Do Vision Language Models Need to Process Image Tokens?

The project is a fresh research repository (7 days old, 0 stars) accompanying an academic paper. It addresses a critical bottleneck in multimodal AI: the high cost of processing hundreds of image tokens through every layer of a large language model. While the insight is valuable—revealing that image representations often 'crystallize' early and don't require full-stack processing—the defensibility is low because it is a methodological discovery rather than a proprietary product or platform. Frontier labs (OpenAI, Google, Anthropic) are already aggressively optimizing VLM inference (e.g., GPT-4o, Gemini Flash) and are likely employing similar 'early-exit' or token-pruning strategies internally. The displacement horizon is very short (6 months) because these optimizations are quickly absorbed into mainstream inference engines like vLLM, TensorRT-LLM, or TGI once the 'proof of concept' is validated by papers like this. The platform domination risk is high as this capability is a feature-level optimization that cloud providers will implement at the infrastructure level to reduce their own COGS (Cost of Goods Sold).