AI RESEARCH
From Prompts to Pavement Through Time: Temporal Grounding in Agentic Scene-to-Plan Reasoning
arXiv CS.AI
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ArXi:2605.19824v1 Announce Type: new Recent attempts to high-level scene interpretation and planning in Autonomous Vehicles (AVs) using ensembles of Large Language Models (LLMs) and Large Multimodal Models (LMMs) continue to treat time as a secondary property. This lack of temporal grounding leads to inconsistencies in reasoning about continuous actions, undermining both safety and interpretability. This work explores whether temporal conditioning within inter-agent communication can preserve or enhance coherence without.