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This paper probes whether visual foundation models maintain persistent spatial states by evaluating four capability levels—spatial structure, object persistence, predictive spatial update, and actionability—using frozen-feature probes on ten representations under streaming egocentric observations, finding that readout accuracy does not guarantee history-based updates or reliable goal-directed action.
论文提出 ViRA 框架，研究视觉基础模型（VFM）表征何时能提升端到端自动驾驶性能，发现 VFM 目标选择与辅助感知监督显著影响规划增益，并推出 ViRA-Diffusion 在 NAVSIM v2 上达到 92.3 EPDMS。