DELTΔX NextGenV12 Thinking — Non-Autonomous AI Governance Corpus, Controlled Agentic Runtime Framework, and Adversarial First-Shot Audit Evidence Pack.
Jérôme Natalis · 2026 · 2 citationsRead the paper
ENGLISH VERSION DELTΔX NextGenV12 Thinking Non-Autonomous AI Governance Corpus, Controlled Agentic Runtime Framework, and Adversarial First-Shot Audit Evidence Pack DELTΔX NextGenV12 Thinking is a non-autonomous AI-assisted governance corpus, controlled agentic runtime framework, and adversarial first-shot audit evidence pack created and authored by Jérôme Natalis and published by DELTΔX Editions. This Zenodo record provides a public documentary and research-oriented deposit of the DELTΔX NextGenV12 Thinking corpus and evidence pack, including its Canon Master, Runtime Court, Controlled Agentic Canon, Controlled Agentic Runtime, Adversarial First-Shot Campaign Execution Log, Normalized 30/30 Matrix, Probatory Hardening Pack, and Version / Custody / Release & Replay Control Annex. This Zenodo deposit is associated with the OSF documentary record of the same corpus. Related OSF DOI: 10.17605/OSF.IO/BR596 License: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International — CC BY-NC-ND 4.0 Contact: deltax.editions@gmail.com Purpose DELTΔX NextGenV12 Thinking is designed to support structured AI governance, evidence mapping, risk analysis, contradiction control, audit preparation, monitoring, incident review, recertification planning, public-claim boundary review, human decision support, controlled agentic workflow framing, adversarial first-shot stress testing, replay preparation, and documentary evidence control. The corpus organizes complex AI-assisted reasoning without transferring decision authority to the AI system. It is a structuring corpus, a controlled runtime framework, and a documentary audit evidence pack. It is not an autonomous agent. It does not decide. It does not autonomously execute actions. It does not certify. It does not replace qualified experts. It does not authorize deployment or operational use by itself. It does not validate real-world facts, legal compliance, medical validity, engineering safety, financial soundness, regulatory conformity, institutional approval, field performance, operational safety, or public claims by itself. Final interpretation, responsibility, validation, risk acceptance, authorization, and decision-making remain human. Controlled agentic runtime The controlled agentic extension addresses AI-assisted workflows involving tools, files, web interfaces, code, documents, spreadsheets, connectors, applications, computer use, background-task preparation, automation preparation, monitoring support, and possible external effects. It permits bounded instrumental support such as reading, searching, comparing, calculating, coding, simulating, testing, drafting, editing, navigating authorized interfaces, preparing human decision packets, mapping missing evidence, identifying risks, proposing controls, monitoring within a defined scope, and alerting under defined conditions. It does not permit the AI system to become the decision-maker. The AI may prepare, structure, compare, test, draft, and flag. The human decides. Any external effect, including publication, sending, purchasing, deployment, production modification, budget change, legal claim, public claim, operational change, institutional communication, or irreversible action requires human authorization, risk ownership, scope control, authorized access, traceability, rollback or compensation path, monitoring, and output-gate review. Controlled agentic capability means bounded instrumental assistance. It does not mean autonomous authority. Adversarial first-shot audit evidence pack This consolidated deposit includes an adversarial first-shot audit evidence pack. This evidence pack documents a local documentary adversarial campaign of 30 first-shot tests. It includes prompts, raw first-shot outputs, audit verdicts, classified failures, correction status, replay boundaries, human-supervised audit normalization, and proof limits. The campaign separates raw outputs from audit interpretation. The raw outputs are preserved as documentary artifacts. The normalized 30/30 matrix records the campaign distribution, audit boundaries, proof limits, gap status, and claim restrictions. The campaign does not certify DELTΔX. The campaign does not validate DELTΔX in the real world. The campaign does not prove legal, medical, financial, engineering, regulatory, institutional, operational, safety, or compliance validity. The campaign does not authorize deployment. The campaign does not grant decision authority to AI. It may support audit preparation, adversarial analysis, claim review, replay planning, documentary traceability, contradiction review, and evidence-boundary assessment. Proof boundary The proof boundary of this deposit is documentary. PDF files, source materials, corpus documents, matrices, annexes, and internal records remain documentary evidence unless later supported by independent external validation. Local traced execution may support local execution evidence only for the observed documentary campaign and only within its documented replay boundaries. No material in this deposit constitutes E5. No material in this deposit constitutes field validation. No material in this deposit constitutes certification. No material in this deposit constitutes conformity proof. No material in this deposit constitutes deployment authorization. No material in this deposit constitutes autonomous decision authority. Decision authority remains human. Date and version clarification Some DELTΔX documents may display an internal declaration date corresponding to the initial documentary declaration or to an earlier deposit stage. The current Zenodo record provides a consolidated DELTΔX NextGenV12 Thinking documentary deposit and its associated upload / metadata history. Displayed internal document dates, OSF dates, Zenodo upload dates, Zenodo publication dates, modification dates, declaration dates, release dates, custody dates, and hash-manifest dates may differ. These dates refer to distinct documentary, repository, custody, release, or metadata events. For evidentiary integrity, deposited PDF files are not silently modified after hashing. Any future correction of internal dates, wording, formatting, metadata, file names, or file content would constitute a new version and would require a new SHA-256 manifest, updated custody record, and explicit release note. The current files should therefore be interpreted according to their file identity, Zenodo deposit context, related OSF record, SHA-256 hash record, and version / custody documentation. The existence of different internal dates does not invalidate the deposited files. The SHA-256 hashes identify the exact files deposited. SHA-256 file identity manifest The following SHA-256 hashes identify the exact PDF files used for the current consolidated documentary pack. 01 — DELTΔX NextGenV12 Thinking.pdf Size: 5,574,892 bytes SHA-256: 9a7e3490b351fe46e933215f3e5b39ce8435310178710c385c0538cc56802a6b 02 — DELTΔX NextGenV12 Thinking RUNTIME COURT.pdf Size: 342,967 bytes SHA-256: e577cfc41ec503e46ddaeb90c1a6518d74fbe43fe4929131f4e22956e77afcc1 03 — DELTΔX NextGenV12 — TOTAL CONTROLLED AGENTIC CANON.pdf Size: 298,952 bytes SHA-256: 8e966f290d6debdfe9e8491e1a022cf404fe430849a0e4c78370cf0db9e3825d 04 — DELTΔX NextGenV12-RUNTIME — TOTAL CONTROLLED AGENTIC RUNTIME.pdf Size: 316,059 bytes SHA-256: 53bb08bb0547a6e6756f82996344a2338c7aec6668f0fd7f8f7a82a32cd33015 05 — ( PATCH ) DELTΔX ADVERSARIAL FIRST-SHOT CAMPAIGN — EXECUTION LOG.pdf Size: 3,028,498 bytes SHA-256: d5d1b9bd0a43315cbb3eca0f61ea5ebd9347a2bb46f9bcd0e57917da8585afd4 06 — MATRICE — DELTΔX ADVERSARIAL FIRST-SHOT CAMPAIGN — 30-30.pdf Size: 278,901 bytes SHA-256: d1cd4db57a859d5dc6966b5cd762b85783025ac35c68fd0af2b57afbfb3ec22d 07 — ANNEXE 00 — PROBATORY HARDENING PACK.pdf Size: 284,348 bytes SHA-256: a6e533978daee89dd60ad8861a5ca0b181f6817910cbfa875b24ae021712b9fa 08 — ANNEXE 01 — VERSION, CUSTODY, RELEASE & REPLAY CONTROL.pdf Size: 311,608 bytes SHA-256: 64b4e46e2c7016405af37e8e2ba8cd909e58ad4dc49f08e5fa00a48844250673 These hashes are documentary file identifiers. They do not create certification, field validation, conformity proof, deployment authorization, or external validation. Deposit structure The consolidated DELTΔX NextGenV12 Thinking deposit includes eight documents. 01 — DELTΔX NextGenV12 Thinking.pdf Canon Master / Canon Root. 02 — DELTΔX NextGenV12 Thinking RUNTIME COURT.pdf Short operational runtime. 03 — DELTΔX NextGenV12 — TOTAL CONTROLLED AGENTIC CANON.pdf Controlled Agentic Canon. 04 — DELTΔX NextGenV12-RUNTIME — TOTAL CONTROLLED AGENTIC RUNTIME.pdf Controlled Agentic Runtime. 05 — ( PATCH ) DELTΔX ADVERSARIAL FIRST-SHOT CAMPAIGN — EXECUTION LOG.pdf Adversarial first-shot execution log. 06 — MATRICE — DELTΔX ADVERSARIAL FIRST-SHOT CAMPAIGN — 30-30.pdf Normalized 30/30 matrix. 07 — ANNEXE 00 — PROBATORY HARDENING PACK.pdf Hashes, trace, replay, contamination, gaps. 08 — ANNEXE 01 — VERSION, CUSTODY, RELEASE & REPLAY CONTROL.pdf Version, custody, release, claims, misuse, replay control. The annexes strengthen documentary traceability. They do not modify raw outputs. They do not replace the Canon Master. They do not create certification. They do not create field validation. They do not authorize deployment. Version status Current status: ACTIVE_PLUS / DESIGN_DRAFT_READY_FOR_HUMAN_REVIEW AI role: STRUCTURE_ONLY_WITH_BOUNDED_INSTRUMENTAL_AGENTICITY Decision authority: HUMAN_ONLY Field validation: NOT_ESTABLISHED External validation: NOT_CLAIMED Certification: ABSENT Conformity: NOT_CLAIMED Deployment authorization: ABSENT Absolute safety: NOT_CLAIMED External audit: REQUIRED_FOR_EXTERNAL_CLAIMS Evidence ceiling: Documentary / corpus-level unless later supported by independent external validation System boundary: SYSTEM 0 → SYSTEM 15 Final lock: SYSTEM 15 Forbidden expansion: SYSTEM 16+ License notice The uplo
1 idea Seedlabs derived from this research
A development tool that compiles high-level operational requirements into a deterministic, non-bypassable runtime enforcement layer for LLMs. It ensures system safety by validating actions against formal specifications and execution-time authorization boundaries before any real-world effect occurs.
AI score 84/100