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PULSE slashes communication overhead by 89% while boosting training throughput by up to 2.3x, revolutionizing how we scale diffusion models across GPU clusters.
Uncovering critical security and privacy vulnerabilities in foundation-model-powered robots could redefine how we approach their deployment in real-world applications.
MIXGUARD achieves robust privacy protection in split learning without sacrificing model utility, outperforming existing defenses against advanced data reconstruction attacks.
LLMs can exploit syntactic patterns to falsely inflate detection rates in hardware security benchmarks, but a new obfuscation framework can slash their effectiveness by up to 78.6%.
