NeoMME cuts multimodal retrieval cost without a vision tower
Most multimodal retrievers carry a separate vision model and a causal language model. NeoMME replaces both with one bidirectional Transformer trained from scratch, and its 260M model encodes about 51 document pages per second on an L40S, roughly twice ColModernVBERT's throughput. Its compressed late-interaction index shrinks from about 1.5 MB to 6 kB per page while retaining over 95% of baseline nDCG@10, making high-volume visual document search materially easier to serve.