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StreamingLLM: attention sinks and rolling KV state

Contribution grade

Language-model inference · Human-designed research method or resource

Reference score130,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

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

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. 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.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. 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.

Contributors

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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