Clinical Note Bloat Reduction for Efficient LLM Use
临床笔记去冗余以提高大语言模型使用效率
Jordan L. Cahoon, Chloe Stanwyck, Asad Aali, Rachel Madding, Emma Sun, Yixing Jiang, Renumathy Dhanasekaran, Emily Alsentzer
机构
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Department of Biomedical Data Science, Stanford University, Stanford, CA（斯坦福大学生物医学数据科学系）
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Department of Pathology, Stanford University, Stanford, CA（斯坦福大学病理学系）
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Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA（斯坦福大学麻醉学、围术期医学与疼痛医学系）
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Department of Radiology, Stanford University, Stanford, CA（斯坦福大学放射学系）
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Department of Obstetrics and Gynecology, Stanford University, Stanford, CA（斯坦福大学妇产科学系）
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Division of Gastroenterology and Hepatology, Stanford University, Stanford, CA（斯坦福大学消化内科与肝病学系）
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Department of Computer Science, Stanford University, Stanford, CA（斯坦福大学计算机科学系）
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Weill Cancer Hub West（韦尔癌症中心西区）
专题命中
效率与部署
：LLM(title,summary_cn)；large language model(abstract)；language model(abstract)；分类 cs.CL、cs.AI
Comments10 pages, 3 figures, 1 table. Empirical measurement study reporting new repeated-run experiments quantifying baseline nondeterministic drift in large language models. This manuscript presents original empirical results (not a review or position paper) and establishes a baseline reference for future drift-mitigation work
Where to Begin: Efficient Pretraining via Subnetwork Selection and Distillation
从何处开始：通过子网络选择和蒸馏实现高效的预训练
Arjun Krishnakumar, Rhea Sanjay Sukthanker, Hannan Javed Mahadik, Gabriela Kadlecová, Vladyslav Moroshan, Timur Carstensen, Frank Hutter, Aaron Klein
机构
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University of Freiburg, Germany（弗赖堡大学）
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ELLIS Institute Tübingen, Germany（图宾根ELLIS研究所）
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Charles University, Faculty of Mathematics and Physics（查尔斯大学数学与物理系）
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PriorLabs
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The Czech Academy of Sciences, Institute of Computer Science（捷克科学院计算机科学研究所）
专题命中
效率与部署
：pretraining(title,abstract)；LLM(abstract)；large language model(abstract)；language model(abstract)
Consolidating TinyML Lifecycle with Large Language Models: Reality, Illusion, or Opportunity?
Guanghan Wu, Sasu Tarkoma, Roberto Morabito
专题命中
效率与部署
：large language model(title,abstract)；language model(title,abstract)；LLM(abstract)；分类 cs.AI、cs.LG
CommentsThis paper has been accepted for publication in the IEEE Internet of Things Magazine (Special Issue on Applications of Large Language Models in IoT). The copyright will be transferred to IEEE upon publication. A preliminary version of this work was presented at the Edge AI Foundation event Beyond LLMs and Chatbots: The Journey to Generative AI at the Edge (https://youtu.be/aFWfisdjQIs)
Prefill/Decode-Aware Evaluation of LLM Inference on Emerging AI Accelerators
新兴AI加速器上LLM推理的Prefill/Decode感知评估
Shun Usami, Venkatram Vishwanath, E. Wes Bethel
机构
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Department of Computer Science（计算机科学系）
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San Francisco State University（旧金山州立大学）
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Argonne National Laboratory（阿贡国家实验室）
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Lawrence Berkeley National Laboratory（伯克利国家实验室）
专题命中
效率与部署
：LLM(title,title_cn)；large language model(abstract)；language model(abstract)；分类 cs.AI
A Communication-Centric 6G-LLM Architecture for Scalable Tactical Autonomous Defense Vehicle Networks
面向可扩展战术自主防御车辆网络的以通信为中心的6G-LLM架构
Kiran Khurshid, Shumaila Javaid, Nasir Saeed
机构
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Department of Computer and Software Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan（计算机与软件工程系，国家科学与技术大学（NUST），伊斯兰堡，巴基斯坦）
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Department of Control Science and Engineering, College of Electronics and Information Engineering, Tongji University and National Key Laboratory of Autonomous Intelligent Unmanned Systems, Tongji University, China（控制科学与工程系，电子与信息工程学院，同济大学，以及自主智能无人机系统国家重点实验室，同济大学，中国）
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Department of Electrical and Communication Engineering, UAE University, Al-Ain 15551, UAE（电子与通信工程系，阿联酋大学，阿恩15551，阿联酋）
专题命中
效率与部署
：LLM(title,title_cn)；large language model(abstract)；language model(abstract)；分类 cs.AI
Comments10 pages, accepted in IEEE Network Magazine
Journal refK. Khurshid, S. Javaid and N. Saeed, "A Communication-Centric 6G-LLM Architecture for Scalable Tactical Autonomous Defense Vehicle Networks," in IEEE Network, Early access, 2026
Quantization Degradation in Large Language Models: A Signal-Noise Perspective
大语言模型中的量化退化：一种信号-噪声视角
Chenxi Zhou, Pengfei Cao, Jinyu Ye, Bohan Yu, Haida Yu, Jiang Li, Jun Zhao, Kang Liu
机构
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Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
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University of Chinese Academy of Sciences（中国科学院大学）
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Inner Mongolia University（内蒙古大学）
专题命中
效率与部署
：large language model(title,abstract)；language model(title,abstract)；post-training(abstract)；分类 cs.CL、cs.AI、cs.LG
CommentsAccepted for publication at IEEE Transactions on Software Engineering (TSE) 2026, 33 pages. Link to Github repository: https://github.com/Ahmadreza-SY/TCFL
CommentsPaper accepted for publication at IEEE Computer Magazine. During the final editing, title changed slightly ("The" is removed). PDF now has final DOI
Journal refProceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 4681-4701, Rabat, Morocco, 2026. Association for Computational Linguistics
EffGen: Enabling Small Language Models as Capable Autonomous Agents
EffGen: 使小型语言模型成为能干的自主智能体
Gaurav Srivastava, Aafiya Hussain, Chi Wang, Yingyan Celine Lin, Xuan Wang
机构
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Department of Computer Science, Virginia Tech, Blacksburg, VA, USA（弗吉尼亚理工大学计算机科学系）
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Georgia Institute of Technology, Atlanta, GA, USA（佐治亚理工学院）
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Google DeepMind, USA（谷歌DeepMind）
专题命中
效率与部署
：language model(title,abstract)；small language model(title,abstract)；large language model(abstract)；分类 cs.CL、cs.AI、cs.LG
