OptFin.org
Dimensional Collapse Simulator
OptFin V3
This interactive model turns the V3 thesis into a visible machine: the tensor learns, the confident core collapses out of active memory, the remaining graph is split into spectral islands, and the asynchronous swarm spends its CPU budget only on the surviving frontier.
Volumen N
Each particle represents a slice of the active decision graph. Bright particles remain unresolved. Dim particles have been permanently extracted into the frozen core and no longer consume active solver memory.
Progreso Fitness
Engine Trace
The timeline below shows how certainty harvest reduces the active search frontier. As the core vanishes, the CPU cost per iteration falls and the swarm can spend more of the budget on difficult residual neighborhoods.
Dimensional Profile
The profile estimates how many variables can be treated safely in each exact kernel, how many spectral islands should be opened, and how patient the thermostat must be before it reheats the search.
Permanent Collapse
Frozen variables are not only fixed. They are removed from the active instance. Their profit or cost is harvested, capacities are updated, and the residual problem becomes physically smaller.
Asynchronous Swarm
Once the graph has been partitioned, each island can carry its own exact or bounded search lane while the central tensor keeps learning from the best structures found so far.