论文

本综述界定了 CAIS 的概念，提出基于组件角色与编排策略的多维分类体系，并分析四种基础范式：Retrieval-Augmented Generation (RAG)、LLM Agents、Multimodal LLMs (MLLMs) 与 Orchestration。This survey defines the concept of CAIS, proposes a multi-dimensional taxonomy based on component roles and orchestration strategies, and analyzes four foundational paradigms: Retrieval-Augmented Generation (RAG), LLM Agents, Multimodal LLMs (MLLMs), and Orchestration.

论文论证了物理先验提供了一种与机器人领域高度相关的归纳偏置，其作用是与数据驱动学习互补而非取代，从而为更通用、数据高效且可信的机器人系统铺平道路。It is argued that physics priors provide a particularly relevant robotics-specific inductive bias, complementing rather than replacing data-driven learning, and paving the way toward more generalizable, data-efficient, and trustworthy robotic systems.

本综述对 RAG 进行了全面考察，将其视为一个模块化且持续演进的范式，能够提升基于 LLM 系统的事实可靠性、适应性与任务对齐，并指出了持续存在的挑战。This survey presents a comprehensive examination of RAG as a modular and evolving paradigm that enhances factual reliability, adaptability, and task alignment in LLM based systems and identifies persistent challenges.

该综述细致梳理了 RAG 范式的演进，涵盖 Naive RAG、Advanced RAG 与 Modular RAG，并对 RAG 框架的三大基础支柱——检索、生成与增强技术——进行了深入审视。This comprehensive review paper offers a detailed examination of the progression of RAG paradigms, encompassing the Naive RAG, the Advanced RAG, and the Modular RAG, and meticulously scrutinizes the tripartite foundation of RAG frameworks, which includes the retrieval, the generation and the augmentation techniques.

本综述对图嵌入文献进行全面回顾，并提出两种图嵌入分类法，分别对应不同图嵌入问题设置中的挑战以及现有工作如何在解决方案中应对这些挑战。This survey conducts a comprehensive review of the literature in graph embedding and proposes two taxonomies ofGraph embedding which correspond to what challenges exist in differentgraph embedding problem settings and how the existing work addresses these challenges in their solutions.