arXiv:2607.00711v2 Announce Type: cross
Abstract: Large Language Models have emerged as programming assistants. However, the efficacy of code generation is constrained by the quality of input require...
By Zheng Fang, Dongming Jin, Yihong dong, Yongmin Li, Kechi Zhang, Zhi Jin, Ge Li
The paper introduces PlanPool, a method for agentic Text-to-SQL systems to manage clarification questions by maintaining a mutable question pool. By requiring agents to explicitly ask or drop each planned question, PlanPool improves ambiguity coverage and reduces silent failures compared to unconstrained or prompt-based approaches. Experiments on benchmarks derived from BIRD-Interact and Spider show that PlanPool achieves competitive execution accuracy while better handling underspecification.
By Wen-Zhi Li, Yue Gong, Konstantinos Kanellis, Balakrishnan Murali Narayanaswamy
arXiv:2606.13120v2 Announce Type: replace
Abstract: Search Agents -- large language models augmented with search tools -- have intensified the need for future-proof evaluation benchmarks. Existing be...
By Yunhan Wang, Jiaan Wang, Lianzhe Huang, Xianfeng Zeng, Fandong Meng
arXiv:2509. 23071v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) agent development is hindered by the lack of executable ground-truth agent-environment interaction trajectories.
By Muzhi Li, Jinhu Qi, Yihong Wu, Minghao Zhao, Liheng Ma, Yifan Li, Xinyu Wang, Zhenghan Tai, Zixing Song, Yingxue Zhang, Ho-fung Leung, Irwin King
arXiv:2506. 13932v3 Announce Type: replace-cross Abstract: The rise of large language models (LLMs) has led to dramatic improvements across a wide range of natural language tasks.
By Saurabh Pujar, Ira Ceka, Irene Manotas, Gail Kaiser, Baishakhi Ray, Shyam Ramji
arXiv:2606. 12387v1 Announce Type: cross Abstract: Large Language Models (LLMs) have democratized database access through Text-to-SQL, but moving from prototypes to production remains difficult.
By Zhiyi Chen, Jie Song, Peng Li