The Deep Research problem — Benedict Evans

DR Tulu: An open, end-to-end training recipe for long-form deep research | Ai2

We introduce Deep Research Tulu (DR Tulu), an open post-training recipe and framework for long-form deep research agents.

swyx on Twitter / X

icymi, openai also open sourced how their deep research prompt rewriter works + the full prompts todayyou can now build your own deep research agent - with full o3/o4-mini deep research quality - in 20LOCif you're a fan of Claude Research they ALSO dropped how to add… pic.twitter.com/5moxXjzqTj— swyx (@swyx) June 27, 2025

Deep Research and Knowledge Value

Deep Research is an AGI product for certain narrow domains; it’s ability to find anything on the Internet will make secret knowledge all the more valuable.

Accelerating scientific discovery with ChatGPT for Academic Researchers

OpenAI is giving 100,000 academic researchers free access to ChatGPT's most advanced AI models to accelerate scientific research, collaboration, and discovery.

Launching the Tacit Knowledge Podcast: How the world's hardest problems really get solved

By Kumar Garg

Infinite Researchers | AI-Powered Scientific Discovery

What happens to the speed of discovery if we have infinite researchers? Explore AI experiments accelerating breakthroughs.

Find Open Datasets for AI and Research | Kaggle

Browse and download hundreds of thousands of open datasets for AI research, model training, and analysis. Join a community of millions of researchers, developers, and builders to share and collaborate on Kaggle.

Deep Research, information vs. insight, and the nature of science

What AI will accelerate in the scientific process, what it cannot do, and how we can prepare for new manners of scientific investigation.

ScientistTwo: Pioneering the Human Knowledge Frontier with Autonomous AI

Scientific discovery is defined by the ability to identify the boundaries of existing knowledge and venture into unexplored territory. The ultimate vision for AI in science is problem-driven autonomous research: given a fundamental challenge by a human expert, the AI independently navigates the scientific landscape, uncovers theoretical and empirical bottlenecks, and systematically expands the frontier of knowledge. In this paper, we introduce ScientistTwo, a fully autonomous multi-agent framework designed to realize this vision. Specifically, ScientistTwo takes an initial problem as input, establishes state-of-the-art baselines, formulates novel hypotheses, and coordinates specialized agents to orchestrate an end-to-end discovery cycle without human intervention. Moreover, the framework rigorously conducts experiments using diverse datasets and metrics, refines methodologies through automated ablation studies, and validates research findings via a closed-loop simulated peer-review rebuttal engine. To evaluate ScientistTwo's capabilities against the highest standards of human scientific achievement, we benchmark it across papers accepted at top-tier conferences such as ICLR, ICML, and NeurIPS. As a result, ScientistTwo autonomously generates expert-level, publishable papers and fully verified, executable codebases. Its solutions consistently outperform human state-of-the-art models, and achieve higher average review ratings than human-authored papers under automated AI review agents. These results show that ScientistTwo is not merely an assistive tool but an autonomous scientific pioneer capable of pushing the frontiers of human discovery. Project website: https://scientist-two.github.io/

OpenAI spent millions to solve this famous math problem — mathematicians are furious

Mathematics relies on a community of experts openly sharing ideas.

OpenAI launches new AI model for life sciences research

Researchers are drowning in data. AI could help.

Deep-Research Agents Can Be Poisoned via User-Generated Content

Deep-research agents, i.e., systems that rely on multi-agent pipelines to iteratively retrieve, synthesize, and cite Web content in order to produce structured reports, are rapidly replacing...

AI has supercharged scientists—but may have shrunk science

Analysis of 41 million papers finds that although AI expands individual impact, it narrows collective scientific exploration