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CausalArena is introduced, a unified and evolvable benchmark for causal discovery under a common protocol, and substantial ranking shifts across SCM families and protocols are revealed, showing that strong performance in one benchmark regime does not reliably transfer to others.
WING achieves state-of-the-art CT synthesis from MRI and CBCT by transforming the regression target into windowed representations, significantly improving detail and accuracy.
GrapNet achieves a remarkable 12.08-point accuracy boost over larger dense networks by enabling structural programmability in neural architectures.
Learning without explicit rewards can yield high-action accuracy and nuanced value inference, challenging traditional reward-based paradigms in reinforcement learning.
Achieve state-of-the-art 3D medical image generation by reformulating deterministic prediction as a multi-objective drifting problem, outperforming GANs, flow-matching, and SDEs in fidelity, realism, and efficiency.
