WORC improves multi-agent LLM reasoning to 82.2% average accuracy by predicting and compensating for the weakest agent via targeted extra sampling rather than uniform reinforcement.
Multi-agent autonomous driving systems with large language models: A survey of recent advances, resources, and future directions
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SwarmDrive uses local SLMs on vehicles for event-triggered semantic V2V intent sharing and consensus, improving occluded intersection success from 68.9% to 94.1% and cutting latency to 151.4 ms in a 5-seed simulation.
This survey synthesizes AI techniques for mixed autonomy traffic simulation and introduces a taxonomy spanning agent-level behavior models, environment-level methods, and cognitive/physics-informed approaches.
citing papers explorer
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Weak-Link Optimization for Multi-Agent Reasoning and Collaboration
WORC improves multi-agent LLM reasoning to 82.2% average accuracy by predicting and compensating for the weakest agent via targeted extra sampling rather than uniform reinforcement.
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SwarmDrive: Semantic V2V Coordination for Latency-Constrained Cooperative Autonomous Driving
SwarmDrive uses local SLMs on vehicles for event-triggered semantic V2V intent sharing and consensus, improving occluded intersection success from 68.9% to 94.1% and cutting latency to 151.4 ms in a 5-seed simulation.
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Artificial Intelligence for Modeling and Simulation of Mixed Automated and Human Traffic
This survey synthesizes AI techniques for mixed autonomy traffic simulation and introduces a taxonomy spanning agent-level behavior models, environment-level methods, and cognitive/physics-informed approaches.