[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"aihot-art-86749":3},{"itemId":4,"vertical":5,"category":6,"source":7,"score":8,"title":9,"summary":10,"analysis":11,"url":12,"coverUrl":13,"direction":13,"marketSignal":13,"publishedAt":14},"86749","ai","论文研究","Apple Machine Learning Research",67,"CLaRa：通过连续潜在推理桥接检索与生成","苹果提出 CLaRa 框架，通过连续潜在推理统一检索与生成，优化长上下文处理。","苹果提出 CLaRa 框架，通过连续潜在推理统一检索与生成。\n· 传统 RAG 面临长上下文和检索-生成分离优化的问题。\n· CLaRa 在共享连续空间中进行嵌入压缩和联合优化。\n· 引入 SCP 数据合成框架，生成语义丰富且可检索的压缩向量。\n· 减少输入给生成器的文档长度，提升效率。\n看点：CLaRa 为 RAG 提供更高效的解决方案，可能推动知识密集型任务的发展。","https:\u002F\u002Fmachinelearning.apple.com\u002Fresearch\u002Fclara-latent-reasoning",null,"2026-07-15 08:00:00"]