Privacy in Fine-tuning Large Language Models: Attacks, Defenses, and Future Directions
Lifelong Learning of Large Language Model based Agents: A Roadmap
A Distributed Collaborative Retrieval Framework Excelling in All Queries and Corpora based on Z...
CodeElo: Benchmarking Competition-level Code Generation of LLMs with Human-comparable Elo Ratings
Don't Do RAG: When Cache-Augmented Generation is All You Need for Knowledge Tasks
Beyond Graph Convolution: Multimodal Recommendation with Topology-aware MLPs
Threshold UCT: Cost-Constrained Monte Carlo Tree Search with Pareto Curves
The Superalignment of Superhuman Intelligence with Large Language Models
CoreInfer: Accelerating Large Language Model Inference with Semantics-Inspired Adaptive Sparse ...
LLMs as mediators: Can they diagnose conflicts accurately?
NushuRescue: Revitalization of the Endangered Nushu Language with AI
Do Large Language Models Defend Inferentialist Semantics?: On the Logical Expressivism and Anti...
AUEB-Archimedes at RIRAG-2025: Is obligation concatenation really all you need?
Unleashing Text-to-Image Diffusion Prior for Zero-Shot Image Captioning
A Survey on Sequential Recommendation
A Guide to Transforming Industries with Vertical AI Agents
Beyond Graph Convolution: Multimodal Recommendation with Topology-aware MLPs
Crafting Personalized Agents through Retrieval-Augmented Generation on Editable Memory Graphs
STAIR: Manipulating Collaborative and Multimodal Information for E-Commerce Recommendation
A Comprehensive Review of Recommender Systems: Transitioning from Theory to Practice
Regional Expected Improvement for Efficient Trust Region Selection in High-Dimensional Bayesian...
Multimodal LLM for Intelligent Transportation Systems
Combining Incomplete Observational and Randomized Data for Heterogeneous Treatment Effects
Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks
Are Your LLMs Capable of Stable Reasoning?
Lorentzian Residual Neural Networks
Hierarchical Subspaces of Policies for Continual Offline Reinforcement Learning
I'm a tech optimist, not a tech utopian.
Machine learning is a core, transformative way by which we are rethinking everything we are doing.
"Compression method to represent vectors using fewer bits while retaining structure."
"AI approach combining information retrieval with text generation."
AMD and John Hopkins University
Agent Laboratory: Using LLM Agents as Research Assistants
Microsoft
Baseline Agentic AI Systems Architecture
Microsoft
AI@Edge Community
Google AI
Google AI Edge: AI on the Edge
Google AI
Google AI News Recap 2024
Google
Google AI Tips & Tricks 2024
"Creating artificial datasets to train or test models."
"Training agents through reward-based feedback for decision-making."
"Artificial intelligence is the new electricity."
"With artificial intelligence, we are summoning the demon."
"Specialized database for storing and querying vector embeddings efficiently."
"AI models trained to understand and generate text."
"AI models generating outputs based on user or situational context."
"Training models to identify patterns without labeled data."
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
"The future is not programmed, it is programmed by the future."
"The implausibility of intelligence explosion."
