'}Brien, Sean and
Sharma, Vasu and
Zhu, Kevin and
Dev, Sunishchal",
editor = "Kamalloo, Ehsan and
Gontier, Nicolas and
Lu, Xing Han and
Dziri, Nouha and
Murty, Shikhar and
Lacoste, Alexandre",
booktitle = "Proceedings of the 1st Workshop for Research on Agent Language Models (REALM 2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.realm-1.31/",
doi = "10.18653/v1/2025.realm-1.31",
pages = "428--453",
ISBN = "979-8-89176-264-0",
abstract = "Large language models (LLMs) have shown remarkable capabilities across various tasks, yet their potential to reason about and construct scientific methodologies remains under explored. This work introduces a novel benchmark evaluating LLMs' capacity to predict methodological details in AI research papers. We construct a dataset of 88 papers with redacted methodology sections and zero-shot prompt several state-of-the-art LLMs to generate methodology predictions. Our evaluation framework then employs a LLM-as-judge system with multiple LLM judges, majority voting, and self-omission techniques to minimize biases. We validate our LLM judge scores against human judgments. We then briefly analyze the judging results of our zero-shot prediction pipeline, suggesting that even state-of-the-art LLMs struggle with the task of methodology generation without more advanced techniques. This benchmark lays the groundwork for future research into evaluating LLMs' potential for aiding in AI research."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="li-etal-2025-frontierscience">
<titleInfo>
<title>FrontierScience Bench: Evaluating AI Research Capabilities in LLMs</title>
</titleInfo>
<name type="personal">
<namePart type="given">Matthew</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Santiago</namePart>
<namePart type="family">Torres-Garcia</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Shayan</namePart>
<namePart type="family">Halder</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Phani</namePart>
<namePart type="family">Kuppa</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sean</namePart>
<namePart type="family">O’Brien</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Vasu</namePart>
<namePart type="family">Sharma</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kevin</namePart>
<namePart type="family">Zhu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sunishchal</namePart>
<namePart type="family">Dev</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 1st Workshop for Research on Agent Language Models (REALM 2025)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Ehsan</namePart>
<namePart type="family">Kamalloo</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nicolas</namePart>
<namePart type="family">Gontier</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xing</namePart>
<namePart type="given">Han</namePart>
<namePart type="family">Lu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nouha</namePart>
<namePart type="family">Dziri</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Shikhar</namePart>
<namePart type="family">Murty</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alexandre</namePart>
<namePart type="family">Lacoste</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Vienna, Austria</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-264-0</identifier>
</relatedItem>
<abstract>Large language models (LLMs) have shown remarkable capabilities across various tasks, yet their potential to reason about and construct scientific methodologies remains under explored. This work introduces a novel benchmark evaluating LLMs’ capacity to predict methodological details in AI research papers. We construct a dataset of 88 papers with redacted methodology sections and zero-shot prompt several state-of-the-art LLMs to generate methodology predictions. Our evaluation framework then employs a LLM-as-judge system with multiple LLM judges, majority voting, and self-omission techniques to minimize biases. We validate our LLM judge scores against human judgments. We then briefly analyze the judging results of our zero-shot prediction pipeline, suggesting that even state-of-the-art LLMs struggle with the task of methodology generation without more advanced techniques. This benchmark lays the groundwork for future research into evaluating LLMs’ potential for aiding in AI research.</abstract>
<identifier type="citekey">li-etal-2025-frontierscience</identifier>
<identifier type="doi">10.18653/v1/2025.realm-1.31</identifier>
<location>
<url>https://aclanthology.org/2025.realm-1.31/</url>
</location>
<part>
<date>2025-07</date>
<extent unit="page">
<start>428</start>
<end>453</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T FrontierScience Bench: Evaluating AI Research Capabilities in LLMs
%A Li, Matthew
%A Torres-Garcia, Santiago
%A Halder, Shayan
%A Kuppa, Phani
%A O’Brien, Sean
%A Sharma, Vasu
%A Zhu, Kevin
%A Dev, Sunishchal
%Y Kamalloo, Ehsan
%Y Gontier, Nicolas
%Y Lu, Xing Han
%Y Dziri, Nouha
%Y Murty, Shikhar
%Y Lacoste, Alexandre
%S Proceedings of the 1st Workshop for Research on Agent Language Models (REALM 2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-264-0
%F li-etal-2025-frontierscience
%X Large language models (LLMs) have shown remarkable capabilities across various tasks, yet their potential to reason about and construct scientific methodologies remains under explored. This work introduces a novel benchmark evaluating LLMs’ capacity to predict methodological details in AI research papers. We construct a dataset of 88 papers with redacted methodology sections and zero-shot prompt several state-of-the-art LLMs to generate methodology predictions. Our evaluation framework then employs a LLM-as-judge system with multiple LLM judges, majority voting, and self-omission techniques to minimize biases. We validate our LLM judge scores against human judgments. We then briefly analyze the judging results of our zero-shot prediction pipeline, suggesting that even state-of-the-art LLMs struggle with the task of methodology generation without more advanced techniques. This benchmark lays the groundwork for future research into evaluating LLMs’ potential for aiding in AI research.
%R 10.18653/v1/2025.realm-1.31
%U https://aclanthology.org/2025.realm-1.31/
%U https://doi.org/10.18653/v1/2025.realm-1.31
%P 428-453
Markdown (Informal)
[FrontierScience Bench: Evaluating AI Research Capabilities in LLMs](https://aclanthology.org/2025.realm-1.31/) (Li et al., REALM 2025)
ACL
- Matthew Li, Santiago Torres-Garcia, Shayan Halder, Phani Kuppa, Sean O’Brien, Vasu Sharma, Kevin Zhu, and Sunishchal Dev. 2025. FrontierScience Bench: Evaluating AI Research Capabilities in LLMs. In Proceedings of the 1st Workshop for Research on Agent Language Models (REALM 2025), pages 428–453, Vienna, Austria. Association for Computational Linguistics.