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Current omnimodal models may excel in perceptual tasks but fundamentally misunderstand music theory, exposing critical reasoning flaws.
Current multimodal LLMs struggle with guideline-constrained clinical reasoning, but a simple multi-agent framework can significantly boost their performance on real-world lung cancer diagnosis and treatment.
By injecting symbolic reasoning into vision-language-action models, NS-VLA achieves remarkable gains in data efficiency and generalization for robotic manipulation.
