Collapse / Resolution Framework (CRF): Discovery Abstract V1.8

Collapse / Resolution Framework (CRF): Discovery Abstract

Collapse / Resolution Framework (CRF) is a computational architecture for studying layered post‑selection dynamics within Born‑aligned probabilistic systems. The project began with a structural question: can complex internal selection behavior emerge beneath globally Born‑consistent outcome statistics?

To examine this, CRF models realization as a multi‑stage process involving:

• Born‑weighted first selection — an exponential‑race implementation of the Born measure

• Detector‑dependent exposure dwell — geometry‑driven variation in microscopic persistence

• Survival / capture filtering — competition among dwell‑conditioned histories

• Final probabilistic resolution — the terminal selection step producing the observed outcome

Extensive parameter sweeps revealed strong geometry‑dependent internal dynamics, including dwell amplification, asymmetry‑driven competition, and configuration‑sensitive survival behavior. Yet despite this internal structure, realized outcome statistics repeatedly returned to Born‑aligned behavior under symmetric detector geometry and stabilization regimes.

A stabilization layer parameterized by coupling strength γ was introduced to regulate runaway asymmetry. This layer suppressed divergence while preserving detector‑dependent internal structure, indicating the presence of admissible regions where rich internal dynamics coexist with stable Born‑consistent outcomes.

CRF does not propose a modification of quantum mechanics, a replacement for the Born rule, or a new physical interpretation. Instead, it provides a constrained computational testbed for exploring how detector geometry, post‑selection dynamics, and stabilization constraints may interact beneath observed probabilistic behavior.

All results are computational, reproducible, and bounded to the tested architecture.