@inproceedings{tong-etal-2026-static,
title = "From Static Inference to Dynamic Interaction: A Survey of Streaming Large Language Models",
author = "Tong, Junlong and
Wang, Zilong and
Ren, YuJie and
Yin, Peiran and
Wu, Hao and
Zhang, Wei and
Shen, Xiaoyu",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.498/",
pages = "10237--10263",
ISBN = "979-8-89176-395-1",
abstract = "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 systematic taxonomy of current streaming LLMs and provide an in-depth discussion of their underlying methodologies across text, speech, and video streaming scenarios. Furthermore, we explore the applications of streaming LLMs in real-world scenarios and outline promising research directions to support ongoing advances in streaming intelligence. We maintain a continuously updated repository of relevant papers at https://github.com/EIT-NLP/Awesome-Streaming-LLMs."
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%0 Conference Proceedings
%T From Static Inference to Dynamic Interaction: A Survey of Streaming Large Language Models
%A Tong, Junlong
%A Wang, Zilong
%A Ren, YuJie
%A Yin, Peiran
%A Wu, Hao
%A Zhang, Wei
%A Shen, Xiaoyu
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F tong-etal-2026-static
%X 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 systematic taxonomy of current streaming LLMs and provide an in-depth discussion of their underlying methodologies across text, speech, and video streaming scenarios. Furthermore, we explore the applications of streaming LLMs in real-world scenarios and outline promising research directions to support ongoing advances in streaming intelligence. We maintain a continuously updated repository of relevant papers at https://github.com/EIT-NLP/Awesome-Streaming-LLMs.
%U https://aclanthology.org/2026.findings-acl.498/
%P 10237-10263
Markdown (Informal)
[From Static Inference to Dynamic Interaction: A Survey of Streaming Large Language Models](https://aclanthology.org/2026.findings-acl.498/) (Tong et al., Findings 2026)
ACL
- Junlong Tong, Zilong Wang, YuJie Ren, Peiran Yin, Hao Wu, Wei Zhang, and Xiaoyu Shen. 2026. From Static Inference to Dynamic Interaction: A Survey of Streaming Large Language Models. In Findings of the Association for Computational Linguistics: ACL 2026, pages 10237–10263, San Diego, California, United States. Association for Computational Linguistics.