​[[{“value”:” Flow-based generative modeling stands out in computational science as a sophisticated approach that facilitates rapid and accurate inferences for complex, high-dimensional datasets. It is particularly relevant in domains requiring efficient inverse problem-solving, such as astrophysics, particle physics, and dynamical system predictions. In these fields,… Read More »This AI Paper from the Technical University of Munich Introduces a Novel Machine Learning Approach to Improving Flow-Based Generative Models with Simulator Feedback Nikhil Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Foundation models hold promise in medicine, especially in assisting complex tasks like Medical Decision-Making (MDM). MDM is a nuanced process requiring clinicians to analyze diverse data sources—like imaging, electronic health records, and genetic information—while adapting to new medical research. LLMs could support MDM by… Read More »MDAgents: A Dynamic Multi-Agent Framework for Enhanced Medical Decision-Making with Large Language Models Sana Hassan Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large Language Models (LLMs) have demonstrated remarkable in-context learning (ICL) capabilities, where they can learn tasks from demonstrations without requiring additional training. A critical challenge in this field is understanding and predicting the relationship between the number of demonstrations provided and the model’s performance… Read More »Predicting and Interpreting In-Context Learning Curves Through Bayesian Scaling Laws Mohammad Asjad Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large language models (LLMs) are getting better at scaling and handling long contexts. As they are being used on a large scale, there has been a growing demand for efficient support of high-throughput inference. However, efficiently serving these long-context LLMs presents challenges related to… Read More »ShadowKV: A High-Throughput Inference System for Long-Context LLM Inference Divyesh Vitthal Jawkhede Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Recent advancements in generative language modeling have propelled natural language processing, making it possible to create contextually rich and coherent text across various applications. Autoregressive (AR) models generate text in a left-to-right sequence and are widely used for tasks like coding and complex reasoning.… Read More »EDLM: A New Energy-based Language Model Embedded with Diffusion Framework Nikhil Artificial Intelligence Category – MarkTechPost

​Natural language generation (NLG) is an enthralling area of artificial intelligence (AI) , or more specifically of natural language processing (NLP) , aimed at enabling machines to produce human-like text that drives human-machine communication for problem-solving. Natural language generation (NLG) is an enthralling area of artificial… Read More »Natural Language Generation Inside Out: Teaching Machines to Write Like Humans Iván Palomares Carrascosa MachineLearningMastery.com

​[[{“value”:” Creating a common semantic space where queries and items can be represented as dense vectors is the main goal of embedding-based retrieval. Instead of depending on precise keyword matches, this method enables effective matching based on semantic similarities. Semantically related things are positioned closer… Read More »pEBR: A Novel Probabilistic Embedding based Retrieval Model to Address the Challenges of Insufficient Retrieval for Head Queries and Irrelevant Retrieval for Tail Queries Tanya Malhotra Artificial Intelligence Category – MarkTechPost

​[[{“value”:” In the fast-paced digital age, AI assistants have become essential tools for enhancing productivity, managing workflows, and providing personalized support in our everyday lives. From voice-activated home devices to advanced chatbots, these AI assistants are designed to simplify tasks, answer questions, and help users… Read More »Top 25 AI Assistants in 2025 Pragati Jhunjhunwala Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Evaluating NLP models has become increasingly complex due to issues like benchmark saturation, data contamination, and the variability in test quality. As interest in language generation grows, standard model benchmarking faces challenges from rapidly saturated evaluation datasets, where top models reach near-human performance levels.… Read More »SMART Filtering: Enhancing Benchmark Quality and Efficiency for NLP Model Evaluation Sana Hassan Artificial Intelligence Category – MarkTechPost