TL;DR
This approach combines classical retrieval methods with the usage of multiple large language model (LLM) agents to generate concise, evidence-based reports to align with DRAGUN’s goal of supporting critical engagement with news.
This work introduces DocCite, to its knowledge the first benchmark for systems that verify and correct page-level citations in document images, and proposes AtomCite, an agentic framework that parses an answer into claims, checks each claim against the image of its cited page, and applies a deterministic repair policy.
Citation analysis has traditionally been organized around citation intent, function, stance, citation context analysis, and cited text span identification. Recent large language models (LLMs) have been applied to these tasks through prompting, in-context learning, fine-tuning, annotation assistance, and retrieval-augme...
This work introduces MUSES, a million-instance benchmark for prospective intellectual-roots retrieval over a fixed 2.33M-paper corpus, and releases CiteRoots, both CiteRoots layers, and a distilled open companion judge for future work on prospective retrieval and intellectual roots.
This study presents a clear and reliable framework for classifying the intent behind scientific citations that combines multi-model reasoning with concepts from social choice theory, and enhances interpretability.
The main implication of this research is the validation of a practical architecture for improving IR systems, offering a viable alternative for domain-specific contexts such as Sequran.
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