​[[{“value”:” Bayesian Optimization, widely used in experimental design and black-box optimization, traditionally relies on regression models for predicting the performance of solutions within fixed search spaces. However, many regression methods are task-specific due to modeling assumptions and input constraints. This issue is especially prevalent in… Read More »Embed-then-Regress: A Versatile Machine Learning Approach for Bayesian Optimization Using String-Based In-Context Regression Sana Hassan Artificial Intelligence Category – MarkTechPost

​[[{“value”:” AI has significantly impacted healthcare, particularly in disease diagnosis and treatment planning. One area gaining attention is the development of Medical Large Vision-Language Models (Med-LVLMs), which combine visual and textual data for advanced diagnostic tools. These models have shown great potential for improving the… Read More »MMed-RAG: A Versatile Multimodal Retrieval-Augmented Generation System Transforming Factual Accuracy in Medical Vision-Language Models Across Multiple Domains Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Dynamical systems are mathematical models that explain how a system evolves due to physical interactions or forces. These systems are fundamental to understanding various phenomena across scientific fields like physics, biology, and engineering. For example, they model fluid dynamics, celestial mechanics, and robotic movements.… Read More »TREAT: A Deep Learning Framework that Achieves High-Precision Modeling for a Wide Range of Dynamical Systems by Injecting Time-Reversal Symmetry as an Inductive Bias Sana Hassan Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Long-context Large language models (LLMs) are designed to handle long input sequences, enabling them to process and understand large amounts of information. As the interference computation power is increased the large language models (LLMs) can perform diverse tasks. Particularly for knowledge-intensive tasks that rely… Read More »This AI Paper from Google DeepMind Explores Inference Scaling in Long-Context RAG Divyesh Vitthal Jawkhede Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large language models (LLMs) have demonstrated consistent scaling laws, revealing a power-law relationship between pretraining performance and computational resources. This relationship, expressed as C = 6ND (where C is compute, N is model size, and D is data quantity), has proven invaluable for optimizing… Read More »Scaling Diffusion transformers (DiT): An AI Framework for Optimizing Text-to-Image Models Across Compute Budgets Mohammad Asjad Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Code generation AI models (Code GenAI) are becoming pivotal in developing automated software demonstrating capabilities in writing, debugging, and reasoning about code. However, their ability to autonomously generate code raises concerns about security vulnerabilities. These models may inadvertently introduce insecure code, which could be… Read More »SecCodePLT: A Unified Platform for Evaluating Security Risks in Code GenAI Nikhil Artificial Intelligence Category – MarkTechPost

​[[{“value”:” The key challenge in the image autoencoding process is to create high-quality reconstructions that can retain fine details, especially when the image data has undergone compression. Traditional autoencoders, which rely on pixel-level losses such as mean squared error (MSE), tend to produce blurry outputs… Read More »Google Unveils ‘Sample What You Can’t Compress’ in AI—A Game-Changer in High-Fidelity Image Compression Aswin Ak Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Recent advancements in large language models (LLMs) have significantly enhanced their ability to handle long contexts, making them highly effective in various tasks, from answering questions to complex reasoning. However, a critical bottleneck has emerged: the memory requirements for storing key-value (KV) caches escalate… Read More »SimLayerKV: An Efficient Solution to KV Cache Challenges in Large Language Models Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large language models (LLMs) have demonstrated significant reasoning capabilities, yet they face issues like hallucinations and the inability to conduct faithful reasoning. These challenges stem from knowledge gaps, leading to factual errors during complex tasks. While knowledge graphs (KGs) are increasingly used to bolster… Read More »Graph-Constrained Reasoning (GCR): A Novel AI Framework that Bridges Structured Knowledge in Knowledge Graphs with Unstructured Reasoning in LLMs Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Training and deploying large-scale language models (LLMs) is complex, requiring significant computational resources, technical expertise, and access to high-performance infrastructure. These barriers limit reproducibility, increase development time, and make experimentation challenging, particularly for academia and smaller research institutions. Addressing these issues requires a lightweight,… Read More »Meta AI Releases Meta Lingua: A Minimal and Fast LLM Training and Inference Library for Research Asif Razzaq Artificial Intelligence Category – MarkTechPost