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ReCite: Agentic Reasoning for Faithful Citation

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.

read2 min views2 publishedSep 9, 2026
ReCite: Agentic Reasoning for Faithful Citation
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  [Submitted on 8 Sep 2026]


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Abstract: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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