THE-Tree: Can Tracing Historical Evolution Enhance Scientific Verification and Reasoning?

• URL: http://arxiv.org/abs/2506.21763v2
• Date: Mon, 21 Jul 2025 06:49:51 GMT
• Title: THE-Tree: Can Tracing Historical Evolution Enhance Scientific Verification and Reasoning?
• Authors: Xin Wang, Jiyao Liu, Yulong Xiao, Junzhi Ning, Lihao Liu, Junjun He, Botian Shi, Kaicheng Yu,
• Abstract summary: We introduce textbfTHE-Tree (textbfTechnology textbfHistory textbfEvolution Tree), a computational framework that constructs such domain-specific evolution trees from scientific literature.
• Score: 16.91455372359864
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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 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%.

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