RAG News

This feed includes only recent releases, roughly from the last 12 months, not the full historical archive. That is why a long-established product may have only a few news items.

6 news

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NVIDIA released Nemotron 3 Embed - open embedding models, No. 1 on RTEB

The 8B model scored 78.5% on RTEB and 75.5% on MMTEB Retrieval - first place overall. The 1B variant reaches 72.4% on RTEB (27% fewer errors than the previous 1B version), and NVFP4 quantization preserves 99%+ of BF16 accuracy at 2x throughput. Weights and training recipes are open and available on Hugging Face.

Claude has no open embedding weights of its own, and the retrieval layer depends on third-party Voyage AI Anthropic Claude →
RAG
LightOn released a multimodal reranker with a single relevance scale for text and scans

LightOn-rerank-LW-2B (2B, a LoRA adapter on Qwen3.5) ranks text passages and document scans with a single model and a single scale - 62.66 NDCG@10 on ViDoRe V3 versus 59.18 for Qwen3-VL-Reranker-2B and 59.40 for jina-reranker-m0. The 4B variant (64.69) beats Qwen3-VL-Reranker-8B (64.23) with half as many parameters. Weights are open on Hugging Face.

built-in hybrid retrieval in Elasticsearch (ELSER); text - scans/images on a unified relevance scale; LightOn handles this with a separate reranker layer on top Elasticsearch →
RAG
Claude Opus 4.6: leap in long-context retrieval

On MRCR v2 (8-needle, 1M tokens), Opus 4.6 scored 76% vs 18.5% for Sonnet 4.5 — better at retrieving and finding facts in large document sets without explicit RAG index. Model via API/cloud only, weights not published.

Llama open weights enable self-hosted retrieval loop without Anthropic cloud dependency Meta Llama →
RAG
Hugging Face replaced MTEB with RTEB - a retrieval benchmark with hidden datasets

RTEB (beta) combines open and private datasets across 20 languages and domains (law, medicine, code, finance) on NDCG@10 metric to prevent model overfitting to public benchmarks—prior MTEB inflated scores due to public dataset memorization. Used as primary benchmark for 2026 embedding model evaluation (Nemotron 3 Embed and others).

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