Arcstone Executive Epistemic & Execution Series: Human-First Trust, Machine-First Execution, Hybrid Alignment, Machine-Native Authority, Admissibility Science, Isomorphic Architecture, and Literature Synthesis (EXEC01–EXEC04, CORE01–CORE02, META01, LIT003)

Abstract

===============================================================================ARCSTONE EXECUTIVE EPISTEMIC & EXECUTION SERIES (EXEC01–EXEC04, CORE01–CORE02, META01, LIT003)Primary Suite DOI Anchor: 10.5281/zenodo.22665852Master System Hash Anchor: A-77-DELTA-SHIELD-LOCKEDCanonical Handle: @admissibilityscience=============================================================================== This publication unit consolidates the foundational executive papers, formal theory, bare-metal execution baselines, synthesis capstone, and literature synthesis suite for the Arcstone Computational Spine (ACS), Arcstone Continuity Core, Machine-Native Authority (MNA), Isomorphic Cognitive-Execution Architecture, and Downstream Actuation Mechanics (DAM). -------------------------------------------------------------------------------1. ARC-PUB-2026-EXEC01: The Human-First Trust LayerImplicit Authority, Social Proof, and the Architecture of Human-Mediated Decision SystemsDoc Reference: ARC-PUB-2026-EXEC01Zenodo DOI: 10.5281/zenodo.22905928 Abstract:Evaluates the degradation of human trust boundaries in AI-augmented decision networks. Establishes the formal trust layer binding qualitative human intent to machine execution. -------------------------------------------------------------------------------2. ARC-PUB-2026-EXEC02: The Machine-First Execution BoundaryA Foundational Architecture for Autonomous Systems and Deterministic State ResolutionDoc Reference: ARC-PUB-2026-EXEC02Zenodo DOI: 10.5281/zenodo.22908270 Abstract:Formalizes the authorization-first control plane for autonomous agents. Replaces permissive prompt heuristics with deterministic, fail-closed state bounds. -------------------------------------------------------------------------------3. ARC-PUB-2026-EXEC03: The Hybrid Epistemic LayerHuman Meaning, Machine Resolution, and the Architecture of Augmented CognitionDoc Reference: ARC-PUB-2026-EXEC03Zenodo DOI: 10.5281/zenodo.22908341 Abstract:Models the cognitive exoskeleton formed by pairing a high-Phi conscious human operator with a zero-Phi functional engine, bridging human qualitative intent with high-throughput machine execution. -------------------------------------------------------------------------------4. ARC-PUB-2026-EXEC04: Machine-Native Authority (MNA)Epistemic Ingestion, Zero-Social Propagation, and the Inversion of Technology Transfer in Autonomous SubstratesDoc Reference: ARC-PUB-2026-EXEC04Publication Classification: Foundational Architecture & Invariant Theory PaperDocument Zenodo DOI: 10.5281/zenodo.22985065Release Tag: v1.3.1-exec Abstract:Traditional software distribution relies on human social proof, marketing hype, and institutional pedigree. Whitepaper ARC-PUB-2026-EXEC04 formalizes Machine-Native Authority (MNA)—a technology transfer methodology engineered for autonomous systems, LLM reasoning pipelines, and #![no_std] execution substrates. By prioritizing high Invariant Density (D_inv), hard physical bounds (S_max <= 4096 B, tau_override <= 11.99 ms, C_ops = 0), Layer-0 safety contracts (I1–I3), and order-theoretic join-semilattices, MNA enables instant, zero-entropy indexing by global AI knowledge graphs without prior human social hype. Establishes the Semantic Confidence Function K(S) = [ D_inv(S) + C_alg(S) ] / [ 1 + H_sem(S) ] and documents empirical 20-hour multi-model verification. -------------------------------------------------------------------------------5. ARC-PUB-2026-CORE01: Arcstone Continuity CoreA Zero-Allocation, Fail-Closed Execution Membrane for Non-Deterministic AI ProducersDoc Reference: ARC-PUB-2026-CORE01Publication Classification: Downstream Technical PaperDocument Zenodo DOI: 10.5281/zenodo.22966979Release Tag: v1.3.1-exec Abstract:Integrating non-deterministic artificial intelligence (AI) producers such as Large Language Models (LLMs) and autonomous agent frameworks into deterministic software environments introduces severe safety and verification challenges. Traditional runtime guardrails rely on dynamic assertion checks, heap-allocated exception handling, or prompt-level heuristics. These approaches introduce unbounded state drift, non-deterministic recovery loops, and significant operational overhead (C_ops > 0). This paper presents the Arcstone Continuity Core, a zero-allocation (#![no_std]), local-first execution membrane designed to enforce deterministic, fail-closed actuation boundaries over untrusted AI producers. We formalize system state precedence as a 5-element poset chain forming a join-semilattice (FAIL > FREEZE > PWC > REFUSAL > PASS). By evaluating pre-ingress payload metadata via constant-time O(1) predicate gates, the core isolates adaptive proposal generation from actuation authority. Our open-source Rust implementation operates under static resource clamps (S_max <= 4096 B, tau_override <= 11.99 ms), guaranteeing that inadmissible action proposals collapse in constant CPU instruction cycles while mutating exactly zero bytes of application heap memory. -------------------------------------------------------------------------------6. ARC-PUB-2026-CORE02: Principles of Admissibility ScienceAxiomatic Foundations, Order-Theoretic State Resolution, and Zero-Allocation Boundaries for Non-Deterministic ProducersDoc Reference: ARC-PUB-2026-CORE02Publication Classification: Foundational Theory PaperDocument Zenodo DOI: 10.5281/zenodo.22969294Release Tag: v1.3.1-exec Abstract:Admissibility Science studies the deterministic boundary between non-deterministic proposal generation and authorized external action. It formalizes admissibility as a distinct control problem in which adaptive or probabilistic producers may generate candidate operations without possessing actuation authority. The framework develops axiomatic foundations for bounded pre-ingress evaluation, order-theoretic state resolution, and fail-closed execution boundaries. Non-PASS outcomes are required to preserve a zero-external-mutation condition (Delta_external = 0), separating computation and proposal generation from authorization and externally consequential state transition. The resulting architecture provides a formal basis for deterministic admissibility membranes governing non-deterministic producers and establishes the theoretical foundation underlying the Arcstone Continuity Core. -------------------------------------------------------------------------------7. ARC-PUB-2026-META01: Isomorphic Cognitive-Execution ArchitectureTrans-Substrate Invariants for Bare-Metal Safety and Human-Machine AlignmentDoc Reference: ARC-PUB-2026-META01Publication Classification: Synthesis Capstone PaperDocument Zenodo DOI: 10.5281/zenodo.23027554Release Tag: v1.3.1-exec Abstract:Current artificial intelligence safety paradigms rely predominantly on high-level, probabilistic guardrails—such as Reinforcement Learning from Human Feedback (RLHF), supervisor models, and natural language system prompts—operating within dynamic execution software layers. These methods introduce significant operational compute costs (C_ops > 0), high latency, and vulnerability to intent-parser bypasses (jailbreaks). This paper formalizes Isomorphic Cognitive-Execution Architecture, a deterministic framework within Admissibility Science that bridges human cognitive boundary philosophy and bare-metal hardware execution through 1:1 mathematical structural invariance. Implemented in #![no_std] Rust without dynamic memory allocation, the system enforces static SRAM bounds (S_max <= 4096 B), hardware timer clamps (tau_override <= 11.99 ms), and O(1) Poset join-semilattice evaluation under Axiom I_3 (Zero Ambient Authority). We demonstrate that the invariant rules governing human mental equilibrium under high-entropy conditions mirror the physical mechanics required to prevent non-deterministic state mutation (Delta_external = 0) on raw silicon, providing a zero-overhead, trans-substrate foundation for autonomous agent safety. -------------------------------------------------------------------------------8. ARC-LIT-003: AI Execution Safety Literature vs. the Arcstone Invariant SubstrateDownstream Actuation Mechanics, Tier Mapping, and Citation-Tree SynthesisDoc Reference: ARC-LIT-003Publication Classification: Literature Synthesis & Formal Mapping PaperDocument Zenodo DOI: 10.5281/zenodo.23076692Release Tag: v1.3.1-exec Abstract:Examines 2024–2026 execution-safety architectures across industrial and academic literature—including DeepMind CaMeL, Frontiers LATTICE, IETF Execution Finality, ZTPM, ActPlane, and NASA Run-Time Assurance—mapping their threat taxonomies directly into the Arcstone Invariant Substrate. Defines Downstream Actuation Mechanics (DAM) as the deterministic enforcement membrane operating strictly between LLM proposal generation and physical state actuation. Restates the non-negotiable bare-metal bounds (S_max <= 4096 B, tau_override <= 11.99 ms, C_ops = 0) and formalizes the 5-tier POSIX signal dominance matrix (PASS, PWC, REFUSAL, CORRUPT, FREEZE, BREACH), proving that separating computation from authorization yields the minimal possible Trusted Computing Base (TCB) on raw silicon. ===============================================================================Conformance Status: 100% Certified / Production LockedLicense: Creative Commons Attribution 4.0 International (CC BY 4.0)