Rethinking Sequence Compression with Test-time Regression
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 compr
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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 compr
This research introduces a novel sequence compression technique that could significantly improve the efficiency of large language and multimodal models. By reframing compression as a test-time regression problem, it offers a general alternative to traditional pooling methods, potentially reducing computational costs and memory footprint for ML engineers working on deploying and scaling these models.