Large language model (LLM)-based agents increasingly face tasks that require reasoning over extremely long range. A key bottleneck is memory design: most prior approaches treat memory as a static, human-designed component, limiting adaptability. Inspired by biological memory evolution, we propose AtomMem, a framework that exposes a set of atomic memory operations—including summarization and CRUD primitives—as tool-like actions. The agent learns a policy over these operations purely via reinforcement learning, using only the task reward. AtomMem allows the agent to dynamically interleave summarization and CRUD to retain important information while managing memory efficiently. We evaluate AtomMem on streaming long-context understanding and web-navigation benchmarks, demonstrating an average improvement of 8.5 points over static memory workflows and 2.1 points over the current learnable-memory baseline. Ablation studies show that both summarization and CRUD are essential to train agent memory. The project can be accessed at https://github.com/RUCBM/AtomMem.

Large language model agents have enabled GUI-based automation, particularly for mobile devices. However, deployment remains limited by noisy data, poor generalization, and lack of support for non-English GUIs. In this work, we present AgentCPM-GUI, an 8B-parameter GUI agent built for robust and efficient on-device GUI interaction. Our training pipeline includes grounding-aware pre-training to enhance perception, supervised fine-tuning on high-quality Chinese and English trajectories to imitate human-like actions, and reinforcement fine-tuning with GRPO to improve reasoning capability. AgentCPM-GUI achieves promising performance on five public benchmarks and our proposed Chinese benchmark CAGUI. To facilitate reproducibility and further research, we publicly release all code, model checkpoint, and evaluation data at: https://github.com/OpenBMB/AgentCPM-GUI

Utilizing Graphic User Interfaces (GUIs) for human-computer interaction is essential for accessing various digital tools. Recent advancements in Vision Language Models (VLMs) reveal significant potential for developing versatile agents that assist humans in navigating GUIs. However, current VLMs face challenges related to fundamental abilities, such as OCR and grounding, as well as a lack of knowledge about GUI elements functionalities and control methods. These limitations hinder their effectiveness as practical GUI agents. To address these challenges, we introduce GUICourse, a series of datasets for training visual-based GUI agents using general VLMs. First, we enhance the OCR and grounding capabilities of VLMs using the GUIEnv dataset. Next, we enrich the GUI knowledge of VLMs using the GUIAct and GUIChat datasets. Our experiments demonstrate that even a small-sized GUI agent (with 3.1 billion parameters) performs effectively on both single-step and multi-step GUI tasks. We further finetune our GUI agents on other GUI tasks with different action spaces (AITW and Mind2Web), and the results show that our agents are better than their baseline VLMs. Additionally, we analyze the impact of OCR and grounding capabilities through an ablation study, revealing a positive correlation with GUI navigation ability.