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.

本文讨论了一种被称为大语言模型涌现能力的不可预测现象——若某项能力在小模型中不存在而在大模型中存在，则称为涌现。This paper discusses an unpredictable phenomenon that is referred to as emergent abilities of large language models, an ability to be emergent if it is not present in smaller models but is present in larger models.