AI
AI, Brain Death Detection, and Islamic Law
Researchers from various fields have come together to address a pressing issue at the intersection of clinical medicine, AI ethics, and Islamic jurisp...
AI
AI
ComNetX: Local Hierarchical Adaptation for Dynamic Community Detection
Researchers have developed ComNetX, a framework for dynamic community detection in graphs. It uses hierarchical adaptation to update only the affected...
AI
Effective Personalized AI Tutors via LLM-Guided Reinforcement Learning
Researchers have developed a personalized AI tutoring system that uses reinforcement learning to adapt the difficulty level of practice problems based...
AI
When Personalization Becomes Bias: Structural and Discursive Religious Framing in AI-Generated Financial Advice
Researchers have found that three large language models (LLMs) used in financial advisory systems often reproduce religious bias. The study analyzed 4...
AI
Education-centered critical policy analysis of AI: Ghana's AI strategy as a case
Ghana's National Artificial Intelligence Strategy for 2025-2035 has been analyzed using a new framework that focuses on education. The strategy is amb...
AI
Average Distance Approximation for Static Large Graphs
Researchers have developed a new method for estimating the average distance between nodes in large-scale networks. They tested two approaches: graph s...
AI
Sparse Coverage: Semantic Center Representations for Patent Prior-Art Retrieval
Researchers have developed a new approach to patent prior-art retrieval called Sparse Coverage. This method maps local spans of text in patents to a s...
AI
CARA: Cognitive Adaptive Recommendation Agent
Researchers have proposed a new recommendation framework called CARA (Cognitive Adaptive Recommendation Agent). CARA aims to improve recommendation sy...
AI
WIP: LLM Odyssey: A Game-Based Platform for Teaching LLM Engineering Concepts
Researchers have developed LLM Odyssey, an open-source gaming platform for teaching Large Language Model engineering concepts. The platform includes 1...
AI
EMAN: Optimization-Driven Capacity Growth through Path Emergence in Multi-Task Learning
Researchers have proposed a new framework for multi-task learning called EMAN (Emergent Modular Atomic Network). Unlike existing methods, EMAN allows ...
AI
Probing the Prefill: Detecting Code Vulnerabilities via Latent Activations
Researchers have developed a method to detect code vulnerabilities by analyzing the internal state of language models used for code generation. They e...
AI
Position: Fairness Failure in Generative Models is an Evaluation Problem
A recent position paper argues that fairness failures in generative models are due to an evaluation problem. The authors claim that current methods fo...