Apple Proposes CLaRa: A Unified RAG Framework
Apple ML Research · rss · 2026-07-15
Apple ML Research proposed CLaRa (Continuous Latent Reasoning), which attempts to unify and optimize "retrieval" and "generation" in Retrieval-Augmented Generation (RAG) within a shared continuous latent space.
The core idea is to use embedding-based compression to condense documents into shorter, yet retrievable vector representations, thereby reducing the context length fed to the generator. Joint training ensures these compressed representations retain semantic info while remaining highly suitable for retrieval.
The paper also introduces SCP, a data construction framework that combines Q&A and paraphrasing to generate data with key semantic fidelity, helping the model learn "compressible, retrievable" vectors.
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