# QUIETT: Query-Independent Table Transformation for Robust Reasoning

arXiv: https://arxiv.org/abs/2602.20017 · HTML: https://mlsanity.com/paper/2602.20017/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 49 references that are in the map.
- First posted on arXiv 23 Feb 2026.

## Builds on (most cited first, 17 of 17)

- [Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks](https://mlsanity.com/paper/2211.12588/) (2022)
- [Compositional Semantic Parsing on Semi-Structured Tables](https://mlsanity.com/paper/1508.00305/) (2015)
- [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://mlsanity.com/paper/2004.02349/) (2020)
- [TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data](https://mlsanity.com/paper/2005.08314/) (2020)
- [UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models](https://mlsanity.com/paper/2201.05966/) (2022)
- [Binding Language Models in Symbolic Languages](https://mlsanity.com/paper/2210.02875/) (2022)
- [Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding](https://mlsanity.com/paper/2401.04398/) (2024)
- [Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering](https://mlsanity.com/paper/2101.00774/) (2021)
- [Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning](https://mlsanity.com/paper/2301.13808/) (2023)
- [HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation](https://mlsanity.com/paper/2108.06712/) (2021)
- [Large Language Models are Complex Table Parsers](https://mlsanity.com/paper/2312.11521/) (2023)
- [Evaluating LLMs' Mathematical Reasoning in Financial Document Question Answering](https://mlsanity.com/paper/2402.11194/) (2024)
- [GraphOTTER: Evolving LLM-based Graph Reasoning for Complex Table Question Answering](https://mlsanity.com/paper/2412.01230/) (2024)
- [NormTab: Improving Symbolic Reasoning in LLMs Through Tabular Data Normalization](https://mlsanity.com/paper/2406.17961/) (2024)
- [Auto-Tables: Synthesizing Multi-Step Transformations to Relationalize Tables without Using Examples](https://mlsanity.com/paper/2307.14565/) (2023)
- [Weaver: Interweaving SQL and LLM for Table Reasoning](https://mlsanity.com/paper/2505.18961/) (2025)
- [Better Call CLAUSE: A Discrepancy Benchmark for Auditing LLMs Legal Reasoning Capabilities](https://mlsanity.com/paper/2511.00340/) (2025)

## Abstract (arXiv)

Real-world tables often contain schema inconsistencies, heterogeneous value formats, and implicit relational structures that degrade table reasoning and question answering. Existing approaches address these issues at query time, repeating normalization and restructuring for every new question and producing representations that do not generalize to unseen queries. We introduce QuIeTT, a transform-first, query-later framework that converts each raw table into a single SQL-ready canonical representation before any evaluation query is observed, under three constraints: no evaluation query access, information preservation, and single-table reuse. QuIeTT operates through a three-stage pipeline: issue probing, where synthetic queries surface structural deficiencies such as ambiguous schemas and heterogeneous formats; plan generation; and structured execution. Experiments on four benchmarks across five model families show consistent improvements over strong baselines in both direct prompting and agentic reasoning paradigms, with particularly strong gains on a manually annotated challenge set of structurally diverse unseen questions.