这篇论文提出了CNeVA，能在Waymo数据集上仿真交通，并且沿速度、加速度等轴直接操控行为，比纯模仿模型多了可控制性，做自动驾驶仿真的可以看看。
论文提出可控神经变分代理（CNeVA），通过闭合形式共轭变分更新从逐通道折扣回报推断每个代理的高斯行为潜变量。采用混合通道掩码课程训练整流流轨迹生成器，实现无分类器引导。在Waymo Open Motion数据集上，CNeVA达到竞争性真实感，同时暴露逐通道可控性——这是排名更高的模仿模型所缺乏的。基于速度和加速度的操控产生单调响应，且引入软资格门（soft eligibility gates）后安全性操控显著且单调。实验表明，必须将操控指标与物理合理性护栏结合阅读，以避免奖励黑客混杂。
Controllable Sim Agents with Behavior Latents
Realistic traffic simulation requires agents that imitate logged behavior and can also be steered along interpretable axes. Such controllability enables engineers to isolate variables, reproduce specific edge cases, and test autonomous systems without real-world risk. We introduce Controllable Neural Variational Agents (CNeVA), a controllable simulated-agent framework that learns to infer a per-agent Gaussian behavior latent from per-channel discounted returns via a closed-form conjugate variational update, conditioning a rectified-flow trajectory generator trained on a mixed channel-mask curriculum for classifier-free guidance. To tackle scarcity in reward signals, we propose soft eligibility gates that replace hard binary thresholds with smooth exponential decay, preserving the gradient signal for near-threshold agents. On the Waymo Open Motion Dataset, CNeVA attains competitive realism on the benchmark while exposing per-channel controllability that the higher-ranked imitation models lack. Speed- and acceleration-based steering produces monotone responses without stall-induced reward hacking. Safety controllability is monotone and substantial with the introduction of soft eligibility. We manage to achieve steerable map compliance under a context-residual return measure. Furthermore, our experiment demonstrates that steering metrics must be read alongside physical-plausibility guardrails to avoid reward-hacking confounds.