The AAMT series has produced three runnable memory systems: topologically protected trit anchors (EXP-01), voxel-steered output eviction over a TERA/HNSW index (WP-21), and Odu coverage maps for loop escape (WP-22). Each currently persists through a different ad-hoc path — TSV, JSON, Python dictionaries, and an HNSW sidecar — so every session pays a full parse-and-rebuild cost, and no two surfaces of the platform (desktop Python, browser visualizer, Unity) can open the same artifact. This paper specifies the. aamt substrate: a single memory-mapped binary format holding a hexadeca-tree spatial index over the 4D TERA cube, with 64-byte cache-line nodes, popcount-indexed sparse children, a zero-copy payload arena, and a deterministic sidecar journal that makes every query bit-exactly replayable. The load-bearing structural result is the orthant correspondence: a binary split of 4D TERA space yields exactly 24 = 16 children per node — the 16 Meji — and two tree levels yield exactly 16 × 16 = 256 cells — the 256 Odu. The Meji/Odu lattice is therefore not a naming convention layered on the index; it is the unique natural spatial index of TERA space, and a query's routing path is literally an Odu word. We position the substrate honestly: it is the geometric memory layer that runs beside a conventional LLM runtime (the architecture WP-21 already established), not a replacement for transformer weights, and its routing cost is O (depth) — bounded and scan-free — not O (1). The substrate's first production integration (Section 9, memoryᵢndexbridge. py) then falsified its own most obvious application: reconstructing a bulk id/label table through the substrate loses to json. load by an order of magnitude, because per-record ctypes marshalling overhead dominates the native tree walk's advantage. What shipped instead — labels stay in JSON; the substrate serves a new O (depth) coarse-routing command alongside HNSW's fine discrimination — is the correction, and it is the more interesting result: the right division of labor was not the one first assumed, and measurement found it in under an hour rather than in production. Section 10 adds a redundancy scheme built from a single exact geometric operation — a true Householder reflection of the TERA cube's T axis, distinguished from the antipodal map (which, in this even dimension, is orientation-preserving and so not actually a mirror) — giving every record a physically separate redundant copy and a gate, verifyChirality, that catches “bad reflections” by re-deriving the relationship rather than trusting stored state; verified against real, injected byte-level corruption, not simulated failure.
Weslyn Cory Whitehead (Thu,) studied this question.