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LLMs struggle significantly at comprehension stages in reverse engineering, revealing a competence limit that challenges assumptions about model size and cost.
DPA-I2P achieves a remarkable 45% reduction in pose estimation errors, setting a new standard for image-to-point cloud registration in autonomous driving.
LLMs are not just tools; they are susceptible to cognitive biases that can drastically undermine their effectiveness in detecting code vulnerabilities.