GLOW is proposed, a unified framework for AW performance prediction that combines the graph-structure modeling ability of GNNs with the topology-aware semantic encoding capability of LLMs and consistently outperforms state-of-the-art baselines in both prediction accuracy and ranking utility.
Wei Guan, Jian Cao, Jin-Yu Cai et al.· IEEE Transactions on Service...· 2 citations
GCA-KBQA is proposed, a step-wise fine-tuned LLM-based framework that employs hop-wise generation, knowledge-assisted calibration, and path-level assembly to construct complete LFs for KBQA, demonstrating its superior performance compared to state-of-the-art baselines.
Ran-Ran Bu, Jian Cao, Jian-Qi Gao et al.· Annual International ACM SIG...· 0 citations
Experiments establish ReTree as an effective self-correcting memory abstraction for long-horizon search, and show that ReTree consistently outperforms Full-Trajectory ReAct in question-answering and search benchmarks.
Aijun Yang, Qianxue Guo, Ziyi Huang et al.· 0 citations
Evaluations on standard KBQA benchmarks show that the proposed ARI-KBQA enhances model performance with a reduced search space, especially in complex multi-hop query scenarios.
Jian-Qi Gao, Hang Yu, Jian Cao et al.· 0 citations
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