Continual Learning for Reliable In-Sequence Molecular Communication

Abstract:This paper considers reliable in-sequence molecular communication, where bio-nanomachines exchange molecule-encoded information in a specified order, and investigates a new simulation-based method to incrementally evaluate communication protocols by gradually increasing the complexity of experimental settings. The proposed method, called Affine Memory Replay (AMR), builds a continual learning (CL) model to estimate communication performance between bio-nanomachines. AMR stores past simulation samples and synthesizes additional replay samples through interpolation and extrapolation between past samples. Through incremental performance estimation tasks, it replays both past and synthetic samples to regularize model updates, helping the model adapt to new experimental settings while retaining knowledge from earlier ones. Experimental results show that AMR significantly outperforms existing CL methods in maintaining overall estimation accuracy across sequentially learned estimation tasks. It effectively balances memory stability and learning plasticity under moderate training cost.

8 pages, 7 figures. Published in 2026 20th IIAI International Congress on Advanced Applied Informatics

Machine Learning (cs.LG)

arXiv:2610.20426 [cs.LG]

(or arXiv:2610.20426v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.20426 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

https://doi.org/10.23919/IIAI-AAICPS00095.2026.00072 Focus to learn more DOI(s) linking to related resources

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.