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Abstract

Multi-agent traffic simulation transforms recorded driving data into interactive closed-loop environments, enabling scalable training and evaluation of autonomous driving systems. Recent methods increasingly adopt tokenized autoregressive generation, but often lack two complementary traffic-domain priors: a relational prior that exposes topology-induced conflicts beyond spatial proximity and a motion-vocabulary prior that addresses under-coverage, redundancy, and implausible primitives inherited from data-driven tokenization. To address these limitations, we propose \textbf{TopoKin}, a unified framework that introduces these dual priors into the agent--agent attention graph and the motion vocabulary. The topology-derived relational prior, implemented through conflict-topology-guided attention, performs agent--lane matching, candidate lane expansion, and lane conflict query before integrating the resulting relations into agent--agent attention. The motion-vocabulary prior, implemented through kinematic-constrained tokenization, proceeds through symmetry augmentation, DTW-based compression, kinematic and shape-support filtering, and tokenization with the refined vocabulary. Evaluated as an augmentation to existing simulators, TopoKin demonstrates overall gains over SMART and complementary effects in ablation, while transfer to CATK and TrafficBots and zero-shot evaluation on four additional datasets support cross-backbone applicability and improved cross-domain distributional alignment.