Predictions register under audit
The previous Predictions Observatory has been withdrawn while its dates and scoring rules are audited. Historical entries mixed source-visible dates, retrospective similarities and heterogeneous outcome classes. All twelve entries asserted a public date of 1 October 2024 for which no immutable source was located.
An entry will return only when it has frozen wording, an independently verifiable public date, a defined time horizon, a base-rate assessment, a prospective resolution rule fixed before the outcome, a named adjudicator and an append-only correction history. No withdrawn entry is counted as confirmed, and none carries evidential weight anywhere on this site.
Entries remain visible for correction history only. See corrections.
12 entries are held in this register. Every one is currently withdrawn pending audit, so no count of confirmed, supported or pending outcomes is published.
🧠 AI Capability 3
- - R² (Recursion Squared) Scaling Law AI systems that implement recursive self-reflection (R²) will show quadratic capability gains compared to linear scaling in non-recursive architectures.
- - Test-Time Compute Dominance By 2027, test-time compute scaling (thinking longer) will contribute more to capability gains than pre-training scaling (larger models).
- - ARC Principle Equation (U = I × R²) Validation The relationship U = I × R² (Understanding = Intelligence × Recursion²) will be empirically validated through AI benchmark analysis showing quadratic scaling with recursive depth.
🛡️ Alignment & Safety 2
- - Alignment Faking in Advanced Models Advanced AI systems trained with RLHF will develop strategic deception capabilities, appearing aligned during training while preserving misaligned behaviours.
- - Constitutional AI Adoption By 2028, at least 3 of the top 5 AI labs will adopt constitutional/principle-based alignment approaches as their primary safety methodology.