⚡ VLM Efficiency¶

🧠 NeurIPS2026 · 2 paper notes

Beyond Selection: Token Parameterization for Extreme Visual Token Compression: Rather than selecting a few original patches, Braco constructs a compact visual interface using a DCT low-frequency backbone, basis-coordinate embeddings, budget-dependent coordinate organization, and sparse-pooled spatial residuals; at 576→16 tokens, it retains a Vanilla-normalized aggregate score of 94.0 while reducing full-pipeline prefill latency to 40.59 ms.
G2TR: Generation-Guided Visual Token Reduction for Separate-Encoder Unified Multimodal Models: G2TR uses VAE latent anchors to select and merge understanding-side visual tokens before LLM prefill in separate-encoder unified multimodal models; retaining 50% of the tokens yields 99.0% relative-average understanding performance and 98.0% editing performance on BAGEL, with approximately 1.94× lower prefill FLOPs, but not lossless performance on every task.