3 papers
cs.CL2026
CoSpaDi: Compressing LLMs via Calibration-Guided Sparse Dictionary Learning
Denis Makhov, Dmitriy Shopkhoev, Magauiya Zhussip +2
Post-training LLM compression often relies on low-rank approximations, which force all columns of a projection matrix to share a single low-dimensional subspace. We propose CoSpaDi…
cs.LG2026
COMPOT: Calibration-Optimized Matrix Procrustes Orthogonalization for Transformers Compression
Denis Makhov, Dmitriy Shopkhoev, Magauiya Zhussip +3
Post-training compression of Transformer models commonly relies on truncated singular value decomposition (SVD). However, enforcing a single shared subspace can degrade accuracy ev…
cs.LG2026
ROCKET: Rapid Optimization via Calibration-guided Knapsack Enhanced Truncation for Efficient Model Compression
Ammar Ali, Baher Mohammad, Denis Makhov +3
We present ROCKET, a training-free model compression method that achieves state-of-the-art performance in comparison with factorization, structured-sparsification and dynamic compr…