@inproceedings{le-etal-2026-evonarrator,
title = "{E}vo{N}arrator: Modeling Scientific Evolution for Feasible Hypothesis Generation",
author = "Le, Xiaoying and
Qian, Pengfei and
Zhai, Yuanzhao and
Zhang, Xu and
Liu, Qian and
Dawei, Feng and
Ding, Bo",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.544/",
pages = "11846--11865",
ISBN = "979-8-89176-390-6",
abstract = "Scientific discovery evolution does not emerge in isolation but stems from the structural deepening and recombination of existing functionalities. However, current automated hypothesis generation methods, constrained by the statistical co-occurrence nature of Large Language Models (LLMs), lack perception of temporal causality and the ``evolutionary patterns'' inherent in scientific development. Consequently, they often yield superficial combinations that are logically infeasible. To address this, we propose EvoNarrator, a framework for hypothesis generation based on evolutionary narratives. We first extract structured P-M-L-F (Problem, Method, Limitation, Future Work) quadruples from citation networks. Subsequently, we introduce the SocketMatch mechanism, which eliminates logical disconnects between methods and problems by assessing their deep semantic compatibility. Finally, utilizing three macro patterns{---}Chain, Divergence, and Convergence{---}we constrain the generation process within historically logical derivation paths. Furthermore, double-blind expert reviews yielded an average score of 4.80/5.00 across novelty, feasibility, theoretical, and Logical. Additionally, hindcasting experiments validated its predictive foresight. Crucially, ablation studies indicate that integrating evolutionary patterns facilitates a paradigm shift from conservative incrementalism to theoretically grounded structural innovation. The code is available at https://github.com/xiyii-star/EvoNarrator."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="le-etal-2026-evonarrator">
<titleInfo>
<title>EvoNarrator: Modeling Scientific Evolution for Feasible Hypothesis Generation</title>
</titleInfo>
<name type="personal">
<namePart type="given">Xiaoying</namePart>
<namePart type="family">Le</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Pengfei</namePart>
<namePart type="family">Qian</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yuanzhao</namePart>
<namePart type="family">Zhai</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xu</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Qian</namePart>
<namePart type="family">Liu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Feng</namePart>
<namePart type="family">Dawei</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Bo</namePart>
<namePart type="family">Ding</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Maria</namePart>
<namePart type="family">Liakata</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Viviane</namePart>
<namePart type="given">P</namePart>
<namePart type="family">Moreira</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jiajun</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">David</namePart>
<namePart type="family">Jurgens</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">San Diego, California, United States</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-390-6</identifier>
</relatedItem>
<abstract>Scientific discovery evolution does not emerge in isolation but stems from the structural deepening and recombination of existing functionalities. However, current automated hypothesis generation methods, constrained by the statistical co-occurrence nature of Large Language Models (LLMs), lack perception of temporal causality and the “evolutionary patterns” inherent in scientific development. Consequently, they often yield superficial combinations that are logically infeasible. To address this, we propose EvoNarrator, a framework for hypothesis generation based on evolutionary narratives. We first extract structured P-M-L-F (Problem, Method, Limitation, Future Work) quadruples from citation networks. Subsequently, we introduce the SocketMatch mechanism, which eliminates logical disconnects between methods and problems by assessing their deep semantic compatibility. Finally, utilizing three macro patterns—Chain, Divergence, and Convergence—we constrain the generation process within historically logical derivation paths. Furthermore, double-blind expert reviews yielded an average score of 4.80/5.00 across novelty, feasibility, theoretical, and Logical. Additionally, hindcasting experiments validated its predictive foresight. Crucially, ablation studies indicate that integrating evolutionary patterns facilitates a paradigm shift from conservative incrementalism to theoretically grounded structural innovation. The code is available at https://github.com/xiyii-star/EvoNarrator.</abstract>
<identifier type="citekey">le-etal-2026-evonarrator</identifier>
<location>
<url>https://aclanthology.org/2026.acl-long.544/</url>
</location>
<part>
<date>2026-07</date>
<extent unit="page">
<start>11846</start>
<end>11865</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T EvoNarrator: Modeling Scientific Evolution for Feasible Hypothesis Generation
%A Le, Xiaoying
%A Qian, Pengfei
%A Zhai, Yuanzhao
%A Zhang, Xu
%A Liu, Qian
%A Dawei, Feng
%A Ding, Bo
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F le-etal-2026-evonarrator
%X Scientific discovery evolution does not emerge in isolation but stems from the structural deepening and recombination of existing functionalities. However, current automated hypothesis generation methods, constrained by the statistical co-occurrence nature of Large Language Models (LLMs), lack perception of temporal causality and the “evolutionary patterns” inherent in scientific development. Consequently, they often yield superficial combinations that are logically infeasible. To address this, we propose EvoNarrator, a framework for hypothesis generation based on evolutionary narratives. We first extract structured P-M-L-F (Problem, Method, Limitation, Future Work) quadruples from citation networks. Subsequently, we introduce the SocketMatch mechanism, which eliminates logical disconnects between methods and problems by assessing their deep semantic compatibility. Finally, utilizing three macro patterns—Chain, Divergence, and Convergence—we constrain the generation process within historically logical derivation paths. Furthermore, double-blind expert reviews yielded an average score of 4.80/5.00 across novelty, feasibility, theoretical, and Logical. Additionally, hindcasting experiments validated its predictive foresight. Crucially, ablation studies indicate that integrating evolutionary patterns facilitates a paradigm shift from conservative incrementalism to theoretically grounded structural innovation. The code is available at https://github.com/xiyii-star/EvoNarrator.
%U https://aclanthology.org/2026.acl-long.544/
%P 11846-11865
Markdown (Informal)
[EvoNarrator: Modeling Scientific Evolution for Feasible Hypothesis Generation](https://aclanthology.org/2026.acl-long.544/) (Le et al., ACL 2026)
ACL
- Xiaoying Le, Pengfei Qian, Yuanzhao Zhai, Xu Zhang, Qian Liu, Feng Dawei, and Bo Ding. 2026. EvoNarrator: Modeling Scientific Evolution for Feasible Hypothesis Generation. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 11846–11865, San Diego, California, United States. Association for Computational Linguistics.