This article surveys Cognitive Edge Computing as a practical and methodical pathway for deploying reasoning-capable Large Language Models (LLMs) and autonomous AI agents on resource-constrained devices at the network edge. We present a unified, cognition-preserving framework spanning: (1) model optimization (quantization, sparsity, low-rank adaptation, distillation) aimed at retaining multi-step reasoning under tight memory/compute budgets; (2) system architecture (on-device inference, elastic offloading, cloud-edge collaboration) that trades off latency, energy, privacy, and capacity; and (3) adaptive intelligence (context compression, dynamic routing, federated personalization) that tailors computation to task difficulty and device constraints. We synthesize advances in efficient Transformer design, multimodal integration, hardware-aware compilation, privacy-preserving learning, and agentic tool use, and map them to edge-specific operating envelopes. We further outline a standardized evaluation protocol covering latency, throughput, energy per token, accuracy, robustness, privacy, and sustainability, with explicit measurement assumptions to enhance comparability. Remaining challenges include modality-aware reasoning benchmarks, transparent and reproducible energy reporting, edge-oriented safety/alignment evaluation, and multi-agent testbeds. We conclude with practitioner guidelines for cross-layer co-design of algorithms, runtime, and hardware to deliver reliable, efficient, and privacy-preserving cognitive capabilities on edge devices.

翻译：本文综述了认知边缘计算作为一种实用且系统化的路径，用于在网络边缘的资源受限设备上部署具备推理能力的大型语言模型（LLMs）和自主AI智能体。我们提出了一个统一的、保持认知能力的框架，涵盖：（1）模型优化（量化、稀疏化、低秩适应、蒸馏），旨在有限的内存/计算预算下保持多步推理能力；（2）系统架构（设备端推理、弹性卸载、云边协同），在延迟、能耗、隐私和容量之间进行权衡；（3）自适应智能（上下文压缩、动态路由、联邦个性化），根据任务难度和设备约束定制计算。我们综合了高效Transformer设计、多模态集成、硬件感知编译、隐私保护学习以及智能体工具使用等方面的进展，并将其映射到边缘特定的运行边界。我们进一步概述了一个标准化的评估协议，涵盖延迟、吞吐量、每令牌能耗、准确性、鲁棒性、隐私和可持续性，并明确了测量假设以增强可比性。剩余的挑战包括模态感知推理基准、透明且可复现的能耗报告、面向边缘的安全/对齐评估以及多智能体测试平台。最后，我们为算法、运行时和硬件的跨层协同设计提供了实践指南，以在边缘设备上实现可靠、高效且保护隐私的认知能力。