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Mohanad Odema

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Preprint Aug 2026

Understanding Calibration and Truncation Error Propagation in Training-Free Low-Rank Compression for LLMs

This work proposes a simple, training-free methodology compatible with existing frameworks to mitigate residual errors in calibration data activations accumulate across layers during compression, causing misalignment between representations simulated at compression time and those experienced at inference.

Mohanad Odema, G. de Micheli, Dayin Gou et al. · 1 citation
#artificial intelligence Preprint Aug 2026

LaMoC: Loss-Aware Modular Compression for LLMs

LaMoC improves joint compression by selecting compression statistics that better align local module reconstruction error with the downstream loss, and reformulate joint modular compression as a two-tiered optimization problem that minimizes module reconstruction error while tuning the activation and gradient information blending rate.

Mohanad Odema, Jacob Song · 0 citations

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