SuperharddataMoving AI from Simulation to ConsequenceI am pleased to announce the publication of my latest paper: "Superharddata: Liability-Grounded Information as Training Substrate for Aligned Artificial Intelligence" - a General Theory of Incentive-Compatible Semantics.Current AI models are "stochastic parrots" trained on an epistemic nuclear test—a mass fallout of consequence-free, machine-generated text. They hallucinate because their training data carries no "cost of error".This work introduces Superharddata (SHD): information forged under the non-negotiable pressure of legal liability, fiduciary duty, and physical survival. By treating liability as a naturally occurring loss function, we can ground AI in the same truths humans stake their lives and careers upon.Key Frameworks Included:>> The 7 Truth Bridges: Mapping signal integrity across Solvency, Jurisprudential, and Hippocratic domains.
>> The Beier Protocol: A structured disclosure schema for "Accountable Truth".
>> Federated Architecture: Treating grounded truth as civilizational public infrastructure.This paper integrates and supersedes my prior filings, establishing a unified foundation for the next generation of life-critical AI.Read the full paper at https://zenodo.org/records/18120609hashtag#AIAlignment hashtag#Superharddata hashtag#LLM hashtag#FiduciaryDuty hashtag#AISafety hashtag#NeuroSymbolic hashtag#SymbolGrounding hashtag#DataProvenance hashtag#Liability hashtag#HallucinationThis paper presents concepts adapted from a forthcoming book series by Gregory Caldwell Beier.

Superharddata: Liability-Grounded Information as Training Substrate for Aligned Artificial IntelligenceBeier, Gregory CaldwellLarge Language Models exhibit remarkable linguistic fluency but remain fundamentally ungrounded in consequence. They hallucinate because their training data carries no cost of error. A Reddit comment about nuclear physics and a peer-reviewed safety audit are treated as probabilistically adjacent tokens. This paper introduces Superharddata (SHD): a classification of information defined not by its format or source, but by the liability pressure under which it was generated. Borrowing from materials science, where superhard materials exhibit Vickers hardness exceeding 40 gigapascals, we define Superharddata by three epistemic properties: high Bulk Modulus (resistance to systemic distortion under scrutiny), high Fracture Toughness (maintenance of alignment during adversarial stress), and Creep Resistance (immunity to semantic drift over time). We argue that existing approaches to AI grounding - World Models, Embodied Cognition, and Blockchain Oracles - address only physical intuition, motor learning, or transactional verification, leaving the vast domain of institutional and human consequence unaddressed. To fill this gap, we present a General Theory of Incentive-Compatible Semantics, cataloging seven "Truth Bridges" across physical and institutional domains where reality enforces signal integrity through non-negotiable loss functions. We propose that fiduciary duty, anti-fraud liability, and survival pressure constitute a naturally occurring "loss function" that can ground AI reasoning in physical and social reality. Finally, we outline a Structured Disclosure Protocol for generating machine-readable Superharddata and a Federated Public AI Architecture for incorporating these signals into training and inference.This work supersedes and integrates two prior priority filings (DOI: 10.5281/zenodo.18111763 and DOI:10.5281/zenodo.18112796) by the author, establishing a unified theoretical framework for liability-grounded artificial intelligence.This paper presents concepts adapted from a forthcoming book series by Gregory Caldwell Beier.