The development of transformer-based large language models (LLMs) has significantly advanced AI-driven applications, particularly conversational agents. However, these models face inherent limitations due to their fixed context windows, which can lead to loss of relevant information over time. While Retrieval-Augmented Generation (RAG) methods provide external knowledge to supplement LLMs, they often rely on static document…
Regression tasks, which involve predicting continuous numeric values, have traditionally relied on numeric heads such as Gaussian parameterizations or pointwise tensor projections. These traditional approaches have strong distributional assumption requirements, require a lot of labeled data, and tend to break down when modeling advanced numerical distributions. New research on large language models introduces a different…
Transformer-based language models process text by analyzing word relationships rather than reading in order. They use attention mechanisms to focus on keywords, but handling longer text is challenging. The Softmax function, which distributes attention, weakens as the input size grows, causing attention fading. This reduces the model’s focus on important words, making it harder to…
Neural Ordinary Differential Equations are significant in scientific modeling and time-series analysis where data changes every other moment. This neural network-inspired framework models continuous-time dynamics with a continuous transformation layer governed by differential equations, which sets them apart from vanilla neural nets. While Neural ODEs have cracked down on handling dynamic series efficiently, cost-effective gradient…
Directed graphs are crucial in modeling complex real-world systems, from gene regulatory networks and flow networks to stochastic processes and graph metanetworks. Representing these directed graphs presents significant challenges, particularly in causal reasoning applications where understanding cause-and-effect relationships is paramount. Current methodologies face a fundamental limitation in balancing directional and distance information within the representation…
AI-powered coding agents have significantly transformed software development in 2025, offering advanced features that enhance productivity and streamline workflows. Below is an overview of some of the leading AI coding agents available today. Devin AI Designed for complex development tasks, Devin AI utilizes multi-agent parallel workflows to manage intricate projects efficiently. Its architecture supports the…
Large language models (LLMs) have become an integral part of various applications, but they remain vulnerable to exploitation. A key concern is the emergence of universal jailbreaks—prompting techniques that bypass safeguards, allowing users to access restricted information. These exploits can be used to facilitate harmful activities, such as synthesizing illegal substances or evading cybersecurity measures.…
Large-scale language models (LLMs) have advanced the field of artificial intelligence as they are used in many applications. Although they can almost perfectly simulate human language, they tend to lose in terms of response diversity. This limitation is particularly problematic in tasks requiring creativity, such as synthetic data generation and storytelling, where diverse outputs are…
Answering open-domain questions in real-world scenarios is challenging, as relevant information is often scattered across diverse sources, including text, databases, and images. While LLMs can break down complex queries into simpler steps to improve retrieval, they usually fail to account for how data is structured, leading to suboptimal results. Agentic RAG introduces iterative retrieval, refining…
OpenAI has introduced Deep Research, a tool designed to assist users in conducting thorough, multi-step investigations on a variety of topics. Unlike traditional search engines, which return a list of links, Deep Research synthesizes information from multiple sources into detailed, well-cited reports. This feature is particularly useful for professionals in fields such as finance, science,…