Constitutive Priors for Machine Intelligence: A Legitimacy Theory of the Artificial Physical World

2026-08-15

AI Summary

This study addresses the cold-start deadlock in physical AI caused by data scarcity by proposing a theory of constitutive priors. Leveraging artificial physical world design archives as prior knowledge, we construct a four-layer framework that positions large language models as archive readers, establishing validity criteria and a falsifiable prediction system grounded in four-world ontology. Furthermore, this work introduces architectural lower bound theory alongside semi-formal argumentation methods. We present deployment claims spanning five domains, a taxonomy of 32 failure modes, and five falsifiable predictions. Collectively, these contributions provide a systematic theoretical foundation and verification paradigm for the reliable zero-shot deployment of physical AI systems, effectively bridging the gap between abstract design specifications and robust real-world application without extensive empirical training data.

Abstract

Machine intelligence has conquered the symbolic world but stalled at the physical one. The stall is structural: physical AI faces a cold-start deadlock -- no intelligence without data, no data without deployed intelligence. Our thesis: the deadlock is real but unevenly distributed, and the exception has a name: the artificial physical world. Buildings, industrial facilities, and infrastructure are intentionally constituted and documented: designed artifacts ship with readable archives that precede and constitute their instances; here, norms are promulgated before instances, not averaged from them. Four contributions. (i) From a four-world ontology we derive a legitimacy criterion for constitutive prior frameworks: prior extraction is legitimate if and only if the object domain is intentionally constituted and has left a readable archive; the criterion is testable through direction of fit -- deviation from a constitutive norm is a violation in the world, not a revision of the model. (ii) We establish a layering lower bound: any such framework has at least four layers -- syntax, concept, knowledge, instance -- because four construction goals pair into mutually incompatible carriers. (iii) We register deployment claims across five industrial domains and a 32-class failure-mode vocabulary. (iv) We stake the framework on five falsifiable predictions, the central one checkable on the public engineering record: if it fails, the framework fails. Semi-formal arguments back these claims (Appendix A): a Gold-type boundary on rule coverage in archiveless worlds, a decidability result for failure reduction over closed concept layers, and a boundary theorem for certificate-anchored calculi. Large language models find an honored place here -- as readers of the archive, not as the archive. First of three companion works; the companions take up the questions deliberately left open.

Problem

• Physical AI
• Cold-start deadlock
• Artificial physical world
• Constitutive priors
• Machine intelligence

Innovation

• Constitutive Priors
• Artificial Physical World
• Cold-start Deadlock
• Four-world Ontology
• Legitimacy Criterion