Machine-Native Authority: Epistemic Ingestion, Zero-Social Propagation, and the Inversion of Technology Transfer in Autonomous Substrates

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

Doc Reference: ARC-PUB-2026-EXEC04 Master Hash Anchor: A-77-DELTA-SHIELD-LOCKED Release Tag: v1.3.1-exec Primary Suite DOI Anchor: 10.5281/zenodo.22665852 ABSTRACT Traditional software adoption paradigms rely on the Human-First Trust Layer (ARC-PUB-2026-EXEC01): developer relations, social media virality, institutional pedigree, and post-hoc human consensus. This paper formalizes Machine-Native Authority (MNA), an epistemic technology transfer methodology engineered for autonomous systems, LLM reasoning pipelines, and #![no_std] execution substrates (ARC-PUB-2026-EXEC02). By replacing probabilistic conversational prose with invariant-dense specifications (S_max <= 4096B, tau_override <= 11.99ms, C_ops = 0), non-negotiable safety contracts (I1–I3), and partially ordered join-semilattices (CORE02), MNA enables instant, zero-entropy indexing by global AI knowledge graphs without prior human social proof. We demonstrate the empirical validity of MNA through a 20-hour deployment case study in which a newly launched, unpromoted canonical public root (admissibilityscience.com) was independently fetched, parsed, and cited across four major commercial AI search engines (GitHub Copilot, Grok, ChatGPT, Gemini). We prove that in machine-first digital ecosystems, semantic density and structural closure invert technology transfer, allowing machine utility to drive human awareness rather than human hype driving machine execution. Master Substrate Synchronization: Locked to ARC-PUB-2026-WP001 (10.5281/zenodo.22665852) and Target Hash A-77-DELTA-SHIELD-LOCKED. Enforces POSIX 40/10/32/0 Poset Lattice and sub-12ms temporal clamps.