做安全关键系统（如机器人规划、控制）的团队终于有了一个既能严格满足约束又不牺牲生成质量的方案，PolyFlow 的零违规和低延迟特性值得直接试试。
PolyFlow 提出了一种新的约束流匹配框架，专门解决在安全关键物理系统中部署生成模型时的约束问题。传统方法通过事后修正来保证安全性，但计算开销大且可能扭曲数据分布。PolyFlow 将约束直接嵌入模型和流动力学中，采用离散时间流公式和无投影架构，无需昂贵的迭代求解器即可严格满足任意多面体约束。实验表明，PolyFlow 在规划和控制任务中实现零约束违规，同时保持高分布保真度，推理延迟显著低于现有方法。代码已开源，适合需要安全生成的应用场景。
PolyFlow: Safe and Efficient Polytope-Constrained Flow Matching with Constraint Embedding and Projection-free Update
While flow-based generative models have demonstrated strong performance across a wide range of domains, deploying them in safety-critical physical systems remains challenging due to strict constraint requirements. Existing approaches typically enforce safety through post-hoc corrections, which incur substantial computational overhead and may distort the learned distribution. We propose PolyFlow, a polytope-constrained flow matching framework that embeds constraints directly into the model and flow dynamics. PolyFlow introduces a discrete-time flow formulation and a projection-free architecture, which eliminate the discretization error and guarantee strict satisfaction of arbitrary polyhedral constraints, without the need for expensive iterative solvers. Experimental results show that PolyFlow achieves zero constraint violation while maintaining high distributional fidelity across a range of planning and control tasks. Compared to state-of-the-art constrained generation baselines, PolyFlow significantly reduces inference latency and demonstrates a favorable trade-off between safety, efficiency, and generative quality. Code is available on https://github.com/MJianM/PolyFlow.