3 citations · 3 across the 5 of their papers we have counts for
7 papers
OLED-MoE: Accelerating MoE-Based dLLM Inference via Inter-Iteration Locality-Aware Expert Offloading
Jingyuan Xiao, Jiayue Wang, Yitao Hu +7
Semi-autoregressive diffusion large language models (dLLMs) improve decoding parallelism through iterative block-wise denoising, but scaling them with mixture-of-experts (MoE) laye…
PatchKV: Efficient KV Cache Recovery for Dynamically Edited LLM Contexts
Guotao Yang, Rui Guo, Siwei He +3
Long-running LLM agent workflows often revise interior context spans while retaining long suffixes. Although suffix tokens remain unchanged, altered causal histories and rotary pos…
RAC: Reference-Aware Activation Compression for Communication-Efficient Split LLM Inference
Guotao Yang, Mingxi Zhao, Haopeng Li +4
Large language model (LLM) agents repeatedly process long, privacy-sensitive contexts, while cloud-only deployment exposes user data beyond the trusted endpoint and fully local dep…
KernelClaw: A Multi-Agent Framework for GPU Kernel Optimization with Dual-Memory Mechanisms
Qitong Sun, Jun Han, Tianlin Li +6
Improving GPU kernel efficiency is crucial for efficient AI systems. Recent work has explored leveraging large language models (LLMs) for GPU kernel generation and optimization. Ho…
Mosaic: Unlocking Long-Context Inference for Diffusion LLMs via Global Memory Planning and Dynamic Peak Taming
Liang Zheng, Bowen Shi, Yitao Hu +5
Diffusion-based large language models (dLLMs) have emerged as a promising paradigm, utilizing simultaneous denoising to enable global planning and iterative refinement. While these…
dots.llm1 Technical Report
Bi Huo, Bin Tu, Cheng Qin +24
Mixture of Experts (MoE) models have emerged as a promising paradigm for scaling language models efficiently by activating only a subset of parameters for each input token. In this…
Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologies
Luping Wang, Sheng Chen, Linnan Jiang +4
The large models, as predicted by scaling raw forecasts, have made groundbreaking progress in many fields, particularly in natural language generation tasks, where they have approa…