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cs.AI2026
HRBench: Benchmarking and Understanding Thinking-Mode Switch Strategies in Hybrid-Reasoning LLMs
Yansong Ning, Mianpeng Liu, Jingwen Ye +2
Hybrid-reasoning large language models (LLMs) expose explicit controls over reasoning effort, allowing users or systems to trade off answer quality against inference cost. However,…
cs.AI2026
ALIVE: Awakening LLM Reasoning via Adversarial Learning and Instructive Verbal Evaluation
Yiwen Duan, Jing Ye, Xinpei Zhao
The quest for expert-level reasoning in Large Language Models (LLMs) has been hampered by a persistent \textit{reward bottleneck}: traditional reinforcement learning (RL) relies on…
cs.AI2026
Beyond Text-to-SQL: Can LLMs Really Debug Enterprise ETL SQL?
Jing Ye, Yiwen Duan, Yonghong Yu +3
SQL is central to enterprise data engineering, yet generating fully correct SQL code in a single attempt remains difficult, even for experienced developers and advanced text-to-SQL…