Wearable assistants require episodic memory over egocentric video, yet current vision-language models face bounded frame budgets, growing visual-token costs, and long-context retrieval failures. Under these practical constraints, we study whether textual captions can serve as reusable episodic memory. We define the Episodic Memory Video Caption QA task and introduce CapMem, a human-annotated benchmark with 75 videos totaling 33.7 hours, and 1,000 multiple-choice questions across 16 scenarios. On long videos (>20 min), full-coverage CaptionQA with 30s and 60s caption windows outperforms direct VideoQA for 10/12 and 8/12 models, respectively. On the same video subset, a matched-frame control across six Qwen models retains mean accuracy gains of 3.22 and 2.55 points, respectively. Our caption-guided retrieve-and-verify harness further improves accuracy by up to 5.3 points. These results support the effectiveness of caption memory for episodic reasoning over long egocentric video.

Vision-language models (VLMs) excel in many downstream tasks but struggle with spatial reasoning, which is crucial for navigation and interaction with physical environments. Specifically, many spatial reasoning tasks rely on fundamental two-dimensional (2D) capabilities, yet our evaluation shows that state-of-the-art VLMs often produce implausible or incorrect solutions for composite spatial problems, including simple pathfinding tasks that humans solve effortlessly at a glance. To address this, we explore an effective approach to enhance 2D spatial reasoning in VLMs by training them solely on basic spatial capabilities. We first disentangle 2D spatial reasoning into three core components: direction comprehension, distance estimation, and localization. Our central hypothesis is that mastering these basic capabilities will significantly boost performance on more complex spatial tasks requiring advanced reasoning and combinatorial problem-solving, as well as generalize to real-world visual-spatial scenarios. To test this hypothesis, we introduce Sparkle, a framework that generates synthetic data to provide targeted supervision for VLMs across these three basic spatial capabilities, producing an instruction dataset for each capability. Our experiments demonstrate that VLMs fine-tuned with Sparkle achieve substantial improvements, not only on basic tasks but also in generalizing to composite and out-of-distribution real-world spatial reasoning tasks. These findings highlight that enhancing basic spatial capabilities through synthetic generalization effectively improves complex spatial reasoning, offering insights into systematic strategies for boosting VLMs’ spatial understanding. Source codes of Sparkle are available at https://github.com/YihongT/Sparkle.

The dream to create AI assistants as capable and versatile as the fictional J.A.R.V.I.S from Iron Man has long captivated imaginations. With the evolution of multi-modal large language models ((M)LLMs), this dream is closer to reality, as (M)LLM-based Agents using computers, mobile phones and web browsers by operating within the environments and interfaces (e.g., Graphical User Interface (GUI) and Command Line Interface (CLI)) provided by operating systems (OS) to automate tasks have significantly advanced. This paper presents a comprehensive survey on these advanced agents, designated as OS Agents. We begin by elucidating the fundamentals of OS Agents, exploring their key components and capabilities. We then examine methodologies for constructing OS Agents, focusing on domain-specific foundation models and agent frameworks. A detailed review of evaluation metrics and benchmarks highlights how OS Agents are assessed across diverse platforms and tasks. Finally, we discuss current challenges and identify promising directions for future research. An open-source GitHub repository is maintained as a dynamic resource to foster further innovation in this field.

Citywalk, a recently popular form of urban travel, requires genuine personalization and understanding of fine-grained requests compared to traditional itinerary planning. In this paper, we introduce the novel task of Open-domain Urban Itinerary Planning (OUIP), which generates personalized urban itineraries from user requests in natural language. We then present ItiNera, an OUIP system that integrates spatial optimization with large language models to provide customized urban itineraries based on user needs. This involves decomposing user requests, selecting candidate points of interest (POIs), ordering the POIs based on cluster-aware spatial optimization, and generating the itinerary. Experiments on real-world datasets and the performance of the deployed system demonstrate our system’s capacity to deliver personalized and spatially coherent itineraries compared to current solutions. Source codes of ItiNera are available at https://github.com/YihongT/ITINERA.