发表机构
Arkadhi Labs（阿卡迪实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究提出CMP架构，通过局部、无梯度更新学习，测试其抵御灾难性遗忘能力。在文本域实验中，CMP反向转移比Transformer好15 - 19倍，还报告了与基线差距等结果，表明该架构在特定条件下抗灾难性遗忘效果优于反向传播。
AI 中文摘要
我们引入了CMP（认知记忆原语）架构，它将输入表示为稀疏关系码，存储在两层竞争记忆中，并完全通过局部、无梯度更新进行学习，网络中任何地方都不使用反向传播。我们用此架构测试一个特定假设：灾难性遗忘通常被视为训练时的缺陷需用重播或正则化修复，而实际上它是反向传播分配信用方式的结构后果，一种局部且稀疏的学习规则应能在无修补情况下抵御它。在跨越15个文本域的受控域增量协议上，三种子复制，CMP的反向转移比用在线EWC训练的匹配大小Transformer好15 - 19倍，结果在域顺序控制下依然成立。我们还报告了与Transformer基线的实际显著准确率差距、在公认视觉基准上的无效结果，以及将此架构与单独提高原始准确率机制结合时的诊断出未解决失败情况。核心主张明确且可证伪：在我们精确说明的条件下，局部、稀疏、无反向传播学习比带标准修复的反向传播更能有效抵御灾难性遗忘。
英文摘要
We introduce CMP (Cognitive Memory Primitive), an architecture that represents inputs as sparse relational codes, stores them in a two-tier competitive memory, and learns entirely through local, gradient-free updates, with no backpropagation anywhere in the network. We use this architecture to test a specific hypothesis: that catastrophic forgetting, usually treated as a training-time defect to be patched with replay or regularization, is instead a structural consequence of how backpropagation assigns credit and that a learning rule that is local and sparse by construction should resist it without a patch. On a controlled domain-incremental protocol across 15 text domains, three-seed replicated, CMP's backward transfer is 15-19x better than a matched-size Transformer trained with online EWC, and the result survives a domain-order control (reported as a range, +0.24 to +0.44, rather than a single figure). We report this alongside a real, substantial accuracy gap versus the Transformer baseline, a null result on a recognized vision benchmark, and a diagnosed, unresolved failure attempting to combine this architecture with a separate mechanism that improves raw accuracy, disclosed because an honest negative result is more useful than an omitted one. The central claim is narrow and falsifiable: local, sparse, non-backpropagation learning measurably resists catastrophic forgetting better than backpropagation with its standard fix, under conditions we state precisely.
Comments23 pages, 12 figures
