TUM improves SVD-based low-rank LLM compression via layer-wise error models and Fisher stats
TUM · hf · 2026-11-03
TUM released work on Hugging Face improving SVD-based low-rank compression of LLMs using layer-wise error modeling with error model search and token-wise Fisher statistics, refining per-layer rank allocation and decomposition quality for better low-rank compression.
More from Research
- Order-Only On-Policy Distillation Matches Token Rankings to Boost Reasoning Without Temperature Tuning — Guangzeng Han · 2026-11-04
- JFPuget's team takes 3rd on both Kaggle ARC-AGI competitions ending the same day — JFPuget · 2026-11-04
- Meta FAIR's Kate Saenko group hiring summer intern for next-gen SAM models — kate_saenko_ · 2026-11-04
- DeepMind-Stanford paper: a red heart in your prompt flips code from 0% to 100% recursive — rohanpaul_ai · 2026-11-04
- A Red Heart Can Steer Code: DeepMind-Stanford Paper Probes Visual Meaning in VLMs — rohanpaul_ai · 2026-11-04
- Ropedia week in review: how human first-person data becomes real robot capability — liuziwei7 · 2026-11-04