arXiv:2607. 22706v1 Announce Type: new Abstract: This paper presents the MPR-CiteG framework, which achieved second place in the ScienceON AI Challenge by addressing two fundamental challenges in generative AI: inefficient retrieval and the absence of source verification.
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...
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.
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...
arXiv:2606. 18385v1 Announce Type: new Abstract: Vision-Language Models (VLMs) remain prone to hallucinations, producing fluent but visually unfaithful outputs.
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.
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...
The paper investigates the initial step of automated research ideation: generating actionable limitations from literature. By creating the ActLim-Bench dataset of 18,279 evidence‑linked supervision triples, the authors develop a three‑stage post‑training pipeline that improves recall over existing methods. Human evaluations show that the generated limitations are preferred 64.9% of the time and enhance downstream ideation systems.
CiteVQA is a benchmark that evaluates multimodal large language models on both answering document questions and providing element‑level bounding‑box citations for the evidence used. It contains 1,897 questions across 711 PDFs in seven domains and two languages, with ground‑truth citations generated via an automated pipeline and strict quality control. The evaluation metric, Strict Attributed Accuracy (SAA), rewards a prediction only when both the answer and the cited region are correct, revealing widespread attribution hallucination in current models.
AtomCite is an agentic framework that verifies and corrects page‑level citations in multi‑page documents by parsing answers into claims, checking each claim against the cited page image, and applying a deterministic repair policy. The authors introduce DocCite, the first benchmark for this task, built on MP‑DocVQA and DUDE, containing 928 injected instances and 1,909 verified natural errors. Across Gemini, Claude, and GPT models, AtomCite achieves about 93% verification accuracy and improves citation precision from 34% to 87‑90%, while also enhancing hallucination detection in open‑source models.
arXiv:2609.22774v1 Announce Type: new Abstract: This paper presents our system for Track 2 of the NLPCC 2026 Shared Task 10 on citation-level faithfulness in AI-assisted scientific reporting. Given a...
arXiv:2602. 20459v2 Announce Type: replace Abstract: Can AI systems trained on the existing scientific record forecast the advances that will follow?
arXiv:2607. 05456v1 Announce Type: new Abstract: While recent advances in large language models have enabled end-to-end automated manuscript generation, existing systems suffer from three critical deficiencies: (i) generated claims are not deterministically grounded in verifiable literature, (ii) experimental results are frequently fabricated rather than executed, and (iii) there exists no standardized, multi-dimensional framework to assess whether AI-generated manuscripts meet the quality and rigor required for real-world publication.