Papers by Yuanfu Sun
AgentGL: Towards Agentic Graph Learning with LLMs via Reinforcement Learning (2026.acl-long)
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| Challenge: | Existing agentic frameworks treat external information as unstructured text and fail to leverage topological dependencies inherent in real-world data. |
| Approach: | They propose to reframe graph learning as an interleaved process of topology-aware navigation and LLM-based inference. |
| Outcome: | The proposed framework outperforms strong GraphLLMs and GraphRAG benchmarks in multiple LLM backbones. |
GraphICL: Unlocking Graph Learning Potential in LLMs through Structured Prompt Design (2025.findings-naacl)
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| Challenge: | Text-Attributed Graphs (TAGs) are a powerful tool for understanding complex systems and relationships. |
| Approach: | They propose a benchmark to evaluate large language models for graph-structured data using prompts. |
| Outcome: | The proposed benchmark outperforms state-of-the-art graph LLMs and graph neural networks on graph learning tasks without training. |