Adaptive KV Cache Filtering Addresses Structural-Role Bias in LLM Inference

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The paper analyzes how attention-based KV cache eviction methods prioritize tokens by accumulated attention mass, which can disproportionately retain non-content tokens like delimiters in schema-dense inputs. It proposes adaptive filtering to correct this structural-role bias during long-context mo...

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This insight helps improve memory efficiency and accuracy in long-context language models by reducing noise retention in the KV cache. Correcting structural-role bias can enhance model performance on complex, nested data formats.