In interactive text-to-SQL, the evidence needed to write correct SQL is not given upfront but distributed across schema, knowledge bases, and users, and must be acquired through costly environment interactions under a finite budget. We observe that strong LLMs often fail not from deficient SQL synthesis, but from premature commitment to SQL before acquiring task-critical evidence. Through controlled trajectory analysis, we formalize this observation as the Information Sufficiency Hypothesis and identify a five-class gap taxonomy that structures the acquisition space. Guided by this diagnosis, we develop PASCAL, a training-free acquisition-control data agent framework that interposes a source-aware information-acquisition layer before SQL generation and schedules acquisition by cost-normalized priority. On BIRD-Interact, PASCAL with a local 35B backbone achieves 51.0% Phase-1 and 37.0% two-phase success rate, outperforming frontier models leaderboard result by up to 14.7 percentage points, with gains concentrated on BI tasks requiring cross-source evidence acquisition.

Entity resolution is a fundamental problem in data management that aims to identify all duplicate entries within collections of multi-attribute tuples. Most existing works focus on supervised learning, relying on large amounts of high-quality labeled data, including both positive and negative tuple pairs that are meticulously prepared. However, in reality, the manual annotation process is labor-intensive; in particular, selecting high-quality negative data for labeling is both important and challenging. In this paper, we propose an end-to-end ER solution, PUER, to address low-resource entity resolution (ER) by leveraging Large Language Models (LLMs) in a Positive-Unlabeled (PU) learning setting, where only a small number of positively labeled examples, e.g., 50, and unlabeled data are provided. Unlike directly fine-tuning LLMs in a supervised manner, we solve the entity matching task using reinforcement learning and propose a self-adaptive reward function in the process of RL. To enhance performance, we design an iterative workflow based on the co-training mechanism that fully utilizes entity blocking component to assist the entity matching. This workflow aims to improve the robustness and quality of pseudo-labels so that the performance of entity matching improves. Comprehensive experimental results on various benchmark datasets demonstrate the superiority of PUER. Full version and code are available.