What Should We Work on Next? Node-Level Compute Allocation Across Open Scientific Questions

Rapid advances in AI capabilities have enabled AI systems to contribute to research on open scientific questions. There are many open scientific questions that AI could help investigate, but the computational cost of frontier models is high. These costs motivate careful allocation of compute across open scientific questions. Two key considerations for allocation are the scientific importance of each question and its difficulty relative to the AI system’s capabilities. However, research-level open questions often require prolonged investigation, leaving limited outcome-based feedback for the difficulty estimates needed to guide compute allocation. This is particularly problematic for question-level allocation, as substantial compute may be invested before sufficient feedback is available to reassess the investment. Therefore, in this paper, we propose node-level compute allocation over evolving AND/OR directed acyclic graphs (DAGs), which record intermediate research progress and dependencies among research tasks. Finer-grained feedback from these tasks guides allocation across nodes from all questions, even before the underlying questions are resolved. We illustrate this allocation process through a case study involving five research-level open questions in quantum error correction (QEC). In controlled comparisons on four mathematical problems, the node-level scheduler ranked first in two of the three value settings and second in the remaining one, giving the best overall performance among the four conditions.