Theory
The OptFin structural hypothesis
A knowledge-bearing AI can repeatedly collapse decisive subgraphs, solve or tightly bound them, and lift those results back into the original problem until lower and upper bounds converge.
OptFin.org
Working Papers and Benchmark Tracks
Working Papers
This page gives the public research shape of the lab: theory, benchmarks, exactness, biological applications, and challenge-driven engineering. It is the bridge between notes and formal papers.
Theory
A knowledge-bearing AI can repeatedly collapse decisive subgraphs, solve or tightly bound them, and lift those results back into the original problem until lower and upper bounds converge.
Benchmarks
Historical challenge families remain the proving ground for a serious optimization engine: assignment, machine reassignment, packing, scheduling, routing, and adaptive segment routing.
Biodesign
Generative systems propose candidates; local exactness verifies and sharpens the specificity frontier in critical interfaces.
Proof
Every report should preserve the instance, feasibility status, objective value, lower bound, upper bound, gap, runtime, and stopping reason in one reproducible bundle.