Conversational AI agents require memory systems that are both scalable and semantically coherent across long interaction horizons. Existing approaches rely predominantly on large language model (LLM)-based summarisation at write time, which introduces non-determinism, escalating token costs, and opacity in pruning decisions. We present the Deterministic Memory Framework (DMF), a CPU-first approach that replaces generative memory compression with a fully deterministic pipeline grounded in classical NLP analysis, vector geometry, and mathematical scoring. DMF assigns each conversational interaction a Survival Score $Ω$ computed from deterministic content signals, conversational cues, and structured provenance, combined through a logistic projection. An interaction-count decay law, denoted as $Ω_{\mathrm{eff}}(Δn)$, governs how relevance evolves as new turns arrive, where $Δn$ is the number of newer interactions rather than wall-clock time, preserving full determinism. We present the mathematical formulation of DMF, its structured recall pipeline, the pruning decision procedure, and the evaluation protocol. Experiments are conducted on a purpose-built benchmark using the LoCoMo and LongMemEval datasets. We compare DMF against Mem0, a popular memory layer for AI agents. DMF achieves comparable accuracy while using zero tokens to prepare the memory context and 5x to 242x fewer tokens over the entire conversation. These results show that it is possible to eliminate LLM calls from the memory-management loop, reducing token costs to nearly zero and enabling deterministic memory systems for conversational AI agents.
MACS: A Hybrid Multi-Agent Framework for Reliable Conversational E-Commerce Recommendation
Stanford University（斯坦福大学）；Google DeepMind（谷歌DeepMind）；University of Southern California（南加州大学）；Amazon（亚马逊公司）；Massachusetts Institute of Technology（麻省理工学院）；MIT Sloan School of Management（麻省理工学院斯隆管理学院）
Securing Multi-Agent GIS Systems: Risk Evaluation and Prompt Hardening Optimization
Department of Systems Design Engineering, University of Waterloo（系统设计工程系，滑铁卢大学）；Department of Mechatronics Engineering, University of Waterloo（机电工程系，滑铁卢大学）；Department of Geomatics Engineering, University of Calgary（测绘工程系，卡尔加里大学）；Department of Geography and Environmental Management, University of Waterloo（地理与环境管理系，滑铁卢大学）
Performance of a large language model-Artificial Intelligence based chatbot for counseling patients with sexually transmitted infections and genital diseases
University College of Medical Sciences and Guru Teg Bahadur Hospital（大学医学院与古鲁·特格·巴哈杜尔医院）；HeHealth Inc.（HeHealth公司）
Agentic AI-based Framework for Mitigating Premature Diagnostic Handoff and Silent Hallucination in Healthcare Applications
Distributed Systems (qCLOUDS) Lab, School of Computing；Information Systems, The University of Melbourne, Australia；2Department of Computer Science；Engineering, School of Electrical；Computer Sciences (SECS), Indian Institute of Technology Bhubaneswar, India；3Department of Computer Science Banaras Hindu University, Varanasi, India
$α^3$-Bench: A Unified Benchmark of Safety, Robustness, and Efficiency for LLM-Based UAV Agents over 6G Networks
Department of Computer and Network Engineering, College of Information Technology, United Arab Emirates University（计算机与网络工程系，信息科技学院，阿联酋大学）；Khalifa University of Science and Technology（科技大学）
