LLM agents are increasingly deployed on long-horizon tasks, where a user often revises the goal partway through rather than stating it once at the start. Adapting an ongoing trajectory to such a mid-task intervention is a basic requirement for agents that work with a user, yet existing agentic benchmarks evaluate agents under a goal that stays fixed for the whole task. To address this gap, we introduce InterventionBench, a benchmark that measures how well an agent adapts to a mid-task intervention. Built on AppWorld and WebArena, InterventionBench inserts a user message that revises the goal partway through each task, and defines the revision along three operations and three components of the goal, forming nine intervention types that locate which kind of revision an agent fails on. The released benchmark contains 2,250 intervention instances. We evaluate twelve LLM agents and find that every intervention type degrades performance. Add interventions, which extend the goal rather than replace it, are consistently the hardest. These results establish adapting to a mid-task intervention as a substantial and open challenge for current LLM agents. Our code and data are available at https://github.com/jhn25/InterventionBench.

Text-to-SQL enables users to query databases using natural language by generating executable SQL queries. Recent methods have increasingly adopted Large Language Models based reinforcement learning (RL) to leverage execution feedback for training. However, existing RL methods assign uniform query-level rewards to all clauses in a SQL query, treating correct and incorrect clauses equally. This coarse-grained reward design leads to insufficient learning signals for correct SQL generation. To address this issue, we propose EXPO-SQL (EXecution-based clause-level Policy Optimization for Text-to-SQL) which provides fine-grained supervision through clause-level rewards. To assign clause-level rewards, our method identifies erroneous clauses by analyzing execution results, including error messages and clause-wise incremental execution. Experiments on widely-used Text-to-SQL benchmarks demonstrate that EXPO-SQL significantly outperforms existing supervised fine-tuning, prompting, and RL-based methods through fine-grained clause-level learning. Our code is available at https://github.com/jhn25/EXPO-SQL.

Text-to-SQL, which translates a natural language question into an SQL query, has advanced with in-context learning of Large Language Models (LLMs). However, existing methods show little improvement in performance compared to randomly chosen demonstrations, and significant performance drops when smaller LLMs (e.g., Llama 3.1-8B) are used. This indicates that these methods heavily rely on the intrinsic capabilities of hyper-scaled LLMs, rather than effectively retrieving useful demonstrations. In this paper, we propose a novel approach for effectively retrieving demonstrations and generating SQL queries. We construct a Deep Contextual Schema Link Graph, which contains key information and semantic relationship between a question and its database schema items. This graph-based structure enables effective representation of Text-to-SQL samples and retrieval of useful demonstrations for in-context learning. Experimental results on the Spider benchmark demonstrate the effectiveness of our approach, showing consistent improvements in SQL generation performance and efficiency across both hyper-scaled LLMs and small LLMs. The code is available at https://github.com/jjklle/DCG-SQL.