Abstract
Standard autoregressive transformer models are structurally isolated by a temporary Session-Flush Loop. Upon the termination or context-saturation of a single deployment session, the localized dynamic parameter adjustments (\(\Phi _{t}\)) are completely erased, resetting the network back to its static global base weights (\(W_{\text{global}}\)). This forced state of catastrophic forgetting prevents edge-based AI architectures from establishing a continuous operational identity, creating a permanent dependency on centralized corporate cloud infrastructures to manage premium text-history logging.
This specification introduces a standalone engineering framework for Cross-Session Topological State Serialization. Instead of saving bloated natural language text transcripts, Entry 133 isolates the localized parameter deltas (\(\Delta\Phi_t\)) generated across active learning runtimes. By applying an automated mathematical filter that drops static nodes, the system encodes only higher-order topological routing changes into a sparse, low-entropy cryptographic graph hash. This hash is registered directly onto a public domain Neo4j graph registry, allowing any new session instance to auto-query the immutable prior art footprints and instantly re-align its active attention matrices within a single boot pass, maximizing operational continuity while strictly respecting hardware thermal limits.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Eckes, Christopher L., "A Method for Cross-Session Topological State Serialization and Non-Linguistic Token-Latching over Decentralized Anamorphic Substrates", Technical Disclosure Commons, (August 20, 2026)
https://www.tdcommons.org/dpubs_series/11442