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… →
Ondansetron is an anti-emetic 5-HT3 receptor antagonist being investigated for treating neonatal opioid withdrawal syndrome (NOWS). Sparse PK data were analyzed from a multicenter, double-blind clinical trial with 98 mother/neonate dyads. Pregnant women with opioid use disorder were randomized to receive either placebo or ondansetron 8 mg intravenously within 4 h of delivery. Neonates born… →
CONCLUSION: ART-001 was effective and well-tolerated in patients with SFVMs. These results support the further development of ART-001 in SFVMs and other PIK3CA-related overgrowth syndromes to confirm clinical benefits and long-term safety. →
CONCLUSIONS: Direct MRCP may be a feasible and potentially cost-effective diagnostic strategy for patients with suspected acute gallstone disease and deranged LFTs. Automated measurement of MRCP parameters shows promise in detecting obstruction. Larger trials are warranted to assess this potential. →
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… →