Papers by Xuanbo Liu
M³GQA: A Multi-Entity Multi-Hop Multi-Setting Graph Question Answering Benchmark (2025.acl-long)
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| Challenge: | GraphRAG systems have achieved remarkable progress in enhancing performance and reliability of large language models. |
| Approach: | They propose a GraphRAG benchmark focusing on multi-entity queries with six settings for comprehensive evaluation. |
| Outcome: | The proposed method can construct diverse data with semantically correct ground-truth reasoning paths. |
Mistake Notebook Learning: Batch-Clustered Failures for Training-Free Agent Adaptation (2026.findings-acl)
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| Challenge: | Mistake Notebook Learning (MNL) is a new memory framework for large language model agents . it allows agents to distill shared error patterns into structured "mistake notes" |
| Approach: | They propose a new memory framework that enables agents to self-curate generalizable guidance from batch-clustered failures. |
| Outcome: | The proposed framework achieves competitive performance compared to existing memory mechanisms. |
MorphoBench: A Benchmark with Difficulty Adaptive to Model Reasoning (2026.findings-acl)
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Xukai Wang, Xuanbo Liu, Mingrui Chen, Haitian Zhong, Xuanlin Yang, Bohan Zeng, Jinbo Hu, Hao Liang, Junbo Niu, Xuchen Li, Ruitao Wu, Ruichuan An, Yang Shi, Liu Liu, Qiang Liu, Zhouchen Lin, Xu-Yao Zhang, Wentao Zhang, Bin Dong
| Challenge: | Existing benchmarks designed to evaluate the reasoning capabilities of large models are limited in scope and lack flexibility to adapt difficulty according to evolving reasoning capacities of models. |
| Approach: | They propose a benchmark that incorporates multidisciplinary questions to evaluate the reasoning capabilities of large models and can adjust and update question difficulty based on the reasoning abilities of advanced models. |
| Outcome: | The proposed benchmark incorporates multidisciplinary questions to evaluate the reasoning capabilities of large models and can adjust and update question difficulty based on the reasoning abilities of advanced models. |
COSMOS: Connectivity-Oriented Submodular Maximization for Optimal Subgraph Retrieval (2026.acl-long)
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| Challenge: | Existing paradigms treat facts independently or employ myopic search, failing to optimize collective subgraph utility. |
| Approach: | They propose a framework that formalizes evidence retrieval as a constrained submodular maximization problem. |
| Outcome: | The proposed framework captures the trade-off between information relevance and structural complexity. |