arXiv:2607. 22854v1 Announce Type: new Abstract: AI agents are enabling a new paradigm of agent-augmented real-time communication (RTC), where humans focus on high-level collaboration, while agents autonomously retrieve, analyze, and generate information in real time to support their interactions.
arXiv:2510. 00481v2 Announce Type: replace-cross Abstract: In 2025, Large Language Model (LLM) services have launched a new feature -- AI video chat -- allowing users to interact with AI agents via real-time video communication (RTC), just like chatting with real people.
arXiv:2606. 14710v1 Announce Type: cross Abstract: Edge-resident AI agents increasingly span home servers, IoT hubs, laptops, and phones, yet their coordination stacks still assume cloud-style transports or a central relay.
arXiv:2609.38652v1 Announce Type: new Abstract: Agentic AI systems increasingly rely on cloud-hosted large language models for planning, tool use, and iterative execution, raising concerns about API...
arXiv:2607. 13465v1 Announce Type: cross Abstract: LLM-based agents have rapidly improved at operating individual digital environments such as mobile applications, desktop systems, and smart homes.
JarvisGUI is a new benchmark that tests GUI agents on cross-device workflows involving Android, Windows, and Ubuntu, requiring transfer of intermediate results and coordination across heterogeneous platforms. It formulates tasks as input-output transformations under a lightweight type system, enabling automatic composition of multi-step, cross-device workflows and dynamic evaluation within a unified framework. The benchmark reveals that state-of-the-art open-source GUI agents struggle with state-transfer awareness, cross-platform contextual reasoning, and long-horizon dependency management, exposing a critical capability gap invisible to existing benchmarks.
arXiv:2512. 12634v4 Announce Type: replace Abstract: Mobile GUI Agents, AI agents capable of interacting with mobile applications on behalf of users, have the potential to transform human computer interaction.
arXiv:2606. 03557v1 Announce Type: new Abstract: As generative AI capabilities expand, AI-driven virtual worlds face a growing architectural challenge.
arXiv:2604. 26508v2 Announce Type: replace-cross Abstract: Deploying Vision-Language Models (VLMs) on edge devices remains challenging due to their substantial computational and memory demands, which exceed the capabilities of resource-constrained embedded platforms.
FRAMEWORKERS is a task‑centric, multi‑agent framework designed for end‑to‑end AI‑generated video production. It uses a central Director to dynamically manage a task stack and an Assistant to execute tasks within a shared Workspace, leveraging modular sub‑agents that can be added without redesigning the workflow. The system is fine‑tuned with supervised learning and policy optimization, outperforming existing LLM planners and fixed pipelines in routing accuracy, failure recovery, and overall video quality.
arXiv:2601.02641v2 Announce Type: replace Abstract: Despite its efficiency, there has been little research on the practical aspects required for real-world deployment of on-device AI models, such as...
arXiv:2606. 09122v1 Announce Type: cross Abstract: Cloud network infrastructure at hyperscale presents unique operational challenges where traditional human-driven incident response cannot keep pace with the volume, velocity, and complexity of failures.
arXiv:2607. 14489v1 Announce Type: cross Abstract: Edge computing brings unique challenges as the resources on the edge are highly diverse in capabilities and capacities, and highly distributed across many users and the physical world.