Recent advancements in large language models (LLMs) have significantly enhanced their capacity to aggregate and process information across multiple modalities, enabling them to perform a wide range of tasks such as multimodal data querying, tool usage, web interactions, and handling long documents. These capabilities pave the way for transforming LLMs from mere chatbots into general-purpose agents capable of interacting with the real world. This paper explores the concept of using a language model as the core component of an operating system (OS), effectively acting as a CPU that processes data stored in a context window, which functions as RAM. A key challenge in realizing such an LM OS is managing the life-long context and ensuring statefulness across sessions, a feature limited by the current session-based interaction paradigm due to context window size limit. To address this, we introduce compressor-retriever, a model-agnostic architecture designed for life-long context management. Unlike other long-context solutions such as retrieval-augmented generation, our approach exclusively uses the base model's forward function to compress and retrieve context, ensuring end-to-end differentiability. Preliminary experiments demonstrate the effectiveness of this architecture in in-context learning tasks, marking a step towards the development of a fully stateful LLM OS. Project repo available at: https://github.com/gblackout/LM-OS
Perception Compressor: A Training-Free Prompt Compression Framework in Long Context Scenarios
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Pengcheng Laboratory（鹏城实验室）；Ant Group（蚂蚁集团）；School of Artificial Intelligence, Sun Yat-sen University（中山大学人工智能学院）
MLP Memory: A Retriever-Pretrained Memory for Large Language Models
Shanghai Jiao Tong University（上海交通大学）；Shanghai AI Laboratory（上海人工智能实验室）；Tsinghua University（清华大学）；SJTU Paris Elite Institute of Technology（上海交通大学巴黎卓越工程师学院）
LightRetriever: A LLM-based Text Retrieval Architecture with Extremely Faster Query Inference
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络安全学院）；Langboat Technology（兰舟科技）
An Information Theoretic Perspective on Agentic System Design
Department of Computer Science, Stanford University（斯坦福大学计算机科学系）；Department of Statistics, Stanford University（斯坦福大学统计学系）；Wu Tsai Neurosciences Institute, Stanford University（斯坦福大学吴泰教授神经科学研究所）
R^2AG: Incorporating Retrieval Information into Retrieval Augmented Generation
School of Computer Science, South China Normal University（华南师范大学计算机学院）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）
IRLab, Informatics Institute, UvA, Amsterdam, the Netherlands（IR实验室、信息研究所、乌得勒支大学、阿姆斯特丹、荷兰）；AI Lab, Pegasystems, Amsterdam, the Netherlands（AI实验室、佩加系统的阿姆斯特丹分部）；LIACS, Leiden University, Leiden, the Netherlands（LIACS、莱顿大学、莱顿、荷兰）
UniRetriever: Multi-task Candidates Selection for Various Context-Adaptive Conversational Retrieval
The Chinese University of Hong Kong（香港中文大学）；Nankai University（南开大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）
New York University（纽约大学）；Modal Labs（Modal实验室）；University of Maryland（马里兰大学）；Princeton University（普林斯顿大学）；Columbia University（哥伦比亚大学）；Harvard University（哈佛大学）；Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）；FAIR at Meta（Meta FAIR实验室）
