AI · Article or course
CLaRa: Bridging Retrieval and Generation with Continuous Latent Reasoning
RAG framework using continuous latent reasoning and embedding-based context compression
CLaRa is an AI research paper and framework by researchers from Apple and the University of Edinburgh that addresses RAG context-length and retrieval bottlenecks. It introduces continuous latent reasoning, compressing documents into dense memory vectors and training reranker and generator components end-to-end via a differentiable top-k estimator. It allows language models to maintain high retrieval and answer accuracy while feeding significantly fewer context tokens to the generator.
What it's for
- Compress long retrieved document contexts into compact latent vectors for LLM prompts
- Train retrieval rerankers and generators jointly via end-to-end language modeling loss
- Study advanced RAG architectures that move beyond discrete text-chunk retrieval
Features
- Joint optimization of reranker and generator modules in a shared continuous space
- SCP data synthesis framework based on question answering and paraphrase supervision
- Differentiable top-k estimator enabling end-to-end gradient flow between retrieval and generation
- Text compression rates up to 16x without severe loss in downstream question-answering accuracy
- Shared continuous latent space aligning retrieval relevance directly with answer generation
Pricing
Free
Details
- Type
- Article or course
- Category
- AI
- Topic
- AI Research Papers
- Open source
- No
- Platforms
- Web
- For
- AI researchers and machine learning engineers working on retrieval-augmented generation
- Alternative to
- Standard RAG chunking pipelines, Dense passage retrieval (DPR)
- Tags
- rag, research paper, embeddings, information retrieval, llm, context compression
Why it was saved
- @officeoptout: Solves traditional RAG semantic retrieval bottlenecks and reduces data size by compressing documents into dense memory tokens while training a query reasoner end-to-end to generate hypothetical answers before retrieval.
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