3 papers
cs.LG2026
Rethinking the Role of Tensor Decompositions in Post-Training LLM Compression
Artur Zagitov, Alexander Miasnikov, Maxim Krutikov +5
Post-training compression is essential for deploying large language models (LLMs) under tight resource constraints. Tensor decompositions have emerged as a promising direction, off…
cs.LG2026
HARP: Hadamard-Preconditioned Adaptive Rotation Processor for Extreme LLM Quantization
Artur Zagitov, Gleb Molodtsov, Aleksandr Beznosikov
Post-training quantization (PTQ) is essential for deploying LLMs under memory and bandwidth constraints. However, extreme low-bit quantization remains highly sensitive to activatio…
cs.CV2026
Neural Network Pruning via QUBO Optimization
Osama Orabi, Artur Zagitov, Hadi Salloum +3
Neural network pruning can be formulated as a combinatorial optimization problem, yet most existing approaches rely on greedy heuristics that ignore complex interactions between fi…