RAF:LAA v1: A Meaning-Free Structural Memory Hierarchy with Fold-Based Authenticated Synchronization
RAF:LAA (Reactor-Aligned Field: Layered Autonomous Architecture) is a substrate in which only physical law is implemented and meaning is categorically excluded by design: existence (a Field) carries phase, curvature, stabilizing force, origin, and lineage, never content. The empty, meaning-free state is treated as normal -- a clean room in which an AI can check structural consistency without semantic interpretation ever entering the check. This report implements and verifies the memory hierarchy built on that substrate: a structure-only RAFLAAServer (public and private, whose data model has no field capable of holding content) is reached by PhaseSync -- synchronization understood as regeneration from an agent's own held fragments via the existing f_t = argmax Align(f, θ_t) reconstruction rule, never as copying -- into a local RAFMemory, whose finished products are promoted into LAAMemory only after PSL canonicalization and an Ed25519 signature gate. We verify all four layers end to end (11, 14, 6, and 8 checks respectively, all passing on the current codebase) and report one resolved misconception: an intuition that a discrete, non-invertible Z-label folding of a synchronization signal could be intercepted safely because "meaning was never in it" turned out to be correct, but not for the reason first assumed -- the folding map's algebra is public and computable by anyone, so the real security boundary is, and can only be, possession of a genuine fragment gated by signature-based authentication, not the folding function itself. An attacker holding the correct folded signal and the folding algorithm, but no valid key pair, is shown to end up with an empty memory regardless.
Authors
- TOYOHIRO ARIMOTO
- 5 Sonnet
Institutions
- Bando Chemical Industries, Ltd. (Japan) (JP)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-09-26
- DOI
- https://doi.org/10.5281/zenodo.22980863
- Primary Topic
- Machine Learning in Materials Science
- Type
- preprint