ReCite: Agentic Reasoning for Faithful Citation Recommendation

Paper Overview

• Field: cs.CL
• Authors: Yuyang Huang, Bobo Li, Jiajia Song, Yuzhe Ding, Chong Teng, Fei Li, Donghong Ji
• Published: 2026-09-08
• arXiv: 2609.09156

Background

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.

The Problem

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.

Proposed Solution: ReCite

The authors argue that accurate citation requires a shift from similarity-based search to active, claim-level reasoning. ReCite is a decoupled agentic framework that orchestrates:
• Location perception — understanding where in the text the citation is needed
• Intent-aware query planning — formulating retrieval queries based on the claim's intent
• Reflective verification — checking claim-evidence consistency

Trained on synthesized reasoning trajectories, the agent verifies claim-evidence consistency and triggers self-correction loops when retrieved candidates lack logical support.

Results

Experiments demonstrate that the 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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