Superharddata: Liability-Grounded Information as Training Substrate for Aligned Artificial Intelligence

Gregory Caldwell Beier
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Gregory Caldwell Beier: Susarb LLC, a Public Benefit LLC

No yhc6t_v1, LawArchive from Center for Open Science

Abstract: Large Language Models exhibit remarkable linguistic fluency but remain fundamentally ungrounded in consequence. Hallucination is inherent to their architecture - probabilistic next-token prediction will always produce some confabulation. But the problem is severely exacerbated when 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, and is available at SSRN: https://ssrn.com/abstract=6004354 or http://dx.doi.org/10.2139/ssrn.6004354 as well. This paper presents concepts adapted from a forthcoming book series by Gregory Caldwell Beier.

Date: 2026-01-02
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DOI: 10.31219/osf.io/yhc6t_v1

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