From Prompts to Pavement Through Time: Temporal Grounding in Agentic Scene-to-Plan Reasoning
Autonomous vehicle planning systems built on LLMs and multimodal models have struggled to reason coherently about time, creating safety and interpretability gaps. This paper addresses a core weakness in agentic AV architectures by embedding temporal grounding into inter-agent communication, testing three progressively sophisticated planner designs against the BDD-X dataset. The work signals growing recognition that foundation models need explicit temporal conditioning to handle real-world sequential decision-making, not just semantic understanding. For practitioners building safety-critical systems, this represents a shift from treating time as metadata to treating it as a first-class reasoning primitive.
Modelwire context
ExplainerThe paper's core contribution is not just that LLMs struggle with time, but that inter-agent communication protocols themselves must encode temporal constraints. This shifts the problem from model capability to system design: the planner's reasoning chain is only as coherent as the messages agents exchange about when events occur.
This work sits alongside two parallel threads in recent AV research. CADENet (May 19) exposed how standard benchmarks mask real-world perception gaps under adverse conditions, and this paper surfaces an analogous problem in the planning layer: temporal incoherence may be invisible in offline evaluation but catastrophic at runtime. Separately, the FineBench benchmark (May 19) highlighted how fine-grained spatial-temporal grounding matters for embodied AI; this paper operationalizes that insight for autonomous vehicle decision-making, where the cost of temporal misalignment is collision risk, not just annotation error.
If the three planner designs show monotonic improvement on the BDD-X dataset but diverge when tested on real-world logged AV trajectories with out-of-distribution weather or traffic patterns, that confirms temporal grounding is brittle to distribution shift. Conversely, if performance holds steady across held-out naturalistic driving data, the approach has production viability; watch whether Waymo, Cruise, or Aurora cite this work in their next safety reports within 12 months.
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MentionsBDD-X dataset · Large Language Models · Large Multimodal Models · Autonomous Vehicles
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