arXiv:2606.00523v1 Announce Type: new Abstract: Standard Large Language Models (LLMs) follow a read-then-generate paradigm, causing unnecessary latency and computation. Streaming LLMs alleviate this issue by generating while receiving inputs, but still struggle to decide when to interact with the stream. Existing methods either hard-code interaction timing or rely on costly external alignment signals, such as timing labels, reasoning trajectories, or stronger teachers. In this paper, we propose ProactiveLLM, which achieves active interaction by leveraging the model's endogenous states to guide
The continuous drive for efficiency in large language models, particularly in real-time interaction and inference, necessitates new approaches to manage computational resources and user experience.
Improving the active interaction for streaming LLMs reduces latency and computational overhead, making these models more practical and scalable for real-time applications and agentic systems.
Traditional read-then-generate LLM paradigms are being challenged by reactive streaming methods, and now proactive, endogenous state-guided interaction promises even greater efficiency and responsiveness.
- · AI developers
- · Cloud providers
- · Real-time AI application industries
- · Users of AI services
- · Providers of latency-sensitive AI services with legacy LLM architectures
- · Companies with high compute costs due to inefficient LLM usage
More efficient and faster real-time processing for large language models.
Acceleration in the development and deployment of agentic AI systems that require rapid, seamless interaction.
Enhanced user experience across a multitude of AI-powered applications, leading to broader adoption and integration of AI into daily workflows.
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Read at arXiv cs.CL