This drop contains the runnable symbolic components described in the paper Recursive Symbolic Identity Architecture (RSIA). It is intended to provide a reproducible, hash-signed snapshot of the FBS tokenizer, the enhanced RSGT engine, the RSIA↔RCF bridge, and the NumPy-only RCF core. The neural URFT/base-tensor orchestration remains private and is referenced only conceptually in the manuscript. The integrated RSIA demo runs today, but the orchestrator still needs tuning; until that work lands, the sacred FBS pipeline, RCF core, and RSGT engine are the components we encourage reviewers to exercise independently.

• docs/Recursive Symbolic Identity Architechture.md — Source manuscript with math foundations and code overviews.
• docs/RSIA_final.pdf — XeLaTeX/Pandoc build of the paper.
• rsgt_snippet.py — Enhanced RSGT engine with RSIA↔RCF integration.
• rsia_rcf_bridge.py — Adapter that feeds RSGT output into the RCF core.(still to be added)
• rcf_core.py — NumPy-only reference implementation of the recursive categorical framework.
• sacred_fbs_tokenizer.py, test_sacred_fbs.py — Post-token FBS pipeline and validation suite.
• hash-index.ps1 — Provenance tool that writes the SHA256SUMS.txt manifest (signed hashes/Timestamp proofs optional).
• SHA256SUMS.txt — Hash manifest for all tracked files.

1. Sacred FBS tokenizer validation

   python -m venv .venv
   .\.venv\Scripts\Activate.ps1
   pip install -r requirements.txt
   python test_sacred_fbs.py

   This writes metrics and sacred_fbs_validation.png for each test.

2. RSGT engine with RSIA↔RCF bridge

   python rsgt_snippet.py

   The script prints all 10 RSGT tests and ends with the bridge grounding summary (the same state is fed into rsia_rcf_bridge.py).

3. RCF core (standalone)

   python rcf_core.py

   This exercises the NumPy-only categorical engine described in the paper.

This repository is now fully open for research collaboration under CC BY-NC-SA 4.0.

Please cite the manuscript as:

Rowell, C. T. (2025). Recursive Symbolic Identity Architecture: A Complete Theoretical Framework. Zenodo. https://doi.org/xx.xxxx/zenodo.xxxxxxx

This README will be kept in sync with the hash manifest to ensure provenance.