Mitigating social bias in large language models (LLMs) has become an increasingly important research objective. However, existing methods often rely on prompt-level control or optimize bias-aware guidance and final prediction within a single model, which may lead to coupled optimization and task interference. To address these limitations, we propose MARD, a Multi-Agent Reinforced Debiasing framework that decomposes debiasing into two role-specialized agents. The Bias-Guard Agent identifies stereotype cues and generates bias-control guidance, while the Evidence-Decision Agent produces evidence-grounded predictions without relying on identity-based assumptions. MARD further introduces retrieved group-related references as negative warning signals to expose potential stereotype cues, and adopts an alternating GRPO optimization strategy to improve role specialization. Experiments on five datasets across four task types demonstrate that MARD achieves strong overall debiasing performance. Further analyses highlight the effectiveness of role decoupling, alternating optimization, format control, and retrieved warning references.

Text-to-SQL is a key natural language processing task that maps natural language questions to SQL queries, enabling intuitive interaction with web-based databases. Although current methods perform well on benchmarks like BIRD and Spider, they struggle with complex reasoning, domain knowledge, and hypothetical queries, and remain costly in enterprise deployment. To address these issues, we propose a framework named IESR(Information Enhanced Structured Reasoning) for Lightweight Large Language Models: (i) leverages Large Language Models (LLMs) for key information understanding and schema linking, and decoupling mathematical computation and SQL generation, (ii) integrates a multi-path reasoning mechanism based on Monte Carlo Tree Search (MCTS) with majority voting, and (iii) introduces a trajectory consistency verification module with a discriminator model to ensure accuracy and consistency. Experimental results demonstrate that IESR achieves state-of-the-art performance on the complex reasoning benchmark LogicCat (24.28 EX) and the Archer dataset (37.28 EX), using only compact 7B and 8B models without fine-tuning. Furthermore, our analysis reveals that current coder models exhibit notable biases and deficiencies in physical knowledge, mathematical computation, and common-sense reasoning, highlighting important directions for future research. We released code at https://github.com/Ffunkytao/IESR-SLM.