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Author

Jian-Qi Gao

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#artificial intelligence Dec 2025

GLOW: Graph-Language Co-Encoding for Agentic Workflow Performance Prediction

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. · 2 citations
Book Open access Jul 2026

GCA-KBQA: A Step-Wise Logical Form Generation Approach for KBQA with Knowledge-Assisted Calibration

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. · 0 citations

Generating then Refining for Reliable Knowledge Base Question Answering

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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