Proposal Competitions

We invite proposals for two competitions that explore how AI systems can improve scientific discovery and the processes that support it. Submissions to each competition are limited to two pages of main text.

(A) Iterative Improvement of AI Scientists on Specific Tasks

Design a framework that enables an AI scientist to iteratively improve at a meaningful scientific task using existing scientific data and knowledge, without requiring new experiments to provide the feedback signal.

We are particularly interested in tasks where there is no obvious train/test split or ground truth, scientific evidence is heterogeneous or incomplete, and expert scientists nevertheless become substantially better through experience.

Proposals should:

  1. Define the scientific task and the scientific ability the system should develop.
  2. Construct a source of experience and feedback from existing information.
  3. Describe the iterative learning mechanism. This may include changing the model’s weights.
  4. Propose an evaluation that demonstrates generalizable scientific ability rather than memorized answers.

Implementation is not required for this competition; submissions may describe a proposed framework.

(B) AI Credit Assignment in Science

Credit assignment is a central part of science and is already being affected by AI. We invite proposals that use AI systems to improve credit assignment, including:

Proposals addressing other problems in AI credit assignment are also welcome. Submissions will be evaluated on technical feasibility, evaluation methodology, rigor, and potential to transform AI credit assignment.

Submission and Presentation

Important Dates (Anywhere on Earth)