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NVIDIA has released Nemotron-3-Embed-1B-BF16, a 1.14 billion parameter text embedding model supporting 34 languages with a 32,768 token context window. The model generates 2048-dimensional embeddings and was derived from Ministral-3-3B-Instruct-2512 through two rounds of structured pruning and distillation, first to 2B then to 1.14B parameters.

NVIDIA's Nemotron-3-Embed-8B-BF16 ranks #1 on the RTEB leaderboard with a 78.5% score, while the 1B variant reduces error rate by 27% over its predecessor. The open-weight models feature 32k context windows and production-ready deployment options including a Blackwell-optimized NVFP4 variant.

Microsoft's Bing team has open-sourced Harrier, a 27-billion-parameter embedding model that supports over 100 languages and features a 131,072-token context window. The model ranks first on the MTEB v2 multilingual benchmark, outperforming proprietary offerings from OpenAI and Amazon, and is available on Hugging Face under the MIT license.

A Google Deepmind paper introduces the first systematic framework for 'AI agent traps'—attacks that exploit autonomous agents' vulnerabilities to external tools and internet access. The researchers identify six attack categories targeting perception, reasoning, memory, actions, multi-agent networks, and human supervisors, with proof-of-concept demonstrations for each.

Chroma has released Context-1, a 20B parameter Mixture of Experts model trained specifically for retrieval tasks that require multi-hop reasoning. The model decomposes complex queries into subqueries, performs parallel tool calls, and actively prunes its own context mid-search—achieving comparable performance to frontier models at a fraction of the cost and up to 10x faster inference speed.

Perplexity has open-sourced two text embedding models designed to match or exceed the performance of Google's and Alibaba's embeddings while requiring significantly less memory. The move brings competitive embedding technology into the open-source ecosystem.