OpenAI Releases LifeSciBench for AI Evaluation

OpenAI research scientist Noam Brown stated that the company's top priority when training new AI models is automating AI research and development, describing recursive self-improvement as the number one goal by a wide margin. While GPT-6 Astra showed improvements across professional tasks including video game design and sheet music transcription, Brown emphasized that these capabilities are secondary to the core objective of enabling AI to improve itself. Brown, who has spent three years at OpenAI focusing on AI reasoning and autonomous agents, discussed these priorities in an interview for The Information's new AI Deep Dive series.

Researchers at Nature Machine Intelligence have demonstrated a dual-model architecture for autonomous crystal materials research using two lightweight large language models working collaboratively. The approach combines reasoning and scientific tool execution while maintaining computational efficiency and local deployability. The method achieves competitive performance without requiring expensive infrastructure, making advanced materials research more accessible.

César de la Fuente's lab is using OpenAI's Codex and ChatGPT to identify new antimicrobial molecules by searching living and extinct genomes. The approach targets drug-resistant infections by leveraging AI to accelerate the discovery of antimicrobial candidates from genomic data. This represents a practical application of large language models to address a significant public health challenge.

An MIT researcher is using GPT-5.6 Sol with Codex to autonomously run quantum computing experiments, including analyzing results and calibrating qubits. The application demonstrates AI's capability to handle complex, iterative scientific workflows without human intervention. This represents a practical use case for large language models in experimental physics and quantum research.