持续学习代理中的表示稳定性
Representation Stability in a Minimal Continual Learning Agent
AI总结：
本文提出一个最小持续学习代理，通过状态向量的动态变化研究表示稳定性，展示在无显式正则化等情况下，系统能实现稳定性与可塑性的平衡。
摘要：
持续学习系统正越来越多地部署在重新训练或重置不可行的环境中，但许多方法更强调任务性能而非随时间推移内部表示的演变。在本工作中，我们研究了一个最小的持续学习代理，旨在隔离表示动态与架构复杂性和优化目标。该代理在每次执行中维护一个持久的状态向量，并随着新文本数据的引入逐步更新它。我们通过连续归一化状态向量之间的余弦相似性来量化表示变化，并在时间区间内定义一个稳定性度量。在八次执行的纵向实验中，揭示了从初始可塑性阶段到稳定表示阶段的转变，在一致输入下。人为引入的语义扰动导致相似性有界下降，随后在后续一致输入下恢复并重新稳定。这些结果表明，在最小、有状态的学习系统中，有意义的稳定性可塑性权衡可以在没有显式正则化、回放或架构复杂性的情况下出现。该工作为研究持续学习系统中的表示积累和适应建立了透明的实证基准。
英文摘要：
Continual learning systems are increasingly deployed in environments where retraining or reset is infeasible, yet many approaches emphasize task performance rather than the evolution of internal representations over time. In this work, we study a minimal continual learning agent designed to isolate representational dynamics from architectural complexity and optimization objectives. The agent maintains a persistent state vector across executions and incrementally updates it as new textual data is introduced. We quantify representational change using cosine similarity between successive normalized state vectors and define a stability metric over time intervals. Longitudinal experiments across eight executions reveal a transition from an initial plastic regime to a stable representational regime under consistent input. A deliberately introduced semantic perturbation produces a bounded decrease in similarity, followed by recovery and restabilization under subsequent coherent input. These results demonstrate that meaningful stability plasticity tradeoffs can emerge in a minimal, stateful learning system without explicit regularization, replay, or architectural complexity. The work establishes a transparent empirical baseline for studying representational accumulation and adaptation in continual learning systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
