Text-to-SQL has advanced rapidly with large language models, but complex database queries still require reasoning beyond one-shot generation, including multi-step decomposition, execution-based diagnosis, and targeted correction. We present EvoSQL, a co-evolution framework that formulates SQL synthesis as an iterative interaction between a generator and a critic. Moreover, EvoSQL maintains a contextualized candidate memory, verifies SQL candidates with both execution signals and LLM-based critique, and updates its memory through utility-guided aggregation. To strengthen the underlying generator–critic pair, we further introduce a Self-Distillation Policy Optimization (SDPO) fine-tuning stage that injects execution-aware supervision into current coding LLM backbones. Experiments on Spider and BIRD show that EvoSQL consistently improves open-source models over Maj@16 baselines, with particularly large gains on BIRD-Dev, ranging from +1.37% for Qwen3-4B to +9.19% for Qwen2.5-Coder-3B. SDPO initialization further improves selected backbones on Spider-Test and BIRD-Dev. These results suggest that memory-grounded co-evolution is an effective path toward more reliable and generalizable Text-to-SQL systems.

Aligning large language models (LLMs) with multiple behavioral objectives from a certain corpus remains challenging, as joint optimization of attributes often leads to interference and trade-offs. While recent representation steering methods enable lightweight control over individual behaviors through hidden-state interventions, their composability for multi-attribute alignment remains underexplored. We present POLAR, Poly-attribute Alignment via Modular Representation Steering, a modular framework that composes independently trained alignment interventions within a frozen LLM. To mitigate conflicts between steering modules, we introduce calibrated and compatibility-aware intervention composition in hidden-state space. Experiments across heterogeneous alignment attributes show that POLAR achieves the best overall trade-off, with a 37.4% improvement in average rank over the strongest multi-attribute baseline while preserving response quality and controllability. The code and data will be publicly available.