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From Static Inference to Dynamic Interaction: A Survey of Streaming Large Language Models

Paper · 2026 · Junlong Tong, Zilong Wang, YuJie Ren et al.

Summary

Most LLMs assume the entire input is available before they respond. This assumption fails for live speech, video, simultaneous translation or agents, where data arrives piece by piece and may be unbounded. Prior work labelled "streaming LLM" has mixed together quite different ideas: token-by-token decoding, chunked encoding of inputs, and full-duplex systems such as GPT-4o.

This Findings of ACL 2026 survey by Tong et al. offers one formal definition based on data flow and on whether reading and writing overlap. A decision function determines which input prefix the model can see at each output step. From this, the authors derive three tiers: output-streaming, sequential-streaming and concurrent-streaming LLMs. Each tier inherits the difficulties of the previous one and adds new ones. Methods for text, speech and video are organised under this scheme, followed by a review of applications and open problems.

The paper reports no experiments. Its contribution is a common vocabulary and research roadmap, plus a maintained paper list (Awesome-Streaming-LLMs on GitHub).

Key points

  • The three paradigms differ in how input is handled:
    • Output-streaming: reads the full input at once, then generates incrementally.
    • Sequential-streaming: encodes input as it arrives, but answers only after the input ends.
    • Concurrent-streaming: reads and generates at the same time (full-duplex), with monotonic, partial input visibility.
  • Output-streaming work covers:
    • Generation styles: token-wise, block-wise (semi-autoregressive SoT, block-diffusion SSD-LM, multi-scale VAR) and refinement-based (LLaDA, LaViDa).
    • Speed-ups: speculative decoding, AdaInfer and layer skipping, and memory savings via StreamingLLM.
  • Sequential-streaming work covers:
    • Incremental encoding, either at fixed intervals (Whisper-Streaming, LLM-SimulMT) or semantically driven (DiSeg, CTC).
    • Context management: memory retention (Flash-VStream), attention-aware KV eviction (StreamKV, StreamingVLM) and compression (KIVI, ZipCache).
  • Concurrent-streaming work covers:
    • Four architecture types: re-encoding (Simul-LLM), concatenated (Qwen3-Omni, Mini-Omni), interleaved (VideoLLM-online, CosyVoice 2, LiveCC) and group (StreamingThinker).
    • Interaction policies that are rule-based, SFT-based or RL-based.
    • Tasks split into perception-level (X→Y, e.g. translation, ASR) and cognition-level (X→Z→Y, e.g. reasoning, tool use).
  • Proposed future directions include:
    • Proactive read/write policies and interpretability of streaming behaviour.
    • Omni-modal parallel streams.
    • Deeper concurrency: 3-level (perceive, reason, generate) and 4-level (adding tool use).

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Shanghai Jiao Tong UniversityShanghai · China149 works · 16,161 citations🎓Eastern Institute of Technology, NingboNingbo · China3 works · 23 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 3,320 works

Cite

Cite as 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. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-acl.498

@inproceedings{tong2026static,
  title = {{From Static Inference to Dynamic Interaction: A Survey of Streaming Large Language Models}},
  author = {Junlong Tong, Zilong Wang, YuJie Ren, Peiran Yin, Hao Wu, Wei Zhang and Xiaoyu Shen},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.498},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.498}
}

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