Tootfinder

Microsoft to bring Copilot Actions, which can use apps to complete certain tasks on local files in Windows 11, to the Windows Insider Program and Copilot Labs (Jordan Novet/CNBC)
https://www.cnbc.com/2025/10/16/microsoft-test-copilot-manus-windows-11.html

Microsoft will test a Copilot AI feature that performs work on local files in Windows 11
Early adopters will be able to tell Microsoft's Copilot assistant to take action on files stored on their Windows 11 PCs.

Responsible AI Adoption in the Public Sector: A Data-Centric Taxonomy of AI Adoption Challenges
Anastasija Nikiforova, Martin Lnenicka, Ulf Melin, David Valle-Cruz, Asif Gill, Cesar Casiano Flores, Emyana Sirait, Mariusz Luterek, Richard Michael Dreyling, Barbora Tesarova
https://arxiv.org/abs/2510.09634

Responsible AI Adoption in the Public Sector: A Data-Centric Taxonomy of AI Adoption Challenges
Despite Artificial Intelligence (AI) transformative potential for public sector services, decision-making, and administrative efficiency, adoption remains uneven due to complex technical, organizational, and institutional challenges. Responsible AI frameworks emphasize fairness, accountability, and transparency, aligning with principles of trustworthy AI and fair AI, yet remain largely aspirational, overlooking technical and institutional realities, especially foundational data and governance. …

Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practice
Kevin Kuo, Chhavi Yadav, Virginia Smith
https://arxiv.org/abs/2510.12595 https://

Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practice
Cross-silo federated learning (FL) is a promising approach to enable cross-organization collaboration in machine learning model development without directly sharing private data. Despite growing organizational interest driven by data protection regulations such as GDPR and HIPAA, the adoption of cross-silo FL remains limited in practice. In this paper, we conduct an interview study to understand the practical challenges associated with cross-silo FL adoption. With interviews spanning a diverse …

Adaptive Deception Framework with Behavioral Analysis for Enhanced Cybersecurity Defense
Basil Abdullah AL-Zahrani
https://arxiv.org/abs/2510.02424 https://

Adaptive Deception Framework with Behavioral Analysis for Enhanced Cybersecurity Defense
This paper presents CADL (Cognitive-Adaptive Deception Layer), an adaptive deception framework achieving 99.88% detection rate with 0.13% false positive rate on the CICIDS2017 dataset. The framework employs ensemble machine learning (Random Forest, XGBoost, Neural Networks) combined with behavioral profiling to identify and adapt responses to network intrusions. Through a coordinated signal bus architecture, security components share real-time intelligence, enabling collective decision-making. …

DACIP-RC: Domain Adaptive Continual Instruction Pre-Training via Reading Comprehension on Business Conversations
Elena Khasanova, Harsh Saini, Md Tahmid Rahman Laskar, Xue-Yong Fu, Cheng Chen, Shashi Bhushan TN
https://arxiv.org/abs/2510.08152

DACIP-RC: Domain Adaptive Continual Instruction Pre-Training via Reading Comprehension on Business Conversations
The rapid advancements in Large Language Models (LLMs) have enabled their adoption in real-world industrial scenarios for various natural language processing tasks. However, the high inference cost of large-scale LLMs makes their deployment impractical, necessitating the use of smaller models. Despite their efficiency, smaller LLMs lack robust zero-shot instruction-following capabilities across diverse domains, limiting their adaptability to dynamic user requirements. Traditional fine-tuning ap…

Bigger is Faster in the Adaptive Immune Response
Jannatul Ferdous, G. Matthew Fricke, Judy L. Cannon, Melanie E. Moses
https://arxiv.org/abs/2510.09678 https://

Bigger is Faster in the Adaptive Immune Response
Zoonotic pathogens represent a growing global risk, yet the speed of adaptive immune activation across mammalian species remains poorly understood. Despite orders-of-magnitude differences in size and metabolic rate, we show that the time to initiate adaptive immunity is remarkably consistent across species. To understand this invariance, we analyse empirical data showing how the numbers and sizes of lymph nodes scale with body mass, finding that larger animals have both more and larger lymph no…

Runtime Composition in Dynamic System of Systems: A Systematic Review of Challenges, Solutions, Tools, and Evaluation Methods
Muhammad Ashfaq, Ahmed R. Sadik, Teerath Das, Muhammad Waseem, Niko Makitalo, Tommi Mikkonen
https://arxiv.org/abs/2510.12616

Runtime Composition in Dynamic System of Systems: A Systematic Review of Challenges, Solutions, Tools, and Evaluation Methods
Context: Modern Systems of Systems (SoSs) increasingly operate in dynamic environments (e.g., smart cities, autonomous vehicles) where runtime composition -- the on-the-fly discovery, integration, and coordination of constituent systems (CSs)--is crucial for adaptability. Despite growing interest, the literature lacks a cohesive synthesis of runtime composition in dynamic SoSs. Objective: This study synthesizes research on runtime composition in dynamic SoSs and identifies core challenges, solu…

Dr.LLM: Dynamic Layer Routing in LLMs
Ahmed Heakl, Martin Gubri, Salman Khan, Sangdoo Yun, Seong Joon Oh
https://arxiv.org/abs/2510.12773 https://arxiv.org…

Dr.LLM: Dynamic Layer Routing in LLMs
Large Language Models (LLMs) process every token through all layers of a transformer stack, causing wasted computation on simple queries and insufficient flexibility for harder ones that need deeper reasoning. Adaptive-depth methods can improve efficiency, but prior approaches rely on costly inference-time search, architectural changes, or large-scale retraining, and in practice often degrade accuracy despite efficiency gains. We introduce Dr.LLM, Dynamic routing of Layers for LLMs, a retrofitt…

As US schools adopt AI, a look at the growing use of AI-powered monitoring tools like GoGuardian to scan students' chatbot conversations for signs of self-harm (Janne Knodler/Bloomberg)
https://www.bloomberg.com/news/articles/20

AI Chatbot Surveillance Tools Are Quietly Watching Kids In Class
As US educators embrace AI in the classroom, firms are selling software to flag mentions of self-harm, raising concerns over privacy and control.

AMAQ: Adaptive Mixed-bit Activation Quantization for Collaborative Parameter Efficient Fine-tuning
Yurun Song, Zhuoyi Yang, Ian G. Harris, Sangeetha Abdu Jyothi
https://arxiv.org/abs/2510.05468

AMAQ: Adaptive Mixed-bit Activation Quantization for Collaborative Parameter Efficient Fine-tuning
Large Language Models (LLMs) are scaling rapidly, creating significant challenges for collaborative server client distributed training, particularly in terms of communication efficiency and computational overheads. To address these challenges, we implement Parameter-efficient Split Learning, which effectively balances efficiency and performance for collaborative training on low-resource devices. To reduce communication overhead in collaborative training, we introduce Adaptive Mixed bit Activa…