{"ID":2835098,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.00329","arxiv_id":"2512.00329","title":"Evidence-Guided Schema Normalization for Temporal Tabular Reasoning","abstract":"Temporal reasoning over evolving semi-structured tables poses a challenge to current QA systems. We propose a SQL-based approach that involves (1) generating a 3NF schema from Wikipedia infoboxes, (2) generating SQL queries, and (3) query execution. Our central finding challenges model scaling assumptions: the quality of schema design has a greater impact on QA precision than model capacity. We establish three evidence-based principles: normalization that preserves context, semantic naming that reduces ambiguity, and consistent temporal anchoring. Our best configuration (Gemini 2.5 Flash schema + Gemini-2.0-Flash queries) achieves 80.39 EM, a 16.8\\% improvement over the baseline (68.89 EM).","short_abstract":"Temporal reasoning over evolving semi-structured tables poses a challenge to current QA systems. We propose a SQL-based approach that involves (1) generating a 3NF schema from Wikipedia infoboxes, (2) generating SQL queries, and (3) query execution. Our central finding challenges model scaling assumptions: the quality...","url_abs":"https://arxiv.org/abs/2512.00329","url_pdf":"https://arxiv.org/pdf/2512.00329v1","authors":"[\"Ashish Thanga\",\"Vibhu Dixit\",\"Abhilash Shankarampeta\",\"Vivek Gupta\"]","published":"2025-11-29T05:40:08Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.AI\",\"cs.IR\"]","methods":"[]","has_code":false}