# CLaRa: Bridging Retrieval and Generation with Continuous Latent Reasoning > Source: > Published: 2026-07-15 00:00:00+00:00 [content type paper](/research/)published July 2026 CLaRa: Bridging Retrieval and Generation with Continuous Latent Reasoning AuthorsJie He†**, Richard He Bai, Sinead Williamson, Jeff Z. Pan†, Navdeep Jaitly, Yizhe Zhang CLaRa: Bridging Retrieval and Generation with Continuous Latent Reasoning AuthorsJie He†**, Richard He Bai, Sinead Williamson, Jeff Z. Pan†, Navdeep Jaitly, Yizhe Zhang 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, thereby reducing the document length fed into the generator, we introduce SCP, a key-preserving data synthesis framework based on question-answering 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, even at a text compression rate of 16, outperforming text-based fine-tuned baselines. Compress and Compare: Interactively Evaluating Efficiency and Behavior Across ML Model Compression Experiments September 27, 2024[research area Human-Computer Interaction](/research/?domain=Human-Computer%20Interaction), [research area Tools, Platforms, Frameworks](/research/?domain=Tools%2C%20Platforms%2C%20Frameworks)[conference IEEE Visualization](/research/?event=IEEE%20Visualization) *Equal Contributors To deploy machine learning models on-device, practitioners use compression algorithms to shrink and speed up models while maintaining their high-quality output. A critical aspect of compression in practice is model comparison, including tracking many compression experiments, identifying subtle changes in model behavior, and negotiating complex accuracy-efficiency trade-offs. However, existing compression tools poorly support… Context Tuning for Retrieval Augmented Generation December 18, 2023[research area Knowledge Bases and Search](/research/?domain=Knowledge%20Bases%20and%20Search), [research area Speech and Natural Language Processing](/research/?domain=Speech%20and%20Natural%20Language%20Processing)[Workshop at EACL](/research/?event=EACL%20Workshop) This paper was accepted at the UncertaiNLP workshop at EACL 2024. Large language models (LLMs) have the remarkable ability to solve new tasks with just a few examples, but they need access to the right tools. Retrieval Augmented Generation (RAG) addresses this problem by retrieving a list of relevant tools for a given task. However, RAG’s tool retrieval step requires all the required information to be explicitly present in the query. This is a…