A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Sina Mohseni, Niloofar Zarei, Eric D. Ragan
A multidisciplinary survey and framework that categorizes XAI design goals and evaluation methods, providing step-by-step guidelines for iterative design and evaluation in XAI teams.
Leslie Lamport
Introduces the Temporal Logic of Actions (TLA), a simple yet powerful logic for specifying and reasoning about concurrent systems, where systems and properties are expressed in the same logic.
Jianghao Lin, Xinyi Dai, Yunjia Xi, et al.
A comprehensive survey of how large language models can be adapted to recommender systems, organized by where LLMs fit in the pipeline and how they are trained and used.
Stephen J. Mellor, Marc Balcer, Ivar Jacoboson
This book defines Executable UML, a profile of UML that enables developers to model subject matters at a higher abstraction level for execution and compilation into complete systems.
Xupeng Miao, Gabriele Oliaro, Zhihao Zhang, et al.
A comprehensive survey of efficient LLM serving techniques spanning algorithmic optimizations and system-level designs to address latency and throughput challenges.
Yingxia Shao, Hongzheng Li, Xizhi Gu, et al.
This survey systematically reviews distributed GNN training, categorizing optimization techniques into data partition, batch generation, execution model, and communication protocol to address communication, accuracy, and imbalance challenges.
Jinke Ren, Guanding Yu, Guangyao Ding
Proposes learning efficiency metric and joint batchsize and resource allocation to accelerate DNN training in wireless federated edge learning for CPU and GPU scenarios.
Syed Junaid Nawaz, Shree Krishna Sharma, Shurjeel Wyne, et al.
This paper reviews machine learning, quantum computing, and quantum machine learning for 6G networks and proposes a QC-assisted QML framework for beyond-5G communication systems.
Zeyu Zhang, Quanyu Dai, Xiaohe Bo, et al.
A comprehensive survey that systematically reviews the memory mechanisms of LLM-based agents, covering design, evaluation, applications, limitations, and future directions.
Yutao Zhu, Huaying Yuan, Shuting Wang, et al.
A comprehensive survey of how large language models are integrated into information retrieval systems, covering query rewriters, retrievers, rerankers, readers, and search agents.
Junda He, Christoph Treude, David Lo
A systematic literature review of LLM-based multi-agent systems across the software development lifecycle, with case studies, research gaps, and a forward-looking agenda.
Juyong Jiang, Fan Wang, Jiasi Shen, et al.
A systematic survey of LLMs for code generation, covering data curation, advances, evaluation, ethics, environmental impact, and applications, with comparative analysis on HumanEval, MBPP, and BigCodeBench.