AI

Rethinking Sequence Compression with Test-time Regression

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 23/10/2026, 04:18:25 UTC.

Briefings

A 2026 study introduces TTR-Compressor, a new sequence compression method using test-time regression for language and multimodal models. It replaces standard pooling with end-to-end learned coefficients, achieving significant gains in sparse attention and matching standard MLP performance with 55.6% fewer visual tokens.

Key points

• TTR-Compressor uses test-time regression where input tokens define a regression problem with fitted coefficients as the compressed representation.
• The method improves RULER performance on block-wise sparse attention by a large margin compared to attention-based compressors.

Context

Sequence compression is a critical bottleneck in efficient language and multimodal models, yet pooling and projection methods underperform. This research offers a general alternative using test-time regression, which is end-to-end learnable while maintaining efficient forward pass computation.

Limitations

Results are limited to specific architectures: block-wise sparse attention and visual token merger for multimodal models. Performance gains are observed on RULER and visual token counts, but broader applicability and generalization across different compression tasks remain unverified.

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