Hybrid Token Compression for Vision-Language Models

Vision-language models (VLMs) have transformed multimodal reasoning, but feeding hundreds of visual patch tokens to LLMs incurs quadratic computational costs, straining memory and context windows. Traditional approaches face a trade-off: continuous compression dilutes high-level semantics like object identities, while discrete quantization loses granular details such as textures. We challenge this by introducing HTC-VLM, a hybrid framework that disentangles semantics and appearance through dual channels, i.e., a continuous pathway for fine-grained details via ViT patches and a discrete pathway for symbolic anchors using MGVQ quantization projected to four tokens. These are fused into a 580-token hybrid sequence and compressed to one token via a disentanglement attention mask and <voco> bottleneck, ensuring efficient, grounded representations. HTC-VLM achieves an average performance retention of 87.2% across seven benchmarks (GQA, VQAv2, MMBench, MME, POPE, SEED-Bench, ScienceQA-Image), outperforming the leading continuous baseline at 81.0% under the same one-token output budget. Attention analyses show the compressed token prioritizes the discrete anchors, supporting their role as semantic guidance. This extended study further examines token-budget behavior, crossarchitecture generalization, inference efficiency, codebook and masking robustness, and formal information-theoretic properties of the hybrid bottleneck. The code is publicly available at https://github.com/jushengzhang/HybridToken-VLM.