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Evidence record 10610 · automatically gathered

Adaptive Filtering of the KV Cache: Diagnosing and Correcting Structural-Role Bias in LLM Inference

Attention-based KV cache eviction (H2O and its descendants) compresses the memory-constrained state of a long-context model by ranking tokens on accumulated attention mass, treated here as signal energy, and keeping the heaviest. On schema-dense input streams such as nested JSON, this score acts as a non-stationary filter that disproportionately retains noise: a non-content sink role (delimiters or whitespace) carries an order of magnitude more energy than any content role, and structural KEY to

Record details

Published: 14 July 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Healthcare · Environment
Retrieved: 16 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Shared topic: Bias & fairness · Shared topic: Healthcare

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Shared topic: Bias & fairness · Shared topic: Healthcare

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Shared topic: Bias & fairness · Shared topic: Healthcare

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How to cite this record

ethics.ai (14 July 2026), “Adaptive Filtering of the KV Cache: Diagnosing and Correcting Structural-Role Bias in LLM Inference,” evidence record 10610, https://ethics.ai/record/10610 (originally published by arXiv).

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