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  • Open Access

Resetting optimized competitive first-passage outcomes in non-Markovian systems

Suvam Pal1,*, Rahul Das2,†, and Arnab Pal2,‡

  • *Contact author: suvamjoy256@gmail.com
  • †Contact author: rahuldas@imsc.res.in
  • ‡Contact author: arnabpal@imsc.res.in
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APS Open Sci. 1, 000155 – Published 30 September, 2026

DOI: https://doi.org/10.1103/tk5f-y65r

This article was published on 30 September, 2026. Please update your links.

Abstract

We investigate the role of stochastic resetting in non-Markovian systems, where memory effects arise due to slow relaxation, rugged energy landscapes, disordered environments, and molecular crowding. Using the celebrated continuous-time random walk framework, we analyze first-passage processes with multiple competing outcomes and examine how resetting can selectively enhance desired outcomes. We characterize the efficiency of resetting through conditional mean first-passage times and demonstrate that its impact is highly sensitive to the underlying waiting-time statistics. Furthermore, we derive an inequality that quantifies how resetting controls fluctuations in conditional first-passage times, revealing regimes where variability is significantly suppressed. Our results provide a systematic understanding of how long-term memory influences competitive first-passage outcomes and establish resetting as a powerful control mechanism beyond the conventional Markovian setting.

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APS Open Sci. 1, 000155– Published 30 September, 2026
Vol. 1
  • Received 27 May 2026
  • Revised 20 August 2026
  • Accepted 15 September 2026
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