{
  "id": 15232,
  "url": "https://arxiv.org/abs/2607.28292v1",
  "title": "CACHE-UK: A Stability-Aware Memory Editor for Sequentially Updated Quantized LLMs in Finance",
  "summary": "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",
  "authors": "Anubhav Lakra, Yue Feng",
  "category": "research",
  "topics": "regulation,environment",
  "orgs": null,
  "regions": "uk",
  "published_at": "2026-07-30T14:36:11.000Z",
  "fetched_at": "2026-07-31T05:10:57.675Z",
  "source_slug": "x-arxiv-cs-ai",
  "source_name": "arXiv cs.AI",
  "source_homepage": "https://arxiv.org/list/cs.AI/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/15232",
  "original_url": "https://arxiv.org/abs/2607.28292v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}