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LLMs and VLMs excel at conceptual tasks but falter in practical execution, achieving only 42.2% success in script generation for VLSI design.
Negative constraints offer a surprisingly robust path to AI alignment, sidestepping the sycophancy issues inherent in preference-based RLHF.
LLMs' true power lies in the "unexplainable" – capabilities that exceed rule-based systems, challenging the pursuit of full interpretability.
