In this tutorial, we will build an advanced AI-powered research agent that can write essays on given topics. This agent follows a structured workflow: Planning: Generates an outline for the essay. Research: Retrieves relevant documents using Tavily. Writing: Uses the research to generate the first draft. Reflection: Critiques the draft for improvements. Iterative Refinement: Conducts…
Large language models (LLMs) struggle with precise computations, symbolic manipulations, and algorithmic tasks, often requiring structured problem-solving approaches. While language models demonstrate strengths in semantic understanding and common sense reasoning, they are not inherently equipped to handle operations that demand high levels of precision, such as mathematical problem-solving or logic-based decision-making. Traditional approaches attempt to…
Mathematical reasoning remains one of the most complex challenges in AI. While AI has advanced in NLP and pattern recognition, its ability to solve complex mathematical problems with human-like logic and reasoning still lags. Many AI models struggle with structured problem-solving, symbolic reasoning, and understanding the deep relationships between mathematical concepts. Addressing this gap requires…
Mathematical reasoning remains a difficult area for artificial intelligence (AI) due to the complexity of problem-solving and the need for structured, logical thinking. While large language models (LLMs) have made significant progress, they often struggle with tasks that require multi-step reasoning. Reinforcement learning (RL) has shown promise in improving these capabilities, yet traditional methods face…
Large language models (LLMs) have demonstrated proficiency in solving complex problems across mathematics, scientific research, and software engineering. Chain-of-thought (CoT) prompting is pivotal in guiding models through intermediate reasoning steps before reaching conclusions. Reinforcement learning (RL) is another essential component that enables structured reasoning, allowing models to recognize and correct errors efficiently. Despite these advancements,…
Recent advancements in LLMs, such as the GPT series and emerging “o1” models, highlight the benefits of scaling training and inference-time computing. While scaling during training—by increasing model size and dataset volume—has been a well-established strategy, recent findings emphasize the advantages of inference-time scaling, where additional computational resources during testing improve output quality and task…
Open-vocabulary object detection (OVD) aims to detect arbitrary objects with user-provided text labels. Although recent progress has enhanced zero-shot detection ability, current techniques handicap themselves with three important challenges. They heavily depend on expensive and large-scale region-level annotations, which are hard to scale. Their captions are typically short and not contextually rich, which makes them…
Developing AI systems that learn from their surroundings during execution involves creating models that adapt dynamically based on new information. In-Context Reinforcement Learning (ICRL) follows this approach by allowing AI agents to learn through trial and error while making decisions. However, this method has significant challenges when applied to complex environments with various tasks. It…
Text-to-speech (TTS) technology has made significant strides in recent years, but challenges remain in creating natural, expressive, and high-fidelity speech synthesis. Many TTS systems struggle to replicate the nuances of human speech, such as intonation, emotion, and accent, often resulting in artificial-sounding voices. Additionally, precise voice cloning remains difficult, limiting the ability to generate personalized…
The International Mathematical Olympiad (IMO) is a globally recognized competition that challenges high school students with complex mathematical problems. Among its four categories, geometry stands out as the most consistent in structure, making it more accessible and well-suited for fundamental reasoning research. Automated geometry problem-solving has traditionally followed two primary approaches: algebraic methods, such as…