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Dual Virtual Hubs enable ICFlowNet to achieve a remarkable 9.13-point improvement in long-range F1 scores, revolutionizing how we predict indirect control-flow in stripped binaries.
Utility misspecification can lead to significant performance drops in RL, but this new framework ensures robustness against such deviations, enhancing real-world applicability.
SWAAP can stealthily degrade the performance of world models by manipulating only a small fraction of training data, revealing a significant vulnerability in model-based learning systems.
