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A 4B-parameter VLA model can beat Gemini-3-Pro in autonomous driving by incorporating physics-informed constraints and training on a specialized dataset of diverse driving styles.
Despite matching or exceeding human expert performance on generating potential diagnoses, current MLLMs struggle to synthesize multimodal clinical evidence for final diagnosis, revealing a critical gap in their clinical reasoning abilities.
Current VLM-driven embodied agents struggle with fundamental skills like navigation and object manipulation when evaluated in realistic, low-level action spaces, severely hindering their performance on complex tasks.
