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This research addresses a critical infrastructure bottleneck for on-device AI agents by proposing HFS, a transport framework that manages network contention between live video streams and agent data flows. By jointly orchestrating these heterogeneous traffic requirements at the application layer, the system significantly enhances real-time collaboration quality while reducing latency without relying on cloud servers. This work is essential as it defines how future local LLMs will operate efficiently within constrained bandwidth environments.
Coordinated Networking for On-Device Agent-Augmented Real-Time Communication
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. These apps enable new experiences across various domains: for example, when corporate employees co-author a legal document, their agents can discuss and draft on their behalf, sparing them the burden of manually reviewing each other's work. As existing cloud-based agents suffer from privacy risks and unscalable server costs, on-device agent-augmented RTC offers a promising alternative. However, this on-device paradigm introduces a new networking challenge: contention between concurrent traffic flows generated by humans (for live video streaming) and agents (for sending context files for analysis). We design HFS, a framework to ensure both high live video quality and low agent response latency in agent-augmented RTC apps. We achieve the goal through an app-guided multi-flow transport approach, where a unified app-layer orchestrator jointly controls the sending rates of live video and agent context flows based on their heterogeneous app requirements. Our prototype built atop WebRTC and llama.cpp demonstrates that HAFS outperforms baselines, achieving 1.5x higher video quality while reducing agent response time by 31%.
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. These apps enable new experiences across various domains: for example, when corporate employees co-author a legal document, their agents can discuss and draft on their behalf, sparing them the burden of manually reviewing each other's work. As existing cloud-based agents suffer from privacy risks and unscalable server costs, on-device agent-augmented RTC offers a promising alternative. However, this on-device paradigm introduces a new networking challenge: contention between concurrent traffic flows generated by humans (for live video streaming) and agents (for sending context files for analysis). We design HFS, a framework to ensure both high live video quality and low agent response latency in agent-augmented RTC apps. We achieve the goal through an app-guided multi-flow transport approach, where a unified app-layer orchestrator jointly controls the sending rates of live video and agent context flows based on their heterogeneous app requirements. Our prototype built atop WebRTC and this http URL demonstrates that HAFS outperforms baselines, achieving 1.5x higher video quality while reducing agent response time by 31%.
Artificial Intelligence (cs.AI)
arXiv:2607.22854 [cs.AI]
(or arXiv:2607.22854v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.22854 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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