[论文] ReCite: Agentic Reasoning for Faithful Citation

论文概要

研究领域: cs.CL 作者: Yuyang Huang, Bobo Li, Jiajia Song, Yuzhe Ding, Chong Teng, Fei Li, Donghong Ji 发布时间: 2026-09-08 arXiv: 2609.09156

中文摘要

准确的引用是学术写作的基石，追溯知识起源并支撑核心主张。然而，手动浏览日益增长的科学文献量越来越困难，促使人们依赖自动引用推荐。虽然现代检索增强架构在很大程度上缓解了虚构不存在论文的问题，但当前依赖语义相似性的系统仍在误归因方面挣扎，经常引用真实但逻辑上无法支持作者主张的论文。为解决这一挑战，我们认为准确引用需要从基于相似性的搜索转向主动的主张级推理。我们提出ReCite，一个解耦的智能体框架，协调位置感知、意图感知查询规划和反思验证。在合成推理轨迹上训练后，我们的智能体验证主张-证据一致性，并在检索到的候选缺乏逻辑支持时触发自校正循环。实验表明，我们的轻量级框架在严格引用准确性方面优于最先进的大规模生成模型。通过将文献匹配建立在可验证的逻辑而非语义重叠之上，ReCite为自动化学术写作奠定了可靠基础。

原文摘要

Accurate citations are the foundation of academic writing, tracing intellectual origins and substantiating core claims. However, manually navigating the growing volume of scientific literature is increasingly difficult, prompting reliance on automatic citation recommendation. While modern retrieval-augmented architectures have largely mitigated the fabrication of non-existent papers, current systems relying on semantic similarity struggle with misattribution, often citing authentic papers that fail to logically support the author's claim. To address this challenge, we argue that accurate citation requires a shift from similarity-based search to active, claim-level reasoning. We propose ReCite, a decoupled agentic framework that orchestrates location perception, intent-aware query planning, and reflective verification. Trained on synthesized reasoning trajectories, our agent verifies claim-evidence consistency and triggers self-correction loops when retrieved candidates lack logical support. Experiments demonstrate that our lightweight framework outperforms state-of-the-art massive generative models in strict citation accuracy. By grounding literature matching in verifiable logic rather than semantic overlap, ReCite establishes a reliable foundation for automated academic writing.

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*自动采集于 2026-09-10*

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