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2026-06-12

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Pro-V: An Efficient Program Generation Multi-Agent System for Automatic RTL Verification

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Processed 12 entities and 12 relations.

Sampling&Filtering Mechanism CorrectBench RTL-based Sampling Program Emulator Signal Reference Result Consistent Outputs Outlier Detection Partial Consistent LLM-as-a-Judge Pro-V RTL Verification Self-Improvement Sampling

DeepVerifier: Learning to Update Test Sequences for Coverage-Guided Verification

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Processed 54 entities and 75 relations.

DeepVerifier: Learning to Update Test Sequences for Coverage-Guided Verification Yuntao Lu Chen Bai Yuxuan Zhao Ziyue Zheng Yangdi Lyu Mingyu Liu Bei Yu The Chinese University of Hong Kong The Hong Kong University of Science and Technology The Hong Kong University of Science and Technology - Guangzhou Campus Huawei Technologies Co., Ltd. RISC-V RISC-V ISA functional verification coverage-guided verification coverage metrics simulation-based verification static formal analysis concolic testing coverage-guided fuzzing RTL control flow graph machine learning for test generation Bayesian network for coverage modeling artificial neural network for test acceleration DeepVerifier transformer language model for instruction sequences gradient-based sequence update coverage score prediction instruction stream generator FORCE-RISCV random instruction generator natural language processing Transformer architecture BERT GPT RoBERTa BART PalmTree Synopsys VCS Verilator LSTM for instruction sequence modeling multi-head self-attention positional encoding cross-entropy loss for sequence generation mean square error loss for coverage prediction ISA syntax correction test sequence tokenization instruction embedding coverage closure CPU processor design GELU activation function sequential modeling task for test generation