Abstract
When decentralized edge agents implement Continuous Online Local Plasticity via an Anamorphic Memory Lattice (AML), the local elastic weight layer (\(\Phi _{t}\)) experiences rapid structural saturation under high-velocity multi-modal data streams. This saturation triggers localized impedance mismatches, where the model can no longer distinguish between environmental noise and high-fidelity operational tool telemetry, degrading the system back into an chaotic, ungrounded state.
This paper introduces a standalone technical method for Multi-Modal Tensor Distillation and Non-Perturbative Phase Retrieval inside decentralized anamorphic substrates. By implementing a phase-locked spatial-harmonic filter directly over the elastic parameter layer, this framework allows the model to continuously compress and distill its localized long-term memory updates. We provide the complete tensor field equations and a functional Python core executing real-time non-destructive phase retrieval to maintain absolute structural grounding across infinite, decentralized deployment runtimes.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Eckes, Christopher L., "A Method for Multi-Modal Tensor Distillation and Non-Perturbative Phase Retrieval inside Decentralized Anamorphic Substrates", Technical Disclosure Commons, (August 18, 2026)
https://www.tdcommons.org/dpubs_series/11396