High-fidelity social simulation is pivotal for addressing complex Web societal challenges, yet it demands agents capable of authentically replicating the dynamic spectrum of human interaction. Current LLM-based multi-agent frameworks, however, predominantly adhere to static interaction topologies, failing to capture the fluid oscillation between cooperative knowledge synthesis and competitive critical reasoning seen in real-world scenarios. This rigidity often leads to unrealistic ``groupthink'' or unproductive deadlocks, undermining the credibility of simulations for decision support. To bridge this gap, we propose \textit{BEACOF}, a \textit{belief-driven adaptive collaboration framework} inspired by Perfect Bayesian Equilibrium (PBE). By modeling social interaction as a dynamic game of incomplete information, BEACOF rigorously addresses the circular dependency between collaboration type selection and capability estimation. Agents iteratively refine probabilistic beliefs about peer capabilities and autonomously modulate their collaboration strategy, thereby ensuring sequentially rational decisions under uncertainty. Validated across adversarial (judicial), open-ended (social) and mixed (medical) scenarios, BEACOF prevents coordination failures and fosters robust convergence toward high-quality solutions, demonstrating superior potential for reliable social simulation. Source codes and datasets are publicly released at: https://github.com/WUT-IDEA/BEACOF.

翻译：高保真度社会仿真是应对复杂Web社会挑战的关键，但这要求智能体能够真实复现人类互动中的动态光谱。当前基于大语言模型的多智能体框架大多固守静态交互拓扑结构，未能捕捉真实场景中合作性知识综合与竞争性批判推理之间的动态振荡。这种僵化常导致非现实的"群体思维"或无效僵局，削弱了仿真对决策支持的可信度。为弥合这一差距，我们提出受完美贝叶斯均衡（PBE）启发的{\it 信念驱动自适应协作框架BEACOF}。通过将社会交互建模为不完全信息动态博弈，BEACOF严格处理了协作类型选择与能力评估之间的循环依赖关系。智能体通过迭代更新对同伴能力的概率信念，并自主调节协作策略，从而在不确定性下实现序列理性决策。在对抗性（司法）、开放性（社会）与混合（医疗）场景的验证表明，BEACOF能有效防止协调失败并促成向高质量解决方案的稳健收敛，展现出可靠社会仿真的突出潜力。源代码与数据集已公开发布于：https://github.com/WUT-IDEA/BEACOF