| title | Beyond Selection: Token Parameterization for Extreme Visual Token Compression |
|---|---|
| authors | Rui Zhong, Yu Li, Zheyu Yan, Cheng Zhuo |
| date | 2026-09-28 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2609.35232 |
| upvotes | 6 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Visual-token compression is effective for improving the efficiency of vision-language models, but under extreme compression budgets, token pruning can break visual grounding while learned resamplers increase parameter count, attention cost, and training complexity. We revisit compression through a token parameterization lens, separating (i) basis transformation and structured truncation (retained subspace/compressibility) from (ii) coordinate organization (optimization and cross-modal alignment). This view yields two coupled objectives, compressibility and learnability, which we formalize as unified functionals. Guided by these objectives, we design Braco, a lightweight four-step coder that combines transform-basis truncation, input-independent basis-coordinate embeddings, budget-dependent orthogonal re-parameterization, and learned spatial residual tokens from lightweight pooling. Experiments show that Braco forms the favorable empirical accuracy-efficiency frontier under 23times--64times compression and remains competitive at 144times, reaching 95.2% accuracy while reducing prefill FLOPs by 84.2%--86.7% relative to the uncompressed upper bound. Against prior methods, Braco matches or improves accuracy while achieving up to approximately 36% end-to-end speedup and using 16.6times/78.8times lower compressor latency/FLOPs. |
cite
@misc{zhong2026beyond,
title={Beyond Selection: Token Parameterization for Extreme Visual Token Compression},
author={Rui Zhong and Yu Li and Zheyu Yan and Cheng Zhuo},
year={2026},
eprint={2609.35232},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2609.35232}
}