The real problem with document retrieval was never just reading more text, it was losing the page before the search even began.

webAI (@thewebAI ) just open-sourced a vision-language retrieval model (ColVec1) that’s now at #1 on ViDoRe V3 by searching document pages directly instead of reading them through OCR first.

ViDoRe V3 benchmark is one of the gold standard for evaluating multimodal document retrieval in enterprise settings.

The models (ColVec1) come in 4B and 9B variants (both are on Huggingface), with Qwen 3.5 vision-language as the backbone, and trained on a dataset of ~2M question-image pairs. Can be easily run on consumer level GPUs

“Frontier-level retrieval. No OCR. No preprocessing. Built for real documents.”

The AI world usually goes chasing size first. webAI has long believed that being smarter matters more than being bigger.

This release clearly shows that page layout is part of meaning, not just formatting noise.

Multimodal RAG has looked like a scale problem for a while, but this release makes a strong case that the real gap was retrieval.

OCR-first systems flatten a page into text and often damage the very clues that carry meaning, like table alignment, chart context, columns, spacing, and scanned layouts.

ColVec1 keeps the page as an image, so retrieval starts from the full visual document rather than a lossy text extraction step.

The model uses one shared VLM for text queries and page images, then turns each token into many vectors instead of one pooled embedding.

That multi-vector late interaction setup lets a query match the best local regions of a page, which helps on manuals, filings, papers, and dense tables where one global vector misses detail.

webAI says it trained on about 2M question-image pairs across DocVQA, PubTables, TAT-QA, ViDoRe-style data, multilingual sets, and synthetic document queries.
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