Real-world tables often exhibit irregular schemas, heterogeneous value formats, and implicit relational structure, which degrade the reliability of downstream table reasoning and question answering. Most existing approaches address these issues in a query-dependent manner, entangling table cleanup with reasoning and thus limiting generalization. We introduce QuIeTT, a query-independent table transformation framework that preprocesses raw tables into a single SQL-ready canonical representation before any test-time queries are observed. QuIeTT performs lossless schema and value normalization, exposes implicit relations, and preserves full provenance via raw table snapshots. By decoupling table transformation from reasoning, QuIeTT enables cleaner, more reliable, and highly efficient querying without modifying downstream models. Experiments on four benchmarks, WikiTQ, HiTab, NQ-Table, and SequentialQA show consistent gains across models and reasoning paradigms, with particularly strong improvements on a challenge set of structurally diverse, unseen questions.

翻译：现实世界中的表格常呈现不规则模式、异构值格式及隐含关系结构，这些问题会降低下游表格推理与问答的可靠性。现有方法大多以查询依赖的方式处理这些挑战，将表格清理与推理过程相耦合，从而限制了泛化能力。本文提出QuIeTT框架，该查询无关的表格转换系统可在观测任何测试时查询前，将原始表格预处理为统一的SQL就绪规范表示。QuIeTT执行无损的模式与值规范化，显式化隐含关系，并通过原始表格快照完整保留数据溯源信息。通过解耦表格转换与推理过程，QuIeTT能够在无需修改下游模型的前提下，实现更清晰、更可靠且高效的数据查询。在WikiTQ、HiTab、NQ-Table和SequentialQA四个基准测试上的实验表明，该方法在不同模型与推理范式中均取得稳定提升，尤其在结构多样、未见问题的挑战集上表现出显著改进。