The mid-training phase has emerged as a critical bridge for enhancing the reasoning capabilities of Vision-Language Models, yet its effectiveness is constrained by the scarcity of high-quality visual reasoning data. While data synthesis offers a promising alternative, conventional stateless synthesis paradigms produce shallow trajectories that quickly succumb to utility plateaus. To overcome this, we propose VisMind, a novel multi-agent collaborative framework that generates deep-reasoning Stateful Synthetic Dialogue (SSD) from visual inputs. By orchestrating iterative problem-solving, where peer student agents engage in bidirectional critique under the ground-truth-anchored guidance of a teacher, VisMind transforms static, fragmented responses into rich, multi-turn logical derivations. Utilizing this interactive paradigm, we construct a dense mid-training corpus of approximately 1.14B tokens. Extensive experiments across the Qwen2-VL and InternVL3 model families demonstrate that applying this corpus via Continued Pretraining consistently outperforms a representative stateless synthesis approach and elevates standard baselines by up to a 2.24% absolute average gain across comprehensive mathematical and logical benchmarks. Furthermore, in-depth analysis reveals that the SSD paradigm exhibits continuous linear scaling without early saturation, fosters intrinsic reasoning generalization, and establishes a robust cognitive foundation that enhances the efficacy of subsequent Reinforcement Learning with Verifiable Rewards.