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cs.LG2026
Not All Drift Is a Mechanism: Tracing Prompt Adaptation in LLMs
Mikhail Aleksandrov, Nail Bashirov, Maksim Ivanov +6
Prompt-based adaptation offers several ways to specialize a frozen language model, ranging from natural-language prompt optimization to learned continuous prompts. These methods ar…
cs.LG2026
Monarch-Muon: Structure-Aware Muon for Fast and Memory-Efficient Pretraining
Alexander Miasnikov, Ruslan Kabirov, Gleb Molodtsov +7
Training large language models is constrained by both wall-clock time and memory footprint, and recent matrix-aware optimizers sharpen this tradeoff: Muon can improve optimization…
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…