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Can AI agents conduct open-ended AI research? Early... | AI Research

Key Takeaways

  • This paper investigates whether AI agents can perform open-ended research, a capability often cited as a prerequisite for explosive progress in AI developmen...
  • Forecasts of explosive AI progress hinge on AI agents automating AI research.
  • But evidence on whether agents can carry out open-ended AI research is thin.
  • We introduce a third way to measure progress towards AI R\&D automation.
  • An agent takes on the central, open-ended research question of a high-quality unpublished paper, and the paper's original authors grade its output.
Paper AbstractExpand

Forecasts of explosive AI progress hinge on AI agents automating AI research. But evidence on whether agents can carry out open-ended AI research is thin. Current evaluations either test agents on narrow, verifiable tasks, which excludes open-ended research, or submit AI-generated papers to blind peer review, which is overstretched, stochastic, and suffers from poor review quality. We introduce a third way to measure progress towards AI R\&D automation. An agent takes on the central, open-ended research question of a high-quality unpublished paper, and the paper's original authors grade its output. We call these shadow evaluations. We ran shadow evaluations on two unpublished NeurIPS 2026 submissions, giving frontier agents six days and thousands of dollars of compute. The agents completed all of the engineering without human help, yet could not make substantial progress towards answering the research questions. As a result, both papers were unambiguously rejected by the authors. We identify five recurring failure modes: poor judgment about the bar for publishable research, uncreative responses to shortcomings in the research design, ineffective backtracking from dead ends, poor resource awareness, and instruction drift. A robustness check with a second model and scaffold reproduced these failures. We release the expert reviews, survey responses, agent repositories, and logs. Our results provide early evidence that today's agents can do the engineering of AI research, but struggle with critical parts of the research lifecycle.

This paper investigates whether AI agents can perform open-ended research, a capability often cited as a prerequisite for explosive progress in AI development. The authors argue that current evaluation methods—such as testing agents on narrow, verifiable tasks or using inconsistent blind peer reviews—are insufficient for measuring true research autonomy.

The Shadow Evaluation Method

To address these limitations, the researchers introduced "shadow evaluations." In this framework, an AI agent is tasked with answering the core, open-ended research question from a high-quality, unpublished paper. The original authors of those papers then grade the agent's output. This approach aims to provide a more rigorous assessment of an agent's ability to navigate the complexities of the research lifecycle compared to existing benchmarks.

Experimental Results

The team conducted shadow evaluations on two unpublished NeurIPS 2026 submissions. They provided frontier AI agents with six days and thousands of dollars in compute resources to complete the research. While the agents successfully handled the engineering components of the projects without human intervention, they failed to make substantial progress toward answering the primary research questions. Consequently, the original authors rejected both agent-generated papers. A robustness check using a second model and scaffold confirmed these findings.

Recurring Failure Modes

The researchers identified five specific areas where the agents struggled:

  • Judgment: Difficulty assessing the standard required for publishable research.

  • Creativity: Inability to generate effective responses to research design flaws.

  • Backtracking: Failure to recover from dead ends during the research process.

  • Resource Awareness: Poor management of compute and time resources.

  • Instruction Drift: A tendency to lose focus on the original research objectives.

Implications for AI Research

The study concludes that while current AI agents are capable of performing the engineering tasks associated with research, they lack the critical skills necessary to manage the broader research lifecycle. These findings suggest that significant gaps remain in the ability of AI to conduct autonomous, open-ended scientific inquiry. The authors have released the expert reviews, survey responses, agent repositories, and logs to support further investigation into these limitations.

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