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