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This paper presents a comprehensive evaluation of five large language models for citation function classification, achieving new state-of-the-art results on the ACL-ARC dataset with a fine-tuned Falcon 7B model. It also introduces the AC3 dataset, which includes a seven-category annotation scheme distinguishing neutral acknowledgments from evaluative stances.
This paper introduces Agents-K1, a knowledge graph system built from 2.46 million papers that improves AI agent research by incorporating text, figures, tables, and equations, along with a five-level citation classification. It significantly boosts performance of top models like Gemini-3 and GPT-5.2 on benchmarks, demonstrating that refining knowledge structure can be more effective than scaling model size.