9 citations · 12 across the 9 of their papers we have counts for
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cs.AI2026
LaMoC: Loss-Aware Modular Compression for LLMs
Mohanad Odema, Jacob Song
Modular compression has enabled considerable parameter reduction in LLMs while preserving strong language understanding and downstream task accuracy. However, existing joint modula…
cs.AI2026
Understanding Calibration and Truncation Error Propagation in Training-Free Low-Rank Compression for LLMs
Mohanad Odema, Gabrielle De Micheli, Dayin Gou +3
Training-free low-rank compression frameworks have been gaining prominence for LLM compression given their effectiveness in reducing model parameter count while maintaining task-le…