{"slug": "recite-agentic-reasoning-for-faithful-citation", "title": "ReCite: Agentic Reasoning for Faithful Citation", "summary": "Researchers introduced ReCite, a decoupled agentic framework that uses claim-level reasoning to improve citation accuracy in academic writing, outperforming state-of-the-art massive generative models in strict citation accuracy. The framework, trained on synthesized reasoning trajectories, orchestrates location perception, intent-aware query planning, and reflective verification to ensure cited papers logically support claims, addressing the misattribution problem in retrieval-augmented systems.", "body_md": "# Computer Science > Computation and Language\n\n  [Submitted on 8 Sep 2026]\n\n# Title:ReCite: Agentic Reasoning for Faithful Citation\n\n[View PDF](/pdf/2609.09156v1)\n\n[HTML (experimental)](https://arxiv.org/html/2609.09156v1)\n\nAbstract: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.\n    \n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer \n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers \n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps \n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations \n\n*(*[What are Smart Citations?](https://www.scite.ai/))\n# Code, Data and Media Associated with this Article\n\nalphaXiv \n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers \n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub \n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub \n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face \n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast \n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))\n# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower \n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender \n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))\n# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [**Learn more about arXivLabs**](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/recite-agentic-reasoning-for-faithful-citation", "canonical_source": "http://arxiv.org/abs/2609.09156v1", "published_at": "2026-09-09 14:35:31+00:00", "updated_at": "2026-09-09 14:43:02.477972+00:00", "lang": "en", "topics": ["artificial-intelligence", "natural-language-processing", "ai-research", "ai-agents"], "entities": ["ReCite"], "alternates": {"html": "https://wpnews.pro/news/recite-agentic-reasoning-for-faithful-citation", "markdown": "https://wpnews.pro/news/recite-agentic-reasoning-for-faithful-citation.md", "text": "https://wpnews.pro/news/recite-agentic-reasoning-for-faithful-citation.txt", "jsonld": "https://wpnews.pro/news/recite-agentic-reasoning-for-faithful-citation.jsonld"}}