这篇论文用具体数字告诉你，统计上合理的轨迹未必结构可行，扩散模型加符号约束能解决。看中型图场景下0.156的无效概率怎么被硬过滤砍掉。
该研究提出一种条件扩散模型，从部分观测生成未来图状态轨迹，并叠加符号约束层（硬过滤、软加权、投影修复）提升结构可行性。在紧凑图场景下，模型无效概率质量为0.002996；在中等复杂度依赖图场景下升至0.155929。硬过滤剔除所有无效轨迹，保留84.4%样本；软加权保持有效样本量但增益有限。分析表明依赖约束是几乎全部不可接纳性的来源。
Bridging the Gap Between Plausibility and Admissibility: Constraint-Aware Flow Maps for Dynamic Graph Systems
Generative models can support decision-making under uncertainty by producing ensembles of plausible future system trajectories, but statistical plausibility does not ensure structural feasibility. This study investigates whether post-sampling symbolic constraints can improve the reliability of generative trajectory modeling in dynamic graph-structured systems. A conditional diffusion model generates future graph-state trajectories from partial observations, while an external symbolic layer applies hard filtering, soft weighting, or projection-based repair. The framework is evaluated on two controlled synthetic regimes: a compact graph and a medium-complexity dependency graph, using metrics for structural validity, sample efficiency, diversity, robustness, and calibration. In the compact regime, the model produces an invalid probability mass of 0.002996, indicating an almost entirely admissible trajectory manifold. Under the same architecture and training protocol, invalid mass increases to 0.155929 in the medium-complexity regime. Hard filtering removes all invalid retained trajectories while preserving 84.4% of generated samples, whereas soft weighting preserves effective sample size but yields only limited validity gains. Family-level analysis shows that dependency constraints account for nearly all observed inadmissibility. These results indicate that statistical plausibility and structural admissibility are distinct reliability properties and that symbolic constraint handling becomes more valuable as graph-structural complexity increases.