Ripeng Li

Shuai Lyu, Haoran Luo, Ripeng Li et al.

Text-to-SQL (Text2SQL) aims to map natural language questions to executable SQL queries. Although large language models (LLMs) have driven significant progress, current approaches struggle with poor transferability to open-source LLMs, limited robustness against logic and function errors in complex queries, and inefficiencies in structured search. We introduce SQL-o1, a self-reward-driven heuristic search framework built on an agent-based architecture to enhance model reasoning capabilities. SQL-o1 leverages Monte Carlo Tree Search (MCTS) for structured, multi-step exploration, and incorporates a dynamic pruning strategy to accelerate inference without sacrificing accuracy. On the Spider and Bird benchmarks, SQL-o1 achieves a +10.8 execution accuracy improvement on the complex Bird dataset, surpassing even GPT-4-based models. Notably, it exhibits strong few-shot generalization and robust cross-model transferability across open-source LLMs. Our code is available at:https://github.com/ShuaiLyu0110/SQL-o1.

Haolang Lu, Minghui Pan, Ripeng Li et al.

Long chain-of-thought (CoT) reasoning improves the performance of large language models, yet hallucinations in such settings often emerge subtly and propagate across reasoning steps. We suggest that hallucination in long CoT reasoning is better understood as an evolving latent state rather than a one-off erroneous event. Accordingly, we treat step-level hallucination judgments as local observations and introduce a cumulative prefix-level hallucination signal that tracks the global evolution of the reasoning state over the entire trajectory. Overall, our approach enables streaming hallucination detection in long CoT reasoning, providing real-time, interpretable evidence.