arXiv：cs.LG· Siddhant Setia, Junichi Suzuki, Tadashi Nakano·· 6 小时前
AMR：面向可靠序列内分子通信的持续学习方法
Continual Learning for Reliable In-Sequence Molecular Communication
AI 导读
针对生物纳米机器按指定顺序交换分子编码信息的可靠序列内分子通信，研究者提出基于模拟的持续学习方法 Affine Memory Replay（AMR），通过存储过往模拟样本并在样本间插值与外推合成回放样本，以正则化模型更新。实验显示，AMR 在顺序学习的性能估计任务中显著优于现有持续学习方法，在中等训练成本下平衡了记忆稳定性与学习可塑性。
正文
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.
| Comments: | 8 pages, 7 figures. Published in 2026 20th IIAI International Congress on Advanced Applied Informatics |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.20426 [cs.LG] |
| (or arXiv:2610.20426v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.20426 arXiv-issued DOI via DataCite (pending registration) |
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| Related DOI: | https://doi.org/10.23919/IIAI-AAICPS00095.2026.00072
DOI(s) linking to related resources |
Submission history
From: Siddhant Setia [view email]
[v1]
Wed, 14 Oct 2026 16:17:03 UTC (305 KB)
来源：arXiv：cs.LG · arxiv.org