{"total":14,"rows":[{"id":"2406.06385","title":"Low-Rank Quantization-Aware Training for LLMs","created":"2024-06-10","cited_by_count":43,"area":null},{"id":"2407.06794","title":"Towards Accurate Post-Training Quantization of Vision Transformers via Error Reduction","created":"2024-07-09","cited_by_count":23,"area":null},{"id":"2411.07762","title":"ASER: Activation Smoothing and Error Reconstruction for Large Language Model Quantization","created":"2024-11-12","cited_by_count":8,"area":null},{"id":"2504.03717","title":"RaanA: A Fast, Flexible, and Data-Efficient Post-Training Quantization Algorithm","created":"2025-03-29","cited_by_count":5,"area":null},{"id":"2603.25284","title":"SliderQuant: Accurate Post-Training Quantization for LLMs","created":"2026-03-26","cited_by_count":5,"area":null},{"id":"2504.13932","title":"Enhancing Ultra-Low-Bit Quantization of Large Language Models Through Saliency-Aware Partial Retraining","created":"2025-04-14","cited_by_count":3,"area":null},{"id":"2601.13655","title":"Why Does the LLM Stop Computing: An Empirical Study of User-Reported Failures in Open-Source LLMs","created":"2026-01-20","cited_by_count":3,"area":null},{"id":"2409.11650","title":"Art and Science of Quantizing Large-Scale Models: A Comprehensive Overview","created":"2024-09-18","cited_by_count":2,"area":null},{"id":"2505.15909","title":"Is (Selective) Round-To-Nearest Quantization All You Need?","created":"2025-05-21","cited_by_count":1,"area":null},{"id":"2509.10334","title":"I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation","created":"2025-09-12","cited_by_count":1,"area":null},{"id":"2511.19705","title":"CafeQ: Calibration-free Quantization via Learned Transformations and Adaptive Rounding","created":"2025-11-24","cited_by_count":1,"area":null},{"id":"2604.07316","title":"SL-FAC: A Communication-Efficient Split Learning Framework with Frequency-Aware Compression","created":"2026-04-08","cited_by_count":1,"area":null},{"id":"2604.07674","title":"Weight Group-wise Post-Training Quantization for Medical Foundation Model","created":"2026-04-09","cited_by_count":0,"area":null},{"id":"2609.33923","title":"Quantization Error Is Spectrally Flat: A Single Random Probe Is a Calibrated, Data-Free Sensitivity Estimator, with Application to Budget-Targeted Mixed-Precision Quantization","created":"2026-09-27","cited_by_count":0,"area":null}],"next_cursor":null}