From Static Inference to Dynamic Interaction: A Survey of Streaming Large Language Models
Standard Large Language Models (LLMs) are predominantly designed for static inference with pre-defined inputs, which limits their applicability in dynamic, real-time scenarios. To address this gap, the streaming LLM paradigm has emerged. However, existing definitions of streaming LLMs remain fragmented, conflating streaming generation, streaming inputs, and interactive streaming architectures, while a systematic taxonomy is still lacking. This paper provides a comprehensive overview and analysis of streaming LLMs. First, we establish a unified definition of streaming LLMs based on data flow and dynamic interaction to clarify existing ambiguities. Building on this definition, we propose a ...
| Authors | Junlong Tong, Zilong Wang, YuJie Ren et al. |
|---|---|
| Primary category | cs.CL |
| Published | 4 March 2026 |
| Last updated at source | 19 April 2026 |
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