Latent Abstraction for Retrieval-Augmented Generation
LAnR proposes a unified architecture where a single LLM handles encoding, retrieval, and generation within its latent space. It replaces explicit text queries with latent vectors from special tokens to retrieve documents directly from the model's representations. This allows for tight integration between retrieval and generation, enabling efficient multi-hop reasoning with an adaptive control head.