Knowledge Graph Question Answering (KGQA) requires multi-step reasoning over a large, structured search space, where free-form LLM planning can hallucinate intermediate subgraphs or query sketches and yield unexecutable reasoning. We propose READS, a constraint-guided discriminative reasoning framework that makes KGQA...
Mu-Fan Xu, Ke-Hai Chen, Xue-Feng Bai et al.· IEEE Transactions on Knowled...· 0 citations
Across open-source and closed-source models, SearchSkill improves exact match on knowledge-intensive QA benchmarks and yields better retrieval behavior, including fewer copied first queries, more atomic hop-focused queries, and more correct answers within a small search budget.
Jinchao Hu, M. Zhong, Ke-Hai Chen et al.· arXiv.org· 0 citations
Understanding where and why Graphical User Interface (GUI) agents fail is essential for building more reliable systems, yet current evaluation relies on step accuracy, a metric that treats each screen independently and overlooks the underlying structure of GUI environments. This leads to two critical blind spots: (1) f...
Shao-Qing Zhang, Ke-Hai Chen, Xue-Feng Bai et al.· 0 citations
The proposed DR 2 identifies and localizes non-deterministic reasoning behaviors, uncovering the underlying semantic representation deficiencies in LLMs, and designs abductive reasoning-based preference learning, which promotes fine-grained semantic discrimination and mitigates non-deterministic reasoning errors.
Ge Liang, Mufan Xu, Kehai Chen et al.· Annual Meeting of the Associ...· 0 citations
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