Temporal conditioning in three LLM-based planner architectures for AV scene-to-plan reasoning yields no statistically significant gains on NLP correctness metrics but enables predictive hazard reasoning and stable corrections on BDD-X subsets.
Agentic ai: The age of reasoning—a review,
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From Prompts to Pavement Through Time: Temporal Grounding in Agentic Scene-to-Plan Reasoning
Temporal conditioning in three LLM-based planner architectures for AV scene-to-plan reasoning yields no statistically significant gains on NLP correctness metrics but enables predictive hazard reasoning and stable corrections on BDD-X subsets.