Tootfinder

Efficient Pre-Training of LLMs via Topology-Aware Communication Alignment on More Than 9600 GPUs
Guoliang He, Youhe Jiang, Wencong Xiao, Kaihua Jiang, Shuguang Wang, Jun Wang, Zixian Du, Zhuo Jiang, Xinlei Zhang, Binhang Yuan, Eiko Yoneki
https://arxiv.org/abs/2509.15940

Efficient Pre-Training of LLMs via Topology-Aware Communication Alignment on More Than 9600 GPUs
The scaling law for large language models (LLMs) depicts that the path towards machine intelligence necessitates training at large scale. Thus, companies continuously build large-scale GPU clusters, and launch training jobs that span over thousands of computing nodes. However, LLM pre-training presents unique challenges due to its complex communication patterns, where GPUs exchange data in sparse yet high-volume bursts within specific groups. Inefficient resource scheduling exacerbates bandwidt…

Tradeoff Between the Number of Transmitted Molecules and the BER Performance in Molecular Communication between Bionanosensors
Dongliang Jing, Linjuan Li, Lin Lin, Andrew W. Eckford
https://arxiv.org/abs/2508.04466

Tradeoff Between the Number of Transmitted Molecules and the BER Performance in Molecular Communication between Bionanosensors
In the domain of molecular communication (MC), information is conveyed through the characteristics of molecules transmitted between the transmitter and the receiver bionanosensors via propagation. The constrained size of the transmitter imposes limitations on its storage capacity, constraining the number of available molecules for transmission, with a resulting effect on communication reliability. This paper primarily focuses on achieving an equilibrium between the number of transmitted molecul…

Graph Alignment via Dual-Pass Spectral Encoding and Latent Space Communication
Maysam Behmanesh, Erkan Turan, Maks Ovsjanikov
https://arxiv.org/abs/2509.09597 https://

Graph Alignment via Dual-Pass Spectral Encoding and Latent Space Communication
Graph alignment-the problem of identifying corresponding nodes across multiple graphs-is fundamental to numerous applications. Most existing unsupervised methods embed node features into latent representations to enable cross-graph comparison without ground-truth correspondences. However, these methods suffer from two critical limitations: the degradation of node distinctiveness due to oversmoothing in GNN-based embeddings, and the misalignment of latent spaces across graphs caused by structura…

Multi-User Generative Semantic Communication with Intent-Aware Semantic-Splitting Multiple Access
Jiayi Lu, Wanting Yang, Zehui Xiong, Rahim Tafazolli, Tony Q. S. Quek, M\'erouane Debbah, Dong In Kim
https://arxiv.org/abs/2507.01333

Multi-User Generative Semantic Communication with Intent-Aware Semantic-Splitting Multiple Access
With the booming development of generative artificial intelligence (GAI), semantic communication (SemCom) has emerged as a new paradigm for reliable and efficient communication. This paper considers a multi-user downlink SemCom system, using vehicular networks as the representative scenario for multi-user content dissemination. To address diverse yet overlapping user demands, we propose a multi-user Generative SemCom-enhanced intent-aware semantic-splitting multiple access (SS-MGSC) framework. …

Towards Stable and Personalised Profiles for Lexical Alignment in Spoken Human-Agent Dialogue
Keara Schaaij, Roel Boumans, Tibor Bosse, Iris Hendrickx
https://arxiv.org/abs/2509.04104

Towards Stable and Personalised Profiles for Lexical Alignment in Spoken Human-Agent Dialogue
Lexical alignment, where speakers start to use similar words across conversation, is known to contribute to successful communication. However, its implementation in conversational agents remains underexplored, particularly considering the recent advancements in large language models (LLMs). As a first step towards enabling lexical alignment in human-agent dialogue, this study draws on strategies for personalising conversational agents and investigates the construction of stable, personalised le…

Gaussian Variation Field Diffusion for High-fidelity Video-to-4D Synthesis
Bowen Zhang, Sicheng Xu, Chuxin Wang, Jiaolong Yang, Feng Zhao, Dong Chen, Baining Guo
https://arxiv.org/abs/2507.23785

Gaussian Variation Field Diffusion for High-fidelity Video-to-4D Synthesis
In this paper, we present a novel framework for video-to-4D generation that creates high-quality dynamic 3D content from single video inputs. Direct 4D diffusion modeling is extremely challenging due to costly data construction and the high-dimensional nature of jointly representing 3D shape, appearance, and motion. We address these challenges by introducing a Direct 4DMesh-to-GS Variation Field VAE that directly encodes canonical Gaussian Splats (GS) and their temporal variations from 3D anima…

Using SBPF to Accelerate Kernel Memory Access From Userspace
Boming Kong, Zhizhou Zhang, Jonathan Balkind
https://arxiv.org/abs/2506.22632 https://arxiv.or…

Using SBPF to Accelerate Kernel Memory Access From Userspace
The cost of communication between the operating system kernel and user applications has long blocked improvements in software performance. Traditionally, operating systems encourage software developers to use the system call interface to transfer (or initiate transfer of) data between user applications and the kernel. This approach not only hurts performance at the software level due to memory copies between user space address spaces and kernel space address spaces, it also hurts system perform…

Enhancing Vehicular Platooning with Wireless Federated Learning: A Resource-Aware Control Framework
Beining Wu, Jun Huang, Qiang Duan, Liang Dong, Zhipeng Cai
https://arxiv.org/abs/2507.00856

Enhancing Vehicular Platooning with Wireless Federated Learning: A Resource-Aware Control Framework
This paper aims to enhance the performance of Vehicular Platooning (VP) systems integrated with Wireless Federated Learning (WFL). In highly dynamic environments, vehicular platoons experience frequent communication changes and resource constraints, which significantly affect information exchange and learning model synchronization. To address these challenges, we first formulate WFL in VP as a joint optimization problem that simultaneously considers Age of Information (AoI) and Federated Learni…