ΔΦ FOLDED-MESH MEMORY ENGINE v1.0 Adaptive Sparse Retrieval Through Folded Geometry, Damage Recovery, and Self-Healing Transport Corridors

This project introduces the ΔΦ Folded-Mesh Memory Engine, a computational prototype exploring whether adaptive folded geometry can improve long-range retrieval efficiency while dramatically reducing activation cost.

The system begins as a flat lattice mesh and introduces strategic long-range “fold” connections that function as latent transport openings. Over repeated routing cycles, the architecture evolves through reinforcement, pruning, structural damage, and adaptive regrowth, eventually converging toward sparse self-healing transport corridors.

Initial experiments showed that folded geometry provides little advantage for short-range retrieval, but produces major gains for distant targets. In the far-target condition, retrieval improved from 58 steps to 12 while reducing activated nodes from 900 to 428.

The model was then extended into an adaptive “latent dermis” architecture that continuously reorganizes itself under repeated structural stress. Across 100 randomized trials, the adaptive system converged toward sparse reinforced retrieval highways averaging approximately 4.15 retrieval steps while activating only ~20 nodes during transport.

The work explores possible connections between:

- sparse inference

- adaptive memory routing

- self-healing network topology

- biologically inspired transport systems

- low-energy AI architectures

Included:

- full Colab notebook

- folded-mesh retrieval experiments

- adaptive dermis simulations

- baseline graph comparisons

- reinforcement/pruning dynamics

- damage and regrowth cycles

- transport efficiency metrics

- visualization outputs

Colab Notebook:

https://share.google/TKSy0g8Lhd6JZEO9a