ORACLE combines preference-conditioned multi-objective DDQN with LLM-guided action masking to size analog circuits, reporting high pass rates and large runtime cuts, but the evaluation compares 10 solutions per target against 1 for a key baseline.
Deep Reinforcement Learning for Analog Circuit Sizing with an Electrical De- sign Space and Sparse Rewards,
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ORACLE: A Multi-Objective Reinforcement Learning-Based Analog Circuit Design Optimizer with Large Language Models-Guided Exploration
ORACLE combines preference-conditioned multi-objective DDQN with LLM-guided action masking to size analog circuits, reporting high pass rates and large runtime cuts, but the evaluation compares 10 solutions per target against 1 for a key baseline.