LLM-based agents increasingly rely on long-term memory to support multi-session reasoning and interaction, yet current systems provide little control over what information is retained. In practice, agents either accumulate large volumes of conversational content, including hallucinated or obsolete facts, or depend on opaque, fully LLM-driven memory policies that are costly and difficult to audit. As a result, memory admission remains a poorly specified and weakly controlled component in agent architectures. To address this gap, we propose Adaptive Memory Admission Control (A-MAC), a framework that treats memory admission as a structured decision problem. A-MAC decomposes memory value into five complementary and interpretable factors: future utility, factual confidence, semantic novelty, temporal recency, and content type prior. The framework combines lightweight rule-based feature extraction with a single LLM-assisted utility assessment, and learns domain-adaptive admission policies through cross-validated optimization. This design enables transparent and efficient control over long-term memory. Experiments on the LoCoMo benchmark show that A-MAC achieves a superior precision-recall tradeoff, improving F1 to 0.583 while reducing latency by 31% compared to state-of-the-art LLM-native memory systems. Ablation results identify content type prior as the most influential factor for reliable memory admission. These findings demonstrate that explicit and interpretable admission control is a critical design principle for scalable and reliable memory in LLM-based agents.
MemRouter: Memory-as-Embedding Routing for Long-Term Conversational Agents
University of Central Florida（佛罗里达大学中央分校）；University of Rochester（罗切斯特大学）；University of Illinois at Chicago（伊利诺伊大学香槟分校）；Case Western Reserve University（凯斯西储大学）
KV Admission: Learning What to Write for Efficient Long-Context LLM Inference
Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan（National Taiwan University 电子工程系）；Research Center for Information Technology Innovation, Academia Sinica, Taipei, Taiwan（Academia Sinica 信息技术创新研究中心）
PACE: Perceived-Latency-Aware Cascading Service Routing and Filler Control for QoE-Efficient Retrieval-Augmented Dialogue Serving
Chongqing University（重庆大学）；National University of Defense Technology（国防科技大学）；Chongqing University of Posts and Telecommunications（重庆邮电大学）；University of Toronto Mississauga（多伦多大学密西沙加分校）；Chongqing Huayuan Zhixin Technology Co., Ltd.（重庆华远智信科技有限公司）；Inspur Yunzhou Industrial Internet Co., Ltd.（浪潮云洲工业互联网有限公司）；Guoqi Zhimo (Chongqing) Technology Co., Ltd.（国器智模（重庆）科技有限公司）
Choosing How to Remember: Adaptive Memory Structures for LLM Agents
University of Technology Sydney（悉尼大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；University of California, Los Angeles（加州大学洛杉矶分校）；The University of Melbourne（墨尔本大学）
Adaptive Memory and Reflection Multi-Agent System for Medical Question Answering
School of Computing and Mathematics, Manchester Metropolitan University（曼彻斯特城市大学计算与数学学院）；Electrical and Computer Engineering, Technion – Israel Institute of Technology（以色列理工学院电气与计算机工程系）；School of Science and Technology, Hong Kong Metropolitan University（香港都会大学科学与科技学院）
ReLMXEL: Adaptive RL-Based Memory Controller with Explainable Energy and Latency Optimization
Department of Mathematics and Computer Science（数学与计算机科学系）；Sri Sathya Sai Institute of Higher Learning（Sri Sathya Sai高等研究院）；Department of Computer Science and Engineering（计算机科学与工程系）；Indian Institute of Technology Ropar（印度理工学院罗帕尔分校）；AI Performance Engineer（AI性能工程师）；Red Hat（红帽公司）
Beyond Similarity: Trustworthy Memory Search for Personal AI Agents
University of Science and Technology of China（中国科学技术大学）；Tsinghua University（清华大学）；National University of Singapore（新加坡国立大学）；University of California, Berkeley（加州大学伯克利分校）
AMA: Adaptive Memory via Multi-Agent Collaboration
The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州））；Shandong University（山东大学）；Nanyang Technological University（南洋理工大学）；Southern University of Science and Technology（南方科技大学）
