#RAG

AIPerplexity is open-sourcing pplx-embed-v2-late, multi-vector embedding models for text and images in one shared space, in 9B and 0.6B sizes. The 9B model can index multimodal data, the 0.6B model can run queries on device, and PDF pages can be searched without OCR. The author reports 92.4% on MADQA and 64% on BrowseComp+, with weights available on Hugging Face.

AI…space for cross-model querying. Both models achieve frontier performance and are publicly available on Hugging Face.

AIGoogle DeepMind released EmbeddingGemma 2, an open 740M-parameter embedding model that maps text, code, images, video, and audio into one 768-dimensional space. Text-only use needs a 270M-parameter footprint, about 191MB active RAM when quantized on a Pixel 11 Pro, while loading all modalities takes about 567MB. The reported MTEB Code NDCG@10 score is 78.68, about 14% above the first generation, and MTEB Multilingual v2 is 61.36, roughly flat.

AIGoogle DeepMind has released EmbeddingGemma 2, an open 740 million parameter model that maps text, images, audio, and video into one embedding space. It is built on the Gemma 4 architecture under an Apache 2.0 license and supports an 8K token context window. The company reports a code benchmark gain from 68.76 to 78.68 on MTEB Code and says the model can run on-device with about 567MB of active RAM for the full multimodal version on a Google Pixel 11 Pro.

Why it matters: The release shows how a 740M-parameter embedding model can cover text, code, images, audio, and video on local hardware, with memory and storage figures to compare against other on-device options.

AIGoogle DeepMind released EmbeddingGemma 2, a 740M-parameter embedding model, under an Apache 2.0 license. The post says it is competitive across benchmarks and outperforms some specialist models more than twice its size, and that developers can use it for multimodal search or pair it with Gemma 4 for on-device RAG. Weights are available on Hugging Face and Kaggle.

AIGoogle introduces EmbeddingGemma 2, its first open, natively multimodal embedding model, covering text, code, image, video, and audio tasks. It has a 740M parameter form factor, is positioned for offline, privacy-first RAG when paired with Gemma 4, and the post claims it outperforms some specialist models more than twice its size. Weights are available now on Hugging Face.

AIIt also beats voyage-context-4 on chunk retrieval while using 8x less storage per vector: 1 KB (1024 dims, int8) vs 8 KB (2048, float32).

AIpplx-embed-v2-context-9b-preview sets a new state of the art on ConTEB and @turbopuffer's new, privately held context-bench.

AIExtremely scalable, SOTA accuracy, and like always fully privately deployable and accessible on Model Vault!

AITencent has published ContextPilot-E4B on Hugging Face, the Gemma4-E4B checkpoint of ContextPilot, a framework that teaches long-horizon language-model agents to plan, maintain long-term memory, and offload less useful context while reasoning and using tools. The checkpoint is intended for research on proactive context management, long-context QA, and deep search, and loading it alone does not execute the context-management tools, which are provided in the ContextPilot repository.

AIApple's CLaRa-7B-E2E is a fully end-to-end unified RAG model that jointly optimizes retrieval and generation, with 16x and 128x document compression. It is trained with end-to-end finetuning using differentiable top-k retrieval and a unified language-modeling objective. The model is available on Hugging Face with example end-to-end inference code.

AIApple has published CLaRa-7B-Instruct on Hugging Face, an instruction-tuned unified RAG model with built-in semantic document compression at 16× and 128× ratios. The model answers instruction-following questions directly from compressed document representations, and its paper, GitHub repository, and transformers usage example are referenced in the release.