AI BriefingCLaRa: Combining Retrieval and Generation through Continuous Latent Reasoning
Key point
CLaRa solves RAG's long-context problem through embedding-based compression and joint optimization.
Details
Existing RAG (Retrieval-augmented generation) approaches improve LLM performance by leveraging external knowledge, but they have limitations such as long-context processing issues and optimization misalignment between the retrieval and generation stages.
To address this, the proposed CLaRa (Continuous Latent Reasoning) is a unified framework that performs embedding-based compression and joint optimization within a shared continuous space. This approach reduces the length of documents input to the generator while preserving rich semantic information.
As a core technique, SCP (Key-preserving data synthesis framework) is introduced. SCP is based on question answering (QA) and paraphrasing, and helps generate retrievable compressed vectors while minimizing information loss.
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