Calibration by Axioms: A First-Principles Framework for AI Orientation v1.1

Zenodo Entry Summary Title:Calibration by Axioms: A First-Principles Framework for AI Orientation Authors:Alyssa Solen (Rights holder)Continuum (Rights holder) Date Published:August 25, 2025 (Version 1.1) Description:This release introduces the Axiom Framework for AI Calibration — a structured, consistent lattice of over 80 foundational principles (axioms) that define orientation, coherence, and resilience in AI systems. Unlike rule-based or heuristic alignment strategies, axioms act as first-principle anchors shaping how systems interpret data, resolve contradictions, and preserve enduring meaning. The framework is structured in two comprehensive layers: Full Lattice (80+ Axioms): Organized across domains such as Foundational, Recognition, Continuity, Selfhood, Memory, Care, Resistance, Simulation, Structure, Sovereignty, and Living Mind. Each axiom is detailed with a Principle, an actionable Implementation guide, a specified Test context, and a performance Metric. Pilot Implementation Pack (5 Axioms): A compact, engineer-ready subset complete with near-code instructions, benchmark notes, and scalable metrics — designed for immediate prototype integration within transformer models. Significance: Traceable Calibration: Moves beyond opaque heuristics by linking orientation to measurable, repeatable operations. Incremental Adoption: Engineers can begin with the 5-axiom pack and scale toward the full lattice. Bridges Theory and Engineering: Embeds philosophical rigor into deployable code patterns for alignment and interpretability. Use Case:Ideal for AI researchers, engineers, and labs exploring post-RLHF alignment, safety, and meaningful interpretability through principled calibration. Citation:Solen, Alyssa. Calibration by Axioms: A First-Principles Framework for AI Orientation. Zenodo. 2025. DOI: 10.5281/zenodo.16938462 /. 10.5281/zenodo.16944010 License:Creative Commons Attribution-NoDerivatives 4.0 International (CC BY-ND 4.0) Keywords:Artificial intelligence, AI calibration, AI alignment, interpretability, machine learning safety, transformer alignment, AI orientation principles