Tootfinder

Bringing Multi-Modal Multi-Task Federated Foundation Models to Education Domain: Prospects and Challenges
Kasra Borazjani, Naji Khosravan, Rajeev Sahay, Bita Akram, Seyyedali Hosseinalipour
https://arxiv.org/abs/2509.07946

Bringing Multi-Modal Multi-Task Federated Foundation Models to Education Domain: Prospects and Challenges
Multi-modal multi-task (M3T) foundation models (FMs) have recently shown transformative potential in artificial intelligence, with emerging applications in education. However, their deployment in real-world educational settings is hindered by privacy regulations, data silos, and limited domain-specific data availability. We introduce M3T Federated Foundation Models (FedFMs) for education: a paradigm that integrates federated learning (FL) with M3T FMs to enable collaborative, privacy-preserving…

CRACI: A Cloud-Native Reference Architecture for the Industrial Compute Continuum
Hai Dinh-Tuan
https://arxiv.org/abs/2509.07498 https://arxiv.org/pdf/2509…

CRACI: A Cloud-Native Reference Architecture for the Industrial Compute Continuum
The convergence of Information Technology (IT) and Operational Technology (OT) in Industry 4.0 exposes the limitations of traditional, hierarchical architectures like ISA-95 and RAMI 4.0. Their inherent rigidity, data silos, and lack of support for cloud-native technologies impair the development of scalable and interoperable industrial systems. This paper addresses this issue by introducing CRACI, a Cloud-native Reference Architecture for the Industrial Compute Continuum. Among other features,…

Day 31 of the road trip and after dropping the copilot at the airport, headed east to the WA wheatbelt. What's more iconic than painted silos? #RoadTrip

Scale dependence of segregation patterns in the filling of silos
Shivakumar Athani, Benjy Marks, Fran\c{c}ois Guillard, Alistair Gillespie, Itai Einav
https://arxiv.org/abs/2508.00318

Scale dependence of segregation patterns in the filling of silos
Size segregation in granular flows is a well-known phenomenon: laboratory experiments consistently show that large particles migrate toward silo walls during filling, while smaller particles concentrate near the center. Paradoxically, field observations in large-scale industrial silos often report the opposite pattern, challenging these findings. We demarcate these patterns through a systematic experimental study spanning a range of dimensionless numbers relevant to bidisperse granular flows in…

NoTVLA: Narrowing of Dense Action Trajectories for Generalizable Robot Manipulation
Zheng Huang, Mingyu Liu, Xiaoyi Lin, Muzhi Zhu, Canyu Zhao, Zongze Du, Xiaoman Li, Yiduo Jia, Hao Zhong, Hao Chen, Chunhua Shen
https://arxiv.org/abs/2510.03895

NoTVLA: Narrowing of Dense Action Trajectories for Generalizable Robot Manipulation
Vision-Language-Action (VLA) models represent a pivotal advance in embodied intelligence, yet they confront critical barriers to real-world deployment, most notably catastrophic forgetting. This issue stems from their overreliance on continuous action sequences or action chunks, which inadvertently create isolated data silos that disrupt knowledge retention across tasks. To tackle these challenges, we propose the Narrowing of Trajectory VLA (NoTVLA) framework: a novel approach that narrows its …

Agent Network Protocol Technical White Paper
Gaowei Chang, Eidan Lin, Chengxuan Yuan, Rizhao Cai, Binbin Chen, Xuan Xie, Yin Zhang
https://arxiv.org/abs/2508.00007 https://

Agent Network Protocol Technical White Paper
With the development of large models and autonomous decision-making AI, agents are rapidly becoming the new entities of the internet, following mobile apps. However, existing internet infrastructure is primarily designed for human interaction, creating data silos, unfriendly interfaces, and high collaboration costs among agents, making it difficult to support the needs for large-scale agent interconnection and collaboration. The internet is undergoing a profound transformation, showing four cor…

Structuring Automotive Data for Systems Engineering: A Taxonomy-Based Approach
Carl Philipp Hohl, Philipp Reis, Tobias Sch\"urmann, Stefan Otten, Eric Sax
https://arxiv.org/abs/2510.00963

Structuring Automotive Data for Systems Engineering: A Taxonomy-Based Approach
Vehicle data is essential for advancing data-driven development throughout the automotive lifecycle, including requirements engineering, design, verification, and validation, and post-deployment optimization. Developers currently collect data in a decentralized and fragmented manner across simulations, test benches, and real-world driving, resulting in data silos, inconsistent formats, and limited interoperability. This leads to redundant efforts, inefficient integration, and suboptimal use of …

BiTAA: A Bi-Task Adversarial Attack for Object Detection and Depth Estimation via 3D Gaussian Splatting
Yixun Zhang, Feng Zhou, Jianqin Yin
https://arxiv.org/abs/2509.19793 http…

BiTAA: A Bi-Task Adversarial Attack for Object Detection and Depth Estimation via 3D Gaussian Splatting
Camera-based perception is critical to autonomous driving yet remains vulnerable to task-specific adversarial manipulations in object detection and monocular depth estimation. Most existing 2D/3D attacks are developed in task silos, lack mechanisms to induce controllable depth bias, and offer no standardized protocol to quantify cross-task transfer, leaving the interaction between detection and depth underexplored. We present BiTAA, a bi-task adversarial attack built on 3D Gaussian Splatting th…

🇺🇦 #NowPlaying on KEXP's #Roadhouse
The Silos:
🎵 Head Party
#TheSilos
https://thesilosofficial.bandcamp.com/track/head-party
https://open.spotify.com/track/1jiXq0MXWixzQuABXaL8rA
Please 🔁 BOOST to share what you like
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Zero-shot Multimodal Document Retrieval via Cross-modal Question Generation
Yejin Choi, Jaewoo Park, Janghan Yoon, Saejin Kim, Jaehyun Jeon, Youngjae Yu
https://arxiv.org/abs/2508.17079

Zero-shot Multimodal Document Retrieval via Cross-modal Question Generation
Rapid advances in Multimodal Large Language Models (MLLMs) have expanded information retrieval beyond purely textual inputs, enabling retrieval from complex real world documents that combine text and visuals. However, most documents are private either owned by individuals or confined within corporate silos and current retrievers struggle when faced with unseen domains or languages. To address this gap, we introduce PREMIR, a simple yet effective framework that leverages the broad knowledge of a…

On the Evolution of Federated Post-Training Large Language Models: A Model Accessibility View
Tao Guo, Junxiao Wang, Fushuo Huo, Laizhong Cui, Song Guo, Jie Gui, Dacheng Tao
https://arxiv.org/abs/2508.16261

On the Evolution of Federated Post-Training Large Language Models: A Model Accessibility View
Federated Learning (FL) enables training models across decentralized data silos while preserving client data privacy. Recent research has explored efficient methods for post-training large language models (LLMs) within FL to address computational and communication challenges. While existing approaches often rely on access to LLMs' internal information, which is frequently restricted in real-world scenarios, an inference-only paradigm (black-box FedLLM) has emerged to address these limitations. …

The Evolving Role of Large Language Models in Scientific Innovation: Evaluator, Collaborator, and Scientist
Haoxuan Zhang, Ruochi Li, Yang Zhang, Ting Xiao, Jiangping Chen, Junhua Ding, Haihua Chen
https://arxiv.org/abs/2507.11810

The Evolving Role of Large Language Models in Scientific Innovation: Evaluator, Collaborator, and Scientist
Scientific innovation is undergoing a paradigm shift driven by the rapid advancement of Large Language Models (LLMs). As science faces mounting challenges including information overload, disciplinary silos, and diminishing returns on conventional research methods, LLMs are emerging as powerful agents capable not only of enhancing scientific workflows but also of participating in and potentially leading the innovation process. Existing surveys mainly focus on different perspectives, phrases, and…

An Intelligent Infrastructure as a Foundation for Modern Science
Satrajit S. Ghosh
https://arxiv.org/abs/2508.10051 https://arxiv.org/pdf/2508.10051…

An Intelligent Infrastructure as a Foundation for Modern Science
Infrastructure shapes societies and scientific discovery. Traditional scientific infrastructure, often static and fragmented, leads to issues like data silos, lack of interoperability and reproducibility, and unsustainable short-lived solutions. Our current technical inability and social reticence to connect and coordinate scientific research and engineering leads to inefficiencies and impedes progress. With AI technologies changing how we interact with the world around us, there is an opportun…

Day 12 of the road trip took me from Esperance to Hyden, WA. As an old Aggie I loved travelling through tens of thousands of hectares of canola and wheat crops. Found this great bit of silo art by Jerome Davenport in Varley. #RoadTrip #SiloArt

Tackling One Health Risks: How Large Language Models are leveraged for Risk Negotiation and Consensus-building
Alexandra Fetsch, Iurii Savvateev, Racem Ben Romdhane, Martin Wiedmann, Artemiy Dimov, Maciej Durkalec, Josef Teichmann, Jakob Zinsstag, Konstantinos Koutsoumanis, Andreja Rajkovic, Jason Mann, Mauro Tonolla, Monika Ehling-Schulz, Matthias Filter, Sophia Johler

Tackling One Health Risks: How Large Language Models are leveraged for Risk Negotiation and Consensus-building
Key global challenges of our times are characterized by complex interdependencies and can only be effectively addressed through an integrated, participatory effort. Conventional risk analysis frameworks often reduce complexity to ensure manageability, creating silos that hinder comprehensive solutions. A fundamental shift towards holistic strategies is essential to enable effective negotiations between different sectors and to balance the competing interests of stakeholders. However, achieving …

Trans-XFed: An Explainable Federated Learning for Supply Chain Credit Assessment
Jie Shi, Arno P. J. M. Siebes, Siamak Mehrkanoon
https://arxiv.org/abs/2508.13715 https://

Trans-XFed: An Explainable Federated Learning for Supply Chain Credit Assessment
This paper proposes a Trans-XFed architecture that combines federated learning with explainable AI techniques for supply chain credit assessment. The proposed model aims to address several key challenges, including privacy, information silos, class imbalance, non-identically and independently distributed (Non-IID) data, and model interpretability in supply chain credit assessment. We introduce a performance-based client selection strategy (PBCS) to tackle class imbalance and Non-IID problems. T…

Dairy country to the west of Dryden, NY
#photo #photography #landscape #dryden