Mathematical Large Language Models (LLMs) have demonstrated strong problem-solving capabilities, but their reasoning ability is often constrained by pattern recognition rather than true conceptual understanding. Current models are heavily based on exposure to similar proofs as part of their training, confining their extrapolation to new mathematical problems. This constraint restricts LLMs from engaging in advanced…
Large language models (LLMs) use extensive computational resources to process and generate human-like text. One emerging technique to enhance reasoning capabilities in LLMs is test-time scaling, which dynamically allocates computational resources during inference. This approach aims to improve the accuracy of responses by refining the model’s reasoning process. As models like OpenAI’s o1 series introduced…
Hypothesis validation is fundamental in scientific discovery, decision-making, and information acquisition. Whether in biology, economics, or policymaking, researchers rely on testing hypotheses to guide their conclusions. Traditionally, this process involves designing experiments, collecting data, and analyzing results to determine the validity of a hypothesis. However, the volume of generated hypotheses has increased dramatically with the…
Modern AI systems have made significant strides, yet many still struggle with complex reasoning tasks. Issues such as inconsistent problem-solving, limited chain-of-thought capabilities, and occasional factual inaccuracies remain. These challenges hinder practical applications in research and software development, where nuanced understanding and precision are crucial. The drive to overcome these limitations has prompted a reexamination…
Vision‐language models (VLMs) have long promised to bridge the gap between image understanding and natural language processing. Yet, practical challenges persist. Traditional VLMs often struggle with variability in image resolution, contextual nuance, and the sheer complexity of converting visual data into accurate textual descriptions. For instance, models may generate concise captions for simple images but…
Ideation processes often require time-consuming analysis and debate. What if we make two LLMs come up with ideas and then make them debate about those ideas? Sounds interesting right? This tutorial exactly shows how to create an AI-powered solution using two LLM agents that collaborate through structured conversation. For achieving this we will be using…
Knowledge graphs (KGs) are the foundation of artificial intelligence applications but are incomplete and sparse, affecting their effectiveness. Well-established KGs such as DBpedia and Wikidata lack essential entity relationships, diminishing their utility in retrieval-augmented generation (RAG) and other machine-learning tasks. Traditional extraction methods are likely to provide sparse graphs with absent important connections or noisy,…
In this tutorial, we will build an interactive text-to-image generator application accessed through Google Colab and a public link using Hugging Face’s Diffusers library and Gradio. You’ll learn how to transform simple text prompts into detailed images by leveraging the state-of-the-art Stable Diffusion model and GPU acceleration. We’ll walk through setting up the environment, installing…
The field of large language models has long been dominated by autoregressive methods that predict text sequentially from left to right. While these approaches power today’s most capable AI systems, they face fundamental limitations in computational efficiency and bidirectional reasoning. A research team from China has now challenged the assumption that autoregressive modeling is the…
Multimodal AI agents are designed to process and integrate various data types, such as images, text, and videos, to perform tasks in digital and physical environments. They are used in robotics, virtual assistants, and user interface automation, where they need to understand and act based on complex multimodal inputs. These systems aim to bridge verbal…