论文

本文综述了 Islamic LLMs 与可信 Islamic AI 这一新兴领域，并论证了仅具备阿拉伯语流利度不足以支撑 Islamic AI，进而提出面向抗幻觉 Islamic AI 系统的研究议程。This survey reviews the emerging field of Islamic LLMs and trustworthy Islamic AI, and argues that fluency in Arabic is not sufficient for Islamic AI, with a research agenda for hallucination-resistant Islamic AI systems.

本文对近期 KV cache 优化技术进行系统综述，将其归纳为五大方向：cache eviction、cache compression、混合内存方案、新型 attention 机制与组合策略，并指出自适应多阶段优化流水线是未来研究的重要方向。This paper provides a systematic review of recent KV cache optimization techniques, organizing them into five principal directions: cache eviction, cache compression, hybrid memory solutions, novel attention mechanisms, and combination strategies, and pointing toward adaptive, multi-stage optimization pipelines as a promising direction for future research.

本文系统梳理了 Agent 系统从 pre-LLM 时代到当前 LLM 驱动架构的演进，将其划分为软件型、物理型与自适应混合型，并突出展示了其在客户服务、软件开发、制造自动化、个性化教育、金融交易和医疗等领域的应用。This paper comprehensively examines the evolution of agent systems from the pre-LLM era to current LLM-powered architectures, categorizing agent systems into software-based, physical, and adaptive hybrid systems, highlighting applications across customer service, software development, manufacturing automation, personalized education, financial trading, and healthcare.

本综述系统梳理了超越基础 Skill 创建的 Skill 演化与评估图景，将其归纳为四种范式：执行反馈、轨迹蒸馏、压缩与强化学习，并指出了构建可泛化、高效且可验证安全的 Skill 生态的开放方向。This survey systematically examines the landscape of skill evolution and evaluation beyond foundational skill creation into four distinct paradigms, spanning execution feedback, trajectory distillation, compression, and reinforcement learning, and identifies open directions for building skill ecosystems that are generalizable, efficient, and verifiably safe.

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.

本综述为 LLM Agent 记忆机制提出一种新的演化框架，将发展过程形式化为三个阶段：Storage（轨迹保存）、Reflection（轨迹精炼）与 Experience（轨迹抽象）。This survey proposes a novel evolutionary framework for LLM agent memory mechanisms, formalizing the development process into three stages: Storage (trajectory preservation), Reflection (trajectory refinement), and Experience (trajectory abstraction).

本文贡献了一组在架构层面具有重要意义的 MLOps 集成与部署指南（共 25 条），分为五类，并阐述其对整体系统架构的影响。This work contributes a collection of 25 architecturally significant MLOps guidelines for model integration and deployment, organized into five categories, and describes their impact on the overall system architecture.

对聚焦MLOps工具的学术文献进行系统综述，揭示其功能、范围及其旨在解决的挑战，并突出真实MLOps pipeline中各工具间互操作性的重要性。A systematic review of the academic literature focused on MLOps tools is conducted to reveal their function, scope, and the challenges they are designed to address and highlight the importance of interoperability across MLOps tools in real-world MLOps pipelines.

本文给出一份关于Agentic RAG系统的分析综述，梳理RAG范式的演进，提出基于Agent数量、控制结构、自主性与知识表示的Agentic RAG架构分类法，并对现有框架的设计权衡进行比较分析。An analytical survey of Agentic RAG systems is presented, which traces the evolution of RAG paradigms, introduces a principled taxonomy of Agentic RAG architectures based on agent cardinality, control structure, autonomy, and knowledge representation, and provides a comparative analysis of design trade-offs across existing frameworks.

本文对 LLM 推理服务方法进行了全面综述，涵盖基础的实例级方法、深入的集群级策略、新兴的场景方向以及其他重要但零散的领域。This paper provides a comprehensive survey of LLM inference serving methods, covering fundamental instance-level approaches, in-depth cluster-level strategies, emerging scenario directions, and other miscellaneous but important areas.

本文聚焦 LLM 驱动的 kernel generation 领域，给出现有方法的结构化综述，涵盖 LLM-based 方法与 agentic optimization workflow，并系统梳理了支撑该领域学习与评测的数据集与 benchmark。This survey addresses the gap in LLM-driven kernel generation by providing a structured overview of existing approaches, spanning LLM-based approaches and agentic optimization workflows, and systematically organizing the datasets and benchmarks that underpin learning and evaluation in this domain.

本文探讨了 cloud platform 与 distributed system 在支撑 LLM 可扩展性、效率与优化方面的作用，涵盖数据管理、资源优化，以及对 microservices、autoscaling 与 hybrid cloud-edge 方案的需求。The role of cloud platforms and distributed systems in supporting the scalability, efficiency, and optimization of LLMs is explored, including data management, resource optimization, and the need for microservices, autoscaling, and hybrid cloud-edge solutions.

本文系统梳理了基于 LLM 的现代智能体中记忆的设计、实现与评估方法，覆盖 2022 年至 2026 年初的相关工作，并将 Agent 记忆形式化为一个涵盖时间范围、表示基底与控制策略的三维分类体系。This survey offers a structured account of how memory is designed, implemented, and evaluated in modern LLM-based agents, covering work from 2022 through early 2026, and formalizes agent memory as a three-dimensional taxonomy spanning temporal scope, representational substrate, and control policy.

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.

该综述全面回顾了生成模型的历史与基本组件，以及 AIGC 在单模态交互与多模态交互方向的最新进展，并介绍了文本与图像生成任务及相关模型。This survey provides a comprehensive review on the history of generative models, and basic components, recent advances in AIGC from unimmodal interaction and multimodal interaction, and introduces the generation tasks and relative models of text and image.

一篇关于基于 LLM 的 Agent 的全面综述，追溯了 Agent 概念从其哲学起源到在 AI 中的发展历程，解释了为何 LLM 适合作为 Agent 的基础，并提出一个包含三个核心组件的通用框架：大脑、感知与行动。A comprehensive survey on LLM-based agents, tracing the concept of agents from its philosophical origins to its development in AI, and explaining why LLMs are suitable foundations for agents, and presenting a general framework, comprising three main components: brain, perception, and action.

本文综述了一些最具代表性的 LLM，包括三大主流 LLM 家族（GPT、LLaMA、PaLM），讨论其特性、贡献与局限性，并概述了构建与增强 LLM 的相关技术。This paper reviews some of the most prominent LLMs, including three popular LLM families (GPT, LLaMA, PaLM), and discusses their characteristics, contributions and limitations, and gives an overview of techniques developed to build, and augment LLMs.

本文对 LLM 的评估方法进行了全面综述，围绕三个关键维度展开：评估什么、在何处评估、如何评估，并为 LLM 评估领域的研究者提供了宝贵洞见。This paper presents a comprehensive review of these evaluation methods for LLMs, focusing on three key dimensions: what to evaluate, where to evaluate, and how to evaluate, and offers invaluable insights to researchers in the realm of LLMs evaluation.

本文描述了一份以模式形式呈现的 prompt 工程技巧目录，这些技巧已被用于解决与 LLMs 对话时的常见问题，以改进 LLM 对话的输出。A catalog of prompt engineering techniques presented in pattern form that have been applied to solve common problems when conversing with LLMs to improve the outputs of LLM conversations is described.

本文旨在建立一套系统化的开放问题与应用成果清单，以便机器学习研究者更快地理解该领域的现状并开展有效工作。This paper aims to establish a systematic set of open problems and application successes so that ML researchers can comprehend the field's current state more quickly and become productive.