retrieval

Amazon Bedrock Managed Knowledge Bases now offers agentic retrieval through a new AgenticRetrieveStream API that decomposes multi-part questions into sub-queries and iterates until it judges evidence sufficient. The managed model costs $4 per 1,000 agentic retrieval calls plus $1 per 1,000 underlying Retrieve API calls.

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.

Amazon has added metadata filtering to its AgentCore Memory service for AI agents. In AWS evaluations across 151 questions, the feature improved overall question-answering accuracy from 40% to 64%, with context-dependent questions jumping from 16% to 69% accuracy. The update allows agents to filter memory retrieval by attributes like priority, department, or time range before semantic search runs.

Mistral AI has released Codestral Embed, its first code-specialized embedding model, priced at $0.15 per million tokens. The model features an 8192-token context window and claims to outperform Voyage Code 3, Cohere Embed v4.0, and OpenAI's large embedding model on code retrieval benchmarks.

AWS released multimodal embedding capabilities for Amazon Nova on Bedrock, allowing manufacturing organizations to retrieve information from technical documents that combine text, engineering diagrams, and images. The model supports configurable dimensions from 256 to 3072 and processes text, images, and multi-page documents into a shared vector space.

IBM released Granite Embedding 311M Multilingual R2, a 311-million parameter dense embedding model with 32,768-token context length and support for 200+ languages. The model scores 64.0 on Multilingual MTEB Retrieval (18 tasks), an 11.8-point improvement over its predecessor, and ships with ONNX and OpenVINO models for production deployment.

A large-scale study testing 34,198 real-world skills reveals that AI agent performance drops drastically when moving from curated benchmarks to realistic conditions. Claude Opus 4.6 saw pass rates fall from 55.4% with hand-selected skills to 38.4% in truly realistic scenarios, while weaker models like Kimi K2.5 actually perform below their no-skill baseline.

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.

Microsoft released the Harrier-OSS embedding model family, comprising three variants with 270M, 600M, and 27B parameters. The largest model achieves 74.3 on the Multilingual MTEB v2 benchmark. All models support 32,768 max tokens and multilingual inputs across 40+ languages.

Microsoft has released Harrier-OSS-v1, a family of multilingual text embedding models trained with contrastive learning and knowledge distillation. The 0.6B parameter variant achieves a 69.0 score on the Multilingual MTEB v2 benchmark with support for 32,768 token context windows and 45+ languages.

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.