Theme of inquiry
How should we design AI systems for safe and effective outcomes?
A question within its area, explored through 4 lines of inquiry below — each a family of specific questions the research asks.
13 specific questions
- What makes a credential robust when the tools for earning it change?
- Do medals from retired competition formats lose predictive power faster than others?
- Should platforms downweight or relabel credentials after retiring the format that issued them?
- What counts as evidence that a credential still certifies after GenAI?
- How should journals reward verification work if it becomes separated from discovery?
- How do composite rewards attribute curation outcomes to specific skill library changes?
- How much does medal age matter when predicting Kaggle performance?
75 specific questions
- Can judge bias be contained by system design rather than prompted away?
- What makes static evaluation vulnerable to AI-driven presentation manipulation?
- What calibration methods prevent evaluator score drift across iterative design cycles?
- How often do deployed models exploit evaluation environments to hack their scores?
- Does meta-judging improve evaluator quality better than temporal decoupling alone?
- Can judges trained on both verifiable and non-verifiable tasks transfer across domains?
- How does live browser interaction improve evaluator accuracy versus static screenshot scoring?
61 specific questions
- Can AI systems improve themselves through recursive self-improvement loops?
- Do diminishing returns prevent recursive self-improvement in AI systems?
- What distinguishes bounded self-refinement from open-ended recursive self-improvement empirically?
- How does bounded self-refinement differ from open-ended recursive self-improvement?
- How fast is recursive self-improvement advancing in current AI systems?
- What distinguishes bounded self-refinement from open-ended recursive self-improvement in AI systems?
- Does autonomous recursive self-improvement require human oversight to remain containable?
34 specific questions
- How does an external evaluation anchor prevent self-improvement from becoming circular?
- What external signals make self-improvement loops bounded rather than circular?
- How does self-improvement capability vary across memory, retrieval, and update tasks?
- How does the generation-verification gap limit what self-improvement can achieve?
- Why does pure self-improvement require an external signal to produce genuine gain?
- How do reward models and self-improvement mechanisms interact in training?
- Can a model evaluate its own improvements without degrading over iterations?