2 papers
cs.MA2026
GenWorld: Empirically Grounded Urban Simulation Infrastructure for Scalable LLM-Agent Studies
Gen Li, Jieyuan Lan, Pengcheng Xu +3
LLM-agent simulation faces a joint grounding and scaling problem: agents should act in environments that reflect real urban constraints, yet direct online LLM calls for city-scale…
cs.RO2026
LA4VLA: Learning to Act without Seeing via Language-Action Pretraining
Tao Lin, Yuxin Du, Yiran Mao +13
Vision-Language-Action (VLA) models are commonly pretrained on robot demonstrations by jointly mapping visual observations and language instructions to actions. However, dense visu…