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cs.AI2026
The Illusion of Equivalency: Statistical Characterization of Quantization Effects in LLMs
Baha Rababah, Shahzeb Qamar, Lorenz Sparrenberg +4
Post-Training Quantization has become widely used to compress large language models to make them deployable on resource-constrained devices. However, the evaluation of quantization…
cs.AI2026
TopoTuner: Topological Finetuning of Large Language Models
Abdulkadir Erol, Yash Mahajan, Vepaul Hariprashad +4
Full fine-tuning remains a strong way to adapt pretrained LLMs, but it updates all weights and can be expensive. LoRA reduces the number of trainable parameters, but it does not di…