@inproceedings{wang-etal-2026-tree,
title = "{THE}-Tree: Can Tracing Historical Evolution Enhance Scientific Verification and Reasoning?",
author = "Wang, Xin and
Liu, Jiyao and
Xiao, Yulong and
Ning, Junzhi and
Zhang, Hairong and
Liu, Lihao and
He, Junjun and
Shi, Botian and
Yu, Kaicheng",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1831/",
pages = "39091--39112",
ISBN = "979-8-89176-462-0",
abstract = "Large Language Models (LLMs) are accelerating scientific idea generation, but rigorously evaluating these numerous, often superficial, AI-generated propositions for novelty and factual accuracy is a critical bottleneck; manual verification is too slow. Existing validation methods are inadequate: LLMs as standalone verifiers may hallucinate and lack domain knowledge (our findings show {\textasciitilde}60{\%} unawareness of relevant papers in specific domains), while traditional citation networks lack explicit causality and narrative surveys are unstructured. This underscores a core challenge: the absence of structured, verifiable, and causally-linked historical data of scientific evolution. To address this, we introduce \textbf{THE-Tree} (\textbf{T}echnology \textbf{H}istory \textbf{E}volution \textbf{Tree}), a computational framework that constructs such domain-specific evolution trees from scientific literature. THE-Tree employs a search algorithm to explore evolutionary paths. During its node expansion, it utilizes a novel ``Think-Verbalize-Cite-Verify'' process: an LLM proposes potential advancements and cites supporting literature. Critically, each proposed evolutionary link is then validated for logical coherence and evidential support by a recovered natural language inference mechanism that interrogates the cited literature, ensuring that each step is grounded. We construct and validate 88 THE-Trees across diverse domains and release a benchmark dataset including up to 71k fact verifications covering 27k papers to foster further research. Experiments demonstrate that i) in graph completion, our THE-Tree improves hit@1 by 8{\%} to 14{\%} across multiple models compared to traditional citation networks; ii) for predicting future scientific developments, it improves hit@1 metric by nearly 10{\%}; and iii) when combined with other methods, it boosts the performance of evaluating important scientific papers by almost 100{\%}. By constructing explicit, verifiable pathways of scientific progression, THE-Tree provides a robust historical foundation for evaluating new hypotheses (human or AI-generated) and enables a computable science history, fostering evidence-based AI-driven scientific discovery."
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<abstract>Large Language Models (LLMs) are accelerating scientific idea generation, but rigorously evaluating these numerous, often superficial, AI-generated propositions for novelty and factual accuracy is a critical bottleneck; manual verification is too slow. Existing validation methods are inadequate: LLMs as standalone verifiers may hallucinate and lack domain knowledge (our findings show ~60% unawareness of relevant papers in specific domains), while traditional citation networks lack explicit causality and narrative surveys are unstructured. This underscores a core challenge: the absence of structured, verifiable, and causally-linked historical data of scientific evolution. To address this, we introduce THE-Tree (Technology History Evolution Tree), a computational framework that constructs such domain-specific evolution trees from scientific literature. THE-Tree employs a search algorithm to explore evolutionary paths. During its node expansion, it utilizes a novel “Think-Verbalize-Cite-Verify” process: an LLM proposes potential advancements and cites supporting literature. Critically, each proposed evolutionary link is then validated for logical coherence and evidential support by a recovered natural language inference mechanism that interrogates the cited literature, ensuring that each step is grounded. We construct and validate 88 THE-Trees across diverse domains and release a benchmark dataset including up to 71k fact verifications covering 27k papers to foster further research. Experiments demonstrate that i) in graph completion, our THE-Tree improves hit@1 by 8% to 14% across multiple models compared to traditional citation networks; ii) for predicting future scientific developments, it improves hit@1 metric by nearly 10%; and iii) when combined with other methods, it boosts the performance of evaluating important scientific papers by almost 100%. By constructing explicit, verifiable pathways of scientific progression, THE-Tree provides a robust historical foundation for evaluating new hypotheses (human or AI-generated) and enables a computable science history, fostering evidence-based AI-driven scientific discovery.</abstract>
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%0 Conference Proceedings
%T THE-Tree: Can Tracing Historical Evolution Enhance Scientific Verification and Reasoning?
%A Wang, Xin
%A Liu, Jiyao
%A Xiao, Yulong
%A Ning, Junzhi
%A Zhang, Hairong
%A Liu, Lihao
%A He, Junjun
%A Shi, Botian
%A Yu, Kaicheng
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F wang-etal-2026-tree
%X Large Language Models (LLMs) are accelerating scientific idea generation, but rigorously evaluating these numerous, often superficial, AI-generated propositions for novelty and factual accuracy is a critical bottleneck; manual verification is too slow. Existing validation methods are inadequate: LLMs as standalone verifiers may hallucinate and lack domain knowledge (our findings show ~60% unawareness of relevant papers in specific domains), while traditional citation networks lack explicit causality and narrative surveys are unstructured. This underscores a core challenge: the absence of structured, verifiable, and causally-linked historical data of scientific evolution. To address this, we introduce THE-Tree (Technology History Evolution Tree), a computational framework that constructs such domain-specific evolution trees from scientific literature. THE-Tree employs a search algorithm to explore evolutionary paths. During its node expansion, it utilizes a novel “Think-Verbalize-Cite-Verify” process: an LLM proposes potential advancements and cites supporting literature. Critically, each proposed evolutionary link is then validated for logical coherence and evidential support by a recovered natural language inference mechanism that interrogates the cited literature, ensuring that each step is grounded. We construct and validate 88 THE-Trees across diverse domains and release a benchmark dataset including up to 71k fact verifications covering 27k papers to foster further research. Experiments demonstrate that i) in graph completion, our THE-Tree improves hit@1 by 8% to 14% across multiple models compared to traditional citation networks; ii) for predicting future scientific developments, it improves hit@1 metric by nearly 10%; and iii) when combined with other methods, it boosts the performance of evaluating important scientific papers by almost 100%. By constructing explicit, verifiable pathways of scientific progression, THE-Tree provides a robust historical foundation for evaluating new hypotheses (human or AI-generated) and enables a computable science history, fostering evidence-based AI-driven scientific discovery.
%U https://aclanthology.org/2026.findings-emnlp.1831/
%P 39091-39112
Markdown (Informal)
[THE-Tree: Can Tracing Historical Evolution Enhance Scientific Verification and Reasoning?](https://aclanthology.org/2026.findings-emnlp.1831/) (Wang et al., Findings 2026)
ACL
- Xin Wang, Jiyao Liu, Yulong Xiao, Junzhi Ning, Hairong Zhang, Lihao Liu, Junjun He, Botian Shi, and Kaicheng Yu. 2026. THE-Tree: Can Tracing Historical Evolution Enhance Scientific Verification and Reasoning?. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 39091–39112, Budapest, Hungary. Association for Computational Linguistics.