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A probabilistic framework reveals that synchronization overhead can negate the expected advantages of full-tree parallelism in distributed search systems.
Surprisingly, larger LLMs benefit from increased repetition of high-quality domain data, challenging conventional wisdom about data diversity in training.
High Artifact Extraction Difficulty regions can be accurately classified with a novel network that adapts to varying extraction challenges, leading to significant improvements in fingerprint PAD.
