AI & ML Research 3 Days

Last updated: July 20, 2026, 8:30 PM ET

AI Model Safety & Deployment

OpenAI shared lessons from deploying long-running AI models, detailing new safety risks and observed failures, alongside improved safeguards developed through iterative deployment lessons from deployment. This follows concerns that AI hiring tools may exhibit greater bias than human recruiters.

LLM Engineering & Applications

Engineers are exploring methods to run Claude code agents for extended periods, aiming to boost productivity. Researchers are also detailing loop engineering techniques for RAG systems, starting with prompt and context engineering, then escalating to loop engineering for question parsing. Further loop engineering approaches involve adaptive parsing for flat tables using Azure and figures with vision LLMs, and adaptive PDF parsing that begins with inexpensive checks before employing heavier parsers. Practical guides are emerging for building AI-native enterprise data platforms, incorporating data agents, AI-powered QA, and governance AI-native platforms.

Data Management & Model Training

New methods enable automatic category assignment to uncategorized rows within Power Query and DAX, crucial for reporting and aggregation assign categories. For those seeking to understand neural network learning, a beginner-friendly explanation of backpropagation is available. In the realm of enterprise decision-making, discussions around Byzantine fault tolerance address challenges in making decisions when trust is uncertain.

AI Economics & Strategy

Despite passing all evaluation metrics, AI agents in finance have been sidelined when their operational costs exceeded those of human counterparts, highlighting cost as a key factor for agent success finance killed it. Meanwhile, the competitive landscape of AI models is evolving, with China's advancements potentially creating friction within the US AI development community.