RosettaSim adapts frozen LLMs via structured autoregressive modeling of scene topology and agent states to reach SOTA short- and long-term traffic simulation on WOSAC, paired with RTE evaluation that correlates better with human-like fidelity.
Advancing multi-agent traffic simulation via r1-style reinforcement fine-tuning
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
A hierarchical Stackelberg MARL plus continuous-motion architecture with hybrid co-training produces smoother and safer closed-loop traffic behavior than standard self-play methods.
CRAFT reduces collisions by 31.2% and traffic violations by 33.2% in closed-loop traffic simulation by discovering context-induced failures in what-if rollouts and using a contextual preference evaluator to reweight autoregressive decoding toward globally coherent behaviors.
A multi-agent LLM system for SUMO decouples simulation tasks across Planner, Builder, Demand, Runner, and Analyst agents with MCP-based orchestration, yielding higher success rates than single-agent baselines in ablation studies.
citing papers explorer
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Decoupled Intelligence: A Multi-Agent LLM Framework for Controllable Traffic Scenario Generation in SUMO
A multi-agent LLM system for SUMO decouples simulation tasks across Planner, Builder, Demand, Runner, and Analyst agents with MCP-based orchestration, yielding higher success rates than single-agent baselines in ablation studies.