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• SAMoE-VLA uses bird's-eye-view (BEV) features to derive MoE routing signals instead of token embeddings.method 90%
• Directly applying existing token-level MoE mechanisms from LLMs to VLA models results in unstable performance and safety degradation in autonomous driving.limitation 85%
• SAMoE-VLA achieves state-of-the-art performance on nuScenes open loop planning dataset and LangAuto closed-loop benchmark.result 90%
• SAMoE-VLA outperforms prior VLA-based and world-model-based approaches with fewer parameters.result 90%
• SAMoE-VLA introduces a Conditional Cross-Modal Causal Attention mechanism for temporally consistent reasoning.method 90%
• Token-based expert specialization in existing MoE mechanisms is misaligned with scene-level decision-making in autonomous driving.technical 85%
• SAMoE-VLA enables scenario-dependent expert weighting and merging tailored to distinct driving conditions.method 85%

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