Ultra-long video understanding remains an open challenge, as existing vision language models (VLMs) falter on such content due to limited context length and inefficient long-term memory retention. To address this, recent works have attempted to construct external knowledge bases and corresponding retrieval agumented generation (RAG) systems, yet these incur enormous storage and computational overhead. In this paper, we propose VideoMem, a novel framework that pioneers models long video understanding as a sequential generation task via adaptive memory management. Specifically, VideoMem dynamically updates a global memory buffer, which adaptively retains critical information while discarding redundant content across the video timeline. To efficiently train VLMs for such long-term tasks, VideoMem integrates the Progressive Grouped Relative Policy Optimization (PRPO) algorithm, equipped with two core modules: Progressive State Propagation (PSP) adaptively retains valid current states, propagates them to the next rollout step, and gradually narrows the model’s exploration space. Temporal Cascading Reward (TCR) further alleviates reward sparsity, improving sample utilization and accelerating convergence. Extensive experiments demonstrate that VideoMem significantly outperforms existing open source models across diverse benchmarks for ultra-long video understanding tasks.

Most existing GUI agents typically depend on non-vision inputs like HTML source code or accessibility trees, limiting flexibility across diverse software environments and platforms. Current multimodal large language models (MLLMs), though excel at using vision to ground real-world objects, often struggle with accurately localizing GUI elements – a critical requirement for effective GUI automation – due to the semantic gap between real-world objects and GUI elements. In this work, we introduce Ponder & Press, a divide-and-conquer framework for general computer control that uses only visual input. Our approach combines a general-purpose MLLM as an ‘interpreter’, responsible for translating high-level user instructions into detailed action descriptions, with a GUI-specific MLLM as a ‘locator’ that precisely locates GUI elements for action placement. By leveraging a purely visual input, our agent offers a versatile, human-like interaction paradigm applicable to various applications. Ponder & Press locator outperforms existing models by +22.5% on the ScreenSpot GUI grounding benchmark. More offline and interactive agent benchmarks across various GUI environments – including web pages, desktop software, and mobile UIs – demonstrate that the Ponder & Press framework achieves state-of-the-art performance, highlighting the potential of visual GUI agents.