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Evidence record 15232 · automatically gathered

CACHE-UK: A Stability-Aware Memory Editor for Sequentially Updated Quantized LLMs in Finance

Large Language Models (LLMs) deployed in dynamic financial environments face a critical challenge: maintaining factual accuracy as market conditions, regulations, and corporate facts change continuously. While 4-bit quantization enables efficient deployment, it severely limits the viability of sequential memory editing: existing methods undergo catastrophic performance degradation under this "quantization stability crisis." We introduce CACHE-UK (Contextual Adaptive Continual Hybrid Editor for U

Record details

Published: 30 July 2026
Source: arXiv cs.AI
Category: Research
Topics: Regulation · Environment
Retrieved: 31 July 2026

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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.

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How to cite this record

ethics.ai (30 July 2026), “CACHE-UK: A Stability-Aware Memory Editor for Sequentially Updated Quantized LLMs in Finance,” evidence record 15232, https://ethics.ai/record/15232 (originally published by arXiv cs.AI).

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