@perdactor: 1/ Meet Argus-Retriever: the first late-interaction visual doc retriever where the document representation adapts to th…
Summary
Argus-Retriever is a new late-interaction visual document retriever that adapts document representation to the query, achieving SOTA performance on ViDoRe benchmarks with a smaller index.
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Cached at: 06/08/26, 07:18 AM
1/👁️ Meet Argus-Retriever: the first late-interaction visual doc retriever where the document representation adapts to the query: D(q).
- 86.0 NDCG@5 on ViDoRe V1+V2 (SOTA open model)
- 📦 1024-dim head, 4.5× smaller index
Sumit (@_reachsumit): Argus-Retriever: Vision-LLM Late-Interaction Retrieval with Region-Aware Query-Conditioned MoE for Visual Document Retrieval
Introduces a query-conditioned late-interaction visual document retriever.
📝 https://t.co/oB5oa4bgKc 👨🏽💻
1/ Meet Argus-Retriever: the first late-interaction visual doc retriever where the document representation adapts to the query: D(q).
- 86.0 NDCG@5 on ViDoRe V1+V2 (SOTA open model)
- 1024-dim head, 4.5× smaller index
2/ The idea: a region-aware Mixture-of-Experts inside the document encoder. A router reads each region’s content, its 2D position, and a pooled query context z_q, then mixes 4 latent experts (+1 shared). The page is now encoded differently per query → D(q). Still MaxSim.
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