# CiteAudit: You Cited It, But Did You Read It? A Benchmark for Verifying Scientific References in the LLM Era

arXiv: https://arxiv.org/abs/2602.23452 · HTML: https://mlsanity.com/paper/2602.23452/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 30 references that are in the map.
- First posted on arXiv 26 Feb 2026.

## Builds on (most cited first, 12 of 12)

- [Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory](https://mlsanity.com/paper/2504.19413/) (2025)
- [A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models](https://mlsanity.com/paper/2401.01313/) (2024)
- [From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review](https://mlsanity.com/paper/2504.19678/) (2025)
- [AutoData: A Multi-Agent System for Open Web Data Collection](https://mlsanity.com/paper/2505.15859/) (2025)
- [HalluCitation Matters: Revealing the Impact of Hallucinated References with 300 Hallucinated Papers in ACL Conferences](https://mlsanity.com/paper/2601.18724/) (2026)
- [MOPI-HFRS: A Multi-objective Personalized Health-aware Food Recommendation System with LLM-enhanced Interpretation](https://mlsanity.com/paper/2412.08847/) (2024)
- [NGQA: A Nutritional Graph Question Answering Benchmark for Personalized Health-aware Nutritional Reasoning](https://mlsanity.com/paper/2412.15547/) (2024)
- [A review of the literature on citation impact indicators](https://mlsanity.com/paper/1507.02099/) (2015)
- [LLMs4All: A Review of Large Language Models Across Academic Disciplines](https://mlsanity.com/paper/2509.19580/) (2025)
- [Hallucination to Truth: A Review of Fact-Checking and Factuality Evaluation in Large Language Models](https://mlsanity.com/paper/2508.03860/) (2025)
- [NG-Router: Graph-Supervised Multi-Agent Collaboration for Nutrition Question Answering](https://mlsanity.com/paper/2510.09854/) (2025)
- [AI-Powered Citation Auditing: A Zero-Assumption Protocol for Systematic Reference Verification in Academic Research](https://mlsanity.com/paper/2511.04683/) (2025)

## Built on by (most cited first, 15 of 15)

- [Cited but Not Verified: Parsing and Evaluating Source Attribution in LLM Deep Research Agents](https://mlsanity.com/paper/2605.06635/) (2026)
- [Detecting and Correcting Reference Hallucinations in Commercial LLMs and Deep Research Agents](https://mlsanity.com/paper/2604.03173/) (2026)
- [Source or It Didn't Happen: A Multi-Agent Framework for Citation Hallucination Detection](https://mlsanity.com/paper/2605.08583/) (2026)
- [BibTeX Citation Errors in Scientific Publishing Agents: Evaluation and Mitigation](https://mlsanity.com/paper/2604.03159/) (2026)
- [HALLMARK: How to Diagnose LLM Citation Verifiers and When to Deploy Them](https://mlsanity.com/paper/2607.18360/) (2026)
- [Citation Grounding Measures the Oracle: Graph Coverage Determines Reported LLM Hallucination Rates in Law](https://mlsanity.com/paper/2606.00898/) (2026)
- [Phantom References: Hallucinated Citations That Survive Peer Review at Top-Tier Conferences](https://mlsanity.com/paper/2607.00738/) (2026)
- [sciwrite-lint: Verification Infrastructure for the Age of Science Vibe-Writing](https://mlsanity.com/paper/2604.08501/) (2026)
- [Pingquanqi (Equalizer): A Cross-Domain Sociotechnical Framework for Human-Agent Interaction Governance](https://mlsanity.com/paper/2606.26573/) (2026)
- [Evaluating and Guarding Citation Faithfulness in Agentic Scientific Synthesis](https://mlsanity.com/paper/2607.20527/) (2026)
- [VetScore: Risk-Weighted Fact Verification for Veterinary Long-Form QA with Citations](https://mlsanity.com/paper/2608.03675/) (2026)
- [Is this Citation on Point?](https://mlsanity.com/paper/2608.12571/) (2026)
- [ATTRICITE: Training an Open 4B Model for Citation Recovery toward Faithful Attribution](https://mlsanity.com/paper/2609.14248/) (2026)
- [PaperDoctor: Evidence-Grounded and Actionable Feedback for Scientific Papers in Progress](https://mlsanity.com/paper/2609.16995/) (2026)
- [How Is Automated Research Evaluated? A Survey of Benchmarks and Evaluation Practices](https://mlsanity.com/paper/2610.11877/) (2026)

## Abstract (arXiv)

Scientific research relies on citation integrity, yet large language models (LLMs) have introduced a critical risk: fabricated references that appear plausible but correspond to no real publications. As manual verification becomes infeasible and existing automated tools remain fragile, we introduce CiteAudit, a comprehensive benchmark and detection framework for hallucinated citations. We design a multi-agent verification pipeline that decomposes citation checking into metadata extraction, memory lookup, web-based retrieval, and final judgment. To evaluate this, we construct a large-scale, human-validated dataset spanning diverse domains and hallucination types. Experiments demonstrate that our framework achieves superior verification performance over state-of-the-art LLMs and commercial baselines. Our work provides the necessary infrastructure to audit citations at scale and safeguard the trustworthiness of scholarly discourse. Code is available at https://github.com/shiiiikw/CiteAudit.