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Trust and traceability in AI-enabled scientific discovery are now recognized as critical pillars for future scientific computing ecosystems.
Contraction-gauge preconditioning can reduce quantized matrix multiplication errors by over 20%, reshaping how we approach low-precision computation.
A novel kernel-based approach reveals that optimal tree structures can significantly reduce summation error, achieving up to 3% prediction accuracy in floating-point operations.
