What is recorded here?
Kept initial attention-sink tokens alongside recent tokens in a fixed rolling KV cache, enabling stable streaming generation beyond the model's training context without fine-tuning.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Evicted middle tokens are not retained as complete semantic memory. Streaming length is not usable all-history context or lossless retrieval; tested million-token stability and speed do not establish indefinite reasoning guarantees.
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People and systems
Guangxuan Xiao; Yuandong Tian; Beidi Chen; Song Han; Mike Lewis
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
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English catalogue narrative
StreamingLLM: attention sinks and rolling KV state
Kept initial attention-sink tokens alongside recent tokens in a fixed rolling KV cache, enabling stable streaming generation beyond the model's training context without fine-tuning.
Scientific scope
Evicted middle tokens are not retained as complete semantic memory. Streaming length is not usable all-history context or lossless retrieval; tested million-token stability and speed do not establish indefinite reasoning guarantees.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Guangxuan Xiao; Yuandong Tian; Beidi Chen; Song Han; Mike Lewis
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.