A quantization vector derived from a donor model via weight-space arithmetic can be added to a receiver model to improve post-PTQ Top-1 accuracy by up to 60 points in 3-bit settings without receiver-side QAT or data.
arXiv preprint arXiv:2505.05530 , year=
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DECO is a sparse MoE architecture with ReLU-based routing, learnable expert scaling, and NormSiLU activation that matches dense Transformer performance at 20% expert activation and delivers 2.93x speedup on Jetson AGX Orin.
A compact Chinese-native T2I U-Net (~0.387B) trained on Sugon K100, distilled to 4 steps, reports GenEval 0.69 and ~1.6–4.5s offline mobile generation.
MARR uses per-module adaptive residual scaling updated by PID feedback to balance error correction against Hessian-approximation bias in low-bit PTQ.
Tiny NeRV models using capacity scaling, frequency-aware distillation, and low-precision quantization achieve favorable quality-efficiency trade-offs with far fewer parameters and lower computational costs than standard NeRV.
citing papers explorer
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Zero-Shot Quantization via Weight-Space Arithmetic
A quantization vector derived from a donor model via weight-space arithmetic can be added to a receiver model to improve post-PTQ Top-1 accuracy by up to 60 points in 3-bit settings without receiver-side QAT or data.
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DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices
DECO is a sparse MoE architecture with ReLU-based routing, learnable expert scaling, and NormSiLU activation that matches dense Transformer performance at 20% expert activation and delivers 2.93x speedup on Jetson AGX Orin.
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JuZhou 1.0 Technical Report: The First Edge-Native Text-to-Image Foundation Model Trained Entirely on China-Developed AI Accelerators
A compact Chinese-native T2I U-Net (~0.387B) trained on Sugon K100, distilled to 4 steps, reports GenEval 0.69 and ~1.6–4.5s offline mobile generation.
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MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization
MARR uses per-module adaptive residual scaling updated by PID feedback to balance error correction against Hessian-approximation bias in low-bit PTQ.
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TinyNeRV: Compact Neural Video Representations via Capacity Scaling, Distillation, and Low-Precision Inference
Tiny NeRV models using capacity scaling, frequency-aware distillation, and low-precision quantization achieve favorable quality-efficiency trade-offs with far fewer parameters and lower computational costs than standard NeRV.