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SliderQuant
SliderQuant: Accurate Post-Training Quantization for LLMsLLM quantization · first seen Mar 26, 2026
current frontier — recent, not yet superseded in the knowledge base
0 papers critique it · 0 beat it on benchmarks
Newer alternatives
Recent methods in the same sub-problem, not yet superseded in the knowledge base.
- Jun 5, 2026
- FAIR-CalibFAIR-Calib: Frontier-Aware Instability-Reweighted Calibration for Post-Training Quantization of Diffusion Large Language ModelsJun 4, 2026
- May 25, 2026
- May 11, 2026
- Activation Residual Hessian Quantization (ARHQ)Technical Report: Activation Residual Hessian Quantization (ARHQ) for Low-Bit LLM QuantizationApr 30, 2026
- Apr 20, 2026
- Apr 14, 2026
- Mar 26, 2026
- Jan 29, 2026
- Reasoning-QATWhat Makes Low-Bit Quantization-Aware Training Work for Reasoning LLMs? A Systematic StudyJan 21, 2026
- Dec 3, 2025
- adaptive transformation selection frameworkAdaptive Layer-Wise Transformations for Post-Training Quantization of Large Language ModelsNov 21, 2025