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提出了一种基于Kolmogorov–Arnold Networks（KAN）的非线性投影方法来增强低秩分解，用于NLP嵌入层压缩，实验表明该方法在固定维度下能生成更忠实的压缩嵌入。
Samsung Labs 释出 LittleBit 项目（NeurIPS 2025 / ICML 2026），通过潜在空间低秩分解与二值化实现大语言模型的亚 1-bit（低至 0.1 bpw）压缩，并在 LittleBit-2 中引入 Joint-ITQ 潜在几何对齐初始化以提升性能。
This paper introduces NOMADD, a post-hoc method to reduce concept drift in tabular models by fitting base models on labeled periods and extrapolating compressed parameter changes. It achieves competitive performance with Drift-Resilient TabPFN at a fraction of training and inference cost.
This paper empirically investigates whether aligning the allocation cost with the output-space objective improves compressed model fidelity in ROCKET, a training-free LLM compression method. Results show a trade-off between accuracy and perplexity, with effects more pronounced at higher compression ratios.