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KeaRepair not only achieves an 83.64% repair rate on C/C++ vulnerabilities but also tackles unique cases that existing methods fail to address.
Adversarial purification can be revolutionized by a novel framework that introduces learnable positive-incentive noise, achieving robust ASV performance with minimal latency.
Optimization-guided diffusion not only ensures feasibility in robot control tasks but also enhances task success rates by up to 23 percentage points without retraining the model.
