arXiv:2606. 07098v1 Announce Type: cross Abstract: We present SigmaScale, a method for learning auxiliary scaling matrices $S$ to aid truncated Singular Value Decomposition (SVD) based Large Language Model (LLM) compression.
arXiv:2607. 18284v1 Announce Type: cross Abstract: To excel at their domain large language models are comprised of billions of parameters.
arXiv:2510. 05544v2 Announce Type: replace-cross Abstract: Large language models (LLM) and vision-language models (VLM) have achieved state-of-the-art performance, but they impose significant memory and computing challenges in deployment.
arXiv:2602.02848v2 Announce Type: replace Abstract: Advances in large language models have driven strong performance across many tasks, but their memory and compute costs still hinder deployment. SVD...
arXiv:2606.21847v2 Announce Type: replace-cross Abstract: Low-rank decomposition is a promising compression paradigm for large language models (LLMs), yet its effectiveness hinges on rank budget allo...
arXiv:2505. 17974v2 Announce Type: replace-cross Abstract: The Fisher information is a fundamental concept for characterizing the sensitivity of parameters in neural networks.
The paper investigates the often-overlooked scale vectors in large language models, showing that despite their tiny size they are crucial for pre‑training performance. The authors provide theoretical insights that scale vectors mainly aid optimization rather than expressivity, and they analyze how weight decay affects different normalization layers. Building on these findings, they propose lightweight improvements—branch‑specific heterogeneity, better placement, and magnitude‑direction reparameterization—that consistently reduce loss across a range of model sizes and training settings.
The paper introduces FACTS, a structured Fisher Approximation for compressing Vision Transformers (ViTs) using Fisher-weighted SVD, which enforces token‑local aggregation while preserving within‑token activation‑gradient dependence. It also presents Constrained Rank Search (CoRS) to optimize layer‑wise rank allocation under a fixed FLOP budget. Experiments on ViTs and hybrid architectures show that FACTS improves accuracy‑efficiency trade‑offs, outperforming the strongest SVD baseline by up to +5.8 percentage points on Swin‑B without requiring finetuning.
arXiv:2607. 03057v1 Announce Type: cross Abstract: The rapid growth in the parameter scale of large language models (LLMs) has created a strong demand for efficient compression techniques.
arXiv:2606. 00573v1 Announce Type: new Abstract: Vision-language models (VLMs) deliver strong multimodal reasoning capabilities, but their large computational cost and high parameter counts make deployment challenging on resource-constrained devices.
arXiv:2605.28573v2 Announce Type: replace-cross Abstract: LLM pretraining is extremely costly; therefore, parameter-efficient LLM architectures have recently emerged as a compelling research directio...
arXiv:2602. 12429v2 Announce Type: replace Abstract: Foundation models have achieved remarkable success, yet their growing parameter counts pose significant computational and memory challenges.
LowRankArena is a standardized evaluation platform for SVD‑based low‑rank compression of large language models, unifying task versions, compression budgets, comparison regimes, and inference measurements. It provides a reproducible pipeline with over 3 TiB of released compressed checkpoints, enabling consistent comparisons across methods. An audit of five representative SVD techniques using LowRankArena shows that prior reported gains are highly conditional, with performance leaders and tiers shifting across backbones and keep ratios, and that nominal low‑rank savings often yield limited end‑to‑end speedups.