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Shared microexponents and microscaling formats for low-precision learning

Contribution grade

Block-scaled numerical formats · Human-designed research method or resource

Reference score135,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Explored block data representations and fine-scale shared exponents, then evaluated an open microscaling-format family with compact elements and shared scaling across training/inference benchmarks.

Scientific scope

Explored block data representations and fine-scale shared exponents, then evaluated an open microscaling-format family with compact elements and shared scaling across training/inference benchmarks. Two2023papers have22/32source-name positions; name variants are retained source-specifically, not identity-resolved. Early multi-level MX designs and later concrete32-element E8M0-scale formats are not silently identical. Quantization/transposition do not commute; vector operations, outputs and master updates remain wider precision. Reported results emulate formats, with distinct direct-cast/PTQ/fine-tuned/training settings; a sample conversion is not the only standards-compliant recipe.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Two2023papers have22/32source-name positions; name variants are retained source-specifically, not identity-resolved. Early multi-level MX designs and later concrete32-element E8M0-scale formats are not silently identical. Quantization/transposition do not commute; vector operations, outputs and master updates remain wider precision. Reported results emulate formats, with distinct direct-cast/PTQ/fine-tuned/training settings; a sample conversion is not the only standards-compliant recipe.

Contributors

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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