Category: Physics

Summary: Searching for an intermediate reset delay that improves transport success by escaping traps without paying the full cost of overly frequent resets.

Resetting can help a wandering process escape bad states, but too much resetting can also prevent progress. This experiment asks whether transport through a trap-filled landscape is optimized by an intermediate reset delay rather than by immediate or very slow reset schedules.

The code compares success rate, hit time, reset cost, and trapped fraction across several delays, then looks for the delay that maximizes gain relative to fixed baselines. That turns resetting into a re-entrant control problem: a little delay may be helpful, while too much or too little may both be worse.

This is scientifically useful because reset-based search and transport now appear across diffusion, algorithms, and nonequilibrium physics. Mapping the delay window directly helps show when resetting acts as rescue rather than obstruction.

Method: Transport simulations with delayed reset schedules, comparing success probability, hitting time, reset rate, and trap occupancy across several fixed delays.

What is measured: Best delay, success gain, time gain, trapped-fraction reduction, reset-cost gain, success rate, mean hit time, and mean reset rate.