Researchers release NeoMME
NeoMME released — points out that ColPali-style visual document retrievers repurpose generative VLMs (a separately pretrained vision encoder + a causal LM) as encoders and so carry parameter and compute overhead into non-generative tasks, and presents a family of 260M and 800M multimodal multilingual encoders that process multilingual text and raw image patches in a single bidirectional transformer encoder. Pretrained with a masked discrete-diffusion text objective conditioned on image patches, it supports a 16,384-token context, and NeoMME-Retriever, fine-tuned with both dense and late-interaction heads, scored 0.523 (260M) and 0.556 (800M) nDCG@10 on ViDoRe v3. Released under Apache 2.0 through Hugging Face Transformers
Why it matters
A strand that, instead of the ColPali lineage of reusing generative models as retrievers, returns to a single tower designed as an encoder from the start — a claim that "encoder-only" is valid again for multimodal retrieval. In the authors' own reporting, about 2 times the encoding throughput of ColModernVBERT at the same resolution, and more than 95% of baseline performance retained even at 255-fold compression through hierarchical token pooling and asymmetric quantization.
Source — Primary sources first; secondary reports are labelled “Press”
Paperarxiv.orghttps://arxiv.org/abs/2609.01657Added in Week 36, 2026 · See that week