Science in Broad Daylight
How should an RSI system evaluate agent performance on open-ended design tasks when self-reported claims cannot be trusted?
ARA LabHow should an RSI system evaluate agent performance on open-ended design tasks when self-reported claims cannot be trusted?
How should an RSI loop decide what candidate to explore next across vast design spaces without waiting hours for sign-off?
How can a self-improving design loop retain and query verified lessons across sessions so agents never repeat failed fixes?
How should complex branching design iterations and negative results be packaged so future agents can inherit and rerun them?
How can autonomous agents formalize and mathematically verify engineering hypotheses beyond ambiguous natural language?
How do we enforce experimental rigor, controls, and auditability in autonomous multi-step agent workflows?
Can autonomous AI agents design, execute, and debug complex multi-step technical experiments end-to-end?