High-variance activation directions are uncorrelated with predictions, transformer blocks grow more linear with depth, and single-block linear replacement yields 34x compression on Mistral's final block at a 1.71 perplexity cost.
Mixed-Precision Quantization of Large Language Models
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
dMX is a differentiable mixed-precision framework that learns per-layer MXFP bit-width assignments for LLMs and outperforms KL-based heuristics on perplexity and zero-shot accuracy under bit-width budgets.
citing papers explorer
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Variance Is Not Importance: Structural Analysis of Transformer Compressibility Across Model Scales
High-variance activation directions are uncorrelated with predictions, transformer blocks grow more linear with depth, and single-block linear replacement yields 34x compression on Mistral's final block at a 1.71 perplexity cost.
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dMX: Differentiable Mixed-Precision Assignment for Low-Precision Floating-Point Formats
dMX is a differentiable mixed-precision framework that learns per-layer MXFP bit-width assignments for LLMs and outperforms KL-based heuristics on perplexity and zero-shot accuracy under bit-width budgets.