Per-Matrix Optimality Is Not Enough: Three-Level Optimization for Low-Rank LLM Compression
Paper recorded by Signals 4 on 2026-09-14 in cs.AI. Abstract reproduced from arXiv; link to the original below.
Published 2026-09-14 on arXiv · recorded by Signals 4 on 2026-09-15
Category: cs.AI · 人工智能 · first seen 2026-09-15
Abstract
Per-matrix singular value decomposition (SVD) truncation is Eckart-Young optimal in the whitened Frobenius norm, but errors from independently compressed matrices compound through the block's nonlinear forward pass. Inspired in part by hierarchical variational optimization in quantum many-body methods, we introduce a three-level chain that widens optimization scope from individual matrices to Tran
Read on arXiv →
Cite this page: Per-Matrix Optimality Is Not Enough: Three-Level Optimization for Low-Rank LLM Compression: the #75 most recent of 300 cs.AI papers we have recorded (as of 2026-09-14). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/per-matrix-optimality-is-not-enough-three-level-optimization-for-low-rank-llm-co.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable:
papers.json
Get 4 AI signals a day by email — free.
Get 4 AI signals a day by email — free