MIT CSAIL researchers built Retrieval Language Models that store full documents outside the model’s context window and let the AI query them via code, slicing, and parallel sub-instances. This approach handles inputs up to 10 million tokens, doubles benchmark performance, and matches or beats the cost of massive-context calls.

Qwen has released the Qwen3-VL-Embedding and Qwen3-VL-Reranker models, designed for advanced multimodal information retrieval and cross-modal understanding. These models support various inputs, including text and images, and enhance retrieval accuracy through a two-stage process of initial recall and precise re-ranking.