Newest papers

Rethinking Sequence Compression with Test-time Regression

Sihan Yang, Jie Huang, Hanxuan Li, Yifan Guo, Weigao Sun, Yiran Zhong, Weiyang Liu · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.31574 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

序列压缩是高效语言和多式模型中的基本操作和关键信息瓶颈,但基于聚合或学习预测的主要方法仍然表现不佳。我们通过测试时间回归的镜头重复了序列压缩:输入代币定义了回归问题,其配套系数形成了压缩表示。重要的是,回归问题是通过后扩散来学习的端到端,而它的系数在前进通道期间被有效地计算在封闭的形式。这导致了TTR-Compressor,输入代币定义了回归问题,其配套系数形成了压缩表示。我们评估了TTR-Compressor作为区块间注意和多式大型语言模型的视觉代币合并器的压缩器。在区块间注意力上,TTR-Compressor

Machine translation (Meta M2M100); the English original follows.

Abstract

Sequence compression is a fundamental operation and a critical information bottleneck in efficient language and multimodal models, and yet prevailing approaches based on pooling or learned projections remain underperforming. We revisit sequence compression through the lens of test-time regression: input tokens define a regression problem whose fitted coefficients form the compressed representation. Importantly, the regression problem is learned end-to-end via backpropagation, while its coefficients are computed efficiently in closed form during the forward pass. This leads to TTR-Compressor, a general alternative to conventional pooling and projection-based compression. We evaluate TTR-Compressor as the compressor for block-wise sparse attention and the visual token merger for multimodal large language models. On block-wise sparse attention, TTR-Compressor improves RULER performance over attention-based compressors by a large margin. For visual token merging, it consistently outperforms the standard MLP merger and matches its performance with 55.6% fewer visual tokens. These results establish test-time regression as an effective primitive for sequence compression in efficient language and multimodal models.

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Topics: Pooling, Sequence (biology), Computer science, Compression (physics), Data compression, Regression

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