arXiv:2605.29522v2 Announce Type: replace
Abstract: As scientific literature grows rapidly and research increasingly involves AI agents, automated survey generation has become a key capability for bo...
By Ziyue Yang, Da Ma, Hanqi Li, Zijian Wang, Tiancheng Huang, Zijian Hu, Chenrun Wang, Yunzhe Zhang, Xiaobao Wu, Kai Yu, Lu Chen
The paper introduces CiteResolve, a benchmark of 423 real-world hallucinated citations annotated for source recoverability, and CiteAnchor, a source-aware grounding framework that adapts retrieval and field-level verification to each source type. CiteAnchor determines citation validity and whether a unique source can be recovered, achieving 0.841 F1 on an existing detection benchmark and 74.7% end-to-end success on CiteResolve, outperforming baselines by significant margins. The authors provide code and data at their GitHub repository.
By Chenyi Tong, Ping Nie, Yu Zhang
arXiv:2609.14248v1 Announce Type: cross
Abstract: Faithful citation attribution begins with identifying the intended source for a scientific claim. We study this source-identification capability thro...
By Yee Man Choi, Xuehang Guo, Songcheng Cai, Yimu Wang, Yi R. Fung, Qingyun Wang
arXiv:2606. 18385v1 Announce Type: new Abstract: Vision-Language Models (VLMs) remain prone to hallucinations, producing fluent but visually unfaithful outputs.
By Sneha Rao, Shaina Raza, Dhanesh Ramachandram
The paper introduces the Fabrication Severity Stack (FSS), a three-tiered framework that classifies fabricated citations by detection difficulty, ranging from non-existent references to subtle misattributions. It proposes a staged heuristic verification workflow that can be run through common large language model (LLM) interfaces, offering a zero-cost method for educators to check citations in dissertations and student reports. A pilot study on 100 citations, with 20% seeded fabrications, showed that mainstream LLMs could correctly flag 9–10 of 10 non-existent references and partially detect metadata corruption, indicating high recall without proprietary tools.
By Hazrat Ali, Mohamed Elawady
arXiv:2608.21808v1 Announce Type: new
Abstract: Multimodal Retrieval-Augmented Generation (RAG) with visual citation is crucial for ensuring the traceability and verifiability of MLLMs. However, curr...
By Suifeng Zhao, Zida Liu, Xinyu Lei, Lei Sun, Jun Gao, Sujian Li