大型语言模型（LLMs）在自动化硬件设计中面临功能正确性与人类专家级优化效率的双重挑战。现有模型生成的电路因固有的“布尔优化障碍”，在门数上通常比人类设计高出38%至1075% 。为应对此挑战，我们提出一个新颖的两阶段AI框架——VeriOptima，旨在打通从自然语言到高效门级网表的完整流程 。 第一阶段ReasoningV 是一个高保真Verilog生成模型，通过高质量数据集与自适应推理机制确保功能正确性 。其性能经过了独立评估，在VerilogEval-Human基准上取得了57.8% 的 pass@1 准确率 ，表现已能与业界顶尖的SOTA（state-of-the-art）模型相媲美。第二阶段CircuitMind 是一个多智能体优化框架，它接收ReasoningV生成的代码并将其精炼至人类水平的效率 。为严格评估，我们引入了首个源自真实设计竞赛的门级基准TC-Bench 。 实验验证了我们集成框架的有效性。首先，ReasoningV在开源模型中达到了顶尖的Verilog生成水平 。更关键的是，在编译优化对比实验中，使用ReasoningV生成的代码作为优化起点，其最终电路的PPA（功耗、性能、面积）指标显著优于使用其他LLM生成的代码。最终，经CircuitMind优化后，55.6%的模型的实现能够达到甚至超越顶级人类专家的效率 。本工作首次提出了系统性克服生成与优化挑战的端到端解决方案，为实现全自动、高质量的电路设计铺平了道路 。 相关代码已在 GitHub 开源：ReasoningV（https://github.com/BUAA-CLab/ReasoningV）与 CircuitMind（https://github.com/BUAA-CLab/CircuitMind）。

Large Language Models (LLMs) face dual challenges in automated hardware design: ensuring functional correctness and achieving human-expert-level optimization efficiency. Circuits generated by existing models often suffer from a fundamental "Boolean optimization barrier," resulting in a gate count that is 38% to 1075% higher than human-expert designs. To address this, we introduce VeriOptima, a novel two-stage AI framework designed to bridge the gap from natural language specifications to highly-optimized gate-level netlists. The first stage, ReasoningV, is a high-fidelity Verilog generation model that ensures functional correctness through a high-quality dataset and an adaptive reasoning mechanism. Its performance was independently evaluated, achieving a 57.8% pass@1 accuracy on the VerilogEval-Human benchmark, which is competitive with top-tier state-of-the-art (SOTA) models. The second stage, CircuitMind, is a multi-agent optimization framework that takes the code generated by ReasoningV and refines it to human-competitive efficiency. For rigorous evaluation, we introduce TC-Bench, the first gate-level benchmark derived from a competitive circuit design platform. Experiments validate the effectiveness of our integrated framework. ReasoningV achieves state-of-the-art performance among open-source Verilog generation models. More critically, in comparative compilation-optimization experiments, using designs from ReasoningV as a starting point yields significantly better final Power, Performance, and Area (PPA) metrics than when starting with code from other LLMs. Ultimately, after refinement by CircuitMind, 55.6% of the implementations reach or surpass the efficiency of top human experts. This work presents the first end-to-end solution to systematically overcome the challenges of both generation and optimization, paving the way for fully automated, high-quality circuit implementation. The related code has been released on GitHub:ReasoningV（https://github.com/BUAA-CLab/ReasoningV）and CircuitMind（https://github.com/BUAA-CLab/CircuitMind）。