No More K-means:Single-Stage Sparse Coding for Efficient Multi-Vector Retrieval @Veritas2026 et al. replace vector clustering with efficient sparse autoencoders & natural inverted indexing to accelerate multi-vector retrieval. 📝arxiv.org/abs/2605.30120 👨🏽‍💻github.com/Y-Research-SBU/SS…

Efficient Multivector Retrieval with Token-Aware Clustering and Hierarchical Indexing Presents a multivector retrieval system that uses token-aware clustering to allocate centroids based on token frequency & semantic variance. 📝arxiv.org/abs/2604.28142 👨🏽‍💻github.com/TusKANNy/tachiom

The new generation of open state-of-the-art single and multi-vector retrieval models is here It's time, DenseOn with the LateOn 🎶 @LightOnIO releases models that leap past existing ones, and everything you need to do the same!

🚀 New model family release with an OMNIMODAL version ! After Eurobert, I'm excited to introduce BidirLM, a family of 5 frontier bidirectional encoders including an OMNIMODAL encoder at just 2.5B parameters. 🧵👇 huggingface.co/BidirLM

Introducing Mixedbread Wholembed v3, our new SOTA retrieval model across all modalities and 100 languages. Wholembed v3 brings best-in-class search to text, audio, images, PDFs, videos... You can now get the best retrieval performance on your data, no matter its format.

Multi-vector embeddings (ColBERT, ColPali) are budget killers. But MUVERA can cut your memory footprint by 70%. Multi-vector models offer incredible retrieval but suffer from massive memory overhead and slow indexing. MUVERA (Multi-Vector Retrieval via Fixed Dimensional Encodings) compresses these into single, fixed-dimensional vectors. How it works: MUVERA condenses a sequence of vectors (e.g., 100x96d) into one vector via: 1️⃣ Space Partitioning: Groups vectors into buckets using SimHash or k-means clustering. 2️⃣ Dimensionality Reduction: Applies random linear projection to compress each sub-vector while preserving dot products. 3️⃣ Repetitions: Repeats the process multiple times and concatenates results to improve accuracy. 4️⃣ Final Projection: Optional final compression (not used in Weaviate's implementation). The impact (LoTTE benchmark): - Memory: 12GB → <1GB. - Indexing: 20 mins → 3-6 mins. - HNSW Graph: 99% smaller. There’s a trade-off: You trade a slight dip in raw recall for massive efficiency gains. However, by tuning the HNSW `ef` parameter (e.g., `ef=512`), you can recover 80-90% recall while keeping costs low. When should you use MUVERA? → Large-scale production RAG → Systems where memory/infrastructure costs are the direct bottleneck → Use cases requiring fast indexing MUVERA in @weaviate_io 1.31 takes just a couple of lines of code. You can tune three parameters (k_sim, d_proj, r_reps) to balance memory usage and retrieval accuracy for your specific use case. Read the full technical deep-dive here: weaviate.io/blog/muvera?utm_…

we just made Claude Code - use 53% fewer tokens - respond 48% faster - give 3.2x better responses just by giving it a better grep

We’ve been running a @mixedbreadai-powered search on the Effect docs for about a month now, and the results speak for themselves: → More relevant results → Fewer “no results” dead ends → Easier discovery of advanced topics Full write-up by @imax153 ⤵️ effect.website/blog/how-mixe…