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generative ai

2,176 papers

#large language models Book Open access Oct 2026

ns3-agent: Fostering Integrated Perception-Communication-Computing Research for Agentic AI Services via Cross-Platform Co-Simulations

The rapid proliferation of Generative AI (GenAI) has catalyzed the emergence of autonomous agentic AI services, spanning large language model (LLM) or vision-language model (VLM) based digital agents to vision-language-action (VLA) based embodied intelligence. Consequently, tokens and multimodal data streams have emerg...

Cheng-Xiang Mi, Ce Wang, Kai Zhang et al. · 0 citations
#large language models Book Open access Oct 2026

ns3-GenAI: Integrating Large Language Models with ns3 for AI-Native Network Simulations

Existing ns3/AI bridges target numeric reinforcement learning (RL) pipelines and cannot handle the text-centric prompt/response exchange, structured output validation, and multi-node orchestration that large language models (LLMs) require. We introduce ns3-GenAI, an open framework that augments the ns3 shared-memory in...

Su-Bin Han, Junkyu Hong, Sangheon Pack · 0 citations
#generative ai Review Open access Dec 2026

Mapping AI Ethics Integration in Postgraduate Health Professions Education: A Scoping Review Through the Lenses of Principlism and Transformative Learning.

This study maps how AI ethics is taught within postgraduate/CPD HPE, drawing on principlism, an ethical framework espousing the principles of autonomy, beneficence, non-maleficence and justice and transformative learning theory (TLT), which explains how critical reflection transforms professional assumptions, perspecti...

T. Wong, Ang Yu Chien Constance, M. S. Hussein et al. · 0 citations
#natural language process... Preprint Oct 2026

Generative AI translations in high-stakes emergency messaging

Emergency messaging such as extreme-weather reports and earthquake instructions can involve high stakes, to the extent that translation errors can lead to tragic consequences. The use of machine translation or generative artificial intelligence might therefore not be recommended. On the other hand, time savings in the...

Nune Ayvazan, Anthony Pym, Yu Hao · 0 citations
#artificial intelligence Preprint Oct 2026

FlowCF: Sparse Counterfactual Explanations for Mixed-Type Tabular Data using Flow Matching

In the field of Explainable AI (XAI), counterfactual (CF) explanations interpret a model's decision by suggesting the changes to the input that would lead to a more favourable outcome. To be useful in practice, such an explanation should change few features and change them as little as possible, properties known as spa...

Emmanouil Panagiotou, E. Ntoutsi · 0 citations
#generative ai Preprint Oct 2026

A Spatiotemporal Semantic Importance-Guided Unified Compression and Editing Framework for AI-Generated Videos

A unified compression and editing framework for AI-generated videos is proposed that incorporates a frozen video generator as a reusable generative prior and designs three spatiotemporal semantic importance-guided techniques that respectively address what to transmit, how much to transmit, and how to use the transmitte...

Xi-Hua Sheng, Dong Liu, Chang-Wen Chen · 0 citations
#generative ai Review Oct 2026

AI-augmented digital workplaces and managerial job satisfaction in the future of work: The role of applied AI literacy, organizational support and regulatory influence

It is found that perceived usefulness of GenAI and perceived applied AI literacy are positively associated with managerial job satisfaction and the positive association between perceived usefulness and job satisfaction is stronger among managers reporting higher applied AI literacy.

J. Z. Zhang, Jimmy Sun, D. Liu · 0 citations
#generative ai Review Oct 2026

From answer machine to collaborative partner: impact of structured generative AI training on scientific problem solving in STEM education

The results suggest that training can enhance students’ revision performance and foster more strategic engagement with GenAI in the short term and underscore the practical need for student development in AI literacy.

Bing C. Huang, Li Xie, Ya-Mei Liu et al. · 0 citations
#generative ai Review Oct 2026

Analyzing the integration of generative AI in the design process through the double-diamond framework

This article investigates how designers integrate AI tools into their design processes, and how they engage with these tools in the conduct of designerly practices through different communication styles and interaction types.

Tuğçe Sözen, N. Börekçi · 0 citations
#generative ai Book Open access Oct 2026

SNDBOX: A Tangible Sand Interface for Observing Behavioral Subordination to Generative AI

We present SNDBOX, a tangible interactive installation that recontextualizes Rangoli, a 5,000-year-old Indian sand-drawing practice, as a spatial interface for observing how people respond to real-time generative AI. Participants shape colored sand on a table; an overhead camera feeds a YOLO object-detection model whos...

Rintaro Fujita, Sugandh Malhotra · 0 citations
#generative ai Book Open access Oct 2026

Human-AI Interactions in AR for Museum Serious Games

Results of a mixed-method evaluation study provide strong evidence that the integration of AR and GenAI in a serious escape game constitutes an effective and engaging approach for enhancing museum-based learning experiences and does not detract from interpersonal dynamics during the experience.

Christos-Spyridon Koulouris, Stylianos Mystakidis, Christopoulos Athanasios et al. · 0 citations

From tech blogs

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Microsoft Research Blog Oct 7, 2026

Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses

Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.

Microsoft Research Blog Oct 6, 2026

What AI gets wrong and what failure teaches us

Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity.  The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.

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