No. 018: Genesis Mission $5B, Challengescape problem directory, team science vs. solo AI agents - The Hallway Track

The Hallway Track No. 018 - How AI is affecting the practice of science

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

No. 011: Sum-product conjecture disproof, AI-generated authorship fraud, OMB grant-cost rules - The Hallway Track

The Hallway Track No. 011 - How AI is affecting the practice of science

Import AI 475: Swarm scaling; Google DeepMind watermarks biology; and the AI science economy

Who chooses what AI gets to do?

No. 019: AlphaFold team disbanded, verification as the bottleneck, Tao's ICM caution - The Hallway Track

The Hallway Track No. 019 - How AI is affecting the practice of science

Why AI Makes Things Worse for Enterprise Teams, by Paul Ford

Why are so few engineering teams reaping the benefits of AI? On this week’s episode, Paul presents Rich with the findings from a recent report from CircleCI and

No. 014: Science's AI productivity paradox, Sakana clears peer review, AI proof search on open Erdős problems - The Hallway Track

The Hallway Track No. 014 - How AI is affecting the practice of science

No. 027: AGMAI advisory group, Millennium Problems for biology, autonomous research pipelines - The Hallway Track

The Hallway Track No. 027 - How AI is affecting the practice of science

No. 010: AutoScientists governance, AI-agent monoculture, nonsense-term retractions - The Hallway Track

The Hallway Track No. 010 - How AI is affecting the practice of science

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/

No. 020: Astra solves ten open math problems, AI agents fail at open-ended research, peer review under strain - The Hallway Track

The Hallway Track No. 020 - How AI is affecting the practice of science

AI Scientists as Engines of Discovery: A Case for Development within Reformed Institutions

Agentic artificial intelligence (AI) systems are beginning to assist, accelerate, and partially automate scientific discovery, performing tasks that span literature synthesis, code generation, data analysis, hypothesis proposal, and model criticism. We argue that this transition is qualitative rather than incremental, and that suitably designed multi-agent systems may evolve from passive computational tools into ``AI scientists'' that can expand the hypothesis-generating and verification capacity of science. Such systems must be developed and deployed within a scientific ecosystem fit for purpose: institutions must be redesigned for verification, accountability, interpretability, and dual-use safety. We sketch how multi-agent architectures, illustrated by the prototype framework \textit{Denario}, accelerate the discovery cycle and traverse model spaces beyond human reach; examine what this implies for authorship, peer review, and the enduring role of human scientists; and close with recommendations for governing AI as an epistemic actor rather than a mere instrument.

No. 009: AI agents p-hacking, submission surge data, Gemini for Science - The Hallway Track

The Hallway Track No. 009 - How AI is affecting the practice of science

Kempner Researchers Create Social Network for “AI Scientists” to Collaborate - Kempner Institute

Science rarely advances exclusively through lone researchers. Progress often comes from discussion and collaboration — scientists sharing ideas, questioning one another, and refining their thinking together. Now, a team of […]