# Continual Learning for Reliable In-Sequence Molecular Communication

arXiv: https://arxiv.org/abs/2610.20426 · HTML: https://mlsanity.com/paper/2610.20426/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 30 references that are in the map.
- First posted on arXiv 14 Oct 2026.

## Builds on (most cited first, 7 of 7)

- [A Comprehensive Survey of Continual Learning: Theory, Method and Application](https://mlsanity.com/paper/2302.00487/) (2023)
- [Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference](https://mlsanity.com/paper/1810.11910/) (2018)
- [CLeaR: An Adaptive Continual Learning Framework for Regression Tasks](https://mlsanity.com/paper/2101.00926/) (2021)
- [TCP-like molecular communications](https://mlsanity.com/paper/1406.4259/) (2014)
- [What Really is `Molecule' in Molecular Communications? The Quest for Physics of Particle-based Information Carriers](https://mlsanity.com/paper/2311.16356/) (2023)
- [Improvements of Dark Experience Replay and Reservoir Sampling towards Better Balance between Consolidation and Plasticity](https://mlsanity.com/paper/2504.20932/) (2025)
- [Continual Learning of Feedback-based Molecular Communication](https://mlsanity.com/paper/2605.01020/) (2026)

## Abstract (arXiv)

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.