Abstract
Current autoregressive transformer scaling laws dictate that foundation model weights must remain static post-training to prevent catastrophic forgetting and alignment drift. Consequently, modern edge agents are restricted to volatile, stateless context manipulations (e.g., Retrieval-Augmented Generation), erasing environmental experiences at every context flush and halting true progression toward decentralized Artificial General Intelligence (AGI).
This paper introduces an architectural blueprint for Continuous Online Local Plasticity utilizing an Anamorphic Memory Lattice (AML) paired with Dynamic Weight Partitioning (DWP). By structurally separating a model's network topology into a frozen global baseline core (\(W_{\text{global}}\)) and an elastic, orthogonal local substrate (\(W_{\text{local}}\)), decentralized agents can permanently retain and internalize localized environmental data in real time. We outline the mathematical mechanics of non-destructive gradient projection and provide a functional Python core executing real-time parameter partitioning without model collapse.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Eckes, Christopher L., "A Method for Continuous Local Plasticity and Catastrophic Forgetting Mitigation via Anamorphic Memory Lattices and Dynamic Weight Partitioning", Technical Disclosure Commons, (August 18, 2026)
https://www.tdcommons.org/dpubs_series/11395