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

arXiv:2607.28292 · cs.CL, cs.AI, cs.CE, cs.LG · Submitted 2026-07-30 · Read on arXiv

Anubhav Lakra, Yue Feng

cs.CL, cs.AI, cs.CE, cs.LG

Submitted: 2026-07-30

Comments: 10 pages, 12 figures

Code: https://github.com/ranaroussi/yfinance

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Quantization — 40× in this paper · explained in 15 episodes
• Test Success — 17× in this paper
• Generalization — 13× in this paper · explained in 14 episodes
• Catastrophic Forgetting — 7× in this paper · explained in 9 episodes
• Edit Success — 6× in this paper
• Editor — 6× in this paper · explained in 2 episodes
• Degradation Score — 5× in this paper
• Accuracy — 4× in this paper · explained in 1 episode
• Language Models (LLMs) — 3× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 3× in this paper · explained in 9 episodes
• RAG — 3× in this paper · explained in 3 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Continual Learning — 2× in this paper · explained in 3 episodes
• Cosine Similarity — 2× in this paper · explained in 3 episodes
• Domain Adaptation — 2× in this paper · explained in 5 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• QLoRA — 2× in this paper · explained in 2 episodes
• Representation Engineering — 2× in this paper · explained in 2 episodes
• Augmentation — 1× in this paper · explained in 2 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Elias — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Evaluation Protocol — 1× in this paper · explained in 2 episodes
• Hyperparameters — 1× in this paper · explained in 2 episodes
• Meta-learning — 1× in this paper · explained in 3 episodes
• Papers — 1× in this paper · explained in 3 episodes
• Post-Training Quantization (PTQ) — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Retrieval-Augmented Generation (RAG) — 1× in this paper · explained in 21 episodes
• Robustness — 1× in this paper · explained in 6 episodes
• Steering — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 16 episodes
• Transformers — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• QLoRA: Efficient Finetuning of Quantized LLMs
• GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
• Transformer Feed-Forward Layers Are Key-Value Memories
• Parameter-Efficient Transfer Learning for NLP
• LoRA: Low-Rank Adaptation of Large Language Models
• Demystifying Domain-adaptive Post-training for Financial LLMs
• Mass-Editing Memory in a Transformer
• Fast Model Editing at Scale
• EasyEdit: An Easy-to-use Knowledge Editing Framework for Large Language Models
• Editing Large Language Models: Problems, Methods, and Opportunities
• Representation Engineering: A Top-Down Approach to AI Transparency

Related papers

• Exploring Solution Divergence and Its Effect on Large Language Model Problem Solving
• Ishigaki-IDS-Bench: A Benchmark for Generating Information Delivery Specification from BIM Information Requirements
• Subliminal Steering: Stronger Encoding of Hidden Signals
• MedStruct-S: A Benchmark for Key Discovery, Key-Conditioned QA and Semi-Structured Extraction from OCR Clinical Reports
• The End of Transformers? On Challenging Attention and the Rise of Sub-Quadratic Architectures
• Untangling the Mechanisms of Misleading Context in Medical Question Answering