Beyond Selection: Token Parameterization for Extreme Visual Token Compression

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 compression ratios from $23\times$ to $64\times$ and remains competitive at $144\times$, 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 $\sim$36\% end-to-end speedup and using $16.6\times$/$78.8\times$ lower compressor latency/FLOPs.