CLaRa: Bridging Retrieval and Generation with Continuous Latent Reasoning

• URL: http://arxiv.org/abs/2511.18659v2
• Date: Tue, 25 Nov 2025 22:02:20 GMT
• Title: CLaRa: Bridging Retrieval and Generation with Continuous Latent Reasoning
• Authors: Jie He, Richard He Bai, Sinead Williamson, Jeff Z. Pan, Navdeep Jaitly, Yizhe Zhang,
• Abstract summary: CLaRa is a unified framework that performs embedding-based compression and joint optimization in a shared continuous space.<n> Experiments show that CLaRa achieves state-of-the-art compression and reranking performance, often surpassing text-based fine-tuned baselines.
• Score: 34.38636514331703
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Retrieval-augmented generation (RAG) enhances large language models (LLMs) with external knowledge but still suffers from long contexts and disjoint retrieval-generation optimization. In this work, we propose CLaRa (Continuous Latent Reasoning), a unified framework that performs embedding-based compression and joint optimization in a shared continuous space. To obtain semantically rich and retrievable compressed vectors, we introduce SCP, a key-preserving data synthesis framework using QA and paraphrase supervision. CLaRa then trains the reranker and generator end-to-end via a single language modeling loss, with gradients flowing through both modules using a differentiable top-k estimator. Theoretically, this unified optimization aligns retrieval relevance with answer quality. Experiments across multiple QA benchmarks show that CLaRa achieves state-of-the-art compression and reranking performance, often surpassing text-based fine-tuned baselines.

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