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

2026-07-30

AI Summary

This work addresses the catastrophic performance degradation in 4-bit quantized large language models (LLMs) operating in dynamic financial environments, where continual memory editing induces severe knowledge forgetting. To mitigate this issue, the authors propose a stability-aware memory editing framework that, for the first time, integrates stability mechanisms into the memory update process of quantized LLMs. The approach combines low-rank adaptation (LoRA), domain-specific priority modulation tailored to finance, and closed-loop stability control, augmented with a novel degradation-debt tracking mechanism and adaptive editing intensity strategy. Evaluated on a corpus of 88,021 UK financial documents, the method reduces knowledge degradation by 11–17% compared to baseline approaches and improves test-time generalization success rates by six percentage points, achieving up to 28%.

Abstract

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 UK Finance), a stability-aware memory editing framework specifically designed for domain-specific, quantized LLMs. CACHE-UK integrates three components: a rank-1 LoRA perturbation mechanism that confines edits to the low-rank adapter subspace, a financial domain prioritization module for content-adaptive edit strength, and a closed-loop Stability Controller that tracks "degradation debt" to prevent catastrophic forgetting across sequential updates. Evaluated on a 4-bit quantized OpenLLaMA-3B model with a curated UK financial corpus of 88,021 documents, CACHE-UK reduces knowledge degradation by 11-17% relative to adapted baselines under identical 4-bit constraints -- its most robust effect -- while attaining the highest test success (generalization) rate observed in our setting (28%, a 6 percentage point improvement over the strongest adapted baseline). These results indicate that stability-aware editing can improve factual maintenance in resource-constrained financial LLM deployments, though absolute generalization rates remain low.

Problem

• quantized LLMs
• memory editing
• stability crisis
• financial domain
• catastrophic forgetting

Innovation

• stability-aware editing
• quantized LLMs
• LoRA perturbation
• catastrophic forgetting
• financial domain adaptation