The Forgetting Engine benchmark program
2D Protein Folding
Approximately 80% relative improvement in the stated comparison
Internal benchmark against the documented Monte Carlo baseline
3D Protein Folding
25.8% success versus 3.9%, approximately 361.8% relative improvement
Largest reported relative gap in this research portfolio
Traveling Salesman
Larger relative gaps were reported at larger tested instances
Benchmark-specific trend, not a universal scaling law
Vehicle Routing
Up to 89.3% improvement at the largest tested scale
Compared with the stated routing baseline and configuration
Neural Architecture Search
Reported accuracy gains ranged from 3.8% to 8.4%
Internal search benchmark; external replication remains needed
Quantum Compilation
27.8% gate reduction and 3.7% fidelity gain were reported
Simulator-based comparison under the documented compilation setup
Important
A 361.8% relative success-rate difference in protein folding is not the same quantity as an 89.3% routing improvement or a 27.8% gate reduction. These numbers should be read within their own experiments, not combined into one universal score.
Complexity inversion is an observed pattern, not a declared law.
In several CONEXUS benchmark series, the relative advantage over the chosen baseline increased at larger tested scales. That is the phenomenon CONEXUS calls complexity inversion. Establishing a general scaling law would require preregistered experiments, stronger competing methods, multiple independent implementations, and replication outside the CONEXUS team.
Larger relative gaps in selected benchmark series as tested scale increased.
A universal rule that the Forgetting Engine improves with every form of complexity or defeats all conventional algorithms.