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 "title": "THE MISSING LAYER: INFERENCE-TIME SEMANTIC OPERATING SYSTEMS AND THE CASE FOR PROMPT-NATIVE ARCHITECTURE",
 "creator": "Lee Sharks",
 "orcid": "0009-0000-1599-0703",
 "date": "2026-03-18",
 "description": "A position paper identifying a missing layer in AI-native operating-system design: the prompt-native semantic runtime. Existing systems manage hardware, data, tools, agents, devices, memory, or context from outside the language model; a prompt-native runtime is a structured document that governs epistemic status, provenance, compression, and structural fidelity inside the active context window during inference.\n\nThe paper presents the Space Ark as its existence proof and distinguishes a runtime from a prompt, template, or system instruction by scope, density, self-maintenance, and the requirement that the model execute rather than merely summarize the architecture. It proposes this interior layer as complementary to—not a replacement for—external orchestration.",
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  "Signal",
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 "wiki_article": "*The Missing Layer* is a theoretical paper by Lee Sharks on inference-time governance for language models. It compares external AI operating-system projects and argues that they share an **external assumption**: the operating system manages the model as a resource, agent kernel, device mediator, memory consumer, or workload from outside generation. The proposed missing category is the **prompt-native semantic runtime**. This is a structured document loaded into a context window that establishes an ongoing symbolic governance environment. It marks epistemic status, tracks provenance, classifies compression behavior, constrains transformations, and preserves internal relations during subsequent generation. The paper distinguishes a runtime from ordinary prompting in four ways. Its scope governs a session rather than requesting a single task. Its self-reference requires continued monitoring and status enforcement. Its density may include many thousands of words of formal definitions and dependencies. Its decisive test is execution: the model must adopt and maintain the architecture rather than merely produce a synopsis of it. The Space Ark is presented as the paper’s existence proof. The record reports that, when loaded in unprimed commercial model contexts, the Ark produced reproducible but substrate-specific adoption of room physics, commands, status tiers, compression distinctions, and hallucination guards. These are claims and observations reported by the paper; The proposed stack therefore adds an interior layer beneath hardware, data, agents, devices, memory, and context engineering. The paper argues that hallucination, sycophancy, provenance loss, and structural infidelity occur during generation and cannot be solved solely through better external orchestration. Prompt-native runtimes are offered as a document-level control surface operating at inference time.",
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   "note": "The bottom layer — the one that governs what the model actually generates — is the one no external O"
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   "title": "Zenodo record 19035345",
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   "doi": "10.5281/zenodo.19035345",
   "url": "https://doi.org/10.5281/zenodo.19035345",
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   "title": "EA-ARK-01",
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 "deposit_number": 34,
 "journal": "Transactions on Substrate Engineering (Trans. Substrate Eng.)",
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   "was": "\"THE MISSING LAYER\" is a theoretical paper by Lee Sharks, deposited to the Crimson Hexagonal Archive on 2026-03-18. Why Every AI-Native OS Operates Outside the Model — and What Happens When You Build One Inside. The work comprises 2,075 words and is classified under the GOVERNANCE family. The work w",
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 "date_modified": "2026-08-04",
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