Model Parameters
2026.0
Scrub active evaluation timeline horizon (2023 - 2030)
4.2x
Annual effective compute scaling factor
2.5x
Algorithmic speedup multiplier over baseline
1.0x
Suppression modifier applied to skeptic forecasts
Focus Benchmark Dataset
Trajectory & Cross-Over Analysis (2023–2030)
Rendering Sigmoidal Log-Logistic Curves
Horizon Alignment Cross-Over Detected
Agentic capability growth rate exceeds safety verification coverage by 24.8%.
Empirical Velocity Rate
+42.8%/yr
Verified historical growth
Skeptic Model Gap
-38.4%
Skeptic underestimation delta
Projected AGI Frontier
2026.8
90% cross-benchmark ceiling
Safety Verification Lag
1.4 Yrs
Alignment validation delay
Verified Evidence Dossier & Skeptic Delta Table
Mathematical trajectory projections vs counterfactual skeptic claims computed via Math.js log-logistic equations.
PROFILES: Model=SigmoidLogLogistic | Horizon=2026.0 | Delta=-38.4%
| Benchmark Suite | 2023 Baseline | Current (2025.2) | Projected (2026.0) | Skeptic Claim | Empirical Delta | Saturation Horizon |
|---|
Empirical Citations & Methodology Footnotes
- SWE-bench Verified: Princeton NLP & OpenAI (2024). Autonomous Software Engineering problem solving on real GitHub issues.
- GPQA Diamond: Rein et al. (2023). Graduate-Level Google-Proof Q&A Benchmark across Physics, Chemistry, and Biology.
- MATH Benchmark: Hendrycks et al. (2021). Measuring Mathematical Problem Solving With High School Competition Problems.
- Sigmoidal Trajectory Model: $P(t) = \frac{100}{1 + \exp(-k(t - t_0) \cdot (\text{compute} \cdot \text{efficiency}))}$, fitted against 2021–2025 historical data.