FactorForge technical whitepaper
Deterministic CPU-First Core.
Reproducible CDS design with explicit constraints, reviewable outputs, and a production path that removes biological hallucination risks from the optimization engine.
Scientific benchmark
CAI optimization, reported with its corpus size.
Static benchmark figures are shown for technical context. They are not experimental validation or a claim of biological performance.
FactorForge
Mean CAI · n=49,257 CDS
Baseline
Mean CAI · n=3,876 CDS
Architecture & transparency
Two lanes, one evidence boundary.
Production (Deterministic)
The CPU-first dynamic-programming path produces the same result for the same inputs and constraints. Provenance is part of every payload and rendered result.
Exploratory Lab (ML)
ML/AI comparisons are isolated as non-deterministic exploration. Any sequence from this lane must pass through Production validation before wet-lab synthesis.
Output provenance
{
"generator": "cpu_deterministic_dp",
"lane": "production",
"validation": "required"
}The generator flag makes the execution lane auditable in exported JSON and review packages.
Read the implementation notes
FactorForge documents its scoring contract, codon reference, and review boundaries so benchmark context stays attached to the software that produced it.