Tootfinder

Voltage Synchronization and Proportional Current Sharing of Grid-Forming Inverters
Qianxi Tang, Li Peng
https://arxiv.org/abs/2509.09277 https://arxiv.org/…

Voltage Synchronization and Proportional Current Sharing of Grid-Forming Inverters
Most previously proposed controllers are analyzed in the small-signal/quasi-steady regime rather than large-signal or transient stability for grid-forming inverters (GFMI). Additionally, methods that presume system-wide data--global measurements and complete grid-model knowledge--are challenging to realize in practice and unsuitable for large-scale operation. Moreover, proportional current sharing is rarely embedded into them. The whole system is a high-order, nonlinear differential system, mak…

Co-TAP: Three-Layer Agent Interaction Protocol Technical Report
Shunyu An, Miao Wang, Yongchao Li, Dong Wan, Lina Wang, Ling Qin, Liqin Gao, Congyao Fan, Zhiyong Mao, Jiange Pu, Wenji Xia, Dong Zhao, Rui Hu, Ji Lu, Guiyue Zhou, Baoyu Tang, Yanqin Gao, Yongsheng Du, Daigang Xu, Lingjun Huang, Baoli Wang, Xiwen Zhang, Luyao Wang, Shilong Liu
https://

Co-TAP: Three-Layer Agent Interaction Protocol Technical Report
This paper proposes Co-TAP (T: Triple, A: Agent, P: Protocol), a three-layer agent interaction protocol designed to address the challenges faced by multi-agent systems across the three core dimensions of Interoperability, Interaction and Collaboration, and Knowledge Sharing. We have designed and proposed a layered solution composed of three core protocols: the Human-Agent Interaction Protocol (HAI), the Unified Agent Protocol (UAP), and the Memory-Extraction-Knowledge Protocol (MEK). HAI focuse…

Key-Augmented Neural Triggers for Knowledge Sharing
Alex Wolf, Marco Edoardo Palma, Pooja Rani, Harald C. Gall
https://arxiv.org/abs/2508.03340 https://arx…

Key-Augmented Neural Triggers for Knowledge Sharing
Repository-level code comprehension and knowledge sharing remain core challenges in software engineering. Large language models (LLMs) have shown promise by generating explanations of program structure and logic. However, these approaches still face limitations: First, relevant knowledge is distributed across multiple files within a repository, aka semantic fragmentation. Second, retrieval inefficiency and attention saturation degrade performance in RAG pipelines, where long, unaligned contexts…

'I requested a guarantee that I could be openly out as a lesbian on stage and perform gay material. I am grateful that I was able to do precisely that — to my knowledge, I am the first openly gay comic to talk about it on stage in SA. I received messages sharing how much it meant to participate in a gay-affirming event. At the same time, I deeply regret participating under the auspices of the Saudi government.'
Lesbian comedian who performed in Saudi Arabia apologizes
https://www.washingtonblade.com/2025/10/07/lesbian-comedian-who-performed-in-saudi-arabia-apologizes/

Capturing and Sharing Know-How through Visual Process Representations: A Human-Centred Approach to Teacher Workflows
Gloria Fern\'andez-Nieto, Vanessa Echeverria, Yuheng Li, Yi-Shan Tsai, Lele Sha, Guanliang Chen, Dragan Gasevic, Zachari Swiecki
https://arxiv.org/abs/2508.04357

Capturing and Sharing Know-How through Visual Process Representations: A Human-Centred Approach to Teacher Workflows
Knowledge Management is crucial for capturing and transferring expertise within universities, especially in high staff turnover contexts where expertise loss disrupts teaching. Documenting teachers' workflows is time-intensive and diverts experts from core responsibilities. Sequential Pattern Mining (SPM) leverages log data to identify expert workflows, offering an automated alternative to represent workflows but requiring transformation into intuitive formats for novice educators. This paper i…

Grouped k-threshold random grid-based visual cryptography scheme
Xiaoli Zhuo, Xuehu Yan, Wei Yan
https://arxiv.org/abs/2508.05394 https://arxiv.org/pdf/250…

Grouped k-threshold random grid-based visual cryptography scheme
Visual cryptography schemes (VCSs) belong to a category of secret image sharing schemes that do not require cryptographic knowledge for decryption, instead relying directly on the human visual system. Among VCSs, random grid-based VCS (RGVCS) has garnered widespread attention as it avoids pixel expansion while requiring no basic matrices design. Contrast, a core metric for RGVCS, directly determines the visual quality of recovered images, rendering its optimization a critical research objective…

Perhaps future work should focus on establishing trusted enclaves of knowledge and expertise.
If you’re a knowledge worker, perhaps sharing your knowledge publicly isn’t the best idea anymore. It’s a real bummer because it makes knowledge a luxury of the elite and wealthy.
Rather than working directly on the thing, you focus on the data that feeds into the machine that works on “the thing”. :\

Virtual Group Knowledge and Group Belief in Topological Evidence Models (Extended Version)
Alexandru Baltag, Malvin Gattinger, Djanira Gomes
https://arxiv.org/abs/2509.00184 htt…

Virtual Group Knowledge and Group Belief in Topological Evidence Models (Extended Version)
We study notions of (virtual) group knowledge and group belief within multi-agent evidence models, obtained by extending the topological semantics of evidence-based belief and fallible knowledge from individuals to groups. We completely axiomatize and show the decidability of the logic of ("hard" and "soft") group evidence, and do the same for an especially interesting fragment of it: the logic of group knowledge and group belief. We also extend these languages with dynamic evidence-sharing ope…

Position: The Current AI Conference Model is Unsustainable! Diagnosing the Crisis of Centralized AI Conference
Nuo Chen, Moming Duan, Andre Huikai Lin, Qian Wang, Jiaying Wu, Bingsheng He
https://arxiv.org/abs/2508.04586

Position: The Current AI Conference Model is Unsustainable! Diagnosing the Crisis of Centralized AI Conference
Artificial Intelligence (AI) conferences are essential for advancing research, sharing knowledge, and fostering academic community. However, their rapid expansion has rendered the centralized conference model increasingly unsustainable. This paper offers a data-driven diagnosis of a structural crisis that threatens the foundational goals of scientific dissemination, equity, and community well-being. We identify four key areas of strain: (1) scientifically, with per-author publication rates more…

"A key principle of PKM is that no one has the right answer, but together we can create better ways of understanding complex systems. We each need to find others who are sharing their knowledge flow and in turn contribute our own. It’s not about being a better digital librarian, it’s about becoming a participating member of a networked organization, economy and society."
@…

Leveraging Temporally Extended Behavior Sharing for Multi-task Reinforcement Learning
Gawon Lee (Seoul National University), Daesol Cho (Seoul National University), H. Jin Kim (Seoul National University)
https://arxiv.org/abs/2509.20766

Leveraging Temporally Extended Behavior Sharing for Multi-task Reinforcement Learning
Multi-task reinforcement learning (MTRL) offers a promising approach to improve sample efficiency and generalization by training agents across multiple tasks, enabling knowledge sharing between them. However, applying MTRL to robotics remains challenging due to the high cost of collecting diverse task data. To address this, we propose MT-Lévy, a novel exploration strategy that enhances sample efficiency in MTRL environments by combining behavior sharing across tasks with temporally extended ex…

TRUST-VL: An Explainable News Assistant for General Multimodal Misinformation Detection
Zehong Yan, Peng Qi, Wynne Hsu, Mong Li Lee
https://arxiv.org/abs/2509.04448 https://

TRUST-VL: An Explainable News Assistant for General Multimodal Misinformation Detection
Multimodal misinformation, encompassing textual, visual, and cross-modal distortions, poses an increasing societal threat that is amplified by generative AI. Existing methods typically focus on a single type of distortion and struggle to generalize to unseen scenarios. In this work, we observe that different distortion types share common reasoning capabilities while also requiring task-specific skills. We hypothesize that joint training across distortion types facilitates knowledge sharing and …

FedProtoKD: Dual Knowledge Distillation with Adaptive Class-wise Prototype Margin for Heterogeneous Federated Learning
Md Anwar Hossen, Fatema Siddika, Wensheng Zhang, Anuj Sharma, Ali Jannesari
https://arxiv.org/abs/2508.19009

FedProtoKD: Dual Knowledge Distillation with Adaptive Class-wise Prototype Margin for Heterogeneous Federated Learning
Heterogeneous Federated Learning (HFL) has gained attention for its ability to accommodate diverse models and heterogeneous data across clients. Prototype-based HFL methods emerge as a promising solution to address statistical heterogeneity and privacy challenges, paving the way for new advancements in HFL research. This method focuses on sharing only class-representative prototypes among heterogeneous clients. However, these prototypes are often aggregated on the server using weighted averagin…

Fostering cultural change in research through innovative knowledge sharing, evaluation, and community engagement strategies
Junsuk Rho, Jinn-Kong Sheu, Andrew Forbes, Din Ping Tsai, Andrea Al\'u, Wei Li, Mark Brongersma, Joonhee Choi, Javier Garcia de Abajo, Laura Na Liu, Alexander Szameit, Tracy Schloemer, Andreas Tittl, Mario Chemnitz, Cheng Wang, Jiejun Zhang, Yuri Kivshar, Tie Jun Cui, Ren-Min Ma, Cheng-Wei Qiu, Cuicui Lu, Yao-Wei Huang, Miguel Angel Solis Prosser, Ileana-Crist…

Fostering cultural change in research through innovative knowledge sharing, evaluation, and community engagement strategies
Scientific research needs a new system that appropriately values science and scientists. Key innovations, within institutions and funding agencies, are driving better assessment of research, with open knowledge and FAIR (findable, accessible, interoperable, and reusable) principles as central pillars. Furthermore, coalitions, agreements, and robust infrastructures have emerged to promote more accurate assessment metrics and efficient knowledge sharing. However, despite these efforts, the system…

A Monoid Ring Approach to Color Visual Cryptography
Maximilian Reif, Jens Zumbr\"agel
https://arxiv.org/abs/2510.00763 https://arxiv.org/pdf/2510.0076…

A Monoid Ring Approach to Color Visual Cryptography
A visual cryptography scheme is a secret sharing scheme in which the secret information is an image and the shares are printed on transparencies, so that the secret image can be recovered by simply stacking the shares on top of each other. Such schemes do therefore not require any knowledge of cryptography tools to recover the secret, and they have widespread applications, for example, when sharing QR codes or medical images. In this work we deal with visual cryptography threshold schemes for c…

The @… meetup is now on YouTube!
As a community, we're committed to sharing knowledge and expertise in PHP development. Our channel will feature recordings of our meetup sessions, so you can learn from the best and stay up-to-date on the latest industry trends. Help us grow our community by sharing this post with your network and subscribing to our channe…

Towards Cognitive Synergy in LLM-Based Multi-Agent Systems: Integrating Theory of Mind and Critical Evaluation
Adam Kostka, Jaros{\l}aw A. Chudziak
https://arxiv.org/abs/2507.21969

Towards Cognitive Synergy in LLM-Based Multi-Agent Systems: Integrating Theory of Mind and Critical Evaluation
Recently, the field of Multi-Agent Systems (MAS) has gained popularity as researchers are trying to develop artificial intelligence capable of efficient collective reasoning. Agents based on Large Language Models (LLMs) perform well in isolated tasks, yet struggle with higher-order cognition required for adaptive collaboration. Human teams achieve synergy not only through knowledge sharing, but also through recursive reasoning, structured critique, and the ability to infer others' mental states…

The @… meetup is now on YouTube!
As a community, we're committed to sharing knowledge and expertise in PHP development. Our channel will feature recordings of our meetup sessions, so you can learn from the best and stay up-to-date on the latest industry trends. Help us grow our community by sharing this post with your network and subscribing to our channe…

Linkage Attacks Expose Identity Risks in Public ECG Data Sharing
Ziyu Wang, Elahe Khatibi, Farshad Firouzi, Sanaz Rahimi Mousavi, Krishnendu Chakrabarty, Amir M. Rahmani
https://arxiv.org/abs/2508.15850

Linkage Attacks Expose Identity Risks in Public ECG Data Sharing
The increasing availability of publicly shared electrocardiogram (ECG) data raises critical privacy concerns, as its biometric properties make individuals vulnerable to linkage attacks. Unlike prior studies that assume idealized adversarial capabilities, we evaluate ECG privacy risks under realistic conditions where attackers operate with partial knowledge. Using data from 109 participants across diverse real-world datasets, our approach achieves 85% accuracy in re-identifying individuals in pu…

MOSA: Mixtures of Simple Adapters Outperform Monolithic Approaches in LLM-based Multilingual ASR
Junjie Li, Jing Peng, Yangui Fang, Shuai Wang, Kai Yu
https://arxiv.org/abs/2508.18998

MOSA: Mixtures of Simple Adapters Outperform Monolithic Approaches in LLM-based Multilingual ASR
End-to-end multilingual ASR aims to transcribe speech from different languages into corresponding text, but is often limited by scarce multilingual data. LLM-based ASR aligns speech encoder outputs with LLM input space via a projector and has achieved notable success. However, prior work mainly improves performance by increasing data, with little focus on cross-lingual knowledge sharing. Moreover, a single complex projector struggles to capture both shared and language-specific features effecti…

Federated Distillation on Edge Devices: Efficient Client-Side Filtering for Non-IID Data
Ahmed Mujtaba, Gleb Radchenko, Radu Prodan, Marc Masana
https://arxiv.org/abs/2508.14769

Federated Distillation on Edge Devices: Efficient Client-Side Filtering for Non-IID Data
Federated distillation has emerged as a promising collaborative machine learning approach, offering enhanced privacy protection and reduced communication compared to traditional federated learning by exchanging model outputs (soft logits) rather than full model parameters. However, existing methods employ complex selective knowledge-sharing strategies that require clients to identify in-distribution proxy data through computationally expensive statistical density ratio estimators. Additionally,…

Replaced article(s) found for cs.HC. https://arxiv.org/list/cs.HC/new
[1/1]:
- The Paradox of Spreadsheet Self-Efficacy: Social Incentives for Informal Knowledge Sharing in End...
Qing (Nancy), Xia, Advait Sarkar, Duncan P. Brumby, Anna Cox

Threshold-Protected Searchable Sharing: Privacy Preserving Aggregated-ANN Search for Collaborative RAG
Ruoyang Rykie Guo
https://arxiv.org/abs/2507.17199 https://

Threshold-Protected Searchable Sharing: Privacy Preserving Aggregated-ANN Search for Collaborative RAG
LLM-powered search services have driven data integration as a significant trend. However, this trend's progress is fundamentally hindered, despite the fact that combining individual knowledge can significantly improve the relevance and quality of responses in specialized queries and make AI more professional at providing services. Two key bottlenecks are private data repositories' locality constraints and the need to maintain compatibility with mainstream search techniques, particularly Hierarc…

ClearFairy: Capturing Creative Workflows through Decision Structuring, In-Situ Questioning, and Rationale Inference
Kihoon Son, DaEun Choi, Tae Soo Kim, Young-Ho Kim, Sangdoo Yun, Juho Kim
https://arxiv.org/abs/2509.14537

ClearFairy: Capturing Creative Workflows through Decision Structuring, In-Situ Questioning, and Rationale Inference
Capturing professionals' decision-making in creative workflows is essential for reflection, collaboration, and knowledge sharing, yet existing methods often leave rationales incomplete and implicit decisions hidden. To address this, we present CLEAR framework that structures reasoning into cognitive decision steps-linked units of actions, artifacts, and self-explanations that make decisions traceable. Building on this framework, we introduce ClearFairy, a think-aloud AI assistant for UI design …

ZORRO: Zero-Knowledge Robustness and Privacy for Split Learning (Full Version)
Nojan Sheybani, Alessandro Pegoraro, Jonathan Knauer, Phillip Rieger, Elissa Mollakuqe, Farinaz Koushanfar, Ahmad-Reza Sadeghi
https://arxiv.org/abs/2509.09787

ZORRO: Zero-Knowledge Robustness and Privacy for Split Learning (Full Version)
Split Learning (SL) is a distributed learning approach that enables resource-constrained clients to collaboratively train deep neural networks (DNNs) by offloading most layers to a central server while keeping in- and output layers on the client-side. This setup enables SL to leverage server computation capacities without sharing data, making it highly effective in resource-constrained environments dealing with sensitive data. However, the distributed nature enables malicious clients to manipul…