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HiFuzz outperforms traditional fuzzing techniques by leveraging hierarchical reinforcement learning to achieve deeper architectural state exploration and improved bug detection.
LLMs can now generate Verilog code with significantly higher compilation success and functional correctness, outperforming specialized and commercial tools, thanks to a novel contrastive learning approach.
LLMs can now generate Verilog RTL code with over 80% functional correctness and 25% less redundant logic, thanks to a novel framework that combines semantic reasoning with state-space exploration.
