Competitive programming has long served as a benchmark for assessing problem-solving and coding skills. These challenges require advanced computational thinking, efficient algorithms, and precise implementations, making them an excellent testbed for evaluating AI systems. While early AI models like Codex demonstrated strong capabilities in program synthesis, they often relied on extensive sampling and heuristic-based selection,…
In many modern Python applications, especially those that handle incoming data (e.g., JSON payloads from an API), ensuring that the data is valid, complete, and properly typed is crucial. Pydantic is a powerful library that allows you to define models for your data using standard Python-type hints and then automatically validate any incoming data against…
The study examines the concept of agency, defined as a system’s ability to direct outcomes toward a goal, and argues that determining whether a system exhibits agency is inherently dependent on the reference frame used for assessment. By analyzing essential properties of agency, the study contends that any evaluation of agency must consider the perspective…
Yann LeCun, Chief AI Scientist at Meta and one of the pioneers of modern AI, recently argued that autoregressive Large Language Models (LLMs) are fundamentally flawed. According to him, the probability of generating a correct response decreases exponentially with each token, making them impractical for long-form, reliable AI interactions. While I deeply respect LeCun’s work…
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…