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Models can identify abstract structures in isolation but fail dramatically when asked to translate that understanding across different contexts, revealing a critical gap in AI's reasoning abilities.
A single shared intervention in the query-key channel can stabilize low-precision transformer training, eliminating the need for individual fault repairs.
StrataCL achieves up to 1.9x faster LLM inference and significantly reduces training times by streamlining communication in distributed AI systems.
