@inproceedings{li-etal-2025-chain-ideas,
title = "Chain of Ideas: Revolutionizing Research Via Novel Idea Development with {LLM} Agents",
author = "Li, Long and
Xu, Weiwen and
Guo, Jiayan and
Zhao, Ruochen and
Li, Xingxuan and
Yuan, Yuqian and
Zhang, Boqiang and
Jiang, Yuming and
Xin, Yifei and
Dang, Ronghao and
Rong, Yu and
Zhao, Deli and
Feng, Tian and
Bing, Lidong",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.477/",
pages = "8971--9004",
ISBN = "979-8-89176-335-7",
abstract = "Research ideation is crucial for scientific progress, but the exponential increase in scientific literature makes it challenging to stay updated and identify impactful directions. Recent developments in large language models(LLMs) offer a promising avenue to automate this process. However, existing methods for idea generation either trivially prompt LLMs or expose LLMs to extensive literature without indicating useful information. Inspired by human research processes, we propose a Chain-of-Ideas (CoI) agent, an LLM-based agent that organizes relevant literature in a chain structure to effectively mirror the progressive development in a research domain. This organization helps LLMs better grasp current advancements, thereby improving ideation capabilities. Further, we present Idea Arena, a protocol for evaluating idea-generation methods from different perspectives, which aligns closely with the preferences of human researchers. Experiments show that CoI agent consistently outperforms existing methods and matches human quality in idea generation. Moreover, CoI agent is budget-friendly, requiring only {\$}0.50 to generate a candidate idea and its experimental design."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="li-etal-2025-chain-ideas">
<titleInfo>
<title>Chain of Ideas: Revolutionizing Research Via Novel Idea Development with LLM Agents</title>
</titleInfo>
<name type="personal">
<namePart type="given">Long</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Weiwen</namePart>
<namePart type="family">Xu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jiayan</namePart>
<namePart type="family">Guo</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ruochen</namePart>
<namePart type="family">Zhao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xingxuan</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yuqian</namePart>
<namePart type="family">Yuan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Boqiang</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yuming</namePart>
<namePart type="family">Jiang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yifei</namePart>
<namePart type="family">Xin</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ronghao</namePart>
<namePart type="family">Dang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yu</namePart>
<namePart type="family">Rong</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Deli</namePart>
<namePart type="family">Zhao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tian</namePart>
<namePart type="family">Feng</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Lidong</namePart>
<namePart type="family">Bing</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-11</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Findings of the Association for Computational Linguistics: EMNLP 2025</title>
</titleInfo>
<name type="personal">
<namePart type="given">Christos</namePart>
<namePart type="family">Christodoulopoulos</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tanmoy</namePart>
<namePart type="family">Chakraborty</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Carolyn</namePart>
<namePart type="family">Rose</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Violet</namePart>
<namePart type="family">Peng</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Suzhou, China</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-335-7</identifier>
</relatedItem>
<abstract>Research ideation is crucial for scientific progress, but the exponential increase in scientific literature makes it challenging to stay updated and identify impactful directions. Recent developments in large language models(LLMs) offer a promising avenue to automate this process. However, existing methods for idea generation either trivially prompt LLMs or expose LLMs to extensive literature without indicating useful information. Inspired by human research processes, we propose a Chain-of-Ideas (CoI) agent, an LLM-based agent that organizes relevant literature in a chain structure to effectively mirror the progressive development in a research domain. This organization helps LLMs better grasp current advancements, thereby improving ideation capabilities. Further, we present Idea Arena, a protocol for evaluating idea-generation methods from different perspectives, which aligns closely with the preferences of human researchers. Experiments show that CoI agent consistently outperforms existing methods and matches human quality in idea generation. Moreover, CoI agent is budget-friendly, requiring only $0.50 to generate a candidate idea and its experimental design.</abstract>
<identifier type="citekey">li-etal-2025-chain-ideas</identifier>
<location>
<url>https://aclanthology.org/2025.findings-emnlp.477/</url>
</location>
<part>
<date>2025-11</date>
<extent unit="page">
<start>8971</start>
<end>9004</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Chain of Ideas: Revolutionizing Research Via Novel Idea Development with LLM Agents
%A Li, Long
%A Xu, Weiwen
%A Guo, Jiayan
%A Zhao, Ruochen
%A Li, Xingxuan
%A Yuan, Yuqian
%A Zhang, Boqiang
%A Jiang, Yuming
%A Xin, Yifei
%A Dang, Ronghao
%A Rong, Yu
%A Zhao, Deli
%A Feng, Tian
%A Bing, Lidong
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F li-etal-2025-chain-ideas
%X Research ideation is crucial for scientific progress, but the exponential increase in scientific literature makes it challenging to stay updated and identify impactful directions. Recent developments in large language models(LLMs) offer a promising avenue to automate this process. However, existing methods for idea generation either trivially prompt LLMs or expose LLMs to extensive literature without indicating useful information. Inspired by human research processes, we propose a Chain-of-Ideas (CoI) agent, an LLM-based agent that organizes relevant literature in a chain structure to effectively mirror the progressive development in a research domain. This organization helps LLMs better grasp current advancements, thereby improving ideation capabilities. Further, we present Idea Arena, a protocol for evaluating idea-generation methods from different perspectives, which aligns closely with the preferences of human researchers. Experiments show that CoI agent consistently outperforms existing methods and matches human quality in idea generation. Moreover, CoI agent is budget-friendly, requiring only $0.50 to generate a candidate idea and its experimental design.
%U https://aclanthology.org/2025.findings-emnlp.477/
%P 8971-9004
Markdown (Informal)
[Chain of Ideas: Revolutionizing Research Via Novel Idea Development with LLM Agents](https://aclanthology.org/2025.findings-emnlp.477/) (Li et al., Findings 2025)
ACL
- Long Li, Weiwen Xu, Jiayan Guo, Ruochen Zhao, Xingxuan Li, Yuqian Yuan, Boqiang Zhang, Yuming Jiang, Yifei Xin, Ronghao Dang, Yu Rong, Deli Zhao, Tian Feng, and Lidong Bing. 2025. Chain of Ideas: Revolutionizing Research Via Novel Idea Development with LLM Agents. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 8971–9004, Suzhou, China. Association for Computational Linguistics.