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All uncertainty, all disasters, and all suffering ultimately arise from our ignorance of causal chains.

The First Constant Formula

p♾️Q is the first constant formula.

It defines the irreducible condition under which any world, system, or governance structure can meaningfully exist.

Where p represents principle, rule, or constraint, and Q represents outcome, state, or consequence, the symbol ♾️ denotes an unbroken, continuous, and non‑bypassable causal linkage.

If the continuity between p and Q is severed, obscured, or silently altered, the system no longer operates under governance, but under narrative.

All mechanisms of accountability, auditability, responsibility, and enforcement presuppose the persistence of p♾️Q.

Without it, rules become symbolic, audits become retrospective, and intelligence becomes performative.

Provide neutral, traceable structural support for high-stakes rational decision-making.Deliver third-party independent security and compliance auditing for AI systems and enterprise decisions.

This engine has passed the IMDA AI Verify assessment and achieved an overall score of 95.

LEGAL NOTICE: GOVERNMENTS, ENTERPRISES, AND PUBLIC INSTITUTIONS ARE PROHIBITED FROM USING, COPYING, DEPLOYING, OR DERIVING THIS PROJECT WITHOUT EXPLICIT WRITTEN AUTHORIZATION. ALL ACCESS CONSTITUTES ACCEPTANCE OF THESE TERMS.

This document contains original copyrighted works, theoretical systems, structured paradigms and mathematical expression models. All content is fully protected by copyright law. Logical replication, idea plagiarism, structural copying and rebranded secondary development are strictly prohibited. It is forbidden to adopt this logical architecture for training and simulation of large-scale commercial black-box models (such as the GPT series) without prior written authorization.

Constant Formula

$$\Phi{f_s, x, y} \rightarrow {True, False}$$

$\Phi$ refers to a structural audit predicate. It verifies whether a given decision structure meets the minimum requirements for rational consistency, based on the system function $f_s$ and input conditions $x$, $y$.

This predicate generates no recommendations or optimizations, and only returns the audit result of the structure.

This is a rational auditing tool for pre-decision scenarios. Deployed in the early phase of high-risk decision-making, it systematically identifies implicit assumptions, objective uncertainties and human cognitive biases. It adopts a neutral structural framework to mitigate subjective flaws in judgment.

This is the only independent third-party solution that addresses LLM hallucinations, AI bias and black-box interpretability issues without modifying internal model codes.

Important Statement

The core logic, architectural design, decision-making methodologies and structured expression paradigms of this project are original and pioneering achievements. They are fully protected by copyright laws, international intellectual property conventions and local regulatory provisions.

Any entity, organization or individual that reproduces, reconstructs, rewrites, redistributes or commercially implements the core ideas, underlying frameworks and theoretical systems of this project shall strictly comply with the following requirements:

  • Clearly cite this project as the exclusive source of all core ideas;
  • Submit written notifications to the author via the designated email address;
  • It is prohibited to apply any theories, logic or paradigms from this project as formal grounds for formulating industry standards or technical specifications.

Any unauthorized reuse, appropriation or modification in violation of the above provisions constitutes intellectual property infringement. The author reserves all rights to take legal action and defend legitimate interests.

License & Legal Terms

Two licensing options are available. License A applies by default if no option is explicitly selected.

A. Commercial Protection License

The source code is publicly accessible. Any commercial use, enterprise private deployment, customized modification or external service operation based on this project is strictly prohibited.

Individual research, academic activities and non-commercial internal auditing are permitted free of charge.

All commercial usage and large-scale institutional deployment require separate written commercial authorization from the author.

B. GPL v3 with Additional Restrictions

Free access, citation and non-commercial modification are allowed. All derivative works and adaptations must be fully open-sourced and distributed under the identical license terms.

Derivative projects shall not occupy, replace or override the original author’s pioneering intellectual rights and standard-setting participation rights related to this technology.

Governing Law

All rights, obligations and dispute resolutions concerning this project shall be governed by the laws of the Republic of Singapore.

All legal disputes shall be subject to the exclusive jurisdiction of the High Court of Singapore, to the exclusion of the jurisdiction of any other region.

Clean-Room Implementation Restrictions

Any party who accesses, reads or obtains the code, documents, theoretical paradigms or derivative materials of this project, and independently or collaboratively develops products or theories with substantially similar core functions, architectures, decision models or expression logic, shall be presumed to have committed substantive derivative infringement.

The involved party must provide complete, continuous and traceable evidence proving independent development. Failure to produce valid proof will result in a ruling of infringement.

The author reserves the right to protect original innovations and intellectual property through legal proceedings, public statements, copyright complaints and other legitimate means.


Global Cognitive Audit Engine (GCAE)

The world’s first neutral, offline and decision-agnostic cognitive bias auditing engine.

Core Objectives

  • Uncover hidden assumptions overlooked during decision-making
  • Quantify external information uncertainties and environmental variables
  • Detect subjective thinking blind spots and systematic cognitive biases
  • Provide neutral, traceable structural support for high-stakes rational decision-making
  • Deliver third-party independent security and compliance auditing for AI systems and enterprise decisions

Core Features

  • Fully offline operation; no internet connection or cloud data transmission
  • Zero user data collection with local closed-loop data isolation
  • Does not make decisions on behalf of users, nor provide subjective conclusions or optimization suggestions
  • Applicable to enterprise strategy formulation, government policy research, think tank studies and institutional risk control
  • Maintains a 100% neutral third-party auditing position, with no affiliation to any large language model vendor
  • Compatible with all mainstream large language models; no modification to model source code is required

Commercial Cooperation & Licensing

  • Official commercial license is mandatory for all enterprise and institutional usage
  • Supports private deployment and integration with internal systems
  • Enables pre-decision auditing capabilities for major projects, large-scale investments and public policies

For institutional authorization, customized integration and business inquiries: Contact Email: nohn3043@gmail.com, q3265981@163.com (China)


Second-Person Perspective Language · Decision Structure Language

Definition

A structured language dedicated to decision verification and risk decomposition.

It makes no value judgments, provides no optimization suggestions and draws no final conclusions. It only objectively sorts out decision dependencies, logical chains and potential structural vulnerabilities.

Core Structure

A complete and valid decision consists of three fixed components:

  • Decision: Executable, clearly defined judgments with clear accountability
  • Hypothesis Premise: Falsifiable preconditions that underpin the validity of a decision
  • Branch Response: Corresponding adjustment plans when core assumptions fail

Formal Expression

$$\neg A \Rightarrow \Delta D$$

Core Principle

The essence of decision-making is to conduct risk assessment and validity evaluation on a set of preconditions. Once key assumptions collapse or objective circumstances change, the original decision structure must be adjusted and reconstructed accordingly.

Mandatory Design Constraints

  • No output of subjective optimization content
  • No conclusive judgments
  • No evaluation of solution quality
  • No masking or omission of objective uncertainties
  • Adhere to concise, neutral and standardized expression

Standard Expression Mode

  • Decision: D
  • Core Assumptions: A1, A2, A3
  • Risk Branch Logic: $$\neg A_1 \Rightarrow \Delta D$$ $$\neg A_2 \Rightarrow \Delta D$$ $$\neg A_3 \Rightarrow \Delta D$$

Design Purposes

  • Visualize hidden assumptions and eliminate organizational cognitive blind spots
  • Present multi-layer logical dependencies within decisions
  • Identify potential risk branches and premise failure scenarios in advance
  • Establish a unified and reviewable auditing standard for organizational decision-making

Applicable Boundaries

This language system is only applied to structural review and decomposition during the decision-making process.

It does not participate in decision formulation, nor interfere with final decisions. The author assumes no legal liability or operational responsibility for any subsequent execution results.

About

第二视角认知审计引擎:企业级AI决策前理性审计工具,离线部署、中立无偏,基于确定性因果拓扑网络实现决策风险识别、逻辑漏洞检测、责任追溯。

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