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When complexity in symbolic systems exceeds a threshold, LLMs risk losing semantic understanding, leading to outputs that are misleadingly coherent but fundamentally flawed.
ThinkingVLA achieves a breakthrough in robotic manipulation by seamlessly interleaving visual and textual reasoning, leading to superior performance in long-horizon tasks.
