Text-to-SQL aims to bridge the gap between human intent and relational databases. While LLMs have shown proficiency in generating simple SQL queries, they struggle with complex analytical tasks. Moreover, models fine-tuned on SQL generation often suffer from catastrophic forgetting, which lose the versatility of procedural reasoning and pertaining to generation constraints. Inspired by the usage of high-resource programming languages as LLM reasoning intermediaries, we propose CORES model, which leverages Python as a procedural reasoning pivot to enhance both complex SQL generation and tabular reasoning. It decomposes complex queries into Python reasoning traces before generating the final SQL, which bridges the gap between procedural reasoning and declarative expression. In order to internalize this reasoning capability, we fine-tune LLMs via GRPO with tailored process reward functions that mitigate the sparse feedback problem. We experimentally verify the effectiveness of CORES on six text-to-SQL benchmarks, where ours outperforms baselines by 6.44% on average, while maintains good capability on three tableQA benchmarks.

Uncertainty estimation for language models often treats reliability as a property of the answer distribution: high confidence, stable samples, or low semantic entropy are taken as signs that an answer can be trusted. Table reasoning exposes a fundamentally different failure mode. A model may consistently return the same answer while linking the wrong entity, selecting the wrong column, applying the wrong operation, or relying on incomplete or insufficient evidence. We introduce MIRAGE—Model Inference Reliability Assessment via Grounded Evidence, a structure-grounded reliability framework for selective table reasoning. MIRAGE estimates residual structural uncertainty from test-time traces, combining outcome consistency with grounding and computation signals, and produces calibrated answer-or-abstain decisions with lightweight reliability certificates. On a controlled four-family diagnostic benchmark, structural reliability signals substantially reduce selective risk compared with generic confidence, sampling, semantic-entropy, and hallucination-detection baselines.