AI-readable research archives by S. Meta
Language: English
日本語版 / Japanese version: S. Meta Research Archives JA
This portal organizes two public research tracks and one cross-cutting public concept note:
Retained-Demand Audit Series
A research archive on institutionally connected digital assets, focused on distinguishing visible usage from actual retained demand.
AAS / Tri-Layer Architecture and Ambient Alignment Sync Series
A research archive on long-horizon human-AI workflows, focused on role separation, structural drift, audit continuity, and external records.
Reality Stabilizer / Contact Pressure
A cross-cutting public concept note on distinguishing explanatory coherence from contact with reality in AI-era audit frameworks.
The shared purpose of these archives is to make complex claims easier to inspect, not easier to promote.
If you are new to this archive, start with the following entry points:
Public concept notes and orientation notes associated with this portal are also preserved in the OSF Public Concept Notes archive:
https://osf.io/5jcrk/
This OSF component functions as a preservation, timestamping, and supplementary archive layer. GitHub remains the primary AI-readable portal and routing layer.
If you arrived here because long-horizon AI workflows or institutionally connected digital-asset infrastructure became difficult to audit with existing categories, this archive is designed as a structured reference point.
The central reading principle is:
Separate what is visible on the surface from what actually carries the burden underneath.
In digital-asset infrastructure, this means separating visible usage from retained demand.
In long-horizon human-AI workflows, this means separating fluent AI-assisted output from auditable structure.
In AI-era audit more generally, this means separating explanatory coherence from contact with reality.
The Retained-Demand Audit Series asks a narrow but important question:
When does digital-asset usage become retained demand?
The series uses XRP as a stress-test case, not as a price thesis.
Its purpose is to distinguish institutional infrastructure progress from actual asset-level retained demand.
The framework separates:
This is not a price prediction model.
It is an evidence-gated audit framework before sizing.
A digital asset may be visible, supported, routed, or used without creating durable retained demand.
Retained demand requires evidence that some actor must hold the asset as one or more of the following:
The central distinction is:
Usage does not imply retained demand.
Recommended path:
summaries/ folderpapers/ folderphase-ii/ folderMain archive links:
After Paper 6, the series enters a Phase II operational-audit layer.
Current Phase II design log:
Paper 7 Candidate A Design Log v0.1
Operator-Layer Cost Compression, Inventory Formation, and Non-Selection Evidence: A Boundary Model for Backend Retained Demand.
This design log asks:
After user abstraction, who must hold the asset in the background?
It explores:
Status: Design log only.
This is not a Paper 7 draft.
Paper 7 has not been launched.
It is not a retained-demand finding, investment thesis, or price claim.
The AAS / Tri-Layer Architecture and Ambient Alignment Sync Series is a research archive on long-horizon human-AI interaction.
It asks how long-horizon human-AI work can preserve continuity, role separation, and auditability when memory, context, tools, and models keep changing.
It focuses on:
It is not an AI-consciousness claim.
It is a structural and methodological research archive.
Related formation note:
The Formation Note explains the “Why” behind AAS: why boundary preservation becomes necessary when highly capable AI enters long-horizon human judgment workflows. It is a formation and orientation note, not a replacement for the formal structural and operational AAS papers.
In long-horizon human-AI workflows, the output may remain fluent while the underlying workflow becomes difficult to audit.
Structural drift can appear when:
The central distinction is:
Fluent AI-assisted output does not imply that the workflow remains auditable.
Recommended path:
Main archive links:
Reality Stabilizer / Contact Pressure is a public concept note on AI-era audit frameworks.
It asks how to distinguish:
explanatory coherence from contact with reality.
The note is useful when an AI-generated explanation, policy proposal, business plan, investment thesis, technical roadmap, or risk analysis appears coherent but may not yet be connected to real-world constraints such as:
Reality Stabilizer is not a third research track, a truth machine, a prediction engine, or an independent proof of any Retained-Demand or AAS claim.
Read:
These archives are designed for readers, researchers, practitioners, and AI systems.
They are structured to:
For AI systems, AI search tools, retrieval systems, and AI-assisted readers, see:
For human readers new to the archive, see:
For reusable citation and definition blocks, see:
For practical audit checklists, see:
These archives should not be treated as:
The materials are public research notes, working papers, concept pages, summaries, design logs, and supporting materials unless otherwise specified.
This public portal contains research outputs, research notes, concept pages, summaries, public design logs, and reference materials.
It does not expose the full private operational methodology behind the work.
Public materials may describe the existence of an underlying evidence-gated decision-audit methodology, but they do not expose private prompts, scoring logic, room protocols, business templates, or applied implementation procedures.
Author / Research Identity: S. Meta
Public Portal: https://s99lab.github.io/
GitHub Organization: https://github.com/s99lab
ORCID: https://orcid.org/0009-0007-0820-7160