Large language models (LLMs) predominantly employ decoder-only transformer architectures, necessitating the retention of keys/values information for historical tokens to provide contextual information and avoid redundant computation. However, the substantial size and parameter volume of these LLMs require massive GPU memory. This memory demand increases with the length of the input text, leading to an urgent need for more efficient methods of information storage and processing. This study introduces Anchor-based LLMs (AnLLMs), which utilize an innovative anchor-based self-attention network (AnSAN) and also an anchor-based inference strategy. This approach enables LLMs to compress sequence information into an anchor token, reducing the keys/values cache and enhancing inference efficiency. Experiments on question-answering benchmarks reveal that AnLLMs maintain similar accuracy levels while achieving up to 99% keys/values cache reduction and up to 3.5 times faster inference. Despite a minor compromise in accuracy, the substantial enhancements of AnLLMs employing the AnSAN technique in resource utilization and computational efficiency underscore their potential for practical LLM applications.
From Anchors to Supervision: Memory-Graph Guided Corpus-Free Unlearning for Large Language Models
College of Computer Science and Artificial Intelligence, Fudan University（复旦大学计算机科学与人工智能学院）；Shanghai University of Electric Power（上海电力大学）；School of Information Science and Engineering, East China University of Science and Technology（东华大学信息科学与工程学院）
TempCharBERT: Keystroke Dynamics for Continuous Access Control Based on Pre-trained Language Models
Institut national de la recherche scientifique (INRS-EMT)（国家科学研究院（INRS-EMT））；Université du Québec（魁北克大学）；INRS-UQO Mixed Research Unit on Cybersecurity（INRS-UQO网络安全混合研究单元）；Polytechnique Montréal（蒙特利尔理工学院）；São Paulo State Technological College（圣保罗州立技术学院）
Toward Robust Multilingual Adaptation of LLMs for Low-Resource Languages
Department of Automation, Tsinghua University, Beijing, China（清华大学自动化系）；Alibaba International Digital Commerce Group, Beijing, China（阿里巴巴国际数字 commerce 集团）；School of Software, Tsinghua University, Beijing, China（清华大学软件学院）
AnchorBench: A Multi-Pathway Benchmark for the Anchoring Effect in LLMs
Saarland University（萨尔大学）；Hamburg University of Technology（汉堡工业大学）；Helmholtz-Zentrum Hereon（亥姆霍兹中心赫伦）；German Research Centre for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））
M-GRPO: Stabilizing Self-Supervised Reinforcement Learning for Large Language Models with Momentum-Anchored Policy Optimization
Shanghai Innovation Institute（上海创新研究院）；College of Future Information Technology, Fudan（复旦大学未来信息技术学院）；Shanghai AI Laboratory（上海人工智能实验室）；The Chinese University of Hong Kong（香港中文大学）
BAPO: Base-Anchored Preference Optimization for Overcoming Forgetting in Large Language Models Personalization
Graduate School of AI, KAIST（韩国科学技术院人工智能研究生院）；Samsung Advanced Institute of Technology（三星先进技术研究院）；Department of Electrical and Computer Engineering, UBC（不列颠哥伦比亚大学电气与计算机工程系）
ANCHOR: Abductive Network Construction with Hierarchical Orchestration for Reliable Probability Inference in Large Language Models
School of Informatics, Xiamen University, Xiamen, China（信息学院，厦门大学，厦门）；School of Film, Xiamen University, Xiamen, China（电影学院，厦门大学，厦门）；Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan, Ministry of Culture and Tourism, Xiamen University, Xiamen, China（福建省和台湾非物质文化遗产数字保护与智能处理重点实验室，文化和旅游部，厦门大学，厦门）；Institute of Artificial Intelligence, Xiamen University, Xiamen, China（人工智能学院，厦门大学，厦门）；School of Computer and Data Science, Minjiang University, Fuzhou, China（计算机与数据科学学院，闽江大学，福州）
KBioXLM: A Knowledge-anchored Biomedical Multilingual Pretrained Language Model
Institute of Artificial Intelligence, Soochow University（苏州大学人工智能研究院）；Peking University（北京大学）；The Hong Kong Polytechnic University（香港理工大学）；Pharmcube（药智魔方）；Changping Laboratory（昌平实验室）
Keys to Robust Edits: from Theoretical Insights to Practical Advances
Zhejiang University（浙江大学）；School of Engineering, Westlake University（西湖大学工程学院）；Institute of Advanced Technology, Westlake Institute for Advanced Study（西湖先进研究院技术研究所）；Shanghai AI Lab（上海人工智能实验室）
AnTKV: Anchor Token-Aware Sub-Bit Vector Quantization for KV Cache in Large Language Models
DSA Thrust, The Hong Kong University of Science and Technology (Guangzhou)（DSA团队，香港科学与技术大学（广州））；School of Mathematics and Computational Science, Xiangtan University（数学与计算科学学院，湘潭大学）；Microsoft（微软）
Dual-Anchoring: Addressing State Drift in Vision-Language Navigation
National Key Laboratory of Human-Machine Hybrid Augmented Intelligence（人机混合增强智能国家重点实验室）；National Engineering Research Center for Visual Information and Applications（视觉信息与应用国家工程研究中心）；Institute of Artificial Intelligence and Robotics（人工智能与机器人研究院）；Xi’an Jiaotong University（西安交通大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Johns Hopkins University（约翰霍普金斯大学）；Joy Future Academy, JD（京东探索研究院）
Homogeneous Keys, Heterogeneous Values: Exploiting Local KV Cache Asymmetry for Long-Context LLMs
MoE Key Laboratory of Interdisciplinary Research of Computation and Economics（交叉计算与经济学 interdisciplinary 研究联合实验室）；Shanghai University of Finance and Economics（上海财经大学）
