Table Reasoning

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9 papers0 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

PARTAB: Partition-Aware Reasoning with Structured Evidence for Scalable Table Understanding
Aug 25, 2026Md Mahadi Hasan Nahid, Davood RafieiTable Reasoning Table QA
University of Alberta
ProgramTab: Boosting Table Reasoning of LLMs via Programmatic Paradigm
Jul 13, 2026Pei Guo, Enjie Liu, Yunzhi Tan +6Table Reasoning Tabular ML
♠Big Data and AI Platform Department, Tencent, China · ♢Institute of Computer Science and Technology, Soochow University, China
TabClaw: An Interactive and Self-Evolving Agent for Spreadsheet Manipulation and Table Reasoning
Jun 9, 2026Mingyue Cheng, Shuo Yu, Daoyu Wang +5Multi-Agent LLM Systems Table Reasoning
State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Hefei, China
Rethinking Stepwise Model Routing: A Cost-Efficient Table Reasoning Perspective
May 28, 2026Shenghao Ye, Yuxiang Wang, Yu Guo +3Table Reasoning Cost-Aware Inference
University of Science and Technology of China · The University of Melbourne · Institute of Artificial Intelligence, Hefei Comprehensive National Science Center
From Table to Cell: Attention for Better Reasoning with TABALIGN
May 14, 2026Tung Sum Thomas Kwok, Zeyong Zhang, Xinyu Wang +6Table QA Table Reasoning
University of California, Los Angeles · SimpleWay · New Jersey Institute of Technology +4
RSAT: Structured Attribution Makes Small Language Models Faithful Table Reasoners
Apr 30, 2026Jugal Gajjar, Kamalasankari SubramaniakuppusamyLLM Reasoning LLM Grounding
Department of Computer Science, The George Washington University, USA
Towards Platonic Representation for Table Reasoning: A Foundation for Permutation-Invariant Retrieval
Apr 13, 2026Willy Carlos Tchuitcheu, Tan Lu, Ann DoomsTable Reasoning Tabular Representation Learning
Department of Mathematics and Data Science, Vrije Universiteit Brussel (VUB), Brussels, Belgium · Data Science Lab, Royal Library of Belgium (KBR), Brussels, Belgium
TableVision: A Large-Scale Benchmark for Spatially Grounded Reasoning over Complex Hierarchical Tables
Apr 4, 2026Xiaoyu Chen, Lu Dai, Hanqing Wang +6Table Reasoning Spatial Reasoning Benchmarks
The Hong Kong University of Science and Technology (Guangzhou) · The Hong Kong University of Science and Technology · The Chinese University of Hong Kong +1
TABQAWORLD: Optimizing Multimodal Reasoning for Multi-Turn Table Question Answering
Apr 3, 2026Tung Sum Thomas Kwok, Xinyu Wang, Xiaofeng Lin +7Table Reasoning Table QA
University of California, Los Angeles · SimpleWay · McGill University +2

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PARTAB: Partition-Aware Reasoning with Structured Evidence for Scalable Table Understanding

Md Mahadi Hasan Nahid, Davood Rafiei

University of Alberta

Table Reasoning Table QA LLM Reasoning Evidence Selection

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Large Language Models (LLMs) have shown strong capabilities in table reasoning, but their effectiveness degrades as tables grow in size and complexity due to irrelevant context and difficulty localizing the evidence required for reasoning. Existing approaches typically reason over either the full table or a single reduced view, which can still obscure important row-column relationships. We introducePARTAB (Partition-Aware Reasoning overTables), a framework that constructs a structured evidence interface between the LLM and the table. PARTAB represents query-relevant evidence as semantically coherent, row-linked table regions and performs hierarchical selection over column groups and row-level partitions before composing the selected evidence for answer generation. We evaluate PARTAB on multiple table reasoning benchmarks, covering question answering, fact verification, and numerical reasoning. PARTAB consistently improves over full-table prompting and several recent table reasoning methods, achieving strong performance on WikiTableQuestions and TabFact while remaining competitive on numerical reasoning. Additional analyses show that semantic partitioning and targeted evidence selection improve evidence localization, substantially reduce the reasoning context, and provide larger benefits on complex tables. These results demonstrate the value of structured, partition aware evidence construction for scalable table reasoning.