军事AI和仿真验证领域的团队终于有了一个可落地的多智能体框架——IFPV不仅生成计划，还能通过对抗仿真找出漏洞，做作战规划或复杂系统验证的开发者可以直接用开源代码复现。
针对现代战场环境下传统作战规划生成不可行、验证不充分的问题，研究者提出IFPV框架。该框架包含多视角分层智能体（MPHA）用于生成可执行的多平台战术动作序列，以及对抗认知仿真引擎（ACSE）用于高保真验证。ACSE引入带有定制世界模型的对手，能预测关键平台未来演化并动态对抗候选计划。在非对称作战战术模拟器（ACTS）中，IFPV相比单步大语言模型基线，任务成功率提升19.4%，作战成本降低41.7%；相比传统规则验证器，平均压制率提高31.8%。代码已开源。
IFPV: An Integrated Multi-Agent Framework for Generative Operational Planning and High-Fidelity Plan Verification
Operational plan generation and verification are critical for modern complex and rapidly changing battlefield environments, yet traditional generation and verification methods still respectively face the challenges of generation infeasibility and verification insufficiency. To alleviate these limitations, we propose an Integrated Multi-Agent Framework for Generative Operational Planning and High-Fidelity Plan Verification (IFPV). IFPV consists of two tightly coupled modules: Multi-Perspective Hierarchical Agents (MPHA) for generative operational planning and an Adversarial Cognitive Simulation Engine (ACSE) for high-fidelity adversarial plan verification. MPHA decomposes commander intent into executable multi-platform tactical action sequences through the collaboration of Pathfinder, Analyst, and Planner agents. ACSE introduces an opponent equipped with a customized world model, which predicts the future evolution of mission-critical platforms and conducts dynamic counteractions against candidate plans. Simulation experiments in the Asymmetric Combat Tactic Simulator (ACTS) show that IFPV improves mission success by 19.4% and reduces operational cost by 41.7% compared with a single-step large language model (LLM) planning baseline. Compared with a traditional rule-based validator, ACSE increases the average suppression rate by 31.8%, indicating that the proposed verification environment is stricter and more discriminative in revealing the latent vulnerabilities of candidate plans. The code for IFPV can be found at https://github.com/zhigao3ks/IFPV.