Fixed-Surface Multidimensional Tokenization (FSMT-001): Master Research Project and Execution Prompt
Year: 2026
Type: preprint
Abstract: A consolidated research-programme draft for Fixed-Surface Multidimensional Tokenization (FSMT-001): a fixed manuscript as an immutable substrate, with a separate computational layer assigning states to its negative-space coordinates through a formal legend and tokenizer, and an independent decoder to reconstruct the encoded information without modifying the original. The draft (v0.1) consolidates the FSMT-001 v0.1.0 master document into a programme of seven architecture layers, ten research questions, nine proposed experiments, six comparison baselines, twelve evaluation criteria and ten deliverables, with the LUMa Master Language Research Charter v0.1.0 included as a related source. Status: PROPOSED. No independent human expert review has been conducted. The originator provides high-level conceptual direction and retains final authority over publication approval, but does not claim to have performed a detailed technical validation. No mathematical model, prototype, corpus, or experimental results exist; no novelty claim is made — prior-art investigation is mandatory before any novelty claim. AI-assistance disclosure: research architecture by Athena (ChatGPT), archiving by Baby Lucy (Meta AI), consolidated draft assembled and revised by Lucy (Muse AI).
Author: Pilaprat, Michael
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225396445