AI 中文总结
研究迈向人工智能原生实时通信，提出将模型视为有状态计算中间盒的新观点，探索网络感知推理调度等三个跨阶段协调机会，构建Conflux验证，结果显示在网络退化时响应延迟等有改善，呼吁建立相关实时通信堆栈。
AI 中文摘要
全双工全知模型正在将人机交互从轮流交流转变为连续的多模态对话，其中说话、倾听和推理同时进行。我们主张一种新观点：模型是以人为中心的反馈回路中的有状态计算中间盒，网络传输、模型服务和用户回放共同塑造交互的发展方式。这种观点打破了围绕局部目标设计的阶段之间的传统界限。我们探索了三个跨阶段协调机会：网络感知推理调度、执行感知传输优先级和考虑网络和模型可变性的回放控制。我们正在构建Conflux来探索这些想法，初步结果表明在网络退化情况下响应延迟和回放期限遵守方面有显著改善。更广泛地说，我们呼吁建立一个解决跨越通信、计算和回放的联合控制问题的人工智能原生实时通信堆栈。
英文摘要
Full-duplex omni models are transforming human--AI interaction from turn-based exchanges into continuous multimodal conversations in which speaking, listening, and reasoning unfold concurrently. Rather than viewing the model as a replacement for a human endpoint, we argue for a new perspective: the model is a stateful computational middlebox inside a human-centered feedback loop, with network transport, model serving, and user playback jointly shaping how the interaction evolves. This perspective breaks the traditional boundaries among stages designed around local objectives. Rather than optimizing them in isolation, an AI-native real-time stack should allow the state of each stage to shape the actions of the others. We explore three cross-stage coordination opportunities: network-aware inference scheduling, execution-aware transport prioritization, and playback control that accounts for both network and model variability. We are building Conflux to explore these ideas, and preliminary results show substantial improvements in response latency and playback deadline adherence under network degradation. More broadly, we call for an AI-native real-time communication stack that resolve the joint control problem spanning communication, computation, and playback.
