Can LLMs Self-Correct Table Reasoning Errors?
Self-correction—the ability of LLMs to detect and fix their own errors—has been studied extensively for mathematical and code reasoning, with limited prior work on table reasoning (primarily multi-agent pipelines such as Table-Critic, ACL 2025, rather than single-model structured prompting). Tables present unique challenges: errors arise from wrong cell retrieval, incorrect computation, flawed logic, and hallucination of values not present in the data. We conduct the first cross-provider single-model self-correction analysis for table reasoning across five providers (Google, Moonshot AI, Zhipu, Alibaba, Min-iMax), testing five models (Gemini 3.1 Pro, Kimi K2.5, GLM 5, Qwen 3.5+, MiniMax M2.5) on WikiTableQuestions and TabFact with a multi-seed paired protocol. We pro-pose Structured Self-Correction (SSC) , a table-specific verification chain that guides models through cell verification, computation checking, logic validation, and completeness assessment. We confirm that the Accuracy-Correction Paradox (terminology from Li (2025)) previously observed in math extends to tables: models with base accuracy in the mid-60s–mid-70s region benefit modestly from self-correction (multi-seed mean SCG up to + 1.3% with within-seed point estimates as high as +3.4%), while stronger models above this region are systematically harmed by over-correction (multi-seed mean SCG down to − 1.3%, with 95% bootstrap CIs significantly below zero). SSC reduces over-correction rates in 9 of 10 conditions, with reductions of 38–69% on TabFact. An inference-mode-controlled probe shows that SSC’s qualitative direction is robust for Qwen 3.5+ across reasoning-ON and reasoning-OFF settings, while GLM 5 exhibits a substantial mode-dependent shift, indicating that mode robustness itself is model-dependent. Stronger