LLMs exhibit striking parallels to neural activity within the human language network, yet the specific linguistic properties that contribute to these brain-like representations remain unclear. Understanding the cognitive mechanisms that enable language comprehension and communication is a key objective in neuroscience. The brain’s language network (LN), a collection of left-lateralized frontotemporal regions, is crucial in… →

The landscape of generative AI and LLMs has experienced a remarkable leap forward with the launch of Mercury by the cutting-edge startup Inception Labs. Introducing the first-ever commercial-scale diffusion large language models (dLLMs), Inception labs promises a paradigm shift in speed, cost-efficiency, and intelligence for text and code generation tasks. Mercury: Setting New Benchmarks in… →

Researchers at The Ohio State University have introduced Finer-CAM, an innovative method that significantly improves the precision and interpretability of image explanations in fine-grained classification tasks. This advanced technique addresses key limitations of existing Class Activation Map (CAM) methods by explicitly highlighting subtle yet critical differences between visually similar categories. Current Challenge with Traditional CAM… →

Large Language Models (LLMs) benefit significantly from reinforcement learning techniques, which enable iterative improvements by learning from rewards. However, training these models efficiently remains challenging, as they often require extensive datasets and human supervision to enhance their capabilities. Developing methods that allow LLMs to self-improve autonomously without additional human input or large-scale architectural modifications has… →

Search engines and recommender systems are essential in online content platforms nowadays. Traditional search methodologies focus on textual content, creating a critical gap in handling illustrated texts and videos that have become crucial components of User-Generated Content (UGC) communities. Current datasets for search and recommendation tasks contain textual information or statistically dense features, severely limiting… →

Large Language Models (LLMs) are essential in fields that require contextual understanding and decision-making. However, their development and deployment come with substantial computational costs, which limits their scalability and accessibility. Researchers have optimized LLMs to improve efficiency, particularly fine-tuning processes, without sacrificing reasoning capabilities or accuracy. This has led to exploring parameter-efficient training methods that… →

In today’s rapidly evolving AI landscape, one persistent challenge is equipping language models with robust decision-making abilities that extend beyond single-turn interactions. Traditional large language models (LLMs) excel at generating coherent responses but often struggle with multi-step problem solving or interacting with dynamic environments. This shortfall largely stems from the nature of the training data,… →

Applying large language models (LLMs) in clinical disease management has numerous critical challenges. Although the models have been effective in diagnostic reasoning, their application in longitudinal disease management, drug prescription, and multi-visit patient care is yet to be tested. The main challenges are limited context understanding across numerous visits, heterogeneous adherence to clinical guidelines, and… →

From business processes to scientific studies, AI agents can process huge datasets, streamline processes, and help in decision-making. Yet, even with all these developments, building and tailoring LLM agents is still a daunting task for most users. The main reason is that AI agent platforms require programming skills, restricting access to a mere fraction of… →

Visual programming has emerged strongly in computer vision and AI, especially regarding image reasoning. Visual programming enables computers to create executable code that interacts with visual content to offer correct responses. These systems form the backbone of object detection, image captioning, and VQA applications. Its effectiveness stems from the ability to modularize multiple reasoning tasks,… →