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    Attention Sink

    AI fundamentals & modelsAttention-Sink Tokens
    Editorially reviewed on: 2 sources
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    The short answer · 18 words

    In short

    Token, which receives unusually much attention weight, although its content does not have to be decisive for this.

    Flow-alerta.cab (2026): Attention Sink. https://flow-alerta.cab/en/glossar/attention-sink. Editorially reviewed on: 09 October 2026.

    How much detail would you like?

    Practical

    Helps to understand limits of interpretation of attention weights and specifics of context processing.

    Example scenario: In a model test, selected initial tokens are preserved in the cache together with recent tokens.

    Relevant for specialized model and inference development; no generally available switch for common AI services.

    At a glance

    Core
    In research, attention sinks are tokens that receive high attention weights from a language model without being correspondingly important to the content. StreamingLLM work observes this especially with initial tokens and uses their preservation in the KV cache for streaming processing.
    Use
    Helps to understand limits of interpretation of attention weights and specifics of context processing.
    Example
    Example scenario: In a model test, selected initial tokens are preserved in the cache together with recent tokens.
    Limits
    High weights automatically read as content importance; equate streaming processing with memory of the entire text.

    In context

    Attention Mechanism

    Mechanism that weights information differently for a calculation step.

    Questions about this term

    Is that a particularly important word?

    Not mandatory.

    Does StreamingLLM save the entire course?

    No, a limited cache does not replace a complete memory.

    All details and background

    In research, attention sinks are tokens that receive high attention weights from a language model without being correspondingly important to the content. StreamingLLM work observes this especially with initial tokens and uses their preservation in the KV cache for streaming processing.

    ELI5 – Explained simply

    A place attracts a lot of computational attention, although there is not necessarily the most important information.

    Why it matters in practice

    Helps to understand limits of interpretation of attention weights and specifics of context processing.

    Example

    Example scenario: In a model test, selected initial tokens are preserved in the cache together with recent tokens.

    Pitfalls and limits

    • High weights automatically read as content importance; equate streaming processing with memory of the entire text.

    Origin & history

    Xiao and others studied the phenomenon in 2023 in “Efficient Streaming Language Models with Attention Sinks”.

    Use in mid-sized companies

    • Relevant for specialized model and inference development; no generally available switch for common AI services.

    Related terms

    Matching Flow-alerta service

    AI opportunity analysis & roadmapAI consulting: AI potential analysis & roadmap on tasks, data and prerequisites of a possible first AI use case.

    Matching tool / use case

    View use casesResearch example: StreamingLLM.

    Sources

    • arxiv.orgXiao et al., Efficient Streaming Language Models with Attention Sinks (2023)
    • hanlab.mit.eduMIT Han Lab, StreamingLLM

    Quellenprüfung: 09.10.2026. Definition und dokumentierte Einordnung abgeglichen; Beispiele beschreiben mögliche Anwendungen.

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