Scientific R&D Engine
Axiom-01
A white-box hypothesis-driven reasoning system designed to discover latent structures between phonetic profiles and biological/contextual traits.
Verified Invariant Benchmarks
| Specification Parameter | Audited Value |
|---|---|
| Core Objective | Phonosemantic Reasoning Engine |
| Dataset Size | 230+ Animals (4 Categories) |
| Hypothesis Threshold | |r| >= 0.20, 70% Consistency |
| GitHub Repository | Zierax/Axiom-01 ★ 0 |
| Primary Language | Python |
| Last Updated | 2026-05-30 |
| License Authorization | Open Source MIT License |
| Framework Integration | Verification Protocol (Active R&D) |
Technical Specifications & Architecture
Axiom-01 processes raw animal naming strings through a three-stage white-box pipeline. Stage 1 (Phonetic Signal Space) converts names into quantitative vectors: syllabic counts (English vs. Latin), plosive-to-soft opening ratios, vowel heaviness indices, and sibilance scores. Stage 2 (Hypothesis Engine) scans a 230+ entry master dataset for statistical correlations between phonetic profiles and biological traits (danger score, speed, mass, habitat overlap), registering only hypotheses that satisfy |r| >= 0.20 across >=70% of data with Z-score gating at 1.8 for anomaly flagging. Stage 3 (Reasoning Engine) provides classify/explain/generate modes -- predicting biological profiles, deconstructing existing names against active hypotheses, or solving for phonetic structures that match target biological constraints -- all with zero hardcoded linguistic rules and 100% traceable hypothesis_id paths.