Tootfinder

Serving Large Language Models on Huawei CloudMatrix384
Pengfei Zuo, Huimin Lin, Junbo Deng, Nan Zou, Xingkun Yang, Yingyu Diao, Weifeng Gao, Ke Xu, Zhangyu Chen, Shirui Lu, Zhao Qiu, Peiyang Li, Xianyu Chang, Zhengzhong Yu, Fangzheng Miao, Jia Zheng, Ying Li, Yuan Feng, Bei Wang, Zaijian Zong, Mosong Zhou, Wenli Zhou, Houjiang Chen, Xingyu Liao, Yipeng Li, Wenxiao Zhang, Ping Zhu, Yinggang Wang, Chuanjie Xiao, Depeng Liang, Dong Cao, Juncheng Liu, Yongqiang Yang, Xiaolong Bai, Yi Li, H…

Serving Large Language Models on Huawei CloudMatrix384
The rapid evolution of large language models (LLMs), driven by growing parameter scales, adoption of mixture-of-experts (MoE) architectures, and expanding context lengths, imposes unprecedented demands on AI infrastructure. Traditional AI clusters face limitations in compute intensity, memory bandwidth, inter-chip communication, and latency, compounded by variable workloads and strict service-level objectives. Addressing these issues requires fundamentally redesigned hardware-software integrati…

Multi-Level Damage-Aware Graph Learning for Resilient UAV Swarm Networks
Huan Lin, Chenguang Zhu, Lianghui Ding, Feng Yang
https://arxiv.org/abs/2506.09703

Multi-Level Damage-Aware Graph Learning for Resilient UAV Swarm Networks
Unmanned aerial vehicle (UAV) swarm networks leverage resilient algorithms to address communication network split issues and restore connectivity. However, existing graph learning-based resilient algorithms face over-aggregation and non-convergence problems caused by uneven and sparse topology under massive damage scenarios. To alleviate these problems, we propose a novel Multi-Level Damage-Aware Graph Learning (ML-DAGL) algorithm, which generates recovery trajectories by mining information fro…

Development of Hardware-in-Loop Framework for Satellite Communication Self-Healing Networks
Sambrama, Venkata Srirama Rohit Kantheti, Liang C Chu, Erik Blasch, Shih-Chun Lin
https://arxiv.org/abs/2506.01210

Development of Hardware-in-Loop Framework for Satellite Communication Self-Healing Networks
The use of Low Earth Orbit (LEO) satellites in the next generation (Next-G) communication systems has been gaining traction over the last few years due to their potential for providing global connectivity with low latency. Since they are the closest to the earth they come with their own set of disadvantages including high vulnerability to jamming and interference. To address these issues, this paper introduces a resilient, self-healing network designed to optimize signal quality under dynamic i…

Literature Review Of Multi-Agent Debate For Problem-Solving
Arne Tillmann
https://arxiv.org/abs/2506.00066 https://arxiv.org/pdf/2506…

Literature Review Of Multi-Agent Debate For Problem-Solving
Multi-agent large language models (MA-LLMs) are a rapidly growing research area that leverages multiple interacting language agents to tackle complex tasks, outperforming single-agent large language models. This literature review synthesizes the latest research on agent profiles, communication structures, and decision-making processes, drawing insights from both traditional multi-agent systems and state-of-the-art MA-LLM studies. In doing so, it aims to address the lack of direct comparisons in…

This https://arxiv.org/abs/2504.14775 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

gLLM: Global Balanced Pipeline Parallelism System for Distributed LLM Serving with Token Throttling
Pipeline parallelism has emerged as a predominant approach for deploying large language models (LLMs) across distributed nodes, owing to its lower communication overhead compared to tensor parallelism. While demonstrating high throughput in request serving, pipeline parallelism often suffers from performance limitations caused by pipeline bubbles, which are primarily resulted from imbalanced computation delays across batches. Existing methods like Sarathi-Serve attempt to address this through h…