Continual learning systems face a fundamental tension between plasticity -- acquiring new knowledge -- and stability -- retaining prior knowledge. We introduce MPCS (Multi-Plasticity Continual System), a neuroplastic architecture that integrates eleven complementary mechanisms: task-driven neurogenesis, Fourier-encoded inputs, EWC regularization, meta-replay, mixed consolidation, hybrid gating, synapse pruning/regeneration, Hebbian updates, task similarity routing, adaptive growth control, and continuous neuron importance tracking. We evaluate MPCS on MEP-BENCH, a multi-track benchmark spanning 31 tasks across regression, classification, logic, and mixed domains, using a three-dimensional Pareto criterion over task performance (Perf), representation diversity (RD), and gradient conflict rate (GCR). Across 15 ablation configurations (3 seeds x 4 tracks x 2000 epochs), MPCS achieves a Normalized Efficiency Score of 94.2, placing it on the Pareto frontier among 9 of 14 gate-passing systems. Key findings: (i) Fourier encoding is the single most critical component (removal drops Perf by 30.7 pp and fails the MEP gate on 14% of tasks); (ii) global EWC degrades performance (NES = -4.2); topology-local EWC reduces this penalty (NES 90.5->91.8) but does not eliminate it; removing EWC entirely yields MPCS_EFFICIENT, the highest-Perf system -- establishing a monotone relationship in the high task-similarity regime (s_bar ~= 0.95): global EWC < topology EWC < no EWC; (iii) the Pareto status assessment is predictive: removing the two Pareto-dominated components (EWC + Hebbian) jointly yields MPCS_EFFICIENT, which improves Perf by 0.6 pp at 4.7x lower compute cost (127 vs. 602 min), validating the Pareto frontier as an actionable model-compression guide.

翻译：持续学习系统面临可塑性（获取新知识）与稳定性（保持已有知识）之间的根本性矛盾。我们提出MPCS（多可塑性持续系统），这是一种融合十一种互补机制的神经可塑性架构：任务驱动神经发生、傅里叶编码输入、EWC正则化、元回放、混合巩固、混合门控、突触修剪/再生、赫布更新、任务相似性路由、自适应生长控制及连续神经元重要性跟踪。我们在涵盖回归、分类、逻辑与混合领域共31项任务的多轨道基准MEP-BENCH上，采用基于任务性能（Perf）、表征多样性（RD）与梯度冲突率（GCR）的三维帕累托准则对MPCS进行评估。在15种消融配置（3种子×4轨道×2000轮次）下，MPCS实现了94.2的标准化效率分数（NES），在14个门控通过系统中占据9个系统的帕累托前沿前沿。关键发现：（i）傅里叶编码是最关键的单一组件（移除后Perf下降30.7个百分点，并在14%任务上未通过MEP门控）；（ii）全局EWC降低性能（NES=-4.2）；拓扑局部化EWC可减轻惩罚（NES从90.5提升至91.8）但未完全消除；完全移除EWC得到最高性能系统MPCS_EFFICIENT，在高任务相似性区域（s_bar≈0.95）形成单调关系：全局EWC < 拓扑EWC < 无EWC；（iii）帕累托状态评估具有预测性：联合移除两个被帕累托支配的组件（EWC+赫布更新）得到MPCS_EFFICIENT，性能提升0.6个百分点的同时计算成本降低4.7倍（127分钟 vs 602分钟），验证了帕累托前沿作为可操作模型压缩指南的有效性。