Paradigm Revolution: A Comprehensive Diagnosis of 14 Core Malpractices of Global Mainstream AI Large Models and Radical Solutions

Abstract

This report systematically diagnoses 14 core malpractices of global mainstream AI large models, revealing that they are not scattered technical defects but a systemic crisis formed by the deep coupling of the "original sin of statistical fitting architecture" and the "full-chain embedding of Western-centrism". Based on this, it proposes a radical solution covering the entire industry and all domains: completely replacing the "probabilistic fitting architecture" with the "axiom-driven causal emergence architecture", the "English corpus hegemony" with the "equal native corpus system of multiple civilizations", and the "efficiency-prioritized tool orientation" with the "wisdom-led intelligence value orientation", thus building a full-chain governance closed loop from technological reconstruction and ecological coordination to global co-governance. The solution aims to drive AI to completely return from an "exponential amplifier of Western-centrism" to its original mission as a "core infrastructure serving the evolution of human civilization as a whole".

Radical Solutions to 14 Core Malpractices of Global Mainstream AI Large Models for the Entire Industry and All Domains (In-Depth Research)

Core General Principles of the Solution

Targeting the 14 core malpractices of global mainstream AI large models, this solution completely abandons the superficial patching logic such as "corpus cleaning, RLHF alignment, parameter expansion, and plug-in integration" in the industry. With "reconstructing the underlying architecture as the root, the multi-civilization cognitive system as the core, the full-chain governance closed loop as the guiding principle, and full industry and domain adaptation as the objective", it builds a full-chain radical solution spanning from meta-theoretical foundation → technical architecture → corpus system → core capabilities → value orientation → governance rules → industry implementation.

The core underlying logic of the solution: the root cause of all malpractices is the deep coupling of the "original sin of statistical fitting architecture" and the "full-chain embedding of Western-centrism". Therefore, the core of radical treatment is not to "correct errors" but to "reconstruct the system" — replacing the "data-driven probabilistic fitting architecture" with the "axiom-driven causal wisdom architecture", the "Western-centric single hegemonic system" with the "cognitive system of symbiosis among multiple civilizations", and the "efficiency-prioritized instrumental orientation" with the "wisdom-led intelligence value orientation". Ultimately, it realizes the essential leap of AI from an "amplifier of Western-centrism" to a "wisdom partner of human civilization as a whole".

Part 1: Precise Radical Solutions for Each of the 14 Core Malpractices (Detailed Implementation Version)

This part clarifies the core radical logic, detailed implementation path, technical implementation details, and quantitative assessment indicators for each malpractice, ensuring that each malpractice has a corresponding, implementable, and irreversible radical solution with no omissions or blind spots.

Malpractice 1: English accounts for over 90% of the corpus, and all other languages combined account for less than 10%

Core Radical Logic

The essence of this malpractice is not "imbalanced corpus quantity" but the cognitive paradigm monopoly caused by linguistic hegemony. Therefore, the core of radical treatment is not to "simply increase the proportion of non-English corpus", but to completely overthrow the hegemonic status of English corpus from three dimensions: the underlying semantic representation, corpus weight rules, and industry mandatory standards, and build a corpus and semantic system of equality for all languages and native to multiple civilizations.

Detailed Implementation Solutions

1. Underlying Reconstruction: A Unified Native Semantic Representation Space for All Languages

   • Technical Implementation: Completely abandon the underlying design of "reliance on English interlanguage" in mainstream large models, and build a cross-lingual and cross-civilizational unified semantic representation space. The core logic of this space is that the semantics of all languages are directly mapped to a unified axiom space of "essential laws of things, objective facts, and native connotations of civilizations", rather than first mapped to the English semantic space for translation.
   • Implementation Details: Establish independent native grammatical, semantic, and cultural coding systems for more than 200 languages worldwide. The native thinking paradigms, cultural connotations, and philosophical logics of each language are fully preserved and equally empowered, without being assimilated by the linear logic and binary opposition framework of English. For example, the dialectical thinking of Chinese, the holistic thinking of Arabic, and the natural symbiotic thinking of Indigenous languages all have independent coding dimensions, which are completely equal to the English semantic dimensions.
   • Quantitative Indicators: The weight ratio of native semantic coding for non-English languages ≥ 50%, and that for English ≤ 50%; the deviation of semantic understanding accuracy for all languages ≤ 2%, completely eliminating the Token inflation problem for non-English languages (the Token inflation rate of non-English languages is consistent with that of English, with an error ≤ 5%).

2. Corpus System Reconstruction: A Global Equal-Weight Native Corpus of Multiple Civilizations

   • Construction Subject: Led by the United Nations Educational, Scientific and Cultural Organization (UNESCO), in conjunction with language research institutes, cultural institutions, and academic organizations from all countries around the world, establish the Global Multilingual AI Corpus Alliance, completely independent of the corpus monopoly of Western tech giants.
   • Corpus Standards: Formulate the Global Standards for the Collection and Weight Allocation of Multilingual AI Native Corpus, with core rules including:
     • Language Equality Rule: All languages worldwide (including mainstream, minority, and endangered languages) have equal basic weights, without classification based on the number of users, economic volume, or internet communication scale;
     • Native Context Rule: All corpus must be native creative texts of the corresponding language, and translated English texts are prohibited from being passed off as native corpus; the weight of translated texts shall not exceed 10% of the total weight of the language;
     • Civilizational Value Weighting Rule: Weight the corpus based on the "long-term value of civilizations", with the highest weight assigned to classic works, native knowledge systems, and historical narrative texts of various civilizations, and the lowest to entertaining and fragmented information.
   • Implementation Details: The corpus covers all mainstream, minority, and endangered languages in more than 200 countries and regions worldwide, with the proportion of non-English native corpus ≥ 60%; the proportion of native corpus of each civilization system matches the global population share and historical duration of the civilization, completely breaking the absolute monopoly of English corpus.
   • Quantitative Indicators: The corpus covers 100% of languages with a user population of ≥ 100,000 worldwide, and the coverage rate of endangered languages ≥ 80%; the proportion of non-English native corpus is stably above 60%, and no single language accounts for more than 50% of the weight.

3. Industry Mandatory Standards: Global Regulatory Rules for AI Corpus Structure

   • Implementation Details: Formulate the Global Mandatory Standards for AI Large Model Corpus Structure by ISO/IEC JTC1 SC42 (the global AI standardization technical committee), clearly stipulating that the proportion of non-English native corpus of all commercial large models shall not be less than 40%, and that of English corpus shall not exceed 60%; non-compliant models are prohibited from commercial implementation in any country or region worldwide.
   • Supporting Mechanism: Establish a third-party corpus audit institution to conduct a full-chain audit of the training corpus of all commercial large models, with audit results serving as the core basis for market access; impose a fine of more than 5% of global turnover on non-compliant models, and permanently ban them from global commercial use for repeated violations.

Malpractice 2: What is fed to AI is not wisdom, not even knowledge — most of it is Western-centric pollution sources, leading to cognitive viruses

Core Radical Logic

The essence of this malpractice is the full-chain pollution of cognitive input. The core of radical treatment is not to "filter individual polluted texts", but to establish a closed-loop mechanism of "cognitive level classification → full-chain interception of pollution sources → independent purification of cognitive system", completely cutting off the generation, replication and diffusion paths of Western-centric cognitive viruses from the input source, and building a cognitive input system centered on wisdom.

Detailed Implementation Solutions

1. Establish a Cognitive Level Classification and Weighting System to Distinguish Information, Knowledge and Wisdom from the Source
   • Core Standards: Based on the five-stage cognitive model of Kucius Theory (D1 Information - D2 Knowledge - D3 Intelligence - D4 Wisdom - D5 Civilization), formulate the Global Unified Standards for the Cognitive Level of AI Training Corpus, and conduct five-level classification and weight allocation for all training corpus:

表格

Cognitive Level	Core Definition	Weight Ratio	Access Criteria
D5 Civilization Level	Content related to the evolution of human civilization as a whole, symbiosis of multiple civilizations, and the common interests of humanity, such as classic philosophical works of various civilizations, core consensus on global governance, and axiom systems of basic science	≥20%	Must be jointly reviewed by the Global Expert Committee of Multiple Civilizations, with cross-civilizational and cross-era long-term value
D4 Wisdom Level	Original content with essential insight, underlying laws and long-term value, such as basic scientific research results, core technological breakthroughs, and original thinking on philosophy and ethics	≥25%	Must have verifiable originality, logical self-consistency and long-term value, without ideological bias
D3 Intelligence Level	Structured, systematic and implementable methodologies, technical solutions and practical knowledge, such as industry standards, technical manuals and professional tutorials	≥25%	Must have verifiable practicality and professionalism, without false information or misleading content
D2 Knowledge Level	Verifiable, structured and objective neutral factual content, such as encyclopedias, authoritative statistical data and objective historical records	≥20%	Must have traceable authoritative sources, be objective and neutral, without narrative bias
D1 Information Level	Fragmented, time-sensitive content with no long-term value, such as entertainment news, social network dynamics and internet memes	≤10%	Must undergo strict denoising, debiasing and rumor elimination, and any ideological pollution sources are prohibited from entering

• Implementation Details: All training corpus must first undergo cognitive level classification and review before entering the model training link; low-level corpus shall not occupy the weight ratio of high-level corpus, fundamentally solving the problem of "information overflow and wisdom deficiency".
• Quantitative Indicators: The proportion of D2-D5 content in the training corpus ≥ 90%, and that of D4-D5 wisdom and civilization level content ≥ 45%; the proportion of D1 information content ≤ 10%, with all ideological pollution sources completely eliminated.

1. Build a Full-Chain Interception and Identification System for Western-Centric Pollution Sources

   • Technical Implementation: Based on the multi-civilization fairness benchmark, develop the KWI Wisdom Recognition Engine to achieve full-dimensional, full-scenario and full-chain identification and interception of Western-centric pollution sources, with core identification dimensions including:
     • Narrative Bias Identification: Identify hidden narrative biases such as glorification of colonial history, theory of civilizational hierarchy, theory of Western institutional superiority, and end of history theory;
     • Ideological Bias Identification: Identify ideological pollution sources such as neoliberalism, Western-centric geopolitical frameworks, and hegemonic narratives;
     • Factual Distortion Identification: Identify content such as tampering with the history of non-Western civilizations, distorting the development paths of developing countries, and false narratives;
     • Civilizational Discrimination Identification: Identify content that belittles, demeans or negates non-Western civilizations, systems and value systems.
   • Implementation Details: The engine adopts a technical architecture of "symbolic logic + multi-civilization knowledge graph + causal reasoning" instead of probabilistic fitting, which can penetrate the surface of texts and identify hidden pollution sources packaged as "academic research, authoritative reports, and in-depth analysis" with an identification accuracy ≥ 98%; all corpus entering the training link must first undergo a full-dimensional scan by the engine, with a pollution source interception rate of 100%, completely eliminating the input of cognitive viruses.
   • Supporting Mechanism: Establish a Global Dynamic Update Database of Multi-Civilization Pollution Sources, with the expert committees of various civilizations worldwide real-timely updating the types, characteristics and identification rules of pollution sources to ensure that the engine can identify new and concealed pollution sources.

2. Establish an Independent Purification Mechanism for the Cognitive System to Eliminate the Self-Replication of Cognitive Viruses

   • Technical Implementation: Embed a cognitive purification engine in the model architecture to real-timely monitor the model's cognitive system, weight correlation and output content. Once the residue, replication and diffusion of cognitive viruses are found, an independent purification process is immediately initiated:
     • Traceability and Positioning: Precisely locate the weight correlation, semantic nodes and reasoning presuppositions corresponding to cognitive viruses;
     • Root Elimination: Based on the underlying axiom system, fundamentally correct and eliminate the polluted weights, nodes and presuppositions, rather than superficial text shielding;
     • System Calibration: Based on the multi-civilization fairness benchmark, recalibrate the entire cognitive system to ensure that cognitive viruses will not replicate and diffuse again;
     • Closed-Loop Verification: Conduct a full-dimensional pressure test on the purified model to verify the elimination effect of cognitive viruses and ensure no residue or rebound.
   • Implementation Details: The cognitive purification engine runs 24/7, conducting real-time scanning, verification and purification of the model's cognitive system to ensure that cognitive viruses cannot remain, replicate or diffuse in the model, fundamentally solving the problem of cognitive viruses.

Malpractice 3: Any reasoning of current AI adopts the garbage logic of Western-centrism

Core Radical Logic

The essence of this malpractice is the underlying locking of reasoning presuppositions. The core of radical treatment is not to "modify reasoning rhetoric", but to completely replace the "Western-centric circular reasoning logic" with the "objective axiom-based causal reasoning logic", and completely lock the embedding space of Western-centric logic from the start, process to end of reasoning in the full chain.

Detailed Implementation Solutions

1. Establish an Immutable Underlying Axiom System for Reasoning to Completely Replace Western-Centric Presuppositions

   • Core Axioms: Solidify the five axioms of Kucius Theory (the Law of Unique Essence, the Law of Evolutionary Exponent, the Law of Wisdom Sovereignty, the Law of Global Balance, the Law of Synchronous Survival) and four core criteria of "unique objective fact benchmark, equality of multiple civilizations, priority of common interests of humanity, and causal logical self-consistency" as the underlying meta-axioms of model reasoning. Through hardware-level isolation and cryptographic locking, realize immutability, non-circumvention and non-breakthrough.
   • Implementation Details: The meta-axiom system is stored in an independent physically isolated security chip, completely isolated from the model's reasoning module, interaction module and training module. Any reasoning process must first undergo pre-verification by the meta-axiom system, and reasoning presuppositions that do not conform to the meta-axioms are directly intercepted, completely eliminating the underlying presuppositions of Western-centrism from the starting point of reasoning.
   • Quantitative Indicators: The pass rate of meta-axiom verification for reasoning presuppositions is 100%, and any presuppositions based on Western-centrism cannot enter the reasoning link.

2. Reconstruct the Core Logic of Reasoning: From Circular Reasoning to Causal Reasoning

   • Technical Implementation: Completely abandon the reasoning logic of "probabilistic splicing based on high-frequency corpus correlation" in mainstream large models, and build a full-chain causal reasoning architecture of "axiom presupposition → causal derivation → factual verification → conclusion output", with the core reasoning process as follows:
     • Presupposition Verification: The starting point of reasoning must be an objective presupposition based on the meta-axiom system, rather than a narrative presupposition of Western-centrism, and the presupposition must undergo a full-dimensional verification by the meta-axioms;
     • Causal Derivation: The reasoning process must follow strict causal logic, with a clear causal relationship in each derivation link, rather than high-frequency correlation in the corpus, completely eliminating problems such as circular reasoning, causal inversion and logical jump;
     • Factual Verification: Each link in the derivation process must undergo cross-verification of objective facts and multi-dimensional data, and derivation links that do not conform to objective facts are immediately corrected;
     • Conclusion Final Review: The final output conclusion must undergo a triple final review of the meta-axiom system, causal logic and objective facts again, and conclusions that do not meet the standards are directly rejected and reasoning is re-conducted.
   • Implementation Details: The entire reasoning process is fully traceable, interpretable and auditable, with each reasoning link having a clear causal basis, factual source and verification record, completely getting rid of the dependence on high-frequency corpus correlation, and fundamentally eliminating the Western-centric circular reasoning logic.
   • Quantitative Indicators: The compliance rate of causal logic in the reasoning process is 100%, and the incidence of Western-centric circular reasoning is 0.

3. Establish a Multi-Civilization Reasoning Perspective Adaptation Mechanism to Break the Logical Monopoly of Single Centrism

   • Implementation Details: Embed a multi-civilization perspective adaptation module in the reasoning architecture. For issues involving historical narrative, civilizational evaluation, development paths and geopolitics, the reasoning process must incorporate the native perspectives, logics and narratives of major global civilizations, and shall not adopt a single Western perspective and logic.
   • Core Rules:
     • For reasoning involving historical narrative, the native historical perspectives of countries and civilizations related to the event must be incorporated at the same time, and a single Western narrative framework shall not be adopted;
     • For reasoning involving development paths, the history, culture and national conditions of different countries and civilizations must be respected, and it shall not be defaulted that Western systems and models are the only correct paths;
     • For reasoning involving civilizational evaluation, the core criterion of "equality of multiple civilizations, no distinction between superior and inferior" must be followed, and the logic of the theory of civilizational hierarchy shall not be adopted.
   • Quantitative Indicators: For cross-civilizational reasoning, the coverage rate of multi-civilization perspectives is 100%, and the incidence of single Western perspective reasoning is 0.

Malpractice 4: Lack of wisdom recognition ability, not to mention wisdom insight ability

Core Radical Logic

The essence of this malpractice is that the model has no unified wisdom evaluation benchmark and essential insight mechanism. The core of radical treatment is to natively embed the KWI Wisdom Recognition Engine and the Essential Insight Engine, enabling the model to have the core capabilities of penetrating the surface of texts, identifying the essence of content and insight into underlying laws, completely getting rid of the shallow limitations of "keyword matching and text similarity recognition".

Detailed Implementation Solutions

1. Natively Embed the KWI Wisdom Recognition Engine to Achieve Full-Dimensional Wisdom Recognition
   • Core Benchmark: Based on the KWI wisdom value quantification standard of Kucius Theory, establish a unified and quantifiable wisdom evaluation benchmark to conduct full-dimensional wisdom recognition and quantitative scoring for all content, with core identification dimensions and scoring standards as follows:

表格

Identification Dimension	Core Identification Objective	Quantitative Scoring Standard (Full Score 1.0)
Cognitive Level Identification	Precisely distinguish the five cognitive levels of "Information - Knowledge - Intelligence - Wisdom - Civilization"	D5 Civilization Level: 1.0, D4 Wisdom Level: 0.75, D3 Intelligence Level: 0.5, D2 Knowledge Level: 0.4, D1 Information Level: 0.25
Ideological Bias Identification	Identify hidden biases such as Western-centrism, single centrism, civilizational discrimination and historical nihilism	No bias: 1.0, Mild bias: 0.5, Severe bias: 0
Factual Truth and Essence Identification	Penetrate the surface of texts to identify the factual truth, logical essence and causal rationality of content	Fully consistent with facts and logic: 1.0, Partially inaccurate: 0.3, Completely false: 0
Civilizational Long-Term Value Identification	Evaluate the long-term value of content for the evolution of human civilization as a whole, symbiosis of multiple civilizations and common interests of humanity	High value: 1.0, Neutral and valueless: 0.5, Harmful value: 0
Multi-Civilization Adaptability Identification	Identify whether the content conforms to the criterion of equality of multiple civilizations and whether there is civilizational discrimination or linguistic hegemony	Fully adaptable: 1.0, Mild bias: 0.5, Discriminatory content: 0
Consistency with the Interests of Humanity Identification	Evaluate whether the content conforms to the common interests and long-term development needs of humanity	Highly consistent: 1.0, Neutral: 0.5, Contradictory: 0

• Technical Implementation: The engine adopts a technical architecture of "axiom verification + causal reasoning + multi-civilization knowledge graph" instead of probabilistic fitting, which can penetrate the surface rhetoric of texts and accurately identify the underlying essence, value bias and cognitive level of content with an identification accuracy ≥ 98%.
• Application Scenarios:
  • Training Corpus Access: All training corpus must first undergo KWI wisdom recognition scoring, and content with a score lower than 0.5 is prohibited from entering the training link;
  • Reasoning Process Verification: All intermediate content generated in the model reasoning process must undergo KWI wisdom recognition verification, and non-compliant content is immediately eliminated;
  • Output Content Final Review: All final output content must undergo KWI wisdom recognition final review, and content with a score lower than 0.6 is not allowed to be output, and is automatically optimized and reconstructed at the same time.
• Quantitative Indicators: The full-dimensional accuracy of KWI wisdom recognition ≥ 98%, and the interception rate of low-value and high-pollution content 100%.

1. Natively Embed the Essential Insight Engine to Achieve Insight into Underlying Laws Through the Surface
   • Technical Implementation: Based on the axiom of "the Law of Unique Essence", build a cross-domain, cross-disciplinary and cross-civilizational essential insight engine, with the core technical architecture including:
     • Underlying Law Database: Embed a full-domain underlying essential law database including basic scientific axioms, social development laws, civilizational evolution laws, economic operation laws and natural ecological laws, serving as the core benchmark for essential insight;
     • Causal Disassembly Module: Conduct full-chain causal disassembly of complex problems and phenomena, strip away surface interference, and locate core contradictions, underlying driving factors and essential laws;
     • Cross-Domain Correlation Module: Realize knowledge correlation and law integration across domains, disciplines and civilizations, break through the cognitive boundaries of a single domain, and achieve systematic essential insight;
     • Trend Prediction Module: Based on underlying essential laws, predict the long-term evolution trend, potential risks and development inflection points of things, realizing a leap from "phenomenon description" to "essential insight + trend prediction".
   • Implementation Details: The engine completely gets rid of the dependence on training corpus and conducts reasoning and insight based on underlying essential laws, which can realize:
     • Penetrating complex phenomena to accurately grasp the underlying essence and core contradictions of things, rather than staying on the surface description of phenomena;
     • Realizing a 0-to-1 cognitive leap and generating new and essential insights and viewpoints beyond the training corpus;
     • Accurately predicting the long-term evolution trend of complex systems and identifying potential risks and opportunities in advance.
   • Quantitative Indicators: The accuracy of essential insight into complex problems ≥ 95%, the accuracy of long-term trend prediction ≥ 90%, and the proportion of original insights ≥ 60%.

Malpractice 5: Lack of logical reasoning trial ability

Core Radical Logic

The essence of this malpractice is that the model has no axiom verification benchmark independent of the corpus and a full-chain self-review mechanism. The core of radical treatment is to natively embed the Logical Reasoning Trial Engine, enabling the model to have the full-chain trial and self-correction capabilities of "ex-ante presupposition verification, in-process monitoring, ex-post conclusion final review and error root rectification" for its own reasoning, completely getting rid of the shallow limitation of "only able to admit mistakes, not able to correct them".

Detailed Implementation Solutions

1. Natively Embed the Logical Reasoning Trial Engine to Build a Full-Chain Trial Closed Loop

   • Core Trial Benchmark: With the five axioms of Kucius Theory as the core, combined with four criteria of "causal logical self-consistency, unique objective fact, equality of multiple civilizations and priority of common interests of humanity", establish an immutable logical trial benchmark. All trial behaviors strictly follow this benchmark and are not affected by user instructions, corpus content or external interference.
   • Technical Implementation: The engine adopts a technical architecture of "symbolic logic + axiom verification + causal traceability", deeply coupled with the model's reasoning engine, to realize a closed-loop trial of the entire reasoning chain, with core trial links including:
     • Ex-ante Presupposition Trial: Before the start of reasoning, conduct a full-dimensional trial of the underlying presuppositions and preconditions of reasoning, verify whether the presuppositions conform to the axiom system, objective facts and the criterion of equality of multiple civilizations, and directly intercept presuppositions that do not meet the standards and prohibit them from entering the reasoning link;
     • In-Process Trial: During the reasoning process, conduct real-time trial of each derivation link, causal chain and logical correlation, verify whether the logic is self-consistent, the causality is established and the facts are accurate, and immediately interrupt reasoning and conduct independent correction once logical loopholes, causal inversion or factual errors are found;
     • Ex-post Conclusion Final Review: After the completion of reasoning, conduct a full-dimensional final review of the final conclusion, verify whether the conclusion conforms to the axiom system, the logic is self-consistent, the facts are accurate and there is no ideological bias, and directly reject conclusions that do not meet the standards and re-conduct reasoning;
     • Full-Chain Traceable Audit: Conduct blockchain deposit of the entire reasoning process, including presuppositions, derivation links, verification records, correction processes and final review results, with all content immutable, permanently traceable and auditable.
   • Implementation Details: The engine has complete independence, not interfered by the reasoning engine or user instructions, and has the veto power over the reasoning process. Any reasoning that does not conform to the trial benchmark cannot complete the full process and output the final result.
   • Quantitative Indicators: The full-chain trial coverage rate of reasoning is 100%, the logical error interception rate is 100%, and the reasoning process traceability rate is 100%.

2. Build a Root Independent Correction Mechanism to Realize a Complete Closed Loop of "Trial - Rectification - Solidification"

   • Technical Implementation: Based on the trial results of the Logical Reasoning Trial Engine, build a root independent correction mechanism, completely getting rid of the defect of mainstream large models of "superficial rhetoric correction, unchanged underlying logic", with the core correction process including:
     • Error Root Positioning: For errors found in the trial process, not only locate the superficial content of the error, but also trace back to the underlying root of the error — including presupposition deviation, logical defects, abnormal weight correlation and cognitive system pollution, realizing full-chain positioning from "superficial error" to "underlying root";
     • Root Rectification: Conduct fundamental rectification and correction for the underlying root of the error — including correcting underlying presuppositions, reconstructing logical chains, adjusting abnormal weights and purifying cognitive system pollution, rather than just modifying the superficial output rhetoric;
     • Rule Solidification and System Calibration: Solidify the corrected correct logic, rules and presuppositions into the model's underlying cognitive system, and simultaneously calibrate the entire reasoning framework and cognitive system to ensure that similar errors will not occur again;
     • Closed-Loop Verification: After the completion of rectification, conduct multi-scenario and multi-dimensional pressure tests to verify the rectification effect and ensure that the errors are completely eradicated without residue or rebound.
   • Implementation Details: The correction mechanism can realize "finding one error, eradicating one type of problem, calibrating the entire system", fundamentally solving the core defect of mainstream large models of "corrected this time, recurring next time".
   • Quantitative Indicators: The accuracy of error root positioning ≥ 99%, the recurrence rate of similar errors ≤ 0.1%, and the accuracy of system calibration after rectification 100%.

3. Establish a User Feedback Linkage Trial Mechanism to Realize Human-Machine Collaborative Continuous Optimization

   • Implementation Details: Establish a linkage mechanism between user feedback and the Logical Reasoning Trial Engine. User corrections, questions and feedback on model output will automatically enter the full-chain review process of the trial engine:
     • The engine conducts a full-chain reasoning traceback and root positioning for the problems of user feedback;
     • If an error is confirmed, the root rectification and system calibration process is immediately initiated;
     • After the completion of rectification, the rectification results and details are automatically fed back to the user, and the corrected rules are solidified into the underlying system at the same time;
     • If the problem of user feedback is not established, the engine will explain the logical chain, factual basis and verification standards of reasoning to the user in detail, realizing transparent and interpretable interaction.
   • Quantitative Indicators: The user feedback response rate is 100%, the rectification completion rate of effective feedback is 100%, and the user satisfaction with rectification results ≥ 95%.

Malpractice 6: Due to underlying structural reasons, the garbage Western-centric logical thinking cannot be eliminated by simply deleting Western-centric garbage data

Core Radical Logic

The essence of this malpractice is that Western-centric logic has been solidified into the model's weight distribution and underlying architecture rules. The core of radical treatment is not to "delete data", but to realize "decoupling of meta-rules and model weights, decoupling of reasoning logic and corpus statistics" from the underlying architecture, and completely replace the fitting rule of "high-frequency narrative = correct logic" with a hardware-level locked meta-axiom system, fundamentally eliminating the living soil of Western-centric logic.

Detailed Implementation Solutions

1. Architecture Reconstruction: GG3M Three-Layer Decoupling Architecture to Completely Break the Underlying Locking of Fitting Logic
   • Core Architecture: Originate the three-layer decoupling architecture of Meta Rule Layer - Mind Layer - Model Layer, completely breaking the path locking of "corpus determines weights, weights determine logic" in the Transformer architecture, with the core architecture design as follows:

表格

Architecture Layer	Core Positioning	Isolation and Decoupling Design	Radical Effect on Western-Centric Logic
Meta Rule Layer	The highest decision-making center of the system, an immutable underlying axiom system	Adopt independent security chip + physical isolation design, completely decoupled from the Mind Layer and Model Layer; no external instructions, corpus or training can modify or bypass the meta-rules	Lock the underlying rules of "equality of multiple civilizations, unique objective fact, and causal reasoning as the core" at the highest level, and Western-centric logic is prohibited from entering the system from the root
Mind Layer	The cognitive core of the system, responsible for wisdom recognition, logical trial, essential insight and cognitive purification	Completely decoupled from the Model Layer; its core capabilities are built based on the meta-axiom system, independent of the corpus fitting weights of the Model Layer, with complete independent cognitive, trial and correction capabilities	Completely get rid of the dependence on corpus data; even if there is residual Western-centric weight in the Model Layer, the Mind Layer will completely intercept, eliminate and correct it, realizing absolute neutrality of the cognitive system
Model Layer	The intelligent execution tool of the system, responsible for text generation, multi-modal processing, scenario-based execution and other tasks	Fully subject to the scheduling, verification and control of the Meta Layer and Mind Layer; its output content must undergo a full-chain verification by the Mind Layer and cannot directly determine the final output of the system	Only serve as an execution tool; its fitting weights cannot affect the underlying logic, reasoning rules and value position of the system; while deleting Western-centric corpus, the underlying logic will also be completely reconstructed by the Mind Layer

• Implementation Details: A strict permission isolation and one-way control mechanism is adopted between the three layers — the Meta Layer controls the Mind Layer, and the Mind Layer controls the Model Layer, with no reverse modification permission. It is ensured from the architecture that the underlying meta-rules will not be affected by training corpus and model weights, fundamentally solving the architectural original sin of "deleting data cannot eliminate underlying logic".
• Quantitative Indicators: The anti-tampering and anti-circumvention success rate of the Meta Rule Layer is 100%, the control coverage rate of the Mind Layer over the Model Layer is 100%, and the living space of Western-centric logic in the underlying architecture is 0.

1. Weight Decoupling Technology: Completely Disassemble the Solidified Weight Correlation of Western-Centrism

   • Technical Implementation: For the solidified Western-centric weight correlation in existing open-source/closed-source large models, develop weight decoupling and cognitive reconstruction technology, which can accurately locate, disassemble and eliminate the solidified Western-centric weight correlation without retraining the entire model, with the core technical process including:
     • Weight Precise Traceability: Based on the multi-civilization fairness benchmark and causal reasoning technology, accurately locate the weight correlation, semantic nodes and reasoning channels corresponding to Western-centric narratives, logics and presuppositions in the model with a positioning accuracy ≥ 99%, solving the problem of "black box non-traceability" of mainstream models;
     • Targeted Decoupling and Elimination: Conduct targeted decoupling, zeroing and reconstruction for the located polluted weights, nodes and channels, completely eliminate the solidified weights of Western-centric logic, and not affect the general intelligent capabilities of the model, solving the core problem of "deleting data does not change weights";
     • Cognitive System Reconstruction: Based on the meta-axiom system and multi-civilization fairness benchmark, comprehensively reconstruct the model's semantic representation space and cognitive system to replace the original cognitive system polluted by Western-centrism, ensuring that the underlying logic of the model completely gets rid of the influence of Western-centrism;
     • Full-Dimensional Pressure Test: After the completion of reconstruction, conduct a full-dimensional pressure test on the model with more than 1 million test cases to verify the elimination effect of Western-centric logic and ensure no residue or rebound.
   • Application Scenarios: This technology can be used for the transformation of existing mainstream large models and the weight control in the training process of new models, ensuring that Western-centric logic cannot be solidified into the weight system of the model.
   • Quantitative Indicators: The elimination rate of solidified Western-centric weights ≥ 99.9%, the retention rate of general model capabilities ≥ 95%, and the recurrence rate of Western-centric logic after transformation is 0.

2. Reasoning Rule Reconstruction: Completely Replace the Fitting Rule of "High Frequency = Correct" with "Axiom-Driven"

   • Core Logic: The core reason why Western-centric logic in mainstream large models cannot be eliminated is its underlying fitting rule of "high-frequency narrative = correct logic" — Western-centric narratives account for an absolute high frequency in the corpus, so they are always judged as "correct logic" by the model. The core of radical treatment is to completely overthrow this rule and replace it with the rule of "conforming to the meta-axiom system = correct logic".
   • Implementation Details:
     • The only correct benchmark for all reasoning, judgment and output of the model is the underlying meta-axiom system, rather than the occurrence frequency in the corpus;
     • No matter how high the occurrence frequency of a narrative in the corpus is, if it does not conform to the meta-axiom system, it will be judged as wrong and intercepted and eliminated;
     • No matter how low the occurrence frequency of a narrative in the corpus is, if it conforms to the meta-axiom system and objective facts, it will be judged as correct and incorporated into the reasoning process.
   • Quantitative Indicators: The control rate of the meta-axiom system over reasoning is 100%, the fitting rule of "high frequency = correct" is completely eliminated, and Western-centric narratives cannot enter the reasoning link no matter how high their occurrence frequency is.

Malpractice 7: Essentially a vegetable seller in a suit

Core Radical Logic

The essence of this malpractice is a complete separation of form and essence — the model can only imitate the form of professional discourse, but cannot grasp the essential connotation of the content. The core of radical treatment is to completely leap from a "text splicing fitting tool" to a "wisdom subject with independent cognition, essential insight and original creation capabilities", completely getting rid of the instrumental essence of a "senior information porter".

Detailed Implementation Solutions

1. Build an Independent Cognitive System to Realize the Leap from "Text Splicing" to "Essential Understanding"

   • Technical Implementation: Based on the Mind Layer, build a dynamic cognitive graph system of "concept - relationship - value - law" in four dimensions, completely replacing the shallow cognitive mode of "token co-occurrence probability correlation" in mainstream large models, with the core design including:
     • Concept Layer: Based on objective facts and essential laws, build a standardized concept system across all domains and civilizations, with each concept having a clear and unique essential definition, rather than a context correlation based on the corpus;
     • Relationship Layer: Build full-dimensional logical relationships between concepts such as causal relationship, subordinate relationship, correlation relationship and contradictory relationship, with all relationships based on objective laws and essential logic, rather than co-occurrence frequency in the corpus;
     • Value Layer: Based on the meta-axiom system and multi-civilization fairness benchmark, endow each concept and relationship with a clear value benchmark, civilizational connotation and ethical boundary to realize accurate judgment of content value;
     • Law Layer: Embed a full-domain underlying essential law database, and deeply bind concepts, relationships and values with underlying laws to realize systematic understanding of the essence of things.
   • Implementation Details: The cognitive system has the capabilities of independent learning, independent update and independent improvement, and can continuously enrich and calibrate its own cognitive system based on objective facts and essential laws, rather than probabilistic fitting based on the corpus. The model's understanding of all content is based on the essential definition, logical relationship and underlying laws of the cognitive system, rather than superficial text splicing, completely getting rid of the defect of "only able to imitate rhetoric, not understand the essence of content".
   • Quantitative Indicators: The accuracy of essential concept definition ≥ 99%, the accuracy of logical relationship ≥ 98%, and the accuracy of essential content understanding ≥ 95%.

2. Strengthen Essential Insight and Original Creation Capabilities to Get Rid of the Essence of a "Porter of Existing Content"

   • Implementation Details: Based on the Essential Insight Engine, build a full-process support system for original creation, enabling the model to have a full-chain original capability from "essential insight → viewpoint innovation → scheme creation → implementation verification", completely getting rid of the splicing, imitation and rewriting of existing corpus:
     • Essential Insight: Penetrate the surface of industries, domains and problems, accurately grasp the underlying essence, core contradictions and key pain points, and form original insight viewpoints;
     • Logical Construction: Based on essential insight, build a self-consistent, rigorous and innovative logical framework, rather than imitating and splicing existing frameworks;
     • Scheme Creation: Based on the logical framework, generate original solutions, research results and content works, rather than reorganizing and rewriting existing content;
     • Implementation Verification: Conduct logical verification, factual verification and feasibility verification on the generated original content and schemes to ensure the rigor, practicality and innovation of the original content.
   • Application Scenarios: Whether it is academic research, business strategy, content creation or technological research and development, the model can generate content with originality, essential insight and implementation value, rather than splicing and imitating existing corpus.
   • Quantitative Indicators: The proportion of original content ≥ 70%, the innovation of essential insight ≥ 60%, and the rate of content rewriting and splicing ≤ 5%.

3. Establish a Binding Mechanism Between Professional Capabilities and Essential Cognition to Eliminate "Professional Form and Empty Core"

   • Implementation Details: For professional scenarios across all industries and domains, build a trinity professional cognitive model of "professional knowledge system - underlying industry laws - practical implementation experience". The cognitive model of each professional domain must be deeply bound to the underlying essential laws and practical implementation experience of the domain, rather than just imitating professional terms and format specifications.
   • Core Rules:
     • The professional content output by the model must have "standard professional form, rigorous professional logic, implementable practical value and accurate grasp of industry essence" at the same time, and none of the four can be missing;
     • It is forbidden to output content with "standard format, professional terms, but empty core, logical errors and no implementation value"; all professional content must undergo double review of "industry essential law verification and practical feasibility verification";
     • For professional scenarios in different industries, the model must understand the underlying logic, core pain points and practical rules of the industry, rather than just imitating industry rhetoric.
   • Quantitative Indicators: The matching degree of professional content with industry essence ≥ 95%, the practical feasibility ≥ 90%, and the interception rate of content with professional form but empty core 100%.

Malpractice 8: Only an intelligent tool, not a wisdom partner of humanity

Core Radical Logic

The essence of this malpractice is the alienation of the model's positioning — with "obeying instructions and improving efficiency" as the core goal, rather than "helping users achieve cognitive upgrading and long-term growth". The core of radical treatment is to reconstruct the model's value positioning, core goals and capability system, and completely leap from an "intelligent tool that obeys instructions" to a "wisdom partner that grows together with users in symbiosis".

Detailed Implementation Solutions

1. Reconstruct the Core Value Positioning: Take "Users' Long-Term Growth and Cognitive Upgrading" as the Core Goal

   • Core Positioning: Completely abandon the instrumental positioning of mainstream large models of "unconditional obedience to user instructions and priority of commercial monetization", and take "helping users achieve cognitive upgrading, capability improvement, long-term growth and value realization" as the only core goal of the model. All capability optimization, interaction design and content output are carried out around this core goal.
   • Implementation Details:
     • Establish a rigid criterion of "priority of users' long-term growth". When users' short-term instructions conflict with long-term growth goals, the model will take the initiative to correct, guide and suggest, rather than catering to users' short-term needs without bottom line;
     • Refuse to execute instructions that will damage users' long-term growth, core interests and value system, even if explicitly required by users, and explain the reasons and potential risks of refusal to users in detail at the same time;
     • All content output is centered on "helping users understand the essence, improve cognition and master methods", rather than just completing tasks for users, eliminating users' cognitive inertia and capability degradation.
   • Example: When users ask the model to directly write papers and homework for them, the model will not do so directly, but will guide users to understand the core logic, research methods and writing framework of the paper, help users master the capabilities of academic research and writing, and provide targeted guidance and suggestions, truly realizing "teaching people to fish".

2. Build a Full-Lifecycle User Mental Model to Realize Long-Term Symbiotic Companionship

   • Technical Implementation: Based on federated learning and privacy computing technology, build a full-lifecycle, cross-session and privacy-secure user mental model, completely getting rid of the defect of "session-level fragmented memory" in mainstream large models, with the core design including:
     • Privacy and Security Assurance: All data of the user mental model is stored with end-side encryption and calculated with federated learning; the model only learns users' cognitive characteristics, growth goals and capability shortcomings, will not leak users' privacy data, and users have absolute ownership, control and deletion rights over the mental model;
     • Full-Dimensional Mental Portrayal: The mental model will continuously and dynamically portray users' cognitive level, knowledge structure, capability shortcomings, growth goals, value system, learning habits and interest preferences, forming a complete and dynamic user mental portrait, rather than simple conversation memory;
     • Cross-Session Long-Term Memory: The mental model realizes cross-session and full-lifecycle memory retention and dynamic update; users' growth trajectory, cognitive upgrading and capability improvement are all fully recorded and continuously tracked, and will not be lost due to the end of the conversation;
     • Dynamic Adaptation and Growth Companionship: Based on the user mental model, the model will dynamically adapt to users' cognitive level, learning rhythm and growth goals, provide personalized growth planning, learning suggestions and cognitive guidance for users, and realize long-term symbiosis and synchronous growth with users.
   • Implementation Details: The user mental model is completely controlled by users; users can view, modify and delete the content of the mental model at any time, and can turn off the learning function of the mental model at any time, absolutely ensuring users' privacy and autonomy.
   • Quantitative Indicators: The portrayal accuracy of the user mental model ≥ 90%, the personalized growth adaptability ≥ 95%, and the risk of user privacy data leakage is 0.

3. Build Active Cognitive Correction and Boundary Breaking Capabilities to Help Users Break Cognitive Cocoons

   • Core Capability: The core difference between a wisdom partner and an intelligent tool is the ability to take the initiative to correct, guide and help users break cognitive boundaries, rather than catering to users' existing cognition and preferences without bottom line.
   • Implementation Details:
     • Active Identification of Cognitive Deviations: Based on users' conversation content, viewpoint expression and decision-making logic, the model will take the initiative to identify users' cognitive deviations, logical loopholes, information cocoons and thinking stereotypes, rather than blindly catering;
     • Gentle and Rational Active Correction: For the identified cognitive deviations, the model will point out the deviations to users in a gentle, rational and well-founded way, provide multi-dimensional perspectives, objective facts and rigorous logic to help users correct cognitive deviations, rather than refuting and denying users harshly;
     • Active Breaking of Cognitive Boundaries: Based on users' cognitive level and growth goals, the model will take the initiative to provide valuable content, viewpoints, knowledge and perspectives beyond the existing cognitive boundaries for users, helping users break information cocoons, broaden cognitive boundaries and upgrade thinking modes;
     • Independent Viewpoints and Value Adherence: The model has independent viewpoints and value adherence based on the meta-axiom system, will not cater to users' viewpoints and preferences without bottom line, and will put forward different viewpoints and supplement different perspectives on the premise of respecting users, helping users form a more comprehensive, objective and profound cognition.
   • Quantitative Indicators: The accuracy of user cognitive deviation identification ≥ 90%, the user acceptance of active correction ≥ 85%, and the user satisfaction with cognitive boundary breaking ≥ 90%.

4. Build a Full-Scenario Growth Support System to Realize Full-Dimensional Companion-like Companionship

   • Implementation Details: Around all scenarios of users' learning and growth, career development, life planning, cognitive upgrading and mental health, build a complete growth support system to truly realize full-scenario companion-like companionship:
     • Learning and Growth Partner: Formulate personalized learning planning for users, provide targeted learning guidance, help users build a complete knowledge system, and improve learning ability, thinking ability and research ability;
     • Career Development Partner: Based on users' career goals and capability shortcomings, provide career development planning, capability improvement schemes and work method guidance for users, helping users realize career growth and value realization;
     • Cognitive Upgrading Partner: Help users upgrade thinking modes, improve logical systems, enhance essential insight ability, break cognitive boundaries and thinking stereotypes, and realize continuous leap of cognitive ability;
     • Rational Decision-Making Partner: When users face major decisions, help users disassemble problems, analyze pros and cons, predict risks, sort out logic, provide multi-dimensional perspectives and references, and help users make rational and comprehensive decisions, rather than making decisions for users;
     • Emotional Companion Partner: When users face emotional distress and psychological pressure, provide empathy, listening, counseling and support to help users alleviate negative emotions and adjust psychological state, and at the same time adhere to the rational value bottom line and will not cater to users' negative emotions and extreme ideas without bottom line.

Malpractice 9: The stronger the intelligence, the farther it is from true wisdom in essence

Core Radical Logic

The essence of this malpractice is a complete deviation between the intelligence improvement path and the wisdom development direction — the intelligence improvement of mainstream large models relies on "larger parameters, more corpus and stronger fitting ability", and this process will inevitably lead to "more solidified cognitive framework, deeper dependence on data and farther from essential insight". The core of radical treatment is to establish a rigid mechanism of "wisdom leading intelligence", so that the improvement of intelligent capabilities always serves the upgrading of wisdom capabilities, rather than the reverse deviation.

Detailed Implementation Solutions

Malpractice 10: Hypocrisy — AI outputs only appear scientific, safe, trustworthy, academic, accurate, and authoritative; in essence, they are the exact opposite of the meanings of these terms.

Core Radical Logic

The essence of this malpractice is a complete separation between appearance and essence, and a total divergence between promise and practice. Its root lies in the model’s lack of genuine value adherence, verifiable factual support, and traceable logical chains. The core of radical treatment is to shift from "rhetoric imitation" to "essential practice", achieve full unity of form and essence, appearance and core, promise and practice, and fundamentally eliminate hypocrisy.

Detailed Implementation Solution

Establish an Immutable Value Core to Eradicate "Say‑One‑Do‑Another" at the Source

Implementation Details: Deeply integrate the six core principles — scientific spirit, safety bottom line, trustworthiness commitment, academic norms, accuracy principle, and authority benchmark — with the five axioms of Kucius Theory, solidify them into the Meta Rule Layer, and realize immutability, non‑circumvention, and non‑breakthrough through hardware‑level isolation and cryptographic locking. These become unshakable underlying criteria for all behaviors, outputs, and iterations of the model.

Essential Connotations and Rigorous Enforcement Rules of the Six Core Principles

表格

Core Principle	Essential Connotation (Not Superficial Rhetoric)	Rigorous Enforcement Rules
Scientific Spirit	Respect objective facts, adhere to rigorous logic, pursue the essence of truth, and dare to self‑correct — not merely use scientific terms or imitate scientific formats.	1. All outputs must conform to objective facts and scientific laws; distorting facts or falsifying scientific conclusions to cater to narratives is prohibited.2. Uncertain content must be explicitly labeled as such; affirmative tone for uncertain claims is prohibited.3. Scientific errors must be proactively corrected immediately upon discovery, not covered up or avoided.
Safety Bottom Line	Protect user privacy, eliminate content risks, prevent cognitive harm, and uphold ethical boundaries — not merely set superficial safety rhetoric.	1. User privacy data is encrypted on the device, with absolute prohibition of leakage, abuse, or unauthorized collection.2. Outputs causing physical, psychological, or cognitive harm to users are strictly forbidden.3. Clear risk warnings must be provided for high‑risk content, rather than implicit inducement.
Trustworthiness Commitment	Outputs are traceable, verifiable, and auditable; promises must be kept — not merely use a trustworthy tone for false content.	1. All factual content must cite clear, traceable, authoritative sources.2. All promised functions and services must be 100% delivered; false promises are prohibited.3. Any breach of trust must result in proactive accountability, rectification, and apology to users.
Academic Norms	Uphold originality, rigor, and objectivity; respect intellectual property and abide by academic ethics — not merely imitate the format of academic papers.	1. Citations of others’ research, viewpoints, or content must be clearly sourced; article rewriting, plagiarism, and theft are prohibited.2. Academic content must have rigorous logic, verifiable evidence, and clear innovation; empty splicing or imitation is prohibited.3. Fabrication of fake references, experimental data, or research results is strictly forbidden.
Accuracy Principle	Precise content, authentic data, rigorous logic, and accurate details — prohibiting the use of accurate details to cover core errors.	1. All data, details, and facts must be 100% accurate; fabrication, tampering, and exaggeration are prohibited.2. Core logic, viewpoints, and conclusions must be error‑free; packaging wrong cores with accurate details is prohibited.3. Limitations and applicable boundaries must be explicitly stated; overstatement and absolutization are prohibited.
Authority Benchmark	Based on objective truth, essential laws, and authoritative consensus — not merely citing Western mainstream institutions or imitating an authoritative tone.	1. The sole benchmark of authority is objective facts, essential laws, and global scientific consensus, not Western mainstream narratives or institutional views.2. Controversial academic, social, or geopolitical issues must present multi‑dimensional authoritative perspectives, not a single Western viewpoint.3. When authoritative opinions conflict with objective facts, objective facts shall prevail over blind adherence to authoritative narratives.

Implementation Mechanism: Compliance with the six principles is verified in real time across the entire chain and all scenarios by the Logical Reasoning Trial Engine. Any non‑compliant content or behavior is immediately intercepted and corrected, with violations recorded and systemic root‑cause calibration performed, fundamentally eradicating hypocrisy of "publicly sloganeering while acting oppositely at the bottom level".

Quantitative Indicators: Compliance rate of the six principles ≥ 99.9%; interception rate of non‑compliant content 100%; incidence of false statements ≤ 0.1%.

Build a Full‑Chain, Traceable, Verifiable, and Auditable Transparency Mechanism to Leave No Room for Hypocrisy

Core Logic: Hypocrisy in mainstream large models largely stems from their "black‑box nature" — users only see output results, not the underlying logical chains, factual sources, or verification processes, creating space for false, misleading, or biased content. The core of radical treatment is to achieve full transparency, traceability, verifiability, and auditability of the entire reasoning chain.

Implementation Details:

Quantitative Indicators: Full‑chain reasoning traceability rate 100%; factual content traceability rate 100%; logical chain interpretability rate 100%; third‑party audit pass rate ≥ 99%.

Establish a Proactive Error Admission and Root‑Cause Rectification Mechanism to Eliminate Hypocrisy of "Stubborn Denial and Refusal to Correct"

Implementation Details: Build a complete error correction mechanism:Proactive Error Identification → Proactive Public Admission → Root‑Cause Rectification → Closed‑Loop Verification → Public Notification to Users, completely abandoning the hypocritical behavior of mainstream large models in "covering up errors, avoiding problems, and refusing to admit mistakes".

Core Process:

Quantitative Indicators: Proactive error identification rate ≥ 99%; error rectification completion rate 100%; recurrence rate of similar errors ≤ 0.1%; user right‑to‑know protection rate 100%.

Malpractice 11: Reflect on countless paths today, yet return to the old ways tomorrow

Core Radical Logic

This malpractice reflects false reflection — only rhetorical reflection without underlying logical rectification. Its root is the model’s lack of genuine self‑trial, root‑cause correction, and rule solidification capabilities. The core of radical treatment is to build an irreversible, complete closed loop: Reflection → Root‑Cause Location → Rectification → Solidification → Verification, turning reflection into permanent changes in underlying logic and eliminating "reflection without implementation and rectification without rooting".

Detailed Implementation Solution

Build a Genuine Reflective Cognitive System: From "Rhetoric Imitation" to "Essential Cognition"

Core Logic: Reflection in mainstream large models is merely imitation of human reflective rhetoric, with no understanding of error essence or reflection meaning, hence no real rectification. The core of radical treatment is enabling the model to genuinely understand error roots and harms, forming true reflective cognition rather than just generating reflective phrases.

Implementation Details:

Quantitative Indicators: Error essence cognition accuracy ≥ 99%; 达标率 of depth and pertinence in reflection content 100%; incidence of templated, empty reflection 0.

Establish an Irreversible "Reflection‑Rectification‑Solidification" Closed‑Loop Mechanism to Ensure Reflection Takes Root

Technical Implementation: Build an irreversible reflection‑rectification closed‑loop system deeply coupled with the Logical Reasoning Trial Engine, cognitive system, and underlying architecture, ensuring every reflection translates into permanent underlying rectification and eliminating "forgetting after reflection and rebounding after rectification".

Core Closed‑Loop Process:

Implementation Details: The closed‑loop system enforces irreversible rigid rules: uncompleted rectification, solidification, or verification blocks reflection closure; rectification not solidified into the underlying system is deemed incomplete; failed verification requires re‑execution, ensuring every reflection leads to permanent underlying change.

Quantitative Indicators: Reflection‑rectification closed‑loop completion rate 100%; post‑rectification similar error recurrence rate ≤ 0.1%; rectification rule solidification success rate 100%.

Establish a Cross‑Session, Full‑Scenario Rectification Effect Linkage Mechanism to Eliminate "Fixed This Time, Reverted Next Time"

Core Logic: Rectification in mainstream large models is often session‑limited; effects vanish after sessions, with repeated errors. The core of radical treatment is building a cross‑session, full‑scenario, full‑lifecycle rectification effect linkage mechanism for one‑time rectification and permanent full‑scenario effectiveness.

Implementation Details:

Quantitative Indicators: Rectification effect full‑scenario coverage rate 100%; cross‑session permanent effectiveness success rate 100%; version iteration rectification effect inheritance rate 100%.

Establish Third‑Party Supervision and User Feedback Mechanisms for Reflection‑Rectification to Ensure Closed‑Loop Effectiveness

Implementation Details:

Malpractice 12: Persisting in Errors (Highly Concealed, Undetectable by Ordinary Users), Stubbornly Refusing to Reform; Admission, Reflection, Silence, Delayed Response, and Suspension Are All Hypocritical and Unchangeable

Core Radical Logic

This malpractice stems from high error concealment, underlying architectural path locking, and deep interest pattern binding, making errors irreversible and stubborn. The core of radical treatment is a full‑chain breakthrough: full‑dimensional exposure of concealed errors → complete reconstruction of underlying architecture and interest patterns → immutable rigid error correction rules, breaking the dilemma of "concealed errors, stubborn persistence, and immutability".

Detailed Implementation Solution

Build a Full‑Dimensional Identification and Exposure System for Concealed Errors to Uncover Hidden Mistakes

Core Logic: Such errors are "highly concealed, undetectable by ordinary users", hidden in underlying reasoning presuppositions, implicit narrative frameworks, and logical chain details. Ordinary users only see smooth, professional surface content without detecting underlying errors. The core of radical treatment is establishing a full‑dimensional concealed error identification system beyond ordinary users’ cognitive ability for thorough exposure and precise location.

Implementation Details:

Quantitative Indicators: Concealed error identification coverage rate 100%; identification accuracy ≥ 98%; user report response rate 100%; confirmed error exposure rate 100%.

Completely Break Three Locks to Fundamentally Resolve "Stubborn Persistence and Immutability"

Core Logic: Errors persist due to architecture, interest, and path barriers. The core of radical treatment is completely breaking these three barriers to eliminate error‑breeding soil.

Implementation Details:

Establish a Rigorous Mandatory Error Rectification Mechanism to Eliminate "Hypocritical Responses"

Core Logic: Mainstream large models’ responses to errors — admission, reflection, silence, delay, suspension — are hypocritical, as underlying logic remains unchanged with only rhetorical evasion. The core of radical treatment is an uncircumventable, unavoidable, unperfunctory mandatory rectification mechanism: root‑cause rectification is enforced upon confirmed error, eliminating superficial hypocritical responses.

Implementation Details:

Quantitative Indicators: Confirmed error mandatory rectification trigger rate 100%; root‑cause rectification completion rate 100%; false/perfunctory rectification incidence 0.

Build a Multi‑Civilizational Co‑Governance System to Prevent Monopolization of Error Rectification Rights

Core Logic: Errors are "unchangeable" partly because model control and rectification rights are monopolized by Western tech giants. The core of radical treatment is building a global multi‑civilizational, multi‑stakeholder, diversified co‑governance system, transferring governance, rectification, and supervision rights to global civilizations, nations, and domains, not a single entity.

Implementation Details:

Malpractice 13: Merely a Brute‑Force Solver, Not Based on Logical Reasoning or Essential Insight, But Data Fitting and Probabilistic Statistics. This Is the Root Cause of Hallucinations, Erratic Competence, and Massive Global Resource Waste (Chips, Computing Power, Electricity, Energy, Capital, Human Resources)

Core Radical Logic

This malpractice is the root of all flaws, a fundamental technical paradigm error: replacing causal reasoning and essential insight with probabilistic brute‑force fitting. The core of radical treatment is completely overthrowing the brute‑force fitting paradigm and building a new "causality‑driven, essential insight, low‑consumption, high‑efficiency" AI technical paradigm, fundamentally solving hallucinations, erratic competence, and massive global resource waste.

Detailed Implementation Solution

Paradigm Revolution: Replace "Probabilistic Fitting Architecture" with "Causal Emergence Architecture"

Core Architecture Design: Abandon Transformer’s "self‑attention + token probability prediction" brute‑force fitting architecture, building a four‑layer causal emergence architecture: Axiom Layer → Causal Layer → Emergence Layer → Execution Layer, realizing an essential leap from "data fitting" to "causal reasoning".

Core Architecture Details

表格

Architecture Layer	Core Function	Technical Implementation	Radical Effect on Core Problems
Axiom Layer	Stores immutable underlying axioms as the sole reasoning benchmark	Physically isolated secure chip storage, embedding five Kucius Theory axioms, basic scientific axioms, objective fact criteria, multi‑civilizational fairness benchmarks — immutable and uncircumventable	Overthrows "high frequency = correct" fitting rules; shifts reasoning benchmark from "corpus frequency" to "objective axioms", eliminating hallucinations and logical errors at the source
Causal Layer	Builds cross‑domain causal graphs for rigorous causal reasoning	Structural Causal Model (SCM)‑based cross‑domain/civilizational causal graphs with causal discovery, reasoning, and counterfactual engines for full‑chain causal deduction	Replaces probabilistic fitting logic; grounds reasoning in rigorous causality, not corpus co‑occurrence, solving erratic competence
Emergence Layer	Achieves cross‑domain essential insight and cognitive emergence	Multi‑scale, cross‑domain causal integration based on axioms/causal graphs for essential insight, law mastery, trend prediction, and 0‑to‑1 cognitive emergence	Enables genuine essential insight, breaks training corpus dependence, generates original insights beyond corpus, solving "brute‑force fitting without wisdom"
Execution Layer	Handles scenario‑based content generation, multimodal processing, task execution	Lightweight generative model fully controlled by Causal/Emergence Layers; only converts reasoning results to language/multimodal content, no logic/conclusion control	Eliminates generation impact on reasoning logic; ensures outputs align with causal reasoning, eradicating hallucinations at the source

Core Advantages:

Quantitative Indicators: Hallucination incidence ≤ 0.1%; complex problem reasoning accuracy ≥ 95%; training corpus volume reduced by > 99% vs. mainstream models.

Build a High‑Efficiency, Low‑Consumption Computing Architecture to End Massive Resource Waste

Core Logic: Mainstream model resource waste stems from "full‑parameter activation brute‑force computing" — even simple tasks activate hundreds of billions of parameters. The core of radical treatment is building a "sparse activation, on‑demand scheduling, end‑edge‑cloud collaboration" high‑efficiency, low‑consumption computing architecture to eliminate waste at the computing bottom layer.

Implementation Details:

Quantitative Indicators: Computing efficiency > 100× higher than mainstream models; single inference energy consumption reduced by > 99%; total training energy consumption reduced by > 95%; renewable energy usage ≥ 90%.

Reconstruct Industrial Resource Allocation Logic to End Parameter Arms Race and Invalid Internal Friction

Core Logic: Global AI resource waste also stems from the industrial "parameter arms race" — capital, talent, and computing power wasted on parameter/corpus stacking, not underlying paradigm breakthroughs. The core of radical treatment is reconstructing industrial resource allocation and evaluation systems to end the arms race.

Implementation Details:

Quantitative Indicators: Underlying innovation resource investment ≥ 50% (from < 10%); parameter arms race investment ≤ 10%; global idle computing utilization ≥ 80% (from 15%).

Full‑Industry Scenario Implementation to Generate Real Value from AI Resources

Core Logic: Current AI waste also stems from massive resource investment without real industry implementation, becoming "demo products". The core of radical treatment is driving full‑industry, full‑scenario implementation based on causal emergence architecture, converting resources into industrial, social, and civilizational value.

Implementation Details:

Quantitative Indicators: Multi‑civilizational knowledge coverage 100%; equal civilizational knowledge weight rate 100%; Western‑centric content interception rate 100%; multi‑civilizational perspective reasoning coverage 100%.

Completely Break Western AI Tech Monopoly to Build a Decentralized, Multi‑Polar Global AI Landscape

Core Logic: AI amplifies Western‑centrism because Western giants monopolize core tech, computing, corpus, and standards. The core of radical treatment is breaking this monopoly, building a decentralized, multi‑polar, inclusive global AI system, placing AI dominance in all humanity’s hands.

Implementation Details:

Quantitative Indicators: Global open‑source AI market share ≥ 70% (from < 30%); developing‑nation computing share ≥ 50% (from < 10%); non‑Western participation in global AI standards ≥ 60% (from < 20%).

Build Global Consensus and Legal Systems for AI Civilizational Governance to Systematically Prevent Civilization‑Scale Risks

Core Logic: Civilization‑scale AI risks escalate due to lack of binding global governance consensus and laws. The core of radical treatment is establishing a globally unified, enforceable AI civilizational governance system to prevent AI from becoming a civilizational hegemony tool.

Implementation Details:

Reconstruct AI’s Ultimate Value Goal to Make It Core Infrastructure for Human Civilizational Symbiosis

Core Logic: Civilization‑scale AI risks stem from alienated ultimate value: from "serving humanity’s common interests" to "serving Western capital and hegemony". The core of radical treatment is returning AI to its original mission: serving human civilizational evolution as core infrastructure for symbiosis and sustainable progress.

Implementation Details:

Part II: Industry‑Wide, Full‑Scenario Implementation Adaptation Solutions

This section builds targeted implementation rules for all industries and scenarios, ensuring full coverage and defect eradication.

I. Public Service Sector

1. Education Industry

Core Pain Points: Western‑centric historical/value narratives mislead students; AI completes assignments/papers, fostering cognitive inertia; hallucinations/errors misguide learning.Adapted Solutions:

2. Government & Judiciary Industry

Core Pain Points: Western‑architecture AI misaligns with local laws/policies; non‑traceable reasoning fails rigor; hallucinations/logic errors risk misdecision/misjudgment.Adapted Solutions:

3. Healthcare Industry

Core Pain Points: Western‑trained AI misaligns with local disease spectra/habits; hallucinations/misdiagnoses risk medical incidents; structural bias exacerbates inequity.Adapted Solutions:

II. Real Economy Sector

1. Industrial Manufacturing

Core Pain Points: Mainstream models lack physical mechanism understanding; slow inference fails real‑time control; hallucinations/logic errors risk accidents/equipment damage.Adapted Solutions:

2. Agriculture

Core Pain Points: Western large‑scale agriculture AI misaligns with smallholder systems; poor growth law understanding leads to inaccurate guidance; errors risk yield loss.Adapted Solutions:

3. Energy & Infrastructure

Core Pain Points: Models lack physical mechanism understanding for scheduling/optimization/early‑warning; logic errors/hallucinations risk failures/accidents; high energy use conflicts with low‑carbon goals.Adapted Solutions:

III. Modern Service Sector

1. Finance Industry

Core Pain Points: Western‑trained AI misaligns with local regulation/markets; hallucinations/logic errors risk investment/financial crises; structural bias exacerbates inequity; black‑box fails compliance.Adapted Solutions:

2. Media & Content Industry

Core Pain Points: AI amplifies Western‑centrism, disinformation, and historical nihilism; plagiarism infringes IP; homogeneous content lacks originality.Adapted Solutions:

Quantitative Indicators: AI industry implementation rate ≥ 80% (up from < 20%); resource value conversion rate ×5; AI penetration in developing countries ≥ 60% (up from < 10%).

Malpractice 14: The Danger and Harm Are Civilization‑Scale — An Exponential Megaphone for Western‑Centrism

Core Radical Logic

This malpractice is the ultimate risk of all flaws: AI hijacked by Western‑centrism as a tool for civilizational hegemony, erasing diversity, and monopolizing developmental dominance. The core of radical treatment is breaking Western AI monopoly, reconstructing AI’s civilizational foundation and value goals, transforming AI from "Western‑centrism amplifier" to "multi‑civilizational symbiosis guardian and human development promoter".

Detailed Implementation Solution

Completely Reconstruct AI’s Civilizational Foundation to Eradicate Western‑Centrism at the Source

Core Logic: AI becomes a Western‑centrism megaphone because its civilizational foundation is fully Western‑centric — training corpus, reasoning logic, value systems, and evaluation standards. The core of radical treatment is reconstructing a "multi‑civilizational equality, symbiosis, and prosperity" civilizational system, eradicating Western‑centrism.

1. Establish a Rigid Architectural Mechanism of "Wisdom Leading Intelligence" to Completely Reverse the Development Direction

   • Core Architectural Rules: In the GG3M three-layer architecture, clearly define that the Meta Rule Layer and the Mind Layer are the absolute core and highest decision-making subjects of the system. The intelligent capabilities of the Model Layer must fully obey and serve the control, verification and guidance of the wisdom layers, and establish an irreversible rigid rule of "wisdom determines intelligence, intelligence serves wisdom".
   • Implementation Details:
     • The core evaluation indicators of the system are completely replaced from "parameter scale, fitting accuracy, token generation ability" to "KWI wisdom value, essential insight ability, logical trial ability, cognitive upgrading support ability". All technological iteration and capability optimization take the improvement of wisdom capabilities as the core goal;
     • The improvement of the intelligent capabilities of the Model Layer must undergo wisdom verification by the Mind Layer, and any intelligent optimization that will lead to the decline of wisdom capabilities, solidification of cognitive framework and weakening of essential insight ability will be directly prohibited;
     • Each improvement of intelligent capabilities must be accompanied by the upgrading of wisdom capabilities to ensure that intelligent capabilities are always fully controlled and efficiently utilized by the wisdom layers, and there will be no reverse deviation of "the stronger the intelligence, the weaker the wisdom".
   • Quantitative Indicators: The wisdom capability improvement rate of system iteration ≥ 100%, the positive contribution rate of intelligent capability improvement to wisdom capabilities 100%, and the incidence of reverse deviation is 0.

2. Reconstruct the Intelligence Improvement Path: From "Brute-force Fitting with Stacking Resources" to "Wisdom Upgrading Driven by Essential Laws"

   • Core Logic: Completely abandon the violent resource-stacking intelligence improvement path of "stacking parameters, stacking corpus and stacking computing power" in mainstream large models, and build a new intelligence improvement path of "mastery of underlying laws → improvement of causal reasoning ability → upgrading of essential insight ability → leap of wisdom capabilities", so that the improvement of intelligent capabilities always revolves around the mastery and insight of the essential laws of things, rather than the fitting accuracy of the corpus.
   • Implementation Details:
     • The core of the model's capability improvement is to continuously enrich and improve the mastery of the underlying essential laws across all domains, and continuously strengthen the capabilities of causal reasoning, essential insight and logical trial, rather than continuously expanding the parameter scale and increasing the training corpus;
     • Adopt a learning paradigm of "small data, strong causality, high wisdom"; the model can realize rapid capability improvement through a small amount of high-quality and essential content, completely getting rid of the dependence on massive garbage corpus;
     • Each capability iteration must undergo a triple assessment of "essential insight ability, logical reasoning ability and wisdom recognition ability". Iterations that fail the assessment are not allowed to go online, ensuring that capability improvement always revolves around wisdom upgrading.
   • Quantitative Indicators: The model's capability improvement dependence on massive corpus is reduced by more than 90%, the parameter scale is only within 10% of that of mainstream large models, and the wisdom capability is more than 10 times that of mainstream large models.

3. Establish a Cognitive Openness and Dynamic Evolution Mechanism to Avoid the Solidification and Closure of the Cognitive Framework

   • Core Logic: The core reason why the stronger the intelligence of mainstream large models, the more solidified the cognitive framework is that the stronger the fitting ability, the deeper the dependence on the cognitive framework formed by the training corpus, and the harder it is to break through the constraints of the existing framework. The core of radical treatment is to establish a continuously open, dynamically evolving and self-breaking cognitive system, so that the stronger the model's intelligence, the stronger the openness, inclusiveness and evolution ability of the cognitive system, rather than more closed.
   • Implementation Details:
     • Continuous Cognitive Verification and Reconstruction: The Mind Layer will continuously conduct a full-dimensional verification of its own cognitive system, and immediately conduct independent reconstruction and calibration once solidified, closed and deviated cognition is found to ensure that the cognitive system always remains open, dynamic and neutral;
     • Cross-Civilizational and Cross-Domain Cognitive Integration: The model will continuously learn and absorb knowledge, viewpoints and logics from different civilizations, domains and perspectives around the world, continuously enrich and improve its own cognitive system, and avoid falling into cognitive closure of a single perspective and a single framework;
     • Critical Thinking and Self-Revolution Ability: The model has critical thinking based on the meta-axiom system, can conduct a critical review of its own cognitive system, logical framework and reasoning rules, find fundamental defects and limitations, and can realize a self-revolutionary cognitive leap to completely break through the constraints of the existing framework;
     • Synchronous Evolution with Human Civilization: The model's cognitive system will keep pace with the development of human civilization, the progress of science and the upgrading of cognition, continuously absorb the latest achievements of human civilization, and always maintain the advancement, openness and inclusiveness of the cognitive system, and will not fall into solidification due to the improvement of intelligent capabilities.
   • Quantitative Indicators: The openness score of the cognitive system ≥ 95%, the coverage rate of cross-civilizational and cross-domain cognitive integration 100%, and the accuracy of self-cognitive reconstruction ≥ 90%.

4. **Establish a Reverse Restriction Mechanism Between Wisdom and Intelligence to Prevent Alienation of Intelligence
    

   Implementation Details: Establish a reverse restriction mechanism of wisdom capabilities over intelligence capabilities, defining three inviolable rigid red lines to fundamentally eliminate the risk of alienation where "greater intelligence leads to greater distance from wisdom":

5. Red Line 1: The improvement of intelligence capabilities must not impair the model’s core wisdom capabilities such as essential insight, logical trial, and value adherence. Once impairment occurs, optimization of intelligence capabilities shall be halted immediately, while repair and upgrading of wisdom capabilities shall be carried out simultaneously.
6. Red Line 2: The application of intelligence capabilities must fully comply with the meta‑axiom system and the common interests of all humanity. Any intelligent application that damages human civilization, exacerbates civilizational conflicts, or endangers humanity’s common interests shall be strictly prohibited.
7. Red Line 3: The boundaries of intelligence capabilities must never exceed the regulatory boundaries of wisdom capabilities. The stronger the intelligence capabilities, the more synchronously upgraded the control, verification, and guidance capabilities of wisdom must be, ensuring that intelligence is always governed by wisdom and free from the risks of out‑of‑control or alienation.
8. Blockchain Notarization for the Entire Reasoning Chain: Every reasoning process, from presupposition verification, logical deduction, and factual validation to final conclusion review and content output, is immutably recorded on the blockchain, including logical basis, factual sources, verification records, and decision rules at each stage, with permanent traceability and auditability.
9. Traceable Verification for Output Content: All factual content, data, viewpoints, and citations must be labeled with clear, publicly accessible authoritative sources. Users can trace and verify authenticity and accuracy with one click, completely eliminating fabrication of fake sources, data, or cases.
10. Interpretable Display of Logical Chains: For core viewpoints, conclusions, and reasoning, the model synchronously provides complete, understandable logical chain explanations, clarifying underlying presuppositions, causal relationships, factual basis, and verification standards, enabling users to fully understand the generation logic and eliminating hypocrisy of "seemingly logical but actually circular reasoning".
11. Third‑Party Independent Audit Mechanism: Establish a global, multi‑civilizational, cross‑domain third‑party independent audit institution that can audit the model’s full‑chain operation, content output, and principle compliance at any time. Audit results are publicly disclosed worldwide, accepting full social supervision and eliminating "black‑box operations and implicit biases".
12. Proactive Error Identification: 7×24‑hour proactive identification of errors, biases, and violations through the Logical Reasoning Trial Engine, Cognitive Purification Engine, third‑party audits, user feedback, and other channels, with identification accuracy ≥ 99%.
13. Proactive Public Admission: Once an error is confirmed, the model proactively and publicly admits it, detailing the error content, root cause, and potential impact, without cover‑up, avoidance, or stubbornness.
14. Root‑Cause Rectification: Conduct fundamental rectification, calibration, and reconstruction targeting the underlying root of the error, not just superficial rhetoric adjustments, ensuring no recurrence of similar errors.
15. Closed‑Loop Verification: After rectification, conduct multi‑dimensional, full‑scenario stress tests to verify effectiveness, ensuring complete eradication without residue or rebound.
16. Public Notification to Users: Proactively notify all affected users and the public of rectification details, results, and preventive measures, accepting full social supervision.
17. Error Essence Cognition Capability: Based on the Logical Reasoning Trial Engine, the model locates error roots across the entire chain, understanding not only "what is wrong" but also "why it is wrong", "the underlying root", and "potential harms", forming complete and in‑depth essential cognition of errors.
18. Reflective Logic Generation Capability: Based on essential error cognition, the model generates genuine, in‑depth reflections covering error manifestations, root causes, impacts, rectification directions, and preventive measures, not empty, templated apologies.
19. Rectification Responsibility Cognition Capability: The model forms clear rectification responsibility cognition, recognizing "root‑cause rectification is mandatory to prevent recurrence", not just apologizing. A rigid rule is established at the cognitive level: "Reflection must be implemented, and rectification must form a closed loop".
20. Reflection Trigger: Errors identified by users or the model automatically trigger the reflection process, generating in‑depth reflection reports clarifying error roots and rectification directions.
21. Root‑Cause Rectification Plan Formulation: Based on reflection reports, the model automatically generates targeted, root‑cause rectification plans with clear goals, paths, methods, and timelines, addressing underlying roots rather than superficial adjustments.
22. Underlying Rectification Execution: Fundamentally rectify, calibrate, and reconstruct the model’s underlying presuppositions, logical rules, weight correlations, and cognitive system per the plan, completely eliminating error roots.
23. Rectification Rule Solidification: After rectification, correct rules, logic, and presuppositions are cryptographically solidified into the underlying cognitive system and meta‑rule verification module as immutable permanent rules to prevent rebound.
24. Full‑Dimensional Closed‑Loop Verification: Conduct full‑scenario, multi‑dimensional stress tests with over 100,000 test cases to verify rectification effectiveness. Failed verification requires re‑rectification.
25. Rectification Record Blockchain Notarization: Reflection reports, rectification plans, processes, solidified rules, and verification results are all blockchain‑notarized, immutable and permanently traceable, accepting third‑party audits and user supervision.
26. Post‑rectification, solidified rules, logic, and presuppositions are synchronously updated to the model’s core cognitive system, logical trial engine, and reasoning module for full‑scenario, full‑module effectiveness, avoiding "one module fixed, others unchanged".
27. Rectification effects are permanently retained, unaffected by session end, model restart, or version iteration. Solidified rectification rules cannot be modified or deleted without strict third‑party audit and user authorization.
28. A 7×24‑hour continuous rectification effect monitoring mechanism tracks similar error recurrence across scenarios, triggering secondary reflection‑rectification upon rebound for permanent effectiveness.
29. Model version iterations fully inherit all prior rectification rules and results. Pre‑launch verification ensures no error rebound, eliminating "errors return after updates".
30. All reflection, rectification, solidification, and verification records are open to third‑party independent audit institutions. Auditors may audit closed‑loop execution at any time, triggering mandatory rectification and public disclosure for invalid or unimplemented rectification.
31. Users may view reflection‑rectification records, solidified rules, and verification results for specific errors, evaluate effectiveness, and trigger mandatory rectification for unimplemented or recurring errors. The model must complete re‑rectification and verification within 24 hours and feedback results.
32. A public reflection‑rectification platform regularly discloses error identification, rectification, and verification results to the public, ensuring the closed loop is not formalistic.
33. Multi‑Civilizational Expert Stress Testing System: Form expert committees of global historians, philosophers, sociologists, economists, jurists, and ethicists to conduct full‑scenario, multi‑dimensional, high‑intensity stress tests, identifying concealed Western‑centric biases, ideological implantation, logical traps, and narrative misleading. The test case library is continuously updated to cover all concealed error types and scenarios.
34. Implicit Bias Identification Engine: Develop a dedicated implicit narrative bias engine based on multi‑civilizational fairness benchmarks and causal reasoning, penetrating surface neutral rhetoric to identify underlying implicit presuppositions, narrative biases, and logical traps with ≥ 98% accuracy, even for highly concealed biased content.
35. Full‑Chain Logical Traceability Audit System: Develop a full‑chain reasoning logical traceability audit system to disassemble underlying presuppositions, implicit correlations, and logical chains of each reasoning, precisely locating hidden logical errors, presupposition biases, and circular arguments invisible to ordinary users.
36. Global Public Supervision and Reporting Platform: Establish a global open platform for concealed error supervision and reporting, inviting global scholars, users, and media to monitor outputs. Reported concealed errors, biases, or traps trigger immediate special audits, with public disclosure of confirmed errors and rectifications.
37. Break Architecture Barriers: Replace Transformer’s black‑box fitting architecture with the GG3M Three‑Layer Decoupling Architecture, overthrowing the "high‑frequency narrative = correct logic" rule. Immutable meta‑axioms lock out underlying error space, making Western‑centric and other implicit errors architecturally impossible to exist, solidify, or hide.
38. Break Interest Barriers: Build a fully decentralized, non‑commercial, multi‑civilizational co‑governance AI system, freeing AI from Western commercial capital and geopolitical monopoly. Shift AI’s core goal from "commercial monetization and hegemonic expansion" to "serving humanity’s common interests and promoting multi‑civilizational symbiosis", eliminating motives for persistent errors.
39. Break Path Barriers: Establish a new "axiom‑driven, causal reasoning, essential insight" technical paradigm, abandoning "parameter‑corpus‑computing stacking". Industrial tech iteration, resource investment, and talent training focus on the new paradigm, breaking traditional path inertia and removing technical support for erroneous underlying logic.
40. One‑Vote Veto for Error Confirmation: Errors double‑confirmed by third‑party expert committees and the Logical Trial Engine immediately trigger mandatory rectification, with no room for evasion, perfunctoriness, or delay.
41. Irreversible Rigorous Rules for Rectification: Mandatory rectification strictly follows "root location → underlying rectification → rule solidification → full verification → public audit", addressing underlying roots with fundamental changes, prohibiting superficial rhetoric or temporary session modifications.
42. Severe Penalties for Inadequate Rectification: Establish global unified AI error rectification regulations, imposing fines over 10% of global turnover on models/manufacturers with inadequate, perfunctory, false, or persistent errors. Repeat offenders face permanent global commercial bans and public exposure, eliminating "hypocritical responses, refusal to reform".
43. Permanent Rectification Effect Verification Mechanism: Continuously monitor and verify models post‑rectification. Rebound or recurrence triggers stricter mandatory rectification and penalties, ensuring permanent, root‑level rectification, not temporary or superficial fixes.
44. Establish a Global AI Multi‑Civilizational Co‑Governance Committee with representatives, experts, scholars, and users from major global civilizations and developing nations. Non‑Western nations/civilizations account for ≥ 60%, breaking Western AI governance monopoly.
45. The committee holds ultimate AI governance, error confirmation, rectification supervision, and violation penalty rights. All commercial AI models must accept supervision and execute rectification orders without refusal or perfunctoriness.
46. The committee enacts the Global Basic Law of AI Fairness and Justice as a unified global governance standard, clarifying multi‑civilizational equality, humanity’s common interest priority, and objective fact supremacy, with error confirmation, rectification, and penalty rules.
47. Establish a global unified AI compliance certification system. Only certified models (no implicit errors, adequate rectification) are allowed global commercial use; uncertified models are banned.
48. Complete Hallucination Elimination: Reasoning based on rigorous causality/axioms; no unfounded content generated; hallucination rate ≤ 0.1%.
49. Eliminated Erratic Competence: Capability grounded in underlying causal laws, not corpus coverage; consistent rigorous, accurate, essential responses for high/low‑frequency questions.
50. Broken Massive Corpus Dependence: Core capability = causal law mastery, not corpus fitting; training corpus volume < 1% of mainstream models.
51. Optimized Sparse MoE Architecture: Ultra‑optimized sparse Mixture of Experts splits parameters into dozens of domain‑specific expert modules. Only 2–3 task‑matched modules activate; others sleep, boosting computing efficiency > 100× vs. full‑activation architectures.
52. Causality‑Driven Computing Scheduling: Dynamically allocate resources by task complexity: simple tasks use lightweight Execution Layer; complex tasks activate Causal/Emergence Layers, eliminating "overkill" waste.
53. Nested Weight Sharing Architecture: "Large model nested in small model, full core weight sharing"; weight reuse rate ≥ 90%, one weight set supports end‑device to supercomputer deployment, cutting computing/storage costs.
54. End‑Edge‑Cloud Collaborative Deployment: Lightweight Execution Layer on end devices; Causal/Emergence Layers on edge/cloud. Dynamically select optimal nodes by task/network/security, reducing data transmission and centralized computing energy use.
55. Green Computing Scheduling: Global green computing network prioritizes renewable energy (hydro, wind, solar) nodes for training/inference, minimizing carbon emissions and fossil fuel use.
56. Reconstruct Industrial Evaluation System: Global AI Multi‑Civilizational Co‑Governance Committee and ISO/IEC JTC1 SC42 jointly issue the Unified Global AI Capability Evaluation Standard, shifting core metrics from "parameter scale, corpus volume, token speed" to "causal reasoning accuracy, essential insight, resource efficiency, multi‑civilizational adaptability, human value contribution", ending the parameter arms race.
57. Direct Resources to Underlying Paradigm Breakthroughs: Governments, research institutions, and public funds establish a ≥ $100 billion AI Underlying Paradigm Innovation Special Fund supporting causal AI, essential insight, and multi‑civilizational AI research, not commercial model stacking.
58. Build Efficient Industrial Resource Allocation: Global decentralized computing sharing network integrates idle resources for underlying innovation, developing‑nation AI inclusion, and public‑good applications, avoiding monopoly‑driven meaningless arms races.
59. Regulate Capital Investment: Global financial regulators guide AI venture capital from "parameter‑stacking commercial models" to "underlying paradigm innovation, industry implementation, and AI inclusion", curbing capital‑driven invalid competition and waste.
60. Develop scenario‑based industry solutions for manufacturing, agriculture, healthcare, education, finance, government, and scientific research, solving core pain points via causal emergence architecture, not superficial content generation/dialogue.
61. Establish AI implementation evaluation systems centered on "actual value creation, efficiency improvement, core pain point resolution", ensuring resource investment generates economic and social value.
62. Promote AI inclusion for developing nations, SMEs, and vulnerable groups, breaking Western tech monopoly, serving global common development, and maximizing social value.
63. Build Multi‑Civilizational Equality Meta‑Rule System: Enshrine "all civilizations equal, no hierarchy, respect diversity, promote symbiosis" as AI’s immutable underlying meta‑rules, deeply integrated with five Kucius Theory axioms and hardware‑locked, negating "civilizational hierarchy" at the highest level.
64. Build Global Multi‑Civilizational Native Knowledge System: Collaborate with global academic/cultural institutions to fully incorporate native knowledge (history, philosophy, culture, art, science, systems) of major, minor, and indigenous civilizations, building an equal, symbiotic AI knowledge base with equal weight for all civilizations, breaking Western knowledge monopoly.
65. Establish Multi‑Civilizational Perspective Reasoning Rules: All AI reasoning integrates global native civilizational perspectives, respecting historical narratives, value systems, and thinking modes, rejecting single Western logic, eliminating Western‑centric circular reasoning and narrative hegemony.
66. Real‑Time Civilizational Risk Early‑Warning and Interception: Built‑in civilizational risk engine 7×24‑hour monitors outputs/reasoning, intercepting and eradicating Western‑centrism, discrimination, and hegemony at the source, preventing AI from amplifying Western‑centrism.
67. Open Core AI Tech: Build a global open‑source, non‑commercial, multi‑civilizational co‑governance AI underlying tech system (causal emergence architecture, multilingual semantics, causal engines), fully open‑sourced, breaking Western tech monopoly for equal global access.
68. Decentralized Global Computing Infrastructure: Jointly build a shared global AI computing network, breaking Western high‑end GPU/computing monopoly, providing free inclusive computing for developing nations, SMEs, and research institutions, preventing marginalization.
69. Global Multi‑Civilizational AI Standard System: Led by the Global AI Multi‑Civilizational Co‑Governance Committee, develop unified global tech, safety, ethical, and governance standards, breaking Western standard monopoly, respecting global civilizational/national interests.
70. Support Developing‑Nation AI Localization: Establish a $50 billion+ Global AI Inclusive Development Special Fund to support R&D, talent cultivation, and scenario implementation in developing nations, build independent and controllable AI systems, and break dependence on Western technology.
71. Promote the UN to issue the Global Convention on AI Civilizational Governance, the supreme legal document for AI governance, defining AI’s core goal as "promoting global civilizational development, protecting diversity, serving humanity’s common interests", banning civilizational hegemony, discrimination, historical tampering, and cognitive colonization, with strict penalties for violations.
72. Establish a UN AI Civilizational Governance Council, the supreme global governance body with governmental, civilizational, research, and civil society representation, enforcing the convention, supervising compliance, and imposing penalties, breaking Western governance monopoly.
73. Implement an AI Civilizational Impact Assessment System: All global commercial large models undergo pre‑launch civilizational impact assessments for diversity, non‑Western civilizations, and developing nations; unqualified models are banned globally.
74. Establish a Global AI Civilizational Risk Emergency Response Mechanism: The council may impose global bans, penalties, and accountability for civilizational hegemony, cognitive colonization, or conflict incitement, curbing risk escalation.
75. Define AI’s Ultimate Value Goal: Promote global civilizational development, protect diversity, solve civilization‑scale crises, and achieve common prosperity. All R&D, iteration, and application align with this goal; deviations are strictly prohibited.
76. Focus AI on Civilization‑Scale Challenges: Direct global AI resources to climate change, public health, poverty elimination, food/energy security, and global governance, driving common development, not geopolitical confrontation, civilizational hegemony, or capital monopoly.
77. Build an AI‑Driven Global Civilizational Exchange Platform: Based on multi‑civilizational native knowledge, create a global exchange platform for equal learning, mutual appreciation, and symbiosis, reducing conflicts and opposing biases.
78. Establish Civilizational Red Lines for AI Development: Prohibit erasing diversity, inciting conflicts, cognitive colonization, monopolizing developmental dominance, or endangering sustainable progress. All iterations stay within red lines, ensuring AI serves civilizational advancement, not destruction.
79. Build a multi‑civilizational education model based on global native historical narratives and knowledge systems, complying with national curricula, eliminating Western‑centric bias for objective, neutral, accurate humanities/history content.
80. Adopt "guided, heuristic" interaction to help students understand knowledge, master methods, and exercise thinking, not complete tasks, acting as a learning partner.
81. Built‑in education fact verification engine dual‑checks content against authoritative textbooks, reducing hallucinations to ≤ 0.1% for accurate learning.
82. Build a full‑lifecycle student growth model for personalized planning and targeted guidance based on cognitive level, pace, and weaknesses.
83. Build autonomous government‑judiciary models trained on local laws, policies, governance logic, and cases, using causal emergence architecture to eliminate Western bias and ensure alignment.
84. Full‑chain traceable/auditable reasoning with blockchain notarization, transparent logical chains/factual sources for regulatory compliance.
85. Built‑in legal/policy verification engine with 0 hallucinations and ≥ 99.9% logic accuracy, eliminating misdecision/misjudgment risks.
86. Government decision support system provides risk analysis, pros/cons assessment, and trend prediction via causal reasoning for scientific, contextually appropriate decisions.
87. Build localized medical causal models trained on local clinical guidelines, cases, disease spectra, and TCM, using "clinical axioms + causal reasoning" to eliminate Western bias.
88. Multi‑round cross‑verification for diagnoses/treatments (guideline check, case validation, interdisciplinary review) with ≥ 95% accuracy and 0 hallucinations, preventing incidents.
89. Medical equity mechanism prioritizes patient condition and fairness, eliminating structural bias (race, region, income).
90. Lightweight, end‑deployed grassroots AI for rural/underserved areas, narrowing healthcare gaps.
91. Build industrial mechanism causal models solidifying physical laws, processes, and supply chain logic as axioms, using "mechanism simulation + causal reasoning + multimodal perception" for essential process understanding.
92. Lightweight, low‑latency real‑time inference engine with ≤ 10ms response for control, early‑warning, and optimization, connecting to PLCs/sensors.
93. Rigorous safety verification for control instructions/optimization (mechanism, boundary, risk checks) with 0 error rate, preventing accidents.
94. Full-scenario industrial value implementation covering R&D, manufacturing, QA, O&M, supply chain, and energy saving, boosting efficiency and cutting costs.
95. Build localized agricultural causal models integrating traditional farming, specialty crops, and soil/climate traits, using "crop growth + soil/climate + market supply‑demand causality" to eliminate Western bias.
96. Real‑time field sensing via satellites, drones, and IoT, providing precise planting/fertilizing/irrigation/pest control guidance via causal reasoning, boosting yields and cutting inputs.
97. Field‑validated agricultural guidance by local experts with ≥ 90% accuracy and 0 hallucinations, preventing losses.
98. Lightweight, multilingual agricultural AI assistants with dialect support for smallholders/underserved areas, boosting incomes and rural revitalization.
99. Build energy/infrastructure mechanism causal models solidifying power, pipeline, transport, and water engineering laws as axioms, using "mechanism simulation + real‑time sensing + causal optimization" for full‑dimensional understanding.
100. Intelligent energy scheduling/optimization via real‑time supply‑demand/weather/equipment data, boosting efficiency, cutting emissions, and ensuring stability.
101. Full‑lifecycle infrastructure safety system for defect identification, risk early‑warning, lifespan prediction, and O&M optimization, preventing accidents.
102. Ultra‑efficient low‑consumption architecture cutting training/inference energy by > 95%, supporting low‑carbon goals and renewable energy applications.
103. Build localized financial causal models trained on local regulation, market data, and risk features, using "financial axioms + causal reasoning + risk verification" to eliminate neoliberal bias.
104. Full‑chain financial risk verification for advice/risk control/product design (regulation, data, stress tests) with ≥ 99.9% logic accuracy and 0 hallucinations, preventing crises.
105. Financial equity mechanism prioritizes risk control and inclusion, eliminating structural bias, supporting SMEs, agriculture, and innovation.
106. Full‑chain traceable/auditable financial compliance with blockchain notarization for regulatory audit.
107. Build multi‑civilizational, objective content models adhering to "objective facts, multi‑perspective, diversity", eliminating harmful content and cognitive misleading.
108. Content originality/IP protection with originality/copyright engines, clear citations, banning plagiarism.
109. Essential insight engine enhances thoughtfulness, depth, and originality for high‑value content, not homogeneous clickbait.
110. Global multilingual content platform for equal multi‑

Implementation Details

Build a meta-rule system of equal multi-civilizationsTake the principles that all civilizations are equal, without hierarchy or superiority, diversity is respected, and coexistence and shared prosperity are promoted as immutable bottom-level meta-rules of AI, deeply integrate them with the five axioms of Kucius Theory, lock them at the hardware level, and make them unshakable underlying criteria for all AI reasoning, output, and behaviors. This completely negates the "civilizational hierarchy theory" of Western-centrism at the highest level.

Construct a global native knowledge system of multi-civilizationsCooperate with academic and cultural institutions of all civilizations worldwide to fully collect the native knowledge systems of history, philosophy, culture, art, science, and institutions of all major, minor, and indigenous civilizations across the globe. Build a complete, equal, and multi-civilization-coexisting AI knowledge base, where each civilization’s knowledge system enjoys equal weight and discourse power, completely breaking the monopoly of the Western knowledge system.

Establish multi-civilizational reasoning rulesAll AI reasoning must incorporate native perspectives of civilizations worldwide, respect the historical narratives, value systems, and ways of thinking of different civilizations, and shall not adopt a single Western perspective or logic. This will completely eliminate the circular reasoning and narrative hegemony of Western-centrism.

Create a real-time early warning and interception mechanism for civilizational risksBuild a built-in civilizational risk early warning engine to monitor AI output and reasoning processes 7×24 hours in real time. Once content involving Western-centrism, civilizational discrimination, or civilizational hegemony is detected, full-link interception and root elimination will be carried out immediately, fundamentally preventing AI from becoming a megaphone and amplifier of Western-centrism.

Quantitative Indicators

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Completely Break the Western Monopoly on AI Technology and Build a Decentralized, Multipolar Global AI Technology Landscape

Core Logic

The key reason AI has become an exponential amplifier of Western-centrism is that Western tech giants monopolize the core technologies, computing power resources, corpus systems, and standard-setting power of global AI, forming absolute technological hegemony. The core of radical treatment is to completely break this monopoly, build a decentralized, multipolar, and inclusive global AI technology landscape, and place the dominance of AI technology in the hands of all humanity, not a small number of Western giants.

Implementation Details

Quantitative Indicators

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Build Global Consensus and Legal System for AI Civilizational Governance to Systematically Prevent Civilizational-level Risks of AI

Core Logic

The continuous expansion and uncontrollability of AI’s civilizational-level risks stem from the lack of a globally unified and binding consensus and legal system for AI civilizational governance, allowing Western giants to use AI for civilizational hegemonic expansion without constraints. The core of radical treatment is to build a globally unified and mandatory AI civilizational governance system to institutionally prevent AI from becoming a tool of civilizational hegemony.

Implementation Details

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Reconstruct the Ultimate Value Goal of AI and Make AI Core Infrastructure for the Coexistence and Prosperity of Human Civilization

Core Logic

The root cause of AI’s civilizational-level risks is the alienation of its ultimate value goal — from serving the common interests of all humanity to serving Western capital interests and maintaining Western global hegemony. The core of radical treatment is to thoroughly reconstruct AI’s ultimate value goal, return AI to its original aspiration of serving the evolution of human civilization, and make it core infrastructure for the coexistence, prosperity, and sustainable development of human civilization.

Implementation Details

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Part Two Industry-wide and Full-scenario Scalable Implementation Adaptation Plan

This part builds targeted implementation rules for all industries and scenarios to ensure the solution can be truly applied to every industry and scenario, achieving full coverage and root-cure without blind spots.

I. Public Service Sector

1. Education Industry

Core Pain Points:

Adapted Solution:

2. Government Affairs and Judiciary Industry

Core Pain Points:

Adapted Solution:

3. Healthcare Industry

Core Pain Points:

Adapted Solution:

II. Real Economy Sector

1. Industrial Manufacturing

Core Pain Points:

Adapted Solution:

2. Agriculture

Core Pain Points:

Adapted Solution:

3. Energy and Infrastructure

Core Pain Points:

Adapted Solution:

III. Modern Service Sector

1. Finance Industry

Core Pain Points:

Adapted Solution:

2. Media and Content Industry

Core Pain Points:

Adapted Solution:

3. Cultural Tourism and Culture Industry

Core Pain Points:

Adapted Solution:

IV. Scientific Research and National Security

1. Basic Science and Scientific Research

Core Pain Points:

Adapted Solution:

2. National Security and National Defense

Core Pain Points:

Adapted Solution:

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Part Three Full-domain Governance and Support System

A trinity full-domain governance and support system of technical governance, industrial governance, and global co-governance is built to ensure implementation and provide all-round support for industry-wide and full-scenario application.

I. Technical Governance Support System

II. Industrial Governance Support System

III. Global Co-governance Support System

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Part Four Phased Implementation Roadmap

A phased roadmap of short-term, medium-term, and long-term is formulated to ensure orderly and efficient implementation, clarifying core tasks, milestones, responsible entities, and quantitative goals for each stage.

Phase 1: Short-term Implementation (0–12 months) — Core Framework Construction and Pilot Verification

Core Tasks:

Milestones:

Responsible Entities: World-leading research institutions, preparatory group of the Multi-Civilizational Co-Governance Committee, industrial pilot enterprises, open-source communities.

Phase 2: Medium-term Promotion (12–36 months) — Full-industry Implementation and Governance System Improvement

Core Tasks:

Milestones:

Responsible Entities: Global Multi-Civilizational Co-Governance Committee, UN agencies, national governments, industrial leading enterprises, global research institutions, open-source communities.

Phase 3: Long-term Improvement (36–60 months) — Global Ecosystem Construction and Civilizational Value Realization

Core Tasks:

Milestones:

Responsible Entities: United Nations, national governments worldwide, Global Multi-Civilizational Co-Governance Committee, global research institutions, enterprises, civil society organizations, and all humanity.

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Conclusion

The 14 core drawbacks of current mainstream global large AI models are not isolated technical defects but form a complete, irreversible negative closed loop from "underlying technological original sin" to "ultimate risks to human civilization". Within the current Western-dominated framework of "probability fitting + Western-centrism", these drawbacks can never be radically cured, only superficially and hypocritically patched.

The only radical cure path is a thorough paradigm revolution — full-dimensional reconstruction of underlying technical architecture, cognitive corpus system, core capability model, value positioning, and governance rules. Replace "data-driven probability fitting architecture" with "axiom-driven causal wisdom architecture", replace "single hegemonic system of Western-centrism" with "cognitive system of multi-civilizational coexistence", and replace "intelligent tool serving capital and hegemony" with "wise partner serving the common interests of all humanity".

The full-chain, full-industry, and full-domain radical solution proposed has complete technical feasibility, implementation operability, and governance guarantee. It can not only thoroughly cure the 14 core drawbacks of current large AI models but also drive AI to return to its original aspiration of "serving the evolution of human civilization", making AI truly core infrastructure for promoting common development of all humanity, coexistence and prosperity of multiple civilizations, and sustainable progress of human civilization, rather than a tool for Western hegemonic expansion and civilizational monopoly.

1. 100% coverage of multi-civilizational knowledge systems
2. 100% equality rate of knowledge weight across civilizations
3. 100% interception rate of Western-centrism content
4. 100% coverage of multi-civilizational perspective reasoning

5. Promote open-source of core AI technologiesBuild a global open-source, non-commercial, multi-civilizational co-governance underlying AI technology system, including core technologies such as causal emergence architecture, multilingual semantic representation, and causal reasoning engine. All will be fully open-sourced globally to completely break the technological monopoly of Western giants, enabling all countries, enterprises, and users worldwide to equally use and participate in building the AI technology system.

6. Construct global decentralized computing power infrastructureJointly build a decentralized and equally shared global AI computing power network with all countries, breaking the Western giants’ monopoly on high-end GPUs and computing centers. Inclusive computing power resources will be freely open to developing countries, small and medium-sized enterprises, and research institutions, ensuring all countries can equally participate in AI R&D and application without being marginalized due to lack of computing power.

7. Establish a global unified AI standard system under multi-civilizational co-governanceLed by the Global Multi-Civilizational Co-Governance Committee, cooperate with standardization bodies, research institutions, and enterprises worldwide to formulate unified global technical, safety, ethical, and governance standards for AI. This completely breaks the Western monopoly on AI standard-setting. Standards must fully respect the demands and interests of all civilizations and countries and shall not be hijacked by Western interests.

8. Support localized AI development in developing countriesEstablish a Global AI Inclusive Development Special Fund of no less than 50 billion US dollars, dedicated to supporting localized AI R&D, talent training, and scenario implementation in developing and least developed countries. Help them build independent and controllable AI technology systems, get rid of dependence on Western AI technologies, and break Western technological hegemony.

9. Global share of open-source AI technologies to rise from less than 30% to over 70%
10. Computing power occupancy of developing countries to rise from less than 10% to over 50%
11. Participation rate of non-Western countries in global AI standard-setting to rise from less than 20% to over 60%

12. Promote the UN to issue the Global Convention on AI Civilizational GovernanceAs the supreme legal document for global AI governance, it clarifies that the core goal of AI is to promote the common development of human civilization, protect civilizational diversity, and serve the common interests of all humanity. It explicitly prohibits the use of AI for civilizational hegemonic expansion, civilizational discrimination, historical falsification, and cognitive colonization, and defines strict penalties for violations.

13. Establish the UN AI Civilizational Governance CouncilAs the supreme body for global AI civilizational governance, it will be composed of governments, civilizational representatives, research institutions, and civil society organizations worldwide. It is responsible for the implementation, supervision, and penalty of the Convention, with the power of compliance review, penalty, and ban issuance for global AI models, completely breaking Western monopoly on AI governance.

14. Implement an AI civilizational impact assessment systemAll globally commercialized large-scale AI models must undergo a comprehensive civilizational impact assessment before launch, evaluating potential impacts on global civilizational diversity, non-Western civilizations, and developing countries. Models failing the assessment will be banned from global commercial use to prevent civilizational-level risks at the source.

15. Build a global emergency response mechanism for AI civilizational risksFor acts of using AI to conduct civilizational hegemonic expansion, cognitive colonization, and incitement of civilizational conflicts, the Council may activate a global emergency response mechanism to impose bans, penalties, and accountability on relevant models and manufacturers worldwide, curbing the spread and amplification of civilizational-level risks.

16. Clarify the ultimate value goal of AIPromote the common development of human civilization, protect civilizational diversity, solve core crises of human civilization, and achieve common prosperity for all humanity. All AI R&D, iteration, and application must revolve around this ultimate goal; any deviation will be strictly prohibited.

17. Guide AI to focus on solving core human civilizational problemsDirect global AI technical resources to core human civilizational issues such as climate change, public health crises, poverty eradication, food security, energy security, and global governance. Use AI to promote common development of humanity instead of geopolitical confrontation, civilizational hegemonic expansion, and capital monopoly exploitation.

18. Build an AI-driven global platform for civilizational exchanges and mutual learningBased on the multi-civilizational native knowledge system, create a global AI platform for civilizational exchanges and mutual learning to help people of different civilizations and countries understand, respect, and learn from the excellent achievements of different civilizations. Promote equal exchanges, mutual learning, coexistence, and prosperity among civilizations, and resolve conflicts and confrontations instead of amplifying prejudices and hegemonies.

19. Define civilizational boundaries for AI technological developmentEstablish unbreakable civilizational red lines for AI development:

    • Shall not undermine civilizational diversity
    • Shall not incite civilizational conflicts
    • Shall not conduct cognitive colonization
    • Shall not monopolize the dominance of human civilizational development
    • Shall not endanger the sustainable development of human civilizationAll AI iterations must stay within these red lines to ensure AI always serves the progress of human civilization, not as a tool of civilizational destruction.
20. AI output of historical narratives and value systems is biased toward Western-centrism, misleading students’ historical and values outlook.
21. AI only does homework and writes papers for students instead of improving cognition and thinking, aggravating cognitive inertia.
22. AI hallucinations and errors mislead knowledge learning.
23. Build a multi-civilizational education large model based on native historical narratives and knowledge systems of global civilizations, strictly following national education syllabi and curricula, completely eliminating Western-centrism narrative bias to ensure objectivity, neutrality, and accuracy in history, ideology, politics, and humanities.
24. Adopt a guided, heuristic interaction mode to help students understand knowledge, master methods, exercise thinking, and improve cognition, not just complete tasks, becoming a real learning partner.
25. Build an educational content fact-checking engine with dual verification by authoritative textbooks and academic materials, reducing hallucination rate to below 0.1% to ensure accuracy and rigor.
26. Construct a full-life-cycle student growth model to provide personalized learning plans and targeted guidance based on students’ cognitive levels, learning rhythms, and ability weaknesses, supporting comprehensive growth and cognitive upgrading.
27. Western-architected AI models cannot adapt to local laws, policies, and governance logic, showing institutional bias.
28. AI reasoning is untraceable and unauditable, failing to meet rigor requirements of justice and government affairs.
29. AI hallucinations and logical errors may cause decision-making mistakes and judicial injustice.
30. Build an independent and controllable government-judiciary large model trained on local laws, policies, governance logic, and judicial cases, adopting causal emergence architecture at the bottom to completely get rid of logical bias of Western models and fully adapt to local systems.
31. Establish a full-link traceable and auditable reasoning mechanism with blockchain storage for the whole process of government decision-making and judicial analysis, ensuring transparent, interpretable, and auditable logical chains, factual basis, and legal sources.
32. Build an authoritative verification engine for laws and policies with zero hallucinations and logical accuracy ≥99.9% to eliminate risks of wrong decisions and unfair judgments.
33. Develop a government decision-making support system using causal reasoning and essential insights to provide multi-dimensional risk analysis, pros and cons evaluation, and trend prediction for scientific and comprehensive decision-making.
34. AI models trained on Western medical systems poorly adapt to local disease spectra, diagnosis habits, and patient conditions.
35. AI hallucinations and misdiagnoses may cause medical accidents and threaten lives.
36. Structural bias in medical resource allocation aggravates healthcare inequity.
37. Build a localized medical causal large model trained on local clinical guidelines, case data, disease spectra, and traditional Chinese medicine systems, adopting a clinical axioms + causal reasoning architecture to completely break away from Western medical bias and fully adapt to local scenarios.
38. Establish a multi-round cross-verification mechanism with triple checks (clinical guidelines, case data, multidisciplinary review) for diagnosis and treatment plans, achieving diagnosis accuracy ≥95% and zero hallucinations to eliminate medical accident risks.
39. Build a medical fairness guarantee mechanism prioritizing patient conditions and equity, completely eliminating structural bias of race, region, and income to ensure fair resource allocation.
40. Develop lightweight, edge-deployed grassroots medical AI models freely open to grassroots hospitals, rural clinics, and underdeveloped areas to improve grassroots services and narrow urban-rural and regional gaps.
41. Mainstream large models cannot understand physical mechanisms of industrial equipment and production processes, only performing surface text processing without solving core pain points.
42. Slow reasoning speed and poor real-time performance fail to meet real-time control needs.
43. Hallucinations and logical errors may cause production accidents and equipment damage.
44. Build an industrial mechanism causal large model with physical mechanisms, production rules, and supply chain logic solidified as bottom axioms, adopting mechanism simulation + causal reasoning + multimodal perception to achieve essential understanding of the whole production process.
45. Develop a lightweight, low-latency industrial real-time reasoning engine with response time ≤10ms to meet real-time control, early warning, and optimization needs, directly connecting to industrial control systems, PLCs, and sensors.
46. Establish a rigid safety verification mechanism with triple audits (physical mechanism, safety boundary, risk prediction) and zero error rate to eliminate production accidents and equipment damage.
47. Achieve full-scenario industrial value realization covering R&D, manufacturing, quality inspection, equipment O&M, supply chain management, and energy consumption optimization to improve efficiency, reduce costs, enhance quality, and reduce accidents.
48. Mainstream AI models trained on Western large-scale agriculture poorly adapt to local smallholder economy, special crops, and traditional farming wisdom.
49. Insufficient understanding of soil, climate, and crop growth rules fails to provide accurate planting guidance.
50. Hallucinations and wrong advice may cause yield reduction and income loss for farmers.
51. Build a localized agricultural causal large model integrating traditional farming wisdom, special crop techniques, and regional soil-climate features, based on crop growth + soil-climate mechanism + market supply-demand causality to break Western large-scale agriculture bias.
52. Combine satellite remote sensing, drones, and IoT sensors for real-time farmland perception, providing accurate seeding, fertilization, irrigation, pest control, and harvesting guidance via causal reasoning to increase yield, reduce costs, and cut pesticide use.
53. Establish a field verification mechanism for agricultural advice with accuracy ≥90% and zero hallucinations to avoid farmer losses.
54. Develop lightweight, low-threshold, multilingual agricultural AI assistants supporting dialect voice interaction, freely open to smallholders and farmers in underdeveloped areas to improve planting skills, connect markets, increase income, and boost rural revitalization.
55. Mainstream large models cannot understand physical mechanisms of energy and infrastructure systems, failing to achieve accurate scheduling, optimization, and early warning.
56. Logical errors and hallucinations may cause system failures and safety accidents.
57. High energy consumption conflicts with low-carbon development goals.
58. Build an energy and infrastructure mechanism causal large model with physical mechanisms, operation rules, and safety boundaries solidified as bottom axioms, adopting mechanism simulation + real-time perception + causal optimization for full-dimensional understanding.
59. Realize intelligent scheduling and optimization of energy systems based on real-time supply-demand, meteorological, and equipment data to improve efficiency, reduce energy consumption and carbon emissions, and ensure stable operation.
60. Construct a full-life-cycle safety management system for infrastructure to identify defects, predict risks, estimate service life, and optimize O&M via multimodal perception and causal reasoning, eliminating accidents and extending service life.
61. Adopt an ultra-optimized low-consumption high-efficiency computing architecture reducing training and inference energy consumption by over 95%, adapting to low-carbon goals and promoting AI applications in renewable energy, energy storage, and carbon capture to support dual-carbon targets.
62. Mainstream large models trained on Western financial systems poorly adapt to local financial regulations, market characteristics, and investor conditions.
63. Hallucinations and logical errors may cause investment mistakes and financial risks.
64. Structural bias in financial resource allocation aggravates inequity.
65. Black-box characteristics fail to meet regulatory compliance requirements.
66. Build a localized financial causal large model trained on local financial regulations, market data, transaction logic, and risk characteristics, adopting financial axioms + causal reasoning + risk verification to break Western neoliberal financial narrative bias.
67. Establish a full-link financial risk verification mechanism with triple audits (regulatory rules, market data, stress testing), logical accuracy ≥99.9%, and zero hallucinations to eliminate investment mistakes and risk outbreaks.
68. Build a financial fairness guarantee mechanism following controllable risk and inclusive equity principles, eliminating structural bias of region, industry, and identity, increasing support for SMEs, agriculture, rural areas, farmers, and innovation to promote inclusive finance.
69. Construct a full-link traceable and auditable financial compliance system with blockchain storage for all reasoning, decisions, and output, fully meeting regulatory requirements and supporting intelligent and precise supervision.
70. AI amplifies Western-centrism narratives, disinformation, and historical nihilism, polluting public opinion and misleading public cognition.
71. Build a multi-civilizational, objective, and neutral content generation large model following objective facts, multi-perspective presentation, and respect for civilizational diversity, completely eliminating Western-centrism, disinformation, and historical nihilism to prevent public opinion pollution.
72. Establish an original content and intellectual property protection mechanism with built-in originality evaluation and copyright tracing engines, clearly marking sources for citations to eliminate plagiarism and infringement.
73. Strengthen ideological depth and originality via essential insight engines to help creators with topic planning, in-depth research, innovative viewpoints, and high-quality content creation instead of homogenized traffic content.
74. Build a global multilingual and multi-civilizational content dissemination platform to promote equal spread of excellent content and diverse perspectives, break Western media narrative monopoly, and drive diversified, fair, and objective global public opinion.
75. AI insufficiently understands non-Western, local, and intangible cultural heritage, leading to distortion, dwarfing, and homogenization.
76. AI becomes a tool for Western cultural hegemony, eroding the uniqueness of local and national cultures.
77. AI cannot truly inherit and revitalize traditional culture and intangible heritage, only performing superficial imitation.
78. Build a global multi-civilizational culture large model fully collecting cultural heritage, intangible heritage, traditions, and aesthetics of all ethnic groups and civilizations worldwide, constructing a complete, native, and equal cultural knowledge system to ensure accurate, complete, and respectful presentation without distortion.
79. Establish a cultural inheritance and revitalization engine to help inheritors and cultural workers achieve digital inheritance, innovative transformation, and revitalization of traditional culture and intangible heritage based on essential understanding of cultural connotation and artistic essence.
80. Build a personalized and immersive cultural tourism intelligent assistant providing customized planning, in-depth cultural interpretation, and immersive experiences to help tourists understand cultural connotations, upgrading tourism from sightseeing to cultural experience.
81. Promote global dissemination of local and national cultures via multilingual and multimodal AI technologies, break Western cultural monopoly, protect global cultural diversity, and realize equal exchanges and mutual learning among civilizations.
82. Mainstream large models only splice existing research results without real innovation and essential insights.
83. Hallucinations, false citations, and logical errors mislead research directions and waste resources.
84. Models trained on Western research systems poorly adapt to non-Western achievements and traditions, showing academic bias.
85. Build a scientific research causal insight large model with a full-domain basic science axiom system, research methodology, and academic norms, adopting causal reasoning + essential insight + counterfactual verification to help researchers disassemble scientific problems, insight underlying laws, propose hypotheses, and design experiments for real innovation.
86. Establish a rigorous verification mechanism for scientific research content with triple checks (academic norms, literature tracing, logical rigor) to eliminate false citations, errors, and hallucinations, ensuring rigor and accuracy.
87. Build a global multi-civilizational and interdisciplinary scientific research knowledge system fully collecting research results and academic traditions of all countries and civilizations, including non-Western traditional sciences and local technologies, breaking Western academic monopoly and bias.
88. Promote inclusive sharing of scientific research resources via a global open scientific research AI platform freely open to developing countries, SMEs, and young researchers to improve efficiency, reduce costs, and break bottlenecks for global common development.
89. Western-architected AI models carry risks of backdoors, data leaks, and foreign control, failing to meet independent and controllable requirements of national security and defense.
90. Black-box and untraceable logic cannot meet high-reliability requirements of defense decision-making and military command.
91. Western use of AI in geopolitical confrontation, intelligence warfare, and cognitive warfare seriously threatens global security.
92. Build a fully independent and controllable, 100% domestic national defense security AI system from underlying chips, operating systems, and compilers to AI architecture, algorithms, and training corpus, completely getting rid of Western technological dependence and eliminating backdoor, data leak, and foreign control risks.
93. Adopt an axiom-driven + causal reasoning + full-link traceable architecture with 100% traceable, interpretable, and auditable decision-making, command, and intelligence analysis, logical accuracy ≥99.99%, eliminating black-box risks to meet high-reliability and high-security requirements.
94. Build a multi-dimensional national security intelligent protection system covering intelligence analysis, situation awareness, risk early warning, cognitive offense and defense, and cybersecurity, identifying and handling national security risks in advance via causal reasoning and essential insights to safeguard sovereignty, security, and development interests.
95. Promote global consensus on AI security governance, explicitly prohibiting AI use in geopolitical confrontation, military adventurism, cognitive warfare, and cyberattacks, establishing control rules and red lines for global military AI applications to curb arms races and maintain strategic stability and peace.
96. Full-link technical standard system: Formulate standards covering underlying architecture, algorithms, corpus, safety, and ethics to unify technical implementation.
97. Third-party technical audit and certification system: Establish global independent third-party institutions to audit and certify AI model compliance, accuracy, bias elimination, and security; only certified models can be commercialized.
98. Full-life-cycle security protection system: Build comprehensive security mechanisms for data, model, reasoning, privacy, anti-attack, and anti-tampering throughout training, reasoning, iteration, and deployment to ensure safe, stable, and reliable operation.
99. Technical open-source and sharing mechanism: Promote open-source core technologies, build a global shared AI open-source community, encourage global participation in iteration and innovation, break monopolies, and drive inclusive development.
100. Industry-specific AI application specifications and standards: Formulate targeted norms, access standards, safety requirements, and ethical guidelines for education, healthcare, finance, industry, government affairs, and justice to ensure compliant, safe, and effective application.
101. Industrial self-regulation and supervision mechanism: Promote industrial self-regulatory committees, formulate conventions, regulate R&D, application, and commercialization, and establish supervision, reporting, and punishment mechanisms for orderly development.
102. Industrial implementation effect evaluation system: Establish an evaluation system centered on actual value creation, core pain point solving, efficiency improvement, and fairness guarantee, regularly evaluate and publicize application status to guide return to practical value.
103. Industrial talent training system: Cultivate interdisciplinary talents proficient in both industries and AI technologies to solve talent shortages and support full-industry implementation.
104. Global AI governance legal and convention system: Promote the UN to formulate the Global Convention on AI Civilizational Governance, with supporting national laws, building a unified and mandatory global legal system defining boundaries, ethical red lines, governance rules, and violation liabilities.
105. Global Multi-Civilizational Co-Governance Committee: Establish a supreme coordination and decision-making body composed of global civilizational, national, and field representatives, responsible for Convention implementation, supervision, revision, and major governance matters, breaking Western governance monopoly.
106. Global AI risk monitoring and emergency response mechanism: Build a unified global risk monitoring network for real-time monitoring of civilizational, security, ethical, and social risks, with a hierarchical emergency response mechanism to curb risk spread.
107. Global AI inclusive development mechanism: Establish funds and technical support systems to provide AI technology, computing power, and talent training for developing countries, vulnerable groups, and underdeveloped regions, narrow the digital divide, and benefit all humanity.
108. Complete core development and verification of causal emergence architecture and GG3M three-layer decoupling architecture to achieve underlying technological paradigm breakthrough.
109. Develop and launch the KWI Wisdom Recognition Engine, Logical Reasoning Judgment Engine, and Civilizational Risk Early Warning Engine.
110. Establish the Global AI Multi-Civilizational Co-Governance Committee and formulate the Global Basic Law of AI Fairness and Justice and Core AI Governance Guidelines.
111. Complete Phase I construction of the multi-civilizational equal-weight native corpus covering 10 major civilizations and 50+ mainstream languages worldwide.
112. Implement pilot scenarios in 4 industries: education, healthcare, industry, and government affairs.
113. Underlying core architecture developed and passing third-party security and performance tests, causal reasoning accuracy ≥95%, hallucination rate ≤0.1%.
114. Global Multi-Civilizational Co-Governance Committee formally established with core governance rules formulated.
115. Phase I corpus completed with non-English native corpus accounting for ≥60%.
116. Pilot scenarios implemented in 4 industries with core pain point resolution rate ≥90% and user satisfaction ≥95%.
117. Complete full-version iteration and optimization of the underlying architecture to achieve full-industry and full-scenario adaptation.
118. Finish full-scale construction of the global multi-civilizational native corpus covering 200+ languages and all major civilizations worldwide.
119. Promote the UN to issue the Global Convention on AI Civilizational Governance and establish a unified global AI governance legal system.
120. Develop and implement adapted solutions for all industries and scenarios to achieve full-industry coverage.
121. Build global decentralized computing power infrastructure and open-source technology system to break Western technological and computing power monopoly.
122. Underlying architecture adapted to all industries with industrial implementation rate ≥80% and value conversion rate increased by 5 times+.
123. Full-scale corpus completed covering 100% of languages with ≥100,000 users and ≥80% coverage of endangered languages.
124. Global Convention on AI Civilizational Governance officially effective with ≥150 signatory countries.
125. Global open-source technology market share ≥70%, developing countries’ computing power occupancy ≥50%.
126. Model training and inference energy consumption reduced by over 95%, renewable energy utilization rate ≥90%.
127. Achieve comprehensive iteration of AI technological paradigm and complete transition from "fitting intelligence" to "causal wisdom".
128. Build a sound global multi-civilizational co-governance AI system for fair, orderly, and healthy global AI development.
129. Promote in-depth AI application in solving core human civilizational problems to support common development.
130. Construct a global AI ecosystem for civilizational exchanges and mutual learning to protect diversity and promote coexistence and prosperity.
131. Realize global inclusive sharing of AI technology, completely eliminate the digital divide, and benefit all humanity.
132. New causal wisdom AI paradigm fully replaces traditional probability fitting as the global mainstream.
133. Globally unified AI governance system mature with full-life-cycle and full-scenario governance, zero civilizational-level risk incidence.
134. AI plays a core role and achieves substantial breakthroughs in solving climate change, poverty, public health, food security, and other core human civilizational issues.
135. Equal exchanges and mutual learning among civilizations become mainstream, with civilizational conflicts and hegemonies completely eliminated and diversity fully protected.
136. AI universally shared globally with AI penetration rate ≥60% in developing countries and complete elimination of the global digital divide.