Tootfinder

NoteBar: An AI-Assisted Note-Taking System for Personal Knowledge Management
Josh Wisoff, Yao Tang, Zhengyu Fang, Jordan Guzman, YuTang Wang, Alex Yu
https://arxiv.org/abs/2509.03610

NoteBar: An AI-Assisted Note-Taking System for Personal Knowledge Management
Note-taking is a critical practice for capturing, organizing, and reflecting on information in both academic and professional settings. The recent success of large language models has accelerated the development of AI-assisted tools, yet existing solutions often struggle with efficiency. We present NoteBar, an AI-assisted note-taking tool that leverages persona information and efficient language models to automatically organize notes into multiple categories and better support user workflows. T…

Encoding Numeric Computations and Infusing Heuristic Knowledge Using Integrity Constraints in stableKanren
Xiangyu Guo, Ajay Bansal
https://arxiv.org/abs/2510.04049 https://

Encoding Numeric Computations and Infusing Heuristic Knowledge Using Integrity Constraints in stableKanren
This paper presents examples of using integrity constraints in stableKanren to encode numeric computations for problem solving. Then, we use one of the examples to introduce multiple ways to infuse heuristic knowledge and reduce solving time. stableKanren is an extension of miniKanren that supports normal logic programs under stable model semantics. stableKanren further supports numeric computation by constructing a constraint store for integrity constraints. There are three ways to extend a re…

Datacenter Energy Optimized Power Profiles
Sreedhar Narayanaswamy, Pratikkumar Dilipkumar Patel, Ian Karlin, Apoorv Gupta, Sudhir Saripalli, Janey Guo
https://arxiv.org/abs/2510.03872

Datacenter Energy Optimized Power Profiles
This paper presents datacenter power profiles, a new NVIDIA software feature released with Blackwell B200, aimed at improving energy efficiency and/or performance. The initial feature provides coarse-grain user control for HPC and AI workloads leveraging hardware and software innovations for intelligent power management and domain knowledge of HPC and AI workloads. The resulting workload-aware optimization recipes maximize computational throughput while operating within strict facility power co…

Hello from #CoRDI2025 👋 !
🛠️ This year @… is presenting how our Culture Information Portal, the Metadata API, and the Culture Knowledge Graph work together to make #culturalheritage

How People Manage Knowledge in their "Second Brains"- A Case Study with Industry Researchers Using Obsidian
Juliana Jansen Ferreira, Vin\'icius Segura, Joana Gabriela Souza, Joao Henrique Gallas Brasil
https://arxiv.org/abs/2509.20187

How People Manage Knowledge in their "Second Brains"- A Case Study with Industry Researchers Using Obsidian
People face overwhelming information during work activities, necessitating effective organization and management strategies. Even in personal lives, individuals must keep, annotate, organize, and retrieve knowledge from daily routines. The collection of records for future reference is known as a personal knowledge base. Note-taking applications are valuable tools for building and maintaining these bases, often called a ''second brain''. This paper presents a case study on how people build and e…

Who Taught the Lie? Responsibility Attribution for Poisoned Knowledge in Retrieval-Augmented Generation
Baolei Zhang, Haoran Xin, Yuxi Chen, Zhuqing Liu, Biao Yi, Tong Li, Lihai Nie, Zheli Liu, Minghong Fang
https://arxiv.org/abs/2509.13772

Who Taught the Lie? Responsibility Attribution for Poisoned Knowledge in Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) integrates external knowledge into large language models to improve response quality. However, recent work has shown that RAG systems are highly vulnerable to poisoning attacks, where malicious texts are inserted into the knowledge database to influence model outputs. While several defenses have been proposed, they are often circumvented by more adaptive or sophisticated attacks. This paper presents RAGOrigin, a black-box responsibility attribution framewo…

Adapting and Evaluating Multimodal Large Language Models for Adolescent Idiopathic Scoliosis Self-Management: A Divide and Conquer Framework
Zhaolong Wu, Pu Luo, Jason Pui Yin Cheung, Teng Zhang
https://arxiv.org/abs/2509.11645

Adapting and Evaluating Multimodal Large Language Models for Adolescent Idiopathic Scoliosis Self-Management: A Divide and Conquer Framework
This study presents the first comprehensive evaluation of Multimodal Large Language Models (MLLMs) for Adolescent Idiopathic Scoliosis (AIS) self-management. We constructed a database of approximately 3,000 anteroposterior X-rays with diagnostic texts and evaluated five MLLMs through a `Divide and Conquer' framework consisting of a visual question-answering task, a domain knowledge assessment task, and a patient education counseling assessment task. Our investigation revealed limitations of MLL…

A Knowledge Distillation-empowered Adaptive Federated Reinforcement Learning Framework for Multi-Domain IoT Applications Scheduling
Zhiyu Wang, Mohammad Goudarzi, Mingming Gong, Rajkumar Buyya
https://arxiv.org/abs/2508.21328

A Knowledge Distillation-empowered Adaptive Federated Reinforcement Learning Framework for Multi-Domain IoT Applications Scheduling
The rapid proliferation of Internet of Things (IoT) applications across heterogeneous Cloud-Edge-IoT environments presents significant challenges in distributed scheduling optimization. Existing approaches face issues, including fixed neural network architectures that are incompatible with computational heterogeneity, non-Independent and Identically Distributed (non-IID) data distributions across IoT scheduling domains, and insufficient cross-domain collaboration mechanisms. This paper proposes…

A Modular KDN-Based Framework for IT/OT Autonomy in Industrial Systems
Tu\u{g}\c{c}e Bilen, Mehmet Ozdem
https://arxiv.org/abs/2509.23389 https://arxiv.org…

A Modular KDN-Based Framework for IT/OT Autonomy in Industrial Systems
The convergence of Information Technology (IT) and Operational Technology (OT) is a critical enabler for achieving autonomous and intelligent industrial systems. However, the increasing complexity, heterogeneity, and real-time demands of industrial environments render traditional rule-based or static management approaches insufficient. In this paper, we present a modular framework based on the Knowledge-Defined Networking (KDN) paradigm, enabling adaptive and autonomous control across IT-OT inf…

TULIP: Adapting Open-Source Large Language Models for Underrepresented Languages and Specialized Financial Tasks
\.Irem Demirta\c{s}, Burak Payzun, Se\c{c}il Arslan
https://arxiv.org/abs/2508.16243

TULIP: Adapting Open-Source Large Language Models for Underrepresented Languages and Specialized Financial Tasks
Thanks to the growing popularity of large language models over the years, there is great potential for their applications in finance. Despite the exceptional performance of larger proprietary models, which are presented as black-box solutions through APIs, smaller models that can be hosted on-premise present opportunities for adaptability and privacy. Especially in cases where the management of sensitive information and application of domain knowledge is important, like finance, enhancing the c…