The Role of Natural Language Processing Tasks in Automatic Literary Character Network Construction
Approximated Variational Bayesian Inverse Reinforcement Learning for Large Language Model Alignment
AUEB-Archimedes at RIRAG-2025: Is obligation concatenation really all you need?
RoLargeSum: A Large Dialect-Aware Romanian News Dataset for Summary, Headline, and Keyword Generation
CORD: Balancing COnsistency and Rank Distillation for Robust Retrieval-Augmented Generation
MiMoTable: A Multi-scale Spreadsheet Benchmark with Meta Operations for Table Reasoning
Beyond Guilt: Legal Judgment Prediction with Trichotomous Reasoning
A Modern Bidirectional Encoder
Crafting Personalized Agents through Retrieval-Augmented Generation on Editable Memory Graphs
STAIR: Manipulating Collaborative and Multimodal Information for E-Commerce Recommendation
Beyond Graph Convolution: Multimodal Recommendation with Topology-aware MLPs
A Comprehensive Review of Recommender Systems: Transitioning from Theory to Practice
Future Sight and Tough Fights: Revolutionizing Sequential Recommendation with FENRec
Are Your LLMs Capable of Stable Reasoning?
Lorentzian Residual Neural Networks
Continuous latent representations for modeling precipitation with deep learning
Introduction to Graph Neural Networks: A Starting Point for Machine Learning Engineers
Hierarchical Subspaces of Policies for Continual Offline Reinforcement Learning
Complexity Control Facilitates Reasoning-Based Compositional Generalization in Transformers
A parametric algorithm is optimal for non-parametric regression of smooth functions
We know how to build AGI.
Machine learning is a core, transformative way by which we are rethinking everything we are doing.
"Vector representations capturing semantic meaning of words."
"Models that generate video content based on text inputs."
Google AI
Google AI News Recap 2024
AMD and John Hopkins University
Agent Laboratory: Using LLM Agents as Research Assistants
Google AI
Google AI Edge: AI on the Edge
Google
Google AI Tips & Tricks 2024
Microsoft
Baseline Agentic AI Systems Architecture
Microsoft
AI@Edge Community
"Allowing users to control specific aspects of generative outputs."
"Creating artificial datasets to train or test models."
"AI could execute tasks more complexly through 'agents' acting on behalf of users."
"With artificial intelligence, we are summoning the demon."
"Combining retrieved knowledge with generative models for better answers."
"AI models that generate or assist in writing code."
"Optimizing input prompts to improve model responses."
"Creating realistic audio, such as music or sound effects, using AI."
In recent years, the AI landscape has undergone a seismic shift, powered by the advent of Large Language Models (LLMs) like GPT-4, Claude, and Llama. These groundbreaking technologies are not just transforming the way we interact with artificial intelligence; they are turning the AI world upside down. Social media is flooded with discussions, research papers, and news showcasing how Agentic AI is shaping the future of technology, work, and enterprise. The rise of AI Copilots has become a defining feature of this revolution. From enhancing workplace productivity to reimagining collaborative workflows, Copilot-like AI systems are emerging as the face of modern AI. These intelligent agents are bridging the gap between humans and machines, creating intuitive and transformative ways to work. They are not only tools but active participants in reshaping industries. The surge in AI research has further amplified this momentum. Academic and industrial spheres alike are producing an unprecedented volume of papers, pushing the boundaries of what AI can achieve. From algorithmic innovations to enterprise-ready solutions, AI is becoming more powerful, adaptable, and ubiquitous. In the enterprise world, AI is rapidly embedding itself into core operations. Algorithms are the backbone of this transformation, driving efficiency and enabling businesses to harness data in new and impactful ways. Social media and news platforms are brimming with stories of AI’s enterprise adoption, making it clear that Agentic AI is not just a trend—it is a revolution defining the next era of technological advancement.Fueling the AI Revolution
"Artificial intelligence is the new electricity."
"Machine learning is the science of getting computers to act without being explicitly programmed."
