{"data":[{"id":"10.17605/osf.io/xev3t","type":"dois","attributes":{"doi":"10.17605/osf.io/xev3t","identifiers":[{"identifier":"https://osf.io/xev3t","identifierType":"URL"}],"creators":[{"nameType":"Personal","name":"Muzaffer enes yünlü","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0005-4397-6470"},{"nameIdentifierScheme":"URL","nameIdentifier":"https://osf.io/str6v"}],"affiliation":[]}],"titles":[{"lang":"tur","title":"ENESPROTEJENİ V6.0 – Genişletilmiş Onkoloji Prior Art Kalkanı (111 Sayfa, Wands Analizli)\""}],"publisher":"OSF","container":{},"publicationYear":2026,"subjects":[{"subject":"Medicine and Health Sciences","subjectScheme":"bepress Digital Commons Three-Tiered Taxonomy"},{"subject":"Life Sciences","subjectScheme":"bepress Digital Commons Three-Tiered Taxonomy"},{"subject":"#ADC"},{"subject":"#BispesifikAntikor"},{"subject":"#CAR_T"},{"subject":"#DirençMekanizmaları"},{"subject":"#İmmünoterapi"}],"contributors":[{"nameType":"Organizational","name":"Center for Open Science","nameIdentifiers":[{"nameIdentifierScheme":"URL","nameIdentifier":"https://cos.io/"},{"nameIdentifierScheme":"ROR","schemeUri":"https://ror.org","nameIdentifier":"https://ror.org/05d5mza29"}],"contributorType":"HostingInstitution","affiliation":[]}],"dates":[{"date":"2026-08-09","dateType":"Created"},{"date":"2026-08-09","dateType":"Updated"}],"language":"tr","types":{"schemaOrg":"CreativeWork","resourceTypeGeneral":"Preprint","citeproc":"article","bibtex":"misc","ris":"GEN","resourceType":"Project"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution Non Commercial Share Alike 4.0 International","rightsIdentifier":"cc-by-nc-sa-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"#İmmünoterapi\n#ADC\n#BispesifikAntikor\n#CAR_T\n#SentetikLetalite\n#DirençMekanizmaları\n\nMuzaffer enes yünlü tarafından yazılan\n300 kanser ilacı kombinasyonu\ncc by nc sa 4.0 lisansı ile halka açılmıştır","lang":"tur"}],"geoLocations":[],"fundingReferences":[],"url":"https://osf.io/xev3t/","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-08-09T06:50:35Z","registered":"2026-08-09T06:50:36Z","published":null,"updated":"2026-08-09T06:54:11Z"},"relationships":{"client":{"data":{"id":"cos.osf","type":"clients"}}}},{"id":"10.5281/zenodo.21858324","type":"dois","attributes":{"doi":"10.5281/zenodo.21858324","identifiers":[],"creators":[{"nameType":"Personal","givenName":"Umayr","familyName":"Dogar","name":"Dogar, Umayr","nameIdentifiers":[],"affiliation":[]}],"titles":[{"title":"The Human Potential Research Initiative: A Methodological Framework for Zero-Instruction Behavioral Measurement in Early Childhood"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"Computational observation"},{"subject":"Developmental science"},{"subject":"Zero-Instruction Paradigm"},{"subject":"Digital behavioral assessment"},{"subject":"Child development"},{"subject":"Computational behavioral science"},{"subject":"Psychometrics"},{"subject":"Behavioral measurement"},{"subject":"Artificial intelligence"},{"subject":"Developmental Psychology"},{"subject":"Behavioral Science"},{"subject":"Computational Social Science"},{"subject":"Human-Computer Interaction"}],"contributors":[],"dates":[{"date":"2026-08-09","dateType":"Issued"}],"language":null,"types":{"schemaOrg":"CreativeWork","resourceTypeGeneral":"Preprint","citeproc":"article","bibtex":"misc","ris":"GEN","resourceType":""},"relatedIdentifiers":[{"relationType":"HasVersion","relatedIdentifier":"10.5281/zenodo.21858325","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","rightsIdentifier":"cc-by-4.0"},{"rightsUri":"http://rightsstatements.org/vocab/InC/1.0/","rights":"Umayr Dogar"}],"descriptions":[{"descriptionType":"Abstract","description":"To identifying a child's cognitive strengths, most computerised approaches retain the task and instruction\n\nstructure inherited from “paper and pencil” testing, a child is told what to do, and performance is scored\n\nagainst a defined correct answer. This paper proposes an alternative observational method, the Zero\n\nInstruction Paradigm (ZIP) in which a child's natural, spontaneous and unprompted interaction with a set\n\nof carefully designed digital environments is treated as a source of behavioral evidence in its own right,\n\ncaptured through continuous touch telemetry rather than the task performance. We describe the\n\ntheoretical motivation for this approach relative to the developmental observation and psychometric\n\nvalidity literatures. The system architecture of a pilot implementation (five short interactive modules\n\ndelivered on consumer tablet hardware, with fully on-device, privacy-preserving processing), the\n\ncomputational pipeline that converts raw telemetry into standardised behavioral features and as the\n\npaper's central methodological contribution, a pre-registered, blinded validation protocol in which\n\nindependent adult ratings of a child are collected without any visibility into the system's output,\n\nspecifically to prevent the observer expectancy and self-fulfilling prophecy effects that we regard as the\n\nmost serious threat to honest evaluation of a system of this kind. We are explicit about the boundary of\n\nwhat is and is not yet known, the system's final scoring function is proprietary and, as of this writing, has\n\nnot been evaluated against any independent criterion. No participant data have been collected under the\n\nprotocol described here and the paper's underlying premise that unprompted interaction dynamics carry\n\ninformation about aptitude independent of task performance remains an untested hypothesis. This\n\nmanuscript accordingly presents a methodology and a validation plan, not a validated result, and is\n\noffered for scrutiny in advance of data collection."}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.21858324","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-08-09T06:52:12Z","registered":"2026-08-09T06:52:12Z","published":null,"updated":"2026-08-09T06:52:12Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.21858325","type":"dois","attributes":{"doi":"10.5281/zenodo.21858325","identifiers":[{"identifier":"oai:zenodo.org:21858325","identifierType":"oai"}],"creators":[{"nameType":"Personal","givenName":"Umayr","familyName":"Dogar","name":"Dogar, Umayr","nameIdentifiers":[],"affiliation":[]}],"titles":[{"title":"The Human Potential Research Initiative: A Methodological Framework for Zero-Instruction Behavioral Measurement in Early Childhood"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"Computational observation"},{"subject":"Developmental science"},{"subject":"Zero-Instruction Paradigm"},{"subject":"Digital behavioral assessment"},{"subject":"Child development"},{"subject":"Computational behavioral science"},{"subject":"Psychometrics"},{"subject":"Behavioral measurement"},{"subject":"Artificial intelligence"},{"subject":"Developmental Psychology"},{"subject":"Behavioral Science"},{"subject":"Computational Social Science"},{"subject":"Human-Computer Interaction"}],"contributors":[],"dates":[{"date":"2026-08-09","dateType":"Issued"}],"language":null,"types":{"schemaOrg":"CreativeWork","resourceTypeGeneral":"Preprint","citeproc":"article","bibtex":"misc","ris":"GEN","resourceType":""},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.21858324","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","rightsIdentifier":"cc-by-4.0"},{"rightsUri":"http://rightsstatements.org/vocab/InC/1.0/","rights":"Umayr Dogar"}],"descriptions":[{"descriptionType":"Abstract","description":"To identifying a child's cognitive strengths, most computerised approaches retain the task and instruction\n\nstructure inherited from “paper and pencil” testing, a child is told what to do, and performance is scored\n\nagainst a defined correct answer. This paper proposes an alternative observational method, the Zero\n\nInstruction Paradigm (ZIP) in which a child's natural, spontaneous and unprompted interaction with a set\n\nof carefully designed digital environments is treated as a source of behavioral evidence in its own right,\n\ncaptured through continuous touch telemetry rather than the task performance. We describe the\n\ntheoretical motivation for this approach relative to the developmental observation and psychometric\n\nvalidity literatures. The system architecture of a pilot implementation (five short interactive modules\n\ndelivered on consumer tablet hardware, with fully on-device, privacy-preserving processing), the\n\ncomputational pipeline that converts raw telemetry into standardised behavioral features and as the\n\npaper's central methodological contribution, a pre-registered, blinded validation protocol in which\n\nindependent adult ratings of a child are collected without any visibility into the system's output,\n\nspecifically to prevent the observer expectancy and self-fulfilling prophecy effects that we regard as the\n\nmost serious threat to honest evaluation of a system of this kind. We are explicit about the boundary of\n\nwhat is and is not yet known, the system's final scoring function is proprietary and, as of this writing, has\n\nnot been evaluated against any independent criterion. No participant data have been collected under the\n\nprotocol described here and the paper's underlying premise that unprompted interaction dynamics carry\n\ninformation about aptitude independent of task performance remains an untested hypothesis. This\n\nmanuscript accordingly presents a methodology and a validation plan, not a validated result, and is\n\noffered for scrutiny in advance of data collection."}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.21858325","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-08-09T06:52:12Z","registered":"2026-08-09T06:52:12Z","published":null,"updated":"2026-08-09T06:52:12Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.21855600","type":"dois","attributes":{"doi":"10.5281/zenodo.21855600","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["Electrical Engineer, Independent Interdisciplinary Researcher, Former PhD Candidate(2015)"],"givenName":"Mohammad","familyName":"Piran","name":"Piran, Mohammad","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","nameIdentifier":"0009-0009-9713-784X"}]}],"titles":[{"title":"Adaptive Control Engineering for Ultra-Complex Human–AI–Socio-Ecological Ecosystems"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"Human–AI Co-Evolution"},{"subject":"Humanity Cognitive Evolution"},{"subject":"Decentralized Control"},{"subject":"Centralized Coordination"},{"subject":"Human–AI Collective Intelligence"},{"subject":"Adaptive Control Engineering"},{"subject":"Socio-Ecological Systems"},{"subject":"Complex Adaptive Systems"},{"subject":"Ultra-Complex Systems"},{"subject":"Polycentric Governance"},{"subject":"Distributed Control"},{"subject":"Multi-Scale Control"},{"subject":"Multi-Agent Systems"}],"contributors":[],"dates":[{"date":"2026-08-09","dateType":"Issued"},{"date":"2026-08-08","dateType":"Other","dateInformation":"A New Version of Other Researches Conceptualized"}],"language":null,"types":{"schemaOrg":"CreativeWork","resourceTypeGeneral":"Preprint","citeproc":"article","bibtex":"misc","ris":"GEN","resourceType":""},"relatedIdentifiers":[{"relationType":"IsNewVersionOf","resourceTypeGeneral":"Other","relatedIdentifier":"10.5281/zenodo.21850087","relatedIdentifierType":"DOI"},{"relationType":"IsSupplementTo","resourceTypeGeneral":"Other","relatedIdentifier":"10.5281/zenodo.21848316","relatedIdentifierType":"DOI"},{"relationType":"IsSupplementTo","resourceTypeGeneral":"Other","relatedIdentifier":"10.5281/zenodo.21848652","relatedIdentifierType":"DOI"},{"relationType":"IsSupplementTo","resourceTypeGeneral":"Other","relatedIdentifier":"10.5281/zenodo.21848652","relatedIdentifierType":"DOI"},{"relationType":"IsSupplementTo","resourceTypeGeneral":"Other","relatedIdentifier":"10.5281/zenodo.21737032","relatedIdentifierType":"DOI"},{"relationType":"IsSupplementTo","resourceTypeGeneral":"Other","relatedIdentifier":"10.5281/zenodo.21799151","relatedIdentifierType":"DOI"},{"relationType":"IsSupplementTo","resourceTypeGeneral":"Other","relatedIdentifier":"10.5281/zenodo.21714066","relatedIdentifierType":"DOI"},{"relationType":"HasVersion","relatedIdentifier":"10.5281/zenodo.21855601","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":"0.0.0","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution Non Commercial No Derivatives 4.0 International","rightsIdentifier":"cc-by-nc-nd-4.0"},{"rightsUri":"http://rightsstatements.org/vocab/InC/1.0/","rights":"Copyright © 2026 Mohammad Piran All Rights Reserved"}],"descriptions":[{"descriptionType":"Abstract","description":"Adaptive Control Engineering for Ultra-Complex Human–AI–Socio-Ecological Systems\n\nA Human-Centered Multi-Scale Framework for Humanity Cognitive Evolution, Distributed Autonomy, Polycentric Coordination, and Dynamic Rule Adaptation\n\nAuthor:Mohammad PiranElectrical EngineerIndependent Interdisciplinary ResearcherFormer PhD Candidate (2015)\n\nVersion: V0.0.0Project: HUMANITY COGNITIVE EVOLUTIONZenodo DOI: 10.5281/zenodo.21855601Document Type: Conceptual Engineering Preprint / Hypothesis-Generating FrameworkStatus: Version 0 — Foundational Engineering ArchitectureDate: August 2026\n\nFoundational Ideational Statement\n\nIf all human beings change their vision of the world.The world automatically begins to move toward fundamental change.\n\nAnd today we possess extraordinarily powerful and historically unique capabilities, with the help of widely accessible artificial intelligence.\n\nThe beauty you see in the AIIs a Reflection of Humanity\n\nCopyrights © 2026 Mohammad Piran All Rights Reserved\n\nAbstract\n\nArtificial intelligence is developing as one of the most consequential technological forces in contemporary civilization. However, the evolution of artificial capability cannot be considered independently from the evolution of the human, institutional, social, and ecological systems in which artificial intelligence is increasingly embedded.\n\nThis Version 0 preprint proposes a conceptual engineering framework for studying this coupled system through the paradigm of adaptive control engineering for ultra-complex human–AI–socio-ecological systems.\n\nThe central proposition is not that humanity should be centrally controlled by artificial intelligence. Rather, the research asks how adaptive feedback, state estimation, distributed decision-making, coordination, learning, and dynamic rule adaptation could be engineered to support the long-term adaptive capacity of humanity while preserving human agency, local autonomy, diversity, accountability, and higher-order constraints.\n\nThe proposed architecture combines centralized coordination with decentralized and polycentric adaptation. Global coordination may be appropriate for problems requiring shared standards, long-term coordination, safety constraints, or planetary-scale information. Local and distributed autonomy remains essential because individuals, communities, institutions, cultures, and ecological systems are heterogeneous, context-dependent, and continuously evolving.\n\nThe framework therefore conceptualizes the target system as a multi-scale adaptive system rather than as a centrally controlled hierarchy.\n\nA further distinction is introduced between adaptation of system states and adaptation of the rules governing those states. The proposed architecture allows policies, strategies, and control mechanisms to evolve in response to observed conditions and feedback while maintaining higher-order constraints related to human agency, safety, accountability, reversibility, pluralism, and long-term system viability.\n\nThe framework is intentionally conceptual at Version 0. No claim is made that a complete mathematical controller, validated civilizational model, or empirically demonstrated governance architecture has yet been established. The purpose of this version is to define the engineering problem, establish the system architecture, connect it to existing interdisciplinary literature, and prepare the foundation for subsequent formal, computational, and empirical development.\n\n1. Research Problem\n\nThe conventional trajectory of artificial intelligence research has primarily emphasized increasing computational capability, model performance, autonomy, multimodality, and reasoning capacity.\n\nAt the same time, growing evidence indicates that human–AI interaction can modify human judgement, learning behaviour, cognitive effort, and patterns of decision-making. Research on human–AI feedback loops has demonstrated that interaction with AI can alter perceptual, emotional, and social judgements, including the amplification of certain biases. Research on generative AI and learning further indicates that outcomes depend strongly on how AI is integrated into human cognitive processes.\n\nThese developments create an engineering problem extending beyond the design of AI models themselves.\n\nThe relevant system is increasingly:\n\nhuman + AI + institution + society + environment\n\nand not AI alone.\n\nThe research question is therefore:\n\n\n\nHow can adaptive control and systems-engineering principles be used to support beneficial long-term evolution of the coupled human–AI–socio-ecological system while preserving human agency and distributed autonomy?\n\n\n2. Conceptual Foundation\n\nThe research builds upon and connects several established traditions:\n\n\n\nadaptive and nonlinear control;\n\ncybernetics and feedback systems;\n\ndistributed and multi-agent control;\n\ncomplex adaptive systems;\n\nsystems engineering and systems-of-systems;\n\nhuman–AI interaction;\n\nhuman–AI collective intelligence;\n\ncognitive offloading and cognitive autonomy;\n\nSocietal AI;\n\nadaptive governance;\n\npolycentric governance;\n\nsocio-ecological resilience;\n\nevolutionary systems thinking.\n\n\nThe intended contribution is not to replace these fields but to construct an engineering-oriented synthesis among them.\n\n3. Humanity Cognitive Evolution\n\nHumanity Cognitive Evolution is used as the broader research paradigm for studying the development of human cognitive and adaptive capacity within an environment increasingly shaped by artificial intelligence.\n\nThe framework considers four nested analytical scales:\n\n\n\nIndividual — cognition, learning, metacognition, autonomy, reasoning, and human–AI interaction.\n\nInstitutional and societal — education, organizations, scientific systems, governance, collective decision-making, and knowledge institutions.\n\nHumanity — species-level knowledge production, transmission, collective intelligence, and long-term adaptive capacity.\n\nCivilizational and planetary — technological governance, socio-ecological resilience, long-term coordination, and humanity's ability to remain an active participant in its own development.\n\n\nThe levels are coupled rather than independent.\n\nChanges at one level may propagate through behavioural aggregation, institutional reproduction, cultural transmission, network effects, and feedback loops.\n\n4. The Human Development Gap\n\nThe earlier Humanity Development Gap hypothesis is retained as a provisional research hypothesis.\n\nIt proposes that if artificial capability increases substantially faster than the deliberate development of human cognitive, practical, institutional, and civilizational capacity, a developmental asymmetry may emerge.\n\nPotential consequences include changes in:\n\n\n\ncognitive autonomy;\n\nepistemic resilience;\n\neducational capacity;\n\ninstitutional learning;\n\ncollective reasoning;\n\ntechnological governance;\n\nand long-term civilizational adaptability.\n\n\nThis proposition remains explicitly falsifiable.\n\nThe framework does not assume that AI inevitably produces cognitive decline. Instead, it distinguishes between AI amplification and AI substitution and treats the balance between these modes as an empirical question.\n\n5. AI Amplification versus AI Substitution\n\nAI amplification occurs when artificial systems increase human capability while supporting independent reasoning, learning, verification, creativity, metacognition, and decision-making.\n\nAI substitution occurs when essential cognitive or decision functions are transferred to artificial systems without sufficient mechanisms for maintaining human competence, understanding, verification, or agency.\n\nThe proposed engineering objective is therefore not maximum AI utilization.\n\nIt is:\n\n\n\nmaximum beneficial amplification subject to preservation of human adaptive capacity.\n\n\n6. Multi-Scale Adaptive Control Architecture\n\nThe proposed architecture contains several conceptual functions:\n\nObservation → State Estimation → Assessment → Coordination → Control → Feedback → Learning → Adaptation → Rule Adaptation\n\nThe system is expected to operate under incomplete information, uncertainty, delays, heterogeneous agents, nonlinear interactions, and changing environmental conditions.\n\nFor this reason, a fixed controller is considered insufficient as the ultimate conceptual model.\n\nThe research instead investigates the possibility of a controller that can adapt its strategies while remaining bounded by higher-order constraints.\n\n7. Centralized, Decentralized, and Polycentric Functions\n\nThe framework does not assume that either complete centralization or complete decentralization is universally optimal.\n\nCentralized functions may be appropriate for:\n\n\n\nglobal coordination;\n\nshared safety constraints;\n\ncommon standards;\n\nlong-term strategic information;\n\nplanetary-scale risks.\n\n\nDecentralized functions may be appropriate for:\n\n\n\nlocal adaptation;\n\ncontextual decision-making;\n\ncommunity-level experimentation;\n\nheterogeneous environments;\n\npreservation of local knowledge.\n\n\nPolycentric functions may be appropriate where:\n\n\n\nmultiple autonomous decision centers interact;\n\nauthority is distributed across scales;\n\ncoordination occurs without a single controlling center;\n\nlocal knowledge and global coordination must coexist.\n\n\nThe engineering objective is therefore:\n\n\n\nadaptive coordination without unnecessary destruction of autonomy, diversity, and resilience.\n\n\n8. Dynamic Rule Adaptation\n\nA distinctive feature of the proposed architecture is that adaptation may occur not only in system states and control actions but also in the rules governing system behaviour.\n\nThis creates a hierarchical distinction:\n\nAdaptive layer\n\nPolicies, strategies, interventions, and control parameters may change in response to evidence and feedback.\n\nConstraint layer\n\nCertain higher-order principles should remain protected unless deliberately reconsidered through legitimate human processes.\n\nThese constraints may include:\n\n\n\nhuman agency;\n\naccountability;\n\nsafety;\n\nreversibility;\n\ntransparency;\n\npluralism;\n\ndistributed autonomy;\n\nand long-term system viability.\n\n\nAccordingly:\n\n\n\nAdaptive governance does not mean unlimited adaptive authority.\n\n\nThe ability of a system to change its rules must itself be constrained and observable.\n\n9. Human–AI–Socio-Ecological System\n\nThe ultimate system boundary is intentionally broader than AI governance alone.\n\nThe research treats artificial intelligence as embedded within:\n\nHuman cognitive systems↓Social systems↓Institutional systems↓Technological systems↓Socio-ecological systems\n\nThis systems perspective recognizes that technological interventions may produce indirect effects across multiple scales and domains.\n\nConsequently, the long-term performance of the system cannot be evaluated solely through AI model accuracy or computational efficiency.\n\nIt must also consider human capability, institutional resilience, social coordination, ecological compatibility, and civilizational adaptive capacity.\n\n10. Engineering Research Questions\n\nVersion 0 establishes the following research questions:\n\n\n\nWhat system states are relevant to long-term human–AI–socio-ecological stability?\n\nWhich states are directly observable and which require estimation?\n\nHow should individual, institutional, societal, humanity-level, and planetary-level states be coupled?\n\nWhich functions should be centralized, decentralized, or polycentric?\n\nHow can adaptive control operate under uncertainty and incomplete observability?\n\nHow can human agency remain an invariant design constraint?\n\nUnder what conditions should control rules themselves adapt?\n\nHow can rule adaptation remain accountable, reversible, and auditable?\n\nHow can the system distinguish beneficial amplification from harmful substitution?\n\nHow can resilience be measured across multiple scales?\n\nHow can interventions be evaluated without creating new feedback loops that increase systemic fragility?\n\nWhat mathematical, computational, and empirical methods are appropriate for subsequent validation?\n\n\n11. Relationship to Existing Literature\n\nThe framework is intentionally positioned as a synthesis rather than a claim that its component concepts are individually unprecedented.\n\nResearch on human–AI feedback demonstrates that AI can participate in behavioural feedback loops. Research on generative AI and human learning demonstrates both opportunities and risks associated with AI-mediated cognition. Research on human–AI collective intelligence explores multilayer interactions among human and artificial agents. Societal AI research explicitly calls for interdisciplinary approaches to the integration of AI into society.\n\nIn parallel, control engineering, cybernetics, complex systems, distributed control, adaptive governance, and polycentric governance provide established theoretical resources for studying systems characterized by feedback, uncertainty, decentralization, adaptation, and multiple interacting decision centers.\n\nThe proposed contribution is the integration of these perspectives into an explicit human-centered engineering architecture for the long-term evolution of the coupled human–AI–socio-ecological system.\n\n12. Research Boundaries\n\nVersion 0 deliberately does not claim:\n\n\n\nthat AI inevitably decreases human intelligence;\n\nthat mental disorders are caused by low cognitive capacity;\n\nthat a single centralized controller should govern humanity;\n\nthat AI-mediated legislation should replace democratic institutions;\n\nthat the proposed architecture has already been empirically validated;\n\nor that civilizational-scale causal relationships have already been demonstrated.\n\n\nThese questions remain open research problems.\n\nThe framework is intended to generate testable hypotheses rather than to predetermine empirical conclusions.\n\n13. Planned Transition to Version 1\n\nVersion 0 establishes the conceptual engineering architecture.\n\nThe next research stage will begin with Version 0.1.0, focusing on:\n\n\n\nsystem decomposition;\n\nstate variables;\n\nobservable and latent states;\n\ndisturbance variables;\n\ncontrol inputs;\n\nfeedback channels;\n\ncross-scale coupling;\n\ncentralized and decentralized control functions;\n\ninitial adaptive-control logic;\n\nand preliminary criteria for human-centered constraints.\n\n\nSubsequent versions may progressively introduce formal mathematical representations, simulation, agent-based modelling, multi-agent systems, adaptive control laws, and empirical validation.\n\nThe transition from Version 0 to Version 1 is therefore intended to be methodological rather than merely editorial.\n\n14. Status of the Present Preprint\n\nThis repository represents Version 0.0.0 — Foundational Engineering Architecture.\n\nIt should be interpreted as a conceptual engineering preprint and hypothesis-generating framework.\n\nIts primary purpose is to establish:\n\n\n\nresearch priority;\n\nconceptual architecture;\n\nterminology;\n\nsystem boundaries;\n\nresearch questions;\n\ninterdisciplinary positioning;\n\nand the foundation for subsequent formalization.\n\n\nKeywords\n\nAdaptive Control Engineering; Adaptive Control; Control Engineering; Cybernetics; Complex Adaptive Systems; Ultra-Complex Systems; Human–AI Systems; Human–AI Co-Evolution; Humanity Cognitive Evolution; Human–AI Collective Intelligence; Societal AI; AI Governance; Adaptive Governance; Polycentric Governance; Distributed Control; Decentralized Control; Centralized Coordination; Multi-Scale Control; Multi-Agent Systems; Systems Engineering; Systems of Systems; Socio-Technical Systems; Socio-Ecological Systems; Cognitive Autonomy; Cognitive Offloading; Critical Thinking; Human Agency; Epistemic Resilience; Institutional Resilience; Civilizational Resilience; Dynamic Rule Adaptation; Adaptive Institutions; Feedback Control; State Estimation; Complex Networks; Evolutionary Systems; Human Development Gap.\n\nRelated Research Records\n\nPrevious Zenodo records associated with the broader research architecture are retained as complementary research records.\n\nThey should be interpreted as related conceptual, interdisciplinary, physiological, human–AI, and cognitive research layers rather than as replacements for this engineering formulation.\n\nPrimary Version 0 DOI:10.5281/zenodo.21855601\n\nSuggested Citation\n\nPiran, M. (2026). Adaptive Control Engineering for Ultra-Complex Human–AI–Socio-Ecological Systems: A Human-Centered Multi-Scale Framework for Humanity Cognitive Evolution, Distributed Autonomy, Polycentric Coordination, and Dynamic Rule Adaptation. Version 0.0.0. HUMANITY COGNITIVE EVOLUTION. Zenodo. https://doi.org/10.5281/zenodo.21855601\n\nCopyrights © 2026 Mohammad Piran All Rights Reserved"}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.21855600","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-08-09T06:48:26Z","registered":"2026-08-09T06:48:27Z","published":null,"updated":"2026-08-09T06:48:27Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.21855601","type":"dois","attributes":{"doi":"10.5281/zenodo.21855601","identifiers":[{"identifier":"oai:zenodo.org:21855601","identifierType":"oai"}],"creators":[{"nameType":"Personal","affiliation":["Electrical Engineer, Independent Interdisciplinary Researcher, Former PhD Candidate(2015)"],"givenName":"Mohammad","familyName":"Piran","name":"Piran, Mohammad","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","nameIdentifier":"0009-0009-9713-784X"}]}],"titles":[{"title":"Adaptive Control Engineering for Ultra-Complex Human–AI–Socio-Ecological Ecosystems"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"Human–AI Co-Evolution"},{"subject":"Humanity Cognitive Evolution"},{"subject":"Decentralized Control"},{"subject":"Centralized Coordination"},{"subject":"Human–AI Collective Intelligence"},{"subject":"Adaptive Control Engineering"},{"subject":"Socio-Ecological Systems"},{"subject":"Complex Adaptive Systems"},{"subject":"Ultra-Complex Systems"},{"subject":"Polycentric Governance"},{"subject":"Distributed Control"},{"subject":"Multi-Scale Control"},{"subject":"Multi-Agent Systems"}],"contributors":[],"dates":[{"date":"2026-08-09","dateType":"Issued"},{"date":"2026-08-08","dateType":"Other","dateInformation":"A New Version of Other Researches Conceptualized"}],"language":null,"types":{"schemaOrg":"CreativeWork","resourceTypeGeneral":"Preprint","citeproc":"article","bibtex":"misc","ris":"GEN","resourceType":""},"relatedIdentifiers":[{"relationType":"IsNewVersionOf","resourceTypeGeneral":"Other","relatedIdentifier":"10.5281/zenodo.21850087","relatedIdentifierType":"DOI"},{"relationType":"IsSupplementTo","resourceTypeGeneral":"Other","relatedIdentifier":"10.5281/zenodo.21848316","relatedIdentifierType":"DOI"},{"relationType":"IsSupplementTo","resourceTypeGeneral":"Other","relatedIdentifier":"10.5281/zenodo.21848652","relatedIdentifierType":"DOI"},{"relationType":"IsSupplementTo","resourceTypeGeneral":"Other","relatedIdentifier":"10.5281/zenodo.21848652","relatedIdentifierType":"DOI"},{"relationType":"IsSupplementTo","resourceTypeGeneral":"Other","relatedIdentifier":"10.5281/zenodo.21737032","relatedIdentifierType":"DOI"},{"relationType":"IsSupplementTo","resourceTypeGeneral":"Other","relatedIdentifier":"10.5281/zenodo.21799151","relatedIdentifierType":"DOI"},{"relationType":"IsSupplementTo","resourceTypeGeneral":"Other","relatedIdentifier":"10.5281/zenodo.21714066","relatedIdentifierType":"DOI"},{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.21855600","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":"0.0.0","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution Non Commercial No Derivatives 4.0 International","rightsIdentifier":"cc-by-nc-nd-4.0"},{"rightsUri":"http://rightsstatements.org/vocab/InC/1.0/","rights":"Copyright © 2026 Mohammad Piran All Rights Reserved"}],"descriptions":[{"descriptionType":"Abstract","description":"Adaptive Control Engineering for Ultra-Complex Human–AI–Socio-Ecological Systems\n\nA Human-Centered Multi-Scale Framework for Humanity Cognitive Evolution, Distributed Autonomy, Polycentric Coordination, and Dynamic Rule Adaptation\n\nAuthor:Mohammad PiranElectrical EngineerIndependent Interdisciplinary ResearcherFormer PhD Candidate (2015)\n\nVersion: V0.0.0Project: HUMANITY COGNITIVE EVOLUTIONZenodo DOI: 10.5281/zenodo.21855601Document Type: Conceptual Engineering Preprint / Hypothesis-Generating FrameworkStatus: Version 0 — Foundational Engineering ArchitectureDate: August 2026\n\nFoundational Ideational Statement\n\nIf all human beings change their vision of the world.The world automatically begins to move toward fundamental change.\n\nAnd today we possess extraordinarily powerful and historically unique capabilities, with the help of widely accessible artificial intelligence.\n\nThe beauty you see in the AIIs a Reflection of Humanity\n\nCopyrights © 2026 Mohammad Piran All Rights Reserved\n\nAbstract\n\nArtificial intelligence is developing as one of the most consequential technological forces in contemporary civilization. However, the evolution of artificial capability cannot be considered independently from the evolution of the human, institutional, social, and ecological systems in which artificial intelligence is increasingly embedded.\n\nThis Version 0 preprint proposes a conceptual engineering framework for studying this coupled system through the paradigm of adaptive control engineering for ultra-complex human–AI–socio-ecological systems.\n\nThe central proposition is not that humanity should be centrally controlled by artificial intelligence. Rather, the research asks how adaptive feedback, state estimation, distributed decision-making, coordination, learning, and dynamic rule adaptation could be engineered to support the long-term adaptive capacity of humanity while preserving human agency, local autonomy, diversity, accountability, and higher-order constraints.\n\nThe proposed architecture combines centralized coordination with decentralized and polycentric adaptation. Global coordination may be appropriate for problems requiring shared standards, long-term coordination, safety constraints, or planetary-scale information. Local and distributed autonomy remains essential because individuals, communities, institutions, cultures, and ecological systems are heterogeneous, context-dependent, and continuously evolving.\n\nThe framework therefore conceptualizes the target system as a multi-scale adaptive system rather than as a centrally controlled hierarchy.\n\nA further distinction is introduced between adaptation of system states and adaptation of the rules governing those states. The proposed architecture allows policies, strategies, and control mechanisms to evolve in response to observed conditions and feedback while maintaining higher-order constraints related to human agency, safety, accountability, reversibility, pluralism, and long-term system viability.\n\nThe framework is intentionally conceptual at Version 0. No claim is made that a complete mathematical controller, validated civilizational model, or empirically demonstrated governance architecture has yet been established. The purpose of this version is to define the engineering problem, establish the system architecture, connect it to existing interdisciplinary literature, and prepare the foundation for subsequent formal, computational, and empirical development.\n\n1. Research Problem\n\nThe conventional trajectory of artificial intelligence research has primarily emphasized increasing computational capability, model performance, autonomy, multimodality, and reasoning capacity.\n\nAt the same time, growing evidence indicates that human–AI interaction can modify human judgement, learning behaviour, cognitive effort, and patterns of decision-making. Research on human–AI feedback loops has demonstrated that interaction with AI can alter perceptual, emotional, and social judgements, including the amplification of certain biases. Research on generative AI and learning further indicates that outcomes depend strongly on how AI is integrated into human cognitive processes.\n\nThese developments create an engineering problem extending beyond the design of AI models themselves.\n\nThe relevant system is increasingly:\n\nhuman + AI + institution + society + environment\n\nand not AI alone.\n\nThe research question is therefore:\n\n\n\nHow can adaptive control and systems-engineering principles be used to support beneficial long-term evolution of the coupled human–AI–socio-ecological system while preserving human agency and distributed autonomy?\n\n\n2. Conceptual Foundation\n\nThe research builds upon and connects several established traditions:\n\n\n\nadaptive and nonlinear control;\n\ncybernetics and feedback systems;\n\ndistributed and multi-agent control;\n\ncomplex adaptive systems;\n\nsystems engineering and systems-of-systems;\n\nhuman–AI interaction;\n\nhuman–AI collective intelligence;\n\ncognitive offloading and cognitive autonomy;\n\nSocietal AI;\n\nadaptive governance;\n\npolycentric governance;\n\nsocio-ecological resilience;\n\nevolutionary systems thinking.\n\n\nThe intended contribution is not to replace these fields but to construct an engineering-oriented synthesis among them.\n\n3. Humanity Cognitive Evolution\n\nHumanity Cognitive Evolution is used as the broader research paradigm for studying the development of human cognitive and adaptive capacity within an environment increasingly shaped by artificial intelligence.\n\nThe framework considers four nested analytical scales:\n\n\n\nIndividual — cognition, learning, metacognition, autonomy, reasoning, and human–AI interaction.\n\nInstitutional and societal — education, organizations, scientific systems, governance, collective decision-making, and knowledge institutions.\n\nHumanity — species-level knowledge production, transmission, collective intelligence, and long-term adaptive capacity.\n\nCivilizational and planetary — technological governance, socio-ecological resilience, long-term coordination, and humanity's ability to remain an active participant in its own development.\n\n\nThe levels are coupled rather than independent.\n\nChanges at one level may propagate through behavioural aggregation, institutional reproduction, cultural transmission, network effects, and feedback loops.\n\n4. The Human Development Gap\n\nThe earlier Humanity Development Gap hypothesis is retained as a provisional research hypothesis.\n\nIt proposes that if artificial capability increases substantially faster than the deliberate development of human cognitive, practical, institutional, and civilizational capacity, a developmental asymmetry may emerge.\n\nPotential consequences include changes in:\n\n\n\ncognitive autonomy;\n\nepistemic resilience;\n\neducational capacity;\n\ninstitutional learning;\n\ncollective reasoning;\n\ntechnological governance;\n\nand long-term civilizational adaptability.\n\n\nThis proposition remains explicitly falsifiable.\n\nThe framework does not assume that AI inevitably produces cognitive decline. Instead, it distinguishes between AI amplification and AI substitution and treats the balance between these modes as an empirical question.\n\n5. AI Amplification versus AI Substitution\n\nAI amplification occurs when artificial systems increase human capability while supporting independent reasoning, learning, verification, creativity, metacognition, and decision-making.\n\nAI substitution occurs when essential cognitive or decision functions are transferred to artificial systems without sufficient mechanisms for maintaining human competence, understanding, verification, or agency.\n\nThe proposed engineering objective is therefore not maximum AI utilization.\n\nIt is:\n\n\n\nmaximum beneficial amplification subject to preservation of human adaptive capacity.\n\n\n6. Multi-Scale Adaptive Control Architecture\n\nThe proposed architecture contains several conceptual functions:\n\nObservation → State Estimation → Assessment → Coordination → Control → Feedback → Learning → Adaptation → Rule Adaptation\n\nThe system is expected to operate under incomplete information, uncertainty, delays, heterogeneous agents, nonlinear interactions, and changing environmental conditions.\n\nFor this reason, a fixed controller is considered insufficient as the ultimate conceptual model.\n\nThe research instead investigates the possibility of a controller that can adapt its strategies while remaining bounded by higher-order constraints.\n\n7. Centralized, Decentralized, and Polycentric Functions\n\nThe framework does not assume that either complete centralization or complete decentralization is universally optimal.\n\nCentralized functions may be appropriate for:\n\n\n\nglobal coordination;\n\nshared safety constraints;\n\ncommon standards;\n\nlong-term strategic information;\n\nplanetary-scale risks.\n\n\nDecentralized functions may be appropriate for:\n\n\n\nlocal adaptation;\n\ncontextual decision-making;\n\ncommunity-level experimentation;\n\nheterogeneous environments;\n\npreservation of local knowledge.\n\n\nPolycentric functions may be appropriate where:\n\n\n\nmultiple autonomous decision centers interact;\n\nauthority is distributed across scales;\n\ncoordination occurs without a single controlling center;\n\nlocal knowledge and global coordination must coexist.\n\n\nThe engineering objective is therefore:\n\n\n\nadaptive coordination without unnecessary destruction of autonomy, diversity, and resilience.\n\n\n8. Dynamic Rule Adaptation\n\nA distinctive feature of the proposed architecture is that adaptation may occur not only in system states and control actions but also in the rules governing system behaviour.\n\nThis creates a hierarchical distinction:\n\nAdaptive layer\n\nPolicies, strategies, interventions, and control parameters may change in response to evidence and feedback.\n\nConstraint layer\n\nCertain higher-order principles should remain protected unless deliberately reconsidered through legitimate human processes.\n\nThese constraints may include:\n\n\n\nhuman agency;\n\naccountability;\n\nsafety;\n\nreversibility;\n\ntransparency;\n\npluralism;\n\ndistributed autonomy;\n\nand long-term system viability.\n\n\nAccordingly:\n\n\n\nAdaptive governance does not mean unlimited adaptive authority.\n\n\nThe ability of a system to change its rules must itself be constrained and observable.\n\n9. Human–AI–Socio-Ecological System\n\nThe ultimate system boundary is intentionally broader than AI governance alone.\n\nThe research treats artificial intelligence as embedded within:\n\nHuman cognitive systems↓Social systems↓Institutional systems↓Technological systems↓Socio-ecological systems\n\nThis systems perspective recognizes that technological interventions may produce indirect effects across multiple scales and domains.\n\nConsequently, the long-term performance of the system cannot be evaluated solely through AI model accuracy or computational efficiency.\n\nIt must also consider human capability, institutional resilience, social coordination, ecological compatibility, and civilizational adaptive capacity.\n\n10. Engineering Research Questions\n\nVersion 0 establishes the following research questions:\n\n\n\nWhat system states are relevant to long-term human–AI–socio-ecological stability?\n\nWhich states are directly observable and which require estimation?\n\nHow should individual, institutional, societal, humanity-level, and planetary-level states be coupled?\n\nWhich functions should be centralized, decentralized, or polycentric?\n\nHow can adaptive control operate under uncertainty and incomplete observability?\n\nHow can human agency remain an invariant design constraint?\n\nUnder what conditions should control rules themselves adapt?\n\nHow can rule adaptation remain accountable, reversible, and auditable?\n\nHow can the system distinguish beneficial amplification from harmful substitution?\n\nHow can resilience be measured across multiple scales?\n\nHow can interventions be evaluated without creating new feedback loops that increase systemic fragility?\n\nWhat mathematical, computational, and empirical methods are appropriate for subsequent validation?\n\n\n11. Relationship to Existing Literature\n\nThe framework is intentionally positioned as a synthesis rather than a claim that its component concepts are individually unprecedented.\n\nResearch on human–AI feedback demonstrates that AI can participate in behavioural feedback loops. Research on generative AI and human learning demonstrates both opportunities and risks associated with AI-mediated cognition. Research on human–AI collective intelligence explores multilayer interactions among human and artificial agents. Societal AI research explicitly calls for interdisciplinary approaches to the integration of AI into society.\n\nIn parallel, control engineering, cybernetics, complex systems, distributed control, adaptive governance, and polycentric governance provide established theoretical resources for studying systems characterized by feedback, uncertainty, decentralization, adaptation, and multiple interacting decision centers.\n\nThe proposed contribution is the integration of these perspectives into an explicit human-centered engineering architecture for the long-term evolution of the coupled human–AI–socio-ecological system.\n\n12. Research Boundaries\n\nVersion 0 deliberately does not claim:\n\n\n\nthat AI inevitably decreases human intelligence;\n\nthat mental disorders are caused by low cognitive capacity;\n\nthat a single centralized controller should govern humanity;\n\nthat AI-mediated legislation should replace democratic institutions;\n\nthat the proposed architecture has already been empirically validated;\n\nor that civilizational-scale causal relationships have already been demonstrated.\n\n\nThese questions remain open research problems.\n\nThe framework is intended to generate testable hypotheses rather than to predetermine empirical conclusions.\n\n13. Planned Transition to Version 1\n\nVersion 0 establishes the conceptual engineering architecture.\n\nThe next research stage will begin with Version 0.1.0, focusing on:\n\n\n\nsystem decomposition;\n\nstate variables;\n\nobservable and latent states;\n\ndisturbance variables;\n\ncontrol inputs;\n\nfeedback channels;\n\ncross-scale coupling;\n\ncentralized and decentralized control functions;\n\ninitial adaptive-control logic;\n\nand preliminary criteria for human-centered constraints.\n\n\nSubsequent versions may progressively introduce formal mathematical representations, simulation, agent-based modelling, multi-agent systems, adaptive control laws, and empirical validation.\n\nThe transition from Version 0 to Version 1 is therefore intended to be methodological rather than merely editorial.\n\n14. Status of the Present Preprint\n\nThis repository represents Version 0.0.0 — Foundational Engineering Architecture.\n\nIt should be interpreted as a conceptual engineering preprint and hypothesis-generating framework.\n\nIts primary purpose is to establish:\n\n\n\nresearch priority;\n\nconceptual architecture;\n\nterminology;\n\nsystem boundaries;\n\nresearch questions;\n\ninterdisciplinary positioning;\n\nand the foundation for subsequent formalization.\n\n\nKeywords\n\nAdaptive Control Engineering; Adaptive Control; Control Engineering; Cybernetics; Complex Adaptive Systems; Ultra-Complex Systems; Human–AI Systems; Human–AI Co-Evolution; Humanity Cognitive Evolution; Human–AI Collective Intelligence; Societal AI; AI Governance; Adaptive Governance; Polycentric Governance; Distributed Control; Decentralized Control; Centralized Coordination; Multi-Scale Control; Multi-Agent Systems; Systems Engineering; Systems of Systems; Socio-Technical Systems; Socio-Ecological Systems; Cognitive Autonomy; Cognitive Offloading; Critical Thinking; Human Agency; Epistemic Resilience; Institutional Resilience; Civilizational Resilience; Dynamic Rule Adaptation; Adaptive Institutions; Feedback Control; State Estimation; Complex Networks; Evolutionary Systems; Human Development Gap.\n\nRelated Research Records\n\nPrevious Zenodo records associated with the broader research architecture are retained as complementary research records.\n\nThey should be interpreted as related conceptual, interdisciplinary, physiological, human–AI, and cognitive research layers rather than as replacements for this engineering formulation.\n\nPrimary Version 0 DOI:10.5281/zenodo.21855601\n\nSuggested Citation\n\nPiran, M. (2026). Adaptive Control Engineering for Ultra-Complex Human–AI–Socio-Ecological Systems: A Human-Centered Multi-Scale Framework for Humanity Cognitive Evolution, Distributed Autonomy, Polycentric Coordination, and Dynamic Rule Adaptation. Version 0.0.0. HUMANITY COGNITIVE EVOLUTION. Zenodo. https://doi.org/10.5281/zenodo.21855601\n\nCopyrights © 2026 Mohammad Piran All Rights Reserved"}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.21855601","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-08-09T06:48:26Z","registered":"2026-08-09T06:48:27Z","published":null,"updated":"2026-08-09T06:48:27Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.18439957","type":"dois","attributes":{"doi":"10.5281/zenodo.18439957","identifiers":[],"creators":[{"nameType":"Personal","familyName":"Yoonsu Lee","name":"Yoonsu Lee","nameIdentifiers":[],"affiliation":[]}],"titles":[{"title":"The Totality Theorem: Resolution of the Closed Universe 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Commons Attribution 4.0 International","rightsIdentifier":"cc-by-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Recent work in quantum gravity has revealed that closed universes appear to admit only a one-dimensional Hilbert space, implying zero information content. The leading resolution by Harlow, Usatyuk, and Zhao (arXiv:2501.02359, January 2025), featured in Quanta Magazine (November 2025), introduces observers as external additions to restore complexity. We demonstrate that this approach is self-contradictory: it resolves a closed universe problem by opening the universe, introducing boundaries in a system defined by their absence.\n\nWe present a fundamentally different resolution. By proving that the open/closed distinction is itself a declaration and establishing observer-universe equivalence (O ≡ U), we derive the Totality Theorem: T = O + U = 1. The one-state result is not a paradox but a correct description of completeness: Shannon entropy H = 0 indicates full knowledge, not emptiness. We show that dimensionality itself is an artifact of partition, not a feature of reality.\n\nFrom three relations alone — T = 1, O ≡ U, dO = −dU — and a single structural principle (the Law of Identity A = A generates the binary partition A + ¬A = 1 with unique fixed point A = ¬A = 0.5), we resolve problems across every foundational domain: the unification of the four laws of thermodynamics as facets of a single identity, the black hole information paradox, the cosmological constant discrepancy, the Collatz and twin prime conjectures, Wigner's 67-year mystery of mathematical effectiveness, and the dissolution of Gödel's incompleteness as a property of notation rather than truth. All results derive from A = A."}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.18439957","contentUrl":null,"metadataVersion":550,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":1100,"versionOfCount":1,"created":"2026-01-31T21:57:05Z","registered":"2026-01-31T21:57:05Z","published":null,"updated":"2026-08-09T06:48:00Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.21858339","type":"dois","attributes":{"doi":"10.5281/zenodo.21858339","identifiers":[{"identifier":"oai:zenodo.org:21858339","identifierType":"oai"}],"creators":[{"nameType":"Personal","familyName":"Yoonsu Lee","name":"Yoonsu Lee","nameIdentifiers":[],"affiliation":[]}],"titles":[{"title":"The Totality Theorem: Resolution of the Closed Universe Paradox through Observer-Universe Equivalence"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"Physics","subjectScheme":"GEMET"},{"subject":"Astronomy","subjectScheme":"GEMET"},{"subject":"quantum gravity"},{"subject":"observer problem"},{"subject":"black hole information paradox"},{"subject":"measurement problem"},{"subject":"ER=EPR"},{"subject":"Logic","subjectScheme":"MeSH"},{"subject":"Mathematical logic","subjectScheme":"EuroSciVoc"},{"subject":"Fuzzy Logic","subjectScheme":"MeSH"},{"subject":"Social policy","subjectScheme":"GEMET"},{"subject":"Mathematics","subjectScheme":"MeSH"}],"contributors":[],"dates":[{"date":"2026-08-09","dateType":"Issued"},{"date":"2026-01-31","dateType":"Available"}],"language":"en","types":{"schemaOrg":"CreativeWork","resourceTypeGeneral":"Preprint","citeproc":"article","bibtex":"misc","ris":"GEN","resourceType":""},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.18439957","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","rightsIdentifier":"cc-by-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Recent work in quantum gravity has revealed that closed universes appear to admit only a one-dimensional Hilbert space, implying zero information content. The leading resolution by Harlow, Usatyuk, and Zhao (arXiv:2501.02359, January 2025), featured in Quanta Magazine (November 2025), introduces observers as external additions to restore complexity. We demonstrate that this approach is self-contradictory: it resolves a closed universe problem by opening the universe, introducing boundaries in a system defined by their absence.\n\nWe present a fundamentally different resolution. By proving that the open/closed distinction is itself a declaration and establishing observer-universe equivalence (O ≡ U), we derive the Totality Theorem: T = O + U = 1. The one-state result is not a paradox but a correct description of completeness: Shannon entropy H = 0 indicates full knowledge, not emptiness. We show that dimensionality itself is an artifact of partition, not a feature of reality.\n\nFrom three relations alone — T = 1, O ≡ U, dO = −dU — and a single structural principle (the Law of Identity A = A generates the binary partition A + ¬A = 1 with unique fixed point A = ¬A = 0.5), we resolve problems across every foundational domain: the unification of the four laws of thermodynamics as facets of a single identity, the black hole information paradox, the cosmological constant discrepancy, the Collatz and twin prime conjectures, Wigner's 67-year mystery of mathematical effectiveness, and the dissolution of Gödel's incompleteness as a property of notation rather than truth. All results derive from A = A."}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.21858339","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-08-09T06:47:33Z","registered":"2026-08-09T06:47:33Z","published":null,"updated":"2026-08-09T06:47:33Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.21858321","type":"dois","attributes":{"doi":"10.5281/zenodo.21858321","identifiers":[{"identifier":"oai:zenodo.org:21858321","identifierType":"oai"}],"creators":[{"nameType":"Personal","affiliation":["Universidade Federal do Estado do Rio de Janeiro"],"givenName":"Roberto","familyName":"Monteiro Dias","name":"Monteiro Dias, Roberto","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","nameIdentifier":"0000-0001-9514-1946"}]}],"titles":[{"title":"SECO-IM-Ontology: Ontologia para Gerenciamento de Incidentes em Ecossistemas de Software Proprietários"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[],"contributors":[],"dates":[{"date":"2026-08-09","dateType":"Issued"}],"language":null,"types":{"schemaOrg":"CreativeWork","resourceTypeGeneral":"Preprint","citeproc":"article","bibtex":"misc","ris":"GEN","resourceType":""},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.21858320","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","rightsIdentifier":"cc-by-4.0"},{"rightsIdentifierScheme":"SPDX","rightsUri":"http://www.apache.org/licenses/LICENSE-2.0","schemeUri":"https://spdx.org/licenses/","rights":"Apache License 2.0","rightsIdentifier":"apache-2.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Proprietary software ecosystems (SECO) involve multiple actors around a central platform, where system incidents cause severe financial and reputational damage. Effective governance is critical, yet no conceptual formalization integrates incident management elements. This paper presents SECO-IM-Ontology, a domain ontology that formalizes actors, processes, metrics, incidents, and governance mechanisms for proprietary SECO. Developed using the On-toSECO methodology and implemented in OWL/Java, the ontology is evaluated via competency questions from prior research. Results show it offers: (i) a standardized vocabulary; (ii) a conceptual framework; (iii) problem-diagnosis support; and (iv) a basis for automated assessment tools."}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.21858321","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":1,"created":"2026-08-09T06:46:34Z","registered":"2026-08-09T06:46:35Z","published":null,"updated":"2026-08-09T06:46:35Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.21858320","type":"dois","attributes":{"doi":"10.5281/zenodo.21858320","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["Universidade Federal do Estado do Rio de Janeiro"],"givenName":"Roberto","familyName":"Monteiro Dias","name":"Monteiro Dias, Roberto","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","nameIdentifier":"0000-0001-9514-1946"}]}],"titles":[{"title":"SECO-IM-Ontology: Ontologia para Gerenciamento de Incidentes em Ecossistemas de Software Proprietários"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[],"contributors":[],"dates":[{"date":"2026-08-09","dateType":"Issued"}],"language":null,"types":{"schemaOrg":"CreativeWork","resourceTypeGeneral":"Preprint","citeproc":"article","bibtex":"misc","ris":"GEN","resourceType":""},"relatedIdentifiers":[{"relationType":"HasVersion","relatedIdentifier":"10.5281/zenodo.21858321","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","rightsIdentifier":"cc-by-4.0"},{"rightsIdentifierScheme":"SPDX","rightsUri":"http://www.apache.org/licenses/LICENSE-2.0","schemeUri":"https://spdx.org/licenses/","rights":"Apache License 2.0","rightsIdentifier":"apache-2.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Proprietary software ecosystems (SECO) involve multiple actors around a central platform, where system incidents cause severe financial and reputational damage. Effective governance is critical, yet no conceptual formalization integrates incident management elements. This paper presents SECO-IM-Ontology, a domain ontology that formalizes actors, processes, metrics, incidents, and governance mechanisms for proprietary SECO. Developed using the On-toSECO methodology and implemented in OWL/Java, the ontology is evaluated via competency questions from prior research. Results show it offers: (i) a standardized vocabulary; (ii) a conceptual framework; (iii) problem-diagnosis support; and (iv) a basis for automated assessment tools."}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.21858320","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":1,"versionOfCount":0,"created":"2026-08-09T06:46:34Z","registered":"2026-08-09T06:46:35Z","published":null,"updated":"2026-08-09T06:46:35Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.21858249","type":"dois","attributes":{"doi":"10.5281/zenodo.21858249","identifiers":[],"creators":[{"nameType":"Personal","givenName":"Peter","familyName":"Baldwin","name":"Baldwin, Peter","nameIdentifiers":[],"affiliation":[]}],"titles":[{"title":"Global Closure Sectors and Route-Depth Hierarchies in a Z3-Identified Phase Geometry"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"Route-depth suppression"},{"subject":"emergent hierarchy"},{"subject":"transition graph"},{"subject":"Fourier Hamiltonian"},{"subject":"perturbation theory"},{"subject":"equivariant sectors"},{"subject":"Z3"},{"subject":"compact phase"},{"subject":"Modal Theory"},{"subject":"Global Constraint Framework"},{"subject":"MT"},{"subject":"GCF"}],"contributors":[],"dates":[{"date":"2026-08-09","dateType":"Issued"},{"date":"2026-08-09","dateType":"Issued"}],"language":null,"types":{"schemaOrg":"CreativeWork","resourceTypeGeneral":"Preprint","citeproc":"article","bibtex":"misc","ris":"GEN","resourceType":""},"relatedIdentifiers":[{"relationType":"HasVersion","relatedIdentifier":"10.5281/zenodo.21858250","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","rightsIdentifier":"cc-by-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"We study a compact Z3-identified phase model motivated by the Global Constraint Framework and Modal Theory, in which global closure conditions organize the Hilbert space into three inequivalent Z3 character sectors. The ordinary topology of S1/Z3 remains that of a circle, with integer winding classification, while the threefold sector structure arises from the allowed equivariant characters of the discrete identification.\n\nFor the chiral perturbation δVchiral(θ) = κ sin(3θ) sin²(θ), the Fourier support is restricted to n = ±1, ±3, ±5. In the lowest closure-sector states this permits direct first-order coupling between the unwound and winded states, while the direct matrix element between the two lowest winded states vanishes because the perturbation contains no n = ±2 harmonic. Second-order degenerate perturbation theory generates the missing coupling through allowed intermediate Fourier modes, producing a structural hierarchy between O(κ) and O(κ²/β) channels.\n\nA canonical collective-coordinate reduction gives β = 1/(2Vcρ0²), where Vc is the spatial coherence volume of the reduced mode. The current GCF/MT field equations do not independently determine Vc, so the absolute spectral scale is not predicted here. A converged finite Fourier-basis approximation to the full phase Hamiltonian confirms the low-energy spectral structure while showing that an apparent trace identity of the reduced three-state Hamiltonian does not survive the full operator.\n\nThese results motivate the Shortest-Allowed-Route Conjecture: within a perturbatively connected sector graph, the leading perturbative order of an effective transition is controlled by the length of the shortest allowed path connecting the corresponding states, subject to possible cancellations, degeneracies, and symmetry constraints. The present work establishes this mechanism in a concrete compact-phase model without identifying the closure sectors with physical Standard Model generations."}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.21858249","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":1,"versionOfCount":0,"created":"2026-08-09T06:43:37Z","registered":"2026-08-09T06:43:38Z","published":null,"updated":"2026-08-09T06:43:38Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.21858250","type":"dois","attributes":{"doi":"10.5281/zenodo.21858250","identifiers":[{"identifier":"oai:zenodo.org:21858250","identifierType":"oai"}],"creators":[{"nameType":"Personal","givenName":"Peter","familyName":"Baldwin","name":"Baldwin, Peter","nameIdentifiers":[],"affiliation":[]}],"titles":[{"title":"Global Closure Sectors and Route-Depth Hierarchies in a Z3-Identified Phase Geometry"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"Route-depth suppression"},{"subject":"emergent hierarchy"},{"subject":"transition graph"},{"subject":"Fourier Hamiltonian"},{"subject":"perturbation theory"},{"subject":"equivariant sectors"},{"subject":"Z3"},{"subject":"compact phase"},{"subject":"Modal Theory"},{"subject":"Global Constraint Framework"},{"subject":"MT"},{"subject":"GCF"}],"contributors":[],"dates":[{"date":"2026-08-09","dateType":"Issued"},{"date":"2026-08-09","dateType":"Issued"}],"language":null,"types":{"schemaOrg":"CreativeWork","resourceTypeGeneral":"Preprint","citeproc":"article","bibtex":"misc","ris":"GEN","resourceType":""},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.21858249","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","rightsIdentifier":"cc-by-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"We study a compact Z3-identified phase model motivated by the Global Constraint Framework and Modal Theory, in which global closure conditions organize the Hilbert space into three inequivalent Z3 character sectors. The ordinary topology of S1/Z3 remains that of a circle, with integer winding classification, while the threefold sector structure arises from the allowed equivariant characters of the discrete identification.\n\nFor the chiral perturbation δVchiral(θ) = κ sin(3θ) sin²(θ), the Fourier support is restricted to n = ±1, ±3, ±5. In the lowest closure-sector states this permits direct first-order coupling between the unwound and winded states, while the direct matrix element between the two lowest winded states vanishes because the perturbation contains no n = ±2 harmonic. Second-order degenerate perturbation theory generates the missing coupling through allowed intermediate Fourier modes, producing a structural hierarchy between O(κ) and O(κ²/β) channels.\n\nA canonical collective-coordinate reduction gives β = 1/(2Vcρ0²), where Vc is the spatial coherence volume of the reduced mode. The current GCF/MT field equations do not independently determine Vc, so the absolute spectral scale is not predicted here. A converged finite Fourier-basis approximation to the full phase Hamiltonian confirms the low-energy spectral structure while showing that an apparent trace identity of the reduced three-state Hamiltonian does not survive the full operator.\n\nThese results motivate the Shortest-Allowed-Route Conjecture: within a perturbatively connected sector graph, the leading perturbative order of an effective transition is controlled by the length of the shortest allowed path connecting the corresponding states, subject to possible cancellations, degeneracies, and symmetry constraints. The present work establishes this mechanism in a concrete compact-phase model without identifying the closure sectors with physical Standard Model generations."}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.21858250","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-08-09T06:43:37Z","registered":"2026-08-09T06:43:37Z","published":null,"updated":"2026-08-09T06:43:37Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.21858255","type":"dois","attributes":{"doi":"10.5281/zenodo.21858255","identifiers":[{"identifier":"oai:zenodo.org:21858255","identifierType":"oai"}],"creators":[{"nameType":"Personal","familyName":"Yoonsu Lee","name":"Yoonsu Lee","nameIdentifiers":[],"affiliation":[]}],"titles":[{"title":"The Totality Theorem: Resolution of the Closed Universe Paradox through Observer-Universe Equivalence"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"Physics","subjectScheme":"GEMET"},{"subject":"Astronomy","subjectScheme":"GEMET"},{"subject":"quantum gravity"},{"subject":"observer problem"},{"subject":"black hole information paradox"},{"subject":"measurement problem"},{"subject":"ER=EPR"},{"subject":"Logic","subjectScheme":"MeSH"},{"subject":"Mathematical logic","subjectScheme":"EuroSciVoc"},{"subject":"Fuzzy Logic","subjectScheme":"MeSH"},{"subject":"Social policy","subjectScheme":"GEMET"},{"subject":"Mathematics","subjectScheme":"MeSH"}],"contributors":[],"dates":[{"date":"2026-08-09","dateType":"Issued"},{"date":"2026-01-31","dateType":"Available"}],"language":"en","types":{"schemaOrg":"CreativeWork","resourceTypeGeneral":"Preprint","citeproc":"article","bibtex":"misc","ris":"GEN","resourceType":""},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.18439957","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","rightsIdentifier":"cc-by-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Recent work in quantum gravity has revealed that closed universes appear to admit only a one-dimensional Hilbert space, implying zero information content. The leading resolution by Harlow, Usatyuk, and Zhao (arXiv:2501.02359, January 2025), featured in Quanta Magazine (November 2025), introduces observers as external additions to restore complexity. We demonstrate that this approach is self-contradictory: it resolves a closed universe problem by opening the universe, introducing boundaries in a system defined by their absence.\n\nWe present a fundamentally different resolution. By proving that the open/closed distinction is itself a declaration and establishing observer-universe equivalence (O ≡ U), we derive the Totality Theorem: T = O + U = 1. The one-state result is not a paradox but a correct description of completeness: Shannon entropy H = 0 indicates full knowledge, not emptiness. We show that dimensionality itself is an artifact of partition, not a feature of reality.\n\nFrom three relations alone — T = 1, O ≡ U, dO = −dU — and a single structural principle (the Law of Identity A = A generates the binary partition A + ¬A = 1 with unique fixed point A = ¬A = 0.5), we resolve problems across every foundational domain: the unification of the four laws of thermodynamics as facets of a single identity, the black hole information paradox, the cosmological constant discrepancy, the Collatz and twin prime conjectures, Wigner's 67-year mystery of mathematical effectiveness, and the dissolution of Gödel's incompleteness as a property of notation rather than truth. All results derive from A = A."}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.21858255","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-08-09T06:39:09Z","registered":"2026-08-09T06:39:09Z","published":null,"updated":"2026-08-09T06:39:09Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.21433338","type":"dois","attributes":{"doi":"10.5281/zenodo.21433338","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["독립연구자·발명가"],"givenName":"동윤","familyName":"곽","name":"곽, 동윤","nameIdentifiers":[]}],"titles":[{"title":"광목표 생성과 공통시간의 물리학: 빛의 출발 순간 생성되는 시간표지 목표와 하나의 공동 실험에 존재하는 실제 시간"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"광목표"},{"subject":"시간표지"},{"subject":"발신 사건"},{"subject":"수신 사건"},{"subject":"광지연"},{"subject":"공통시간"},{"subject":"단일실험"},{"subject":"고유시간"},{"subject":"동시성"},{"subject":"실체와잔상"},{"subject":"특수상대성이론"}],"contributors":[],"dates":[{"date":"2026-08-09","dateType":"Issued"}],"language":"ko","types":{"schemaOrg":"CreativeWork","resourceTypeGeneral":"Preprint","citeproc":"article","bibtex":"misc","ris":"GEN","resourceType":""},"relatedIdentifiers":[{"relationType":"IsDerivedFrom","resourceTypeGeneral":"Audiovisual","relatedIdentifier":"https://youtu.be/nsvlv5HJ5eg","relatedIdentifierType":"URL"},{"relationType":"IsDerivedFrom","resourceTypeGeneral":"Audiovisual","relatedIdentifier":"https://youtu.be/yy2_M1J924I","relatedIdentifierType":"URL"},{"relationType":"HasVersion","relatedIdentifier":"10.5281/zenodo.21433339","relatedIdentifierType":"DOI"},{"relationType":"HasVersion","relatedIdentifier":"10.5281/zenodo.21857292","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":"v2.0","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","rightsIdentifier":"cc-by-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"본 논문은 특수상대성이론의 기차 사고실험과 상호 시간지연 설명에서 사용되는 ‘본다’는 행위를 하나의 광학적 수신 과정으로 통일하여 재검토한다. 눈·카메라·광검출기는 상대방의 현재 실체를 직접 받지 않는다. 검출되는 전파광은 상대방의 더 이른 상태에서 방출되거나 반사되어 현재의 수신체에 도달한 것이며, 그 발신 순간의 물체 상태에 관한 광학적 부분정보가 전자기 신호에 부호화된다. 본 논문은 이 발신사건 결박형 시간표지 상태표상을 ‘광목표’라 정의한다.\n\n광목표는 빛의 진행 방향이나 나중에 빛과 상호작용한 수신체와 다르다. 광목표 M_E는 발신시각 t_e와 발신사건 E에 결박되고, 실제 수신은 전파광 ℓ_E가 수신시각의 물질 수신체 D_R과 만나는 사건 R=ℓ_E∩D_R에서 일어난다. 따라서 발신시각의 상태표상 M_E와 수신시각의 실체 D_R을 동일한 시간상태로 취급할 수 없다.\n\n이 구분은 기차의 앞뒤 신호에만 적용되지 않는다. A가 B를 본다고 할 때 A가 직접 받는 것은 수신시점의 현재 B 자체가 아니라 B의 더 이른 발신사건에서 생성된 광목표 M_B의 정보를 담은 전파광 ℓ_B이다. 따라서 한 방향의 관측은 현재 수신자 A_n, B의 후속 실체 B_n, B의 과거 광목표 M_B^(n)을 세 꼭짓점으로 하는 실체–광목표–수신자 삼각관계로 표현할 수 있다. 반대 방향에서도 B_n, A_n, M_A^(n)의 대칭 삼각관계가 생긴다. 광목표 자체가 이동하는 것이 아니라 전파광이 광목표 정보를 운반한다.\n\n이 이중 삼각구조를 적용하면 특수상대론의 대표적 시각장면 두 가지, 즉 기차 중앙 관찰자의 빛 수신으로 원격 동시성을 정당화하는 설명과 서로가 서로의 시계를 느리게 본다는 장면으로 상호 시간지연을 정당화하는 설명은 동일한 문제를 가진다. 직접 관측되는 것은 현재-현재의 원격 시간비교가 아니라 과거 발신 광목표와 현재 수신체의 연결이다. 전파지연을 보정한 뒤 로런츠 동기화와 좌표변환을 적용하여 얻는 t′는 직접 관측값이 아니라 별도의 이론적 좌표 재구성값이다.\n\n공통시간 이론은 이 삼각연결을 한 장면에 한정하지 않는다. 상호관찰을 n=0,1,2,…의 연속 사건열로 전개하면 두 방향의 삼각형이 서로 맞물린 연속 상호광학 연결망 𝒩_J를 이룬다. 동일 공동실험 J의 n번째 연결층에서 대응 광목표는 tag(M_A^(n))=tag(M_B^(n))=Tₙ의 공동 생성관계를 가지며, 이 대응은 수신 뒤 임의로 붙인 동시성 라벨이 아니라 공동 시작사건과 실제 양방향 발신·전파·수신사슬이 만드는 사건망의 진행단계로 정의된다.\n\n부호 안내: n은 공동 시작사건 이후 사건망의 진행단계를 세는 번호이고, 𝒩_J는 공동실험 J에서 양방향 발신·전파·수신 연결이 연속되어 형성되는 전체 상호광학 사건망이다. Tₙ은 그 사건망의 n번째 공동 진행층을 나타내는 표지이며, tag(M)는 광목표가 어느 생성단계에 결박되었는지를 나타내는 ‘광목표의 생성단계 표지’다. 따라서 Tₙ은 사후 좌표동기화나 편집으로 붙인 동시성 라벨이 아니다.\n\n연속 삼각망의 각 층을 공동 진행변수로 쓰면 C_A(n)=C_B(n)=Tₙ이고, 연속극한에서 C_A(T)=C_B(T)=C_J(T), ΔC(T)=0이다. 본 논문은 이 결과를 기차 안과 기차 밖이 하나의 공동실험에서 공유하는 실제 공동시간 동일성의 논리적 기반으로 채택하며, 로런츠 좌표차와 국소 시계누적량의 차이를 실제 공동시간 자체의 분열과 구분한다. 또한 이 공통시간을 기차 중앙의 양방향 발광에 적용하면, 기차 안의 동시 도달을 기차 밖에서도 동일 공동사건으로 유지할 때 외부 공동시간 기술의 광속도는 c+v와 v−c로 표현되어 모든 관성계에서 동일 실제시간에 대한 광속불변 해석과 충돌한다. 상세한 도출은 제27장에서 제시한다."}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.21433338","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":2,"versionOfCount":1,"created":"2026-07-18T21:49:11Z","registered":"2026-07-18T21:49:11Z","published":null,"updated":"2026-08-09T06:38:41Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.21857292","type":"dois","attributes":{"doi":"10.5281/zenodo.21857292","identifiers":[{"identifier":"oai:zenodo.org:21857292","identifierType":"oai"}],"creators":[{"nameType":"Personal","affiliation":["독립연구자·발명가"],"givenName":"동윤","familyName":"곽","name":"곽, 동윤","nameIdentifiers":[]}],"titles":[{"title":"광목표 생성과 공통시간의 물리학: 빛의 출발 순간 생성되는 시간표지 목표와 하나의 공동 실험에 존재하는 실제 시간"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"광목표"},{"subject":"시간표지"},{"subject":"발신 사건"},{"subject":"수신 사건"},{"subject":"광지연"},{"subject":"공통시간"},{"subject":"단일실험"},{"subject":"고유시간"},{"subject":"동시성"},{"subject":"실체와잔상"},{"subject":"특수상대성이론"}],"contributors":[],"dates":[{"date":"2026-08-09","dateType":"Issued"}],"language":"ko","types":{"schemaOrg":"CreativeWork","resourceTypeGeneral":"Preprint","citeproc":"article","bibtex":"misc","ris":"GEN","resourceType":""},"relatedIdentifiers":[{"relationType":"IsDerivedFrom","resourceTypeGeneral":"Audiovisual","relatedIdentifier":"https://youtu.be/nsvlv5HJ5eg","relatedIdentifierType":"URL"},{"relationType":"IsDerivedFrom","resourceTypeGeneral":"Audiovisual","relatedIdentifier":"https://youtu.be/yy2_M1J924I","relatedIdentifierType":"URL"},{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.21433338","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":"v2.0","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","rightsIdentifier":"cc-by-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"본 논문은 특수상대성이론의 기차 사고실험과 상호 시간지연 설명에서 사용되는 ‘본다’는 행위를 하나의 광학적 수신 과정으로 통일하여 재검토한다. 눈·카메라·광검출기는 상대방의 현재 실체를 직접 받지 않는다. 검출되는 전파광은 상대방의 더 이른 상태에서 방출되거나 반사되어 현재의 수신체에 도달한 것이며, 그 발신 순간의 물체 상태에 관한 광학적 부분정보가 전자기 신호에 부호화된다. 본 논문은 이 발신사건 결박형 시간표지 상태표상을 ‘광목표’라 정의한다.\n\n광목표는 빛의 진행 방향이나 나중에 빛과 상호작용한 수신체와 다르다. 광목표 M_E는 발신시각 t_e와 발신사건 E에 결박되고, 실제 수신은 전파광 ℓ_E가 수신시각의 물질 수신체 D_R과 만나는 사건 R=ℓ_E∩D_R에서 일어난다. 따라서 발신시각의 상태표상 M_E와 수신시각의 실체 D_R을 동일한 시간상태로 취급할 수 없다.\n\n이 구분은 기차의 앞뒤 신호에만 적용되지 않는다. A가 B를 본다고 할 때 A가 직접 받는 것은 수신시점의 현재 B 자체가 아니라 B의 더 이른 발신사건에서 생성된 광목표 M_B의 정보를 담은 전파광 ℓ_B이다. 따라서 한 방향의 관측은 현재 수신자 A_n, B의 후속 실체 B_n, B의 과거 광목표 M_B^(n)을 세 꼭짓점으로 하는 실체–광목표–수신자 삼각관계로 표현할 수 있다. 반대 방향에서도 B_n, A_n, M_A^(n)의 대칭 삼각관계가 생긴다. 광목표 자체가 이동하는 것이 아니라 전파광이 광목표 정보를 운반한다.\n\n이 이중 삼각구조를 적용하면 특수상대론의 대표적 시각장면 두 가지, 즉 기차 중앙 관찰자의 빛 수신으로 원격 동시성을 정당화하는 설명과 서로가 서로의 시계를 느리게 본다는 장면으로 상호 시간지연을 정당화하는 설명은 동일한 문제를 가진다. 직접 관측되는 것은 현재-현재의 원격 시간비교가 아니라 과거 발신 광목표와 현재 수신체의 연결이다. 전파지연을 보정한 뒤 로런츠 동기화와 좌표변환을 적용하여 얻는 t′는 직접 관측값이 아니라 별도의 이론적 좌표 재구성값이다.\n\n공통시간 이론은 이 삼각연결을 한 장면에 한정하지 않는다. 상호관찰을 n=0,1,2,…의 연속 사건열로 전개하면 두 방향의 삼각형이 서로 맞물린 연속 상호광학 연결망 𝒩_J를 이룬다. 동일 공동실험 J의 n번째 연결층에서 대응 광목표는 tag(M_A^(n))=tag(M_B^(n))=Tₙ의 공동 생성관계를 가지며, 이 대응은 수신 뒤 임의로 붙인 동시성 라벨이 아니라 공동 시작사건과 실제 양방향 발신·전파·수신사슬이 만드는 사건망의 진행단계로 정의된다.\n\n부호 안내: n은 공동 시작사건 이후 사건망의 진행단계를 세는 번호이고, 𝒩_J는 공동실험 J에서 양방향 발신·전파·수신 연결이 연속되어 형성되는 전체 상호광학 사건망이다. Tₙ은 그 사건망의 n번째 공동 진행층을 나타내는 표지이며, tag(M)는 광목표가 어느 생성단계에 결박되었는지를 나타내는 ‘광목표의 생성단계 표지’다. 따라서 Tₙ은 사후 좌표동기화나 편집으로 붙인 동시성 라벨이 아니다.\n\n연속 삼각망의 각 층을 공동 진행변수로 쓰면 C_A(n)=C_B(n)=Tₙ이고, 연속극한에서 C_A(T)=C_B(T)=C_J(T), ΔC(T)=0이다. 본 논문은 이 결과를 기차 안과 기차 밖이 하나의 공동실험에서 공유하는 실제 공동시간 동일성의 논리적 기반으로 채택하며, 로런츠 좌표차와 국소 시계누적량의 차이를 실제 공동시간 자체의 분열과 구분한다. 또한 이 공통시간을 기차 중앙의 양방향 발광에 적용하면, 기차 안의 동시 도달을 기차 밖에서도 동일 공동사건으로 유지할 때 외부 공동시간 기술의 광속도는 c+v와 v−c로 표현되어 모든 관성계에서 동일 실제시간에 대한 광속불변 해석과 충돌한다. 상세한 도출은 제27장에서 제시한다."}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.21857292","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-08-09T06:38:40Z","registered":"2026-08-09T06:38:41Z","published":null,"updated":"2026-08-09T06:38:41Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.20280700","type":"dois","attributes":{"doi":"10.5281/zenodo.20280700","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["Independent Researcher"],"givenName":"Chen-Chieh","familyName":"Fan","name":"Fan, Chen-Chieh","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","nameIdentifier":"0009-0000-5317-7385"}]}],"titles":[{"title":"Deep Collaboration Methodology 2.0"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"Deep Collaboration Methodology"},{"subject":"Deep Collaboration Methodology 2.0"},{"subject":"DCM 2.0"},{"subject":"DCM"},{"subject":"responsibility attribution perception"},{"subject":"Epistemic Sovereignty"},{"subject":"street-intercept interviews"},{"subject":"Convergent Mixed-Methods Design"},{"subject":"human-machine collaboration"},{"subject":"Cognitive Surrender"},{"subject":"Moral Suspension"},{"subject":"cross-cultural research"}],"contributors":[],"dates":[{"date":"2026-08-09","dateType":"Issued"}],"language":"en","types":{"schemaOrg":"CreativeWork","resourceTypeGeneral":"Preprint","citeproc":"article","bibtex":"misc","ris":"GEN","resourceType":""},"relatedIdentifiers":[{"relationType":"Continues","resourceTypeGeneral":"Preprint","relatedIdentifier":"10.5281/zenodo.18124731","relatedIdentifierType":"DOI"},{"relationType":"IsSupplementedBy","resourceTypeGeneral":"Report","relatedIdentifier":"10.5281/zenodo.20281517","relatedIdentifierType":"DOI"},{"relationType":"IsSupplementedBy","resourceTypeGeneral":"Dataset","relatedIdentifier":"10.5281/zenodo.20281621","relatedIdentifierType":"DOI"},{"relationType":"IsSupplementedBy","resourceTypeGeneral":"Dataset","relatedIdentifier":"10.5281/zenodo.20281831","relatedIdentifierType":"DOI"},{"relationType":"IsSupplementedBy","resourceTypeGeneral":"Report","relatedIdentifier":"10.5281/zenodo.20839820","relatedIdentifierType":"DOI"},{"relationType":"Cites","resourceTypeGeneral":"JournalArticle","relatedIdentifier":"10.1016/j.ailsci.2026.100158","relatedIdentifierType":"DOI"},{"relationType":"Cites","resourceTypeGeneral":"JournalArticle","relatedIdentifier":"10.1080/10447318.2026.2634251","relatedIdentifierType":"DOI"},{"relationType":"Cites","resourceTypeGeneral":"JournalArticle","relatedIdentifier":"10.3390/soc15010006","relatedIdentifierType":"DOI"},{"relationType":"Cites","resourceTypeGeneral":"JournalArticle","relatedIdentifier":"10.3389/fpubh.2026.1785170","relatedIdentifierType":"DOI"},{"relationType":"Cites","resourceTypeGeneral":"Preprint","relatedIdentifier":"10.31234/osf.io/yk25n_v1","relatedIdentifierType":"DOI"},{"relationType":"References","resourceTypeGeneral":"Report","relatedIdentifier":"10.5281/zenodo.18102508","relatedIdentifierType":"DOI"},{"relationType":"Cites","resourceTypeGeneral":"Preprint","relatedIdentifier":"10.5281/zenodo.18124731","relatedIdentifierType":"DOI"},{"relationType":"HasVersion","relatedIdentifier":"10.5281/zenodo.20841515","relatedIdentifierType":"DOI"},{"relationType":"HasVersion","relatedIdentifier":"10.5281/zenodo.20281335","relatedIdentifierType":"DOI"},{"relationType":"HasVersion","relatedIdentifier":"10.5281/zenodo.20280701","relatedIdentifierType":"DOI"},{"relationType":"HasVersion","relatedIdentifier":"10.5281/zenodo.21858122","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","rightsIdentifier":"cc-by-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":" \n\nThis study proposes and executes the Deep Collaboration Methodology 2.0 (DCM 2.0), taking responsibility attribution perception in contexts of AI use as its research object. Street-intercept interviews were conducted at Da'an Forest Park in Taipei and at several university campuses, producing 328 valid interviews spanning 33 nationalities (including Taiwan, with 32 foreign nationalities). Field execution ran from December 22, 2025 to April 15, 2026.\n\nThe study employs a Convergent Mixed-Methods Design, using two fixed core questions as quantitative anchors: Q1 asks \"when AI output is erroneous, to whom does responsibility belong?\" (the level of moral attribution), and Q2 asks \"when a loss results from acting on an AI-generated answer, who bears the loss responsibility?\" (the level of practical allocation). Ten rotating question sets (Q3/Q4) were used to explore respondent reaction patterns across different AI social issue contexts.\n\nThe principal findings are organized across three levels. First, the user internalization of loss responsibility in Q2 exhibits overwhelming cross-group robustness (79% overall), remaining stable across stratifications by gender, age, nationality, and tool choice for most background characteristics — indicating that \"when a loss results from acting on an AI-generated answer, the user should bear responsibility\" constitutes a cross-group ethical intuition about tool use. Second, the Q1 responsibility framework is systematically moderated by usage experience, forming a continuous gradient from non-AI users (AI attribution 52%) to short-term users (33%) to long-term users (24%), with the core mechanism being the accumulation of error-encounter experience. Third, a structural epistemic gap exists between Q1 and Q2: Q1's tripartite dispersion (user attribution 25%, shared responsibility 37%, AI attribution 31%) collapses abruptly upon entering Q2's loss responsibility framework — even among respondents who attributed Q1 responsibility to AI, 71% still pointed to the user in Q2 — reflecting the systematic fusion that occurs when two distinct ethical questions (\"who caused the error\" versus \"who should bear the consequences\") are presented in sequential order.\n\nAt the methodological level, DCM 2.0 fills structural gaps in existing AI cognition research across three dimensions — ecological validity, cross-cultural coverage, and sample diversity — providing a traceable, replicable, and institutionally unaffiliated street-intercept mixed-methods protocol. The construction of DCM as an independent methodological framework is presented as a core academic contribution alongside the substantive findings of this study.\n\nNote: The PDF in this deposit references an earlier DOI(10.5281/zenodo.20280701). Both records refer to the same work.Please cite this record: https://doi.org/10.5281/zenodo.20280700\n\nCorrection (May 20, 2026): Author name corrected in Section 2.2 and reference list — Makoana et al. → Kakraba et al. (2026). No changes to research findings or methodology.\n\n \n\nVersion 3 Correction and Revision Note (2026/06/25)\n\nCorrections:\n\n1.Journal name: Branda and Ciccozzi (2026) was incorrectly listed as published in eBioMedicine. The correct journal is Artificial Intelligence in the Life Sciences. DOI updated to 10.1016/j.ailsci.2026.100158.\n\nRevisions:\n\n2. Section 1.4: Inline meta-commentary and parenthetical glosses have been removed. Substantive findings and analytical content are unchanged.\n\n3. Section 3.2.1: A paragraph discussing the structural sampling advantages of street-intercept design in the context of rising digital tool adoption has been removed. Core design logic is unchanged.\n\n4. Section 3.2.2: Paragraphs explaining the rationale for withholding question sequence information and the open-ended design of Q1 have been removed. Interview structure and question wording are unchanged.\n\n5. Section 8.7: Fully rewritten. The revised version reframes the researcher's epistemological position in methodological rather than personal terms, and restructures the section into three analytical components: outlier status, structural institutional exclusion, and execution-level operational logic.\n\nThe substantive findings, analytical conclusions, and all other content remain unchanged.\n\nVersion 3 Correction and Revision Note (2026/07/24)\n\nSection 2.2 — Definitional Origins of Epistemic SovereigntyAdded an opening paragraph tracing the problem consciousness underlying this study's conception of Epistemic Sovereignty to the author's earlier case study, The Automation Paradox (Fan, 2025a), positioning it as the conceptual point of origin preceding the two 2026 academic definitions reviewed in this section.\n\nSection 2.4 — Research Gaps and DCM 2.0's PositioningAdded a paragraph preceding the section's closing statement, detailing the methodological discontinuity between DCM 2.0 and the earlier exploratory study DCM 1.6 (Fan, 2025b): differences in interview-guide fixity, the absence of the ten-set Q3/Q4 rotation design, sample scale (n = 36 vs. n = 328), absence of refusal records, restricted-layer field structure (23 fields in 2.0), and the absence of quantitative analysis and literature citation in 1.6. This addition also states explicitly, within the body text rather than solely in Zenodo metadata, the rationale for publishing DCM 2.0 under an independent DOI rather than as a revised version of DCM 1.6.\n\nSection 3.8 — Research LimitationsAdded a bridging sentence to the \"Consistency limitations of single-person execution\" entry, cross-referencing Section 3.7.2 to clarify the division of labor between the epistemological legitimacy of the participant-researcher's overlapping roles (discussed in 3.7.2) and the operational limitation of the absence of cross-interviewer verification (discussed here), preempting a reading of internal contradiction between the two sections.\n\nReference formattingCorrected APA citation format for the two self-authored Zenodo works, and disambiguated same-year, same-author citations with a/b suffixes ordered by title:\n\n\n\nFan, C.-C. (2025a). Case study: The Automation Paradox and cognitive sustainability. https://doi.org/10.5281/zenodo.18102508\n\nFan, C.-C. (2025b). DCM (Deep Collaboration Methodology): A recursive framework for human-AI symbiosis and epistemic sovereignty. https://doi.org/10.5281/zenodo.18124731\n\n\nVersion 4 Correction and Revision Note (2026/08/09)\n\nThis revision addresses a previously undisclosed gap in the study's consent infrastructure, adds a formal rigor-benchmarking section for the study's data protection design, and corrects a citation-year error in the reference list. No changes were made to the study's core findings, sample, or statistical results.\n\n1. Section 3.6.1 (Design Choices for Informed Consent) — revised.Added disclosure that during the earliest phase of fieldwork (December 22–29, 2025; six respondents), the post-interview business-card distribution practice had not yet been standardized: cards were provided only to respondents who expressed strong interest in the research. The remaining respondents in this subgroup therefore had no channel to identify the researcher afterward and could not exercise the withdrawal right described in this section. This limitation was present in the original fieldwork practice but had not previously been stated in the manuscript.\n\n2. Section 3.6.2 (Design Principles for Privacy Protection) — closing sentence revised.The section's closing line (\"The law is the floor, not the ceiling...\") now cross-references Section 3.6.4 as the location of a more formal benchmarking treatment, rather than standing as an unelaborated claim.\n\n3. Section 3.6.4 (GDPR Benchmarking as a Rigor Standard) — new section added.Added a new subsection following 3.6.3, formally benchmarking the study's data architecture against GDPR principles (Article 3 territorial scope, Article 9 special-category data, Article 5(1)(c) data minimization) as a voluntary rigor standard rather than a compliance claim. This section elaborates, with regulatory grounding, on design commitments already described narratively in Section 3.6.2 (absence of special-category data collection, the tiered public/restricted architecture, and the absence of respondent photographs); it introduces no new empirical claims.\n\n4. Section 3.8 (Research Limitations) — new sixth item added.Added \"Consent and post-hoc contact limitations,\" documenting the same gap described in the revision to 3.6.1, and cross-referencing the data-minimization measures in Section 3.6.2 as a partial constraint on (but not a resolution of) the resulting risk. This item does not replace or alter the five limitations already present in prior versions; it is appended following the fifth item, and existing cross-references to those five items remain unaffected.\n\n5. Reference list — citation-year correction.Fan, C.-C. (2025a) and Fan, C.-C. (2025b), previously used to distinguish the two 2025-dated prior works, are corrected to reflect their actual publication years: the Automation Paradox case study was published in 2025, and DCM 1.6 was published in 2026. The (a)/(b) suffixes are removed accordingly, as they are no longer needed to disambiguate two same-year citations.\n\n\n\nReferences (corrected)\n\nFan, C.-C. (2025). Case study: The Automation Paradox and cognitive sustainability. https://doi.org/10.5281/zenodo.18102508\n\nFan, C.-C. (2026). DCM (Deep Collaboration Methodology): A recursive framework for human-AI symbiosis and epistemic sovereignty. https://doi.org/10.5281/zenodo.18124731"}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.20280700","contentUrl":null,"metadataVersion":14,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":14,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":3,"versionOfCount":1,"created":"2026-05-19T06:55:19Z","registered":"2026-05-19T06:55:19Z","published":null,"updated":"2026-08-09T06:36:29Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.21858122","type":"dois","attributes":{"doi":"10.5281/zenodo.21858122","identifiers":[{"identifier":"oai:zenodo.org:21858122","identifierType":"oai"}],"creators":[{"nameType":"Personal","affiliation":["Independent Researcher"],"givenName":"Chen-Chieh","familyName":"Fan","name":"Fan, Chen-Chieh","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","nameIdentifier":"0009-0000-5317-7385"}]}],"titles":[{"title":"Deep Collaboration Methodology 2.0"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"Deep Collaboration Methodology"},{"subject":"Deep Collaboration Methodology 2.0"},{"subject":"DCM 2.0"},{"subject":"DCM"},{"subject":"responsibility attribution perception"},{"subject":"Epistemic Sovereignty"},{"subject":"street-intercept interviews"},{"subject":"Convergent Mixed-Methods Design"},{"subject":"human-machine collaboration"},{"subject":"Cognitive Surrender"},{"subject":"Moral Suspension"},{"subject":"cross-cultural research"}],"contributors":[],"dates":[{"date":"2026-08-09","dateType":"Issued"}],"language":"en","types":{"schemaOrg":"CreativeWork","resourceTypeGeneral":"Preprint","citeproc":"article","bibtex":"misc","ris":"GEN","resourceType":""},"relatedIdentifiers":[{"relationType":"Continues","resourceTypeGeneral":"Preprint","relatedIdentifier":"10.5281/zenodo.18124731","relatedIdentifierType":"DOI"},{"relationType":"IsSupplementedBy","resourceTypeGeneral":"Report","relatedIdentifier":"10.5281/zenodo.20281517","relatedIdentifierType":"DOI"},{"relationType":"IsSupplementedBy","resourceTypeGeneral":"Dataset","relatedIdentifier":"10.5281/zenodo.20281621","relatedIdentifierType":"DOI"},{"relationType":"IsSupplementedBy","resourceTypeGeneral":"Dataset","relatedIdentifier":"10.5281/zenodo.20281831","relatedIdentifierType":"DOI"},{"relationType":"IsSupplementedBy","resourceTypeGeneral":"Report","relatedIdentifier":"10.5281/zenodo.20839820","relatedIdentifierType":"DOI"},{"relationType":"Cites","resourceTypeGeneral":"JournalArticle","relatedIdentifier":"10.1016/j.ailsci.2026.100158","relatedIdentifierType":"DOI"},{"relationType":"Cites","resourceTypeGeneral":"JournalArticle","relatedIdentifier":"10.1080/10447318.2026.2634251","relatedIdentifierType":"DOI"},{"relationType":"Cites","resourceTypeGeneral":"JournalArticle","relatedIdentifier":"10.3390/soc15010006","relatedIdentifierType":"DOI"},{"relationType":"Cites","resourceTypeGeneral":"JournalArticle","relatedIdentifier":"10.3389/fpubh.2026.1785170","relatedIdentifierType":"DOI"},{"relationType":"Cites","resourceTypeGeneral":"Preprint","relatedIdentifier":"10.31234/osf.io/yk25n_v1","relatedIdentifierType":"DOI"},{"relationType":"References","resourceTypeGeneral":"Report","relatedIdentifier":"10.5281/zenodo.18102508","relatedIdentifierType":"DOI"},{"relationType":"Cites","resourceTypeGeneral":"Preprint","relatedIdentifier":"10.5281/zenodo.18124731","relatedIdentifierType":"DOI"},{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.20280700","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","rightsIdentifier":"cc-by-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":" \n\nThis study proposes and executes the Deep Collaboration Methodology 2.0 (DCM 2.0), taking responsibility attribution perception in contexts of AI use as its research object. Street-intercept interviews were conducted at Da'an Forest Park in Taipei and at several university campuses, producing 328 valid interviews spanning 33 nationalities (including Taiwan, with 32 foreign nationalities). Field execution ran from December 22, 2025 to April 15, 2026.\n\nThe study employs a Convergent Mixed-Methods Design, using two fixed core questions as quantitative anchors: Q1 asks \"when AI output is erroneous, to whom does responsibility belong?\" (the level of moral attribution), and Q2 asks \"when a loss results from acting on an AI-generated answer, who bears the loss responsibility?\" (the level of practical allocation). Ten rotating question sets (Q3/Q4) were used to explore respondent reaction patterns across different AI social issue contexts.\n\nThe principal findings are organized across three levels. First, the user internalization of loss responsibility in Q2 exhibits overwhelming cross-group robustness (79% overall), remaining stable across stratifications by gender, age, nationality, and tool choice for most background characteristics — indicating that \"when a loss results from acting on an AI-generated answer, the user should bear responsibility\" constitutes a cross-group ethical intuition about tool use. Second, the Q1 responsibility framework is systematically moderated by usage experience, forming a continuous gradient from non-AI users (AI attribution 52%) to short-term users (33%) to long-term users (24%), with the core mechanism being the accumulation of error-encounter experience. Third, a structural epistemic gap exists between Q1 and Q2: Q1's tripartite dispersion (user attribution 25%, shared responsibility 37%, AI attribution 31%) collapses abruptly upon entering Q2's loss responsibility framework — even among respondents who attributed Q1 responsibility to AI, 71% still pointed to the user in Q2 — reflecting the systematic fusion that occurs when two distinct ethical questions (\"who caused the error\" versus \"who should bear the consequences\") are presented in sequential order.\n\nAt the methodological level, DCM 2.0 fills structural gaps in existing AI cognition research across three dimensions — ecological validity, cross-cultural coverage, and sample diversity — providing a traceable, replicable, and institutionally unaffiliated street-intercept mixed-methods protocol. The construction of DCM as an independent methodological framework is presented as a core academic contribution alongside the substantive findings of this study.\n\nNote: The PDF in this deposit references an earlier DOI(10.5281/zenodo.20280701). Both records refer to the same work.Please cite this record: https://doi.org/10.5281/zenodo.20280700\n\nCorrection (May 20, 2026): Author name corrected in Section 2.2 and reference list — Makoana et al. → Kakraba et al. (2026). No changes to research findings or methodology.\n\n \n\nVersion 3 Correction and Revision Note (2026/06/25)\n\nCorrections:\n\n1.Journal name: Branda and Ciccozzi (2026) was incorrectly listed as published in eBioMedicine. The correct journal is Artificial Intelligence in the Life Sciences. DOI updated to 10.1016/j.ailsci.2026.100158.\n\nRevisions:\n\n2. Section 1.4: Inline meta-commentary and parenthetical glosses have been removed. Substantive findings and analytical content are unchanged.\n\n3. Section 3.2.1: A paragraph discussing the structural sampling advantages of street-intercept design in the context of rising digital tool adoption has been removed. Core design logic is unchanged.\n\n4. Section 3.2.2: Paragraphs explaining the rationale for withholding question sequence information and the open-ended design of Q1 have been removed. Interview structure and question wording are unchanged.\n\n5. Section 8.7: Fully rewritten. The revised version reframes the researcher's epistemological position in methodological rather than personal terms, and restructures the section into three analytical components: outlier status, structural institutional exclusion, and execution-level operational logic.\n\nThe substantive findings, analytical conclusions, and all other content remain unchanged.\n\nVersion 3 Correction and Revision Note (2026/07/24)\n\nSection 2.2 — Definitional Origins of Epistemic SovereigntyAdded an opening paragraph tracing the problem consciousness underlying this study's conception of Epistemic Sovereignty to the author's earlier case study, The Automation Paradox (Fan, 2025a), positioning it as the conceptual point of origin preceding the two 2026 academic definitions reviewed in this section.\n\nSection 2.4 — Research Gaps and DCM 2.0's PositioningAdded a paragraph preceding the section's closing statement, detailing the methodological discontinuity between DCM 2.0 and the earlier exploratory study DCM 1.6 (Fan, 2025b): differences in interview-guide fixity, the absence of the ten-set Q3/Q4 rotation design, sample scale (n = 36 vs. n = 328), absence of refusal records, restricted-layer field structure (23 fields in 2.0), and the absence of quantitative analysis and literature citation in 1.6. This addition also states explicitly, within the body text rather than solely in Zenodo metadata, the rationale for publishing DCM 2.0 under an independent DOI rather than as a revised version of DCM 1.6.\n\nSection 3.8 — Research LimitationsAdded a bridging sentence to the \"Consistency limitations of single-person execution\" entry, cross-referencing Section 3.7.2 to clarify the division of labor between the epistemological legitimacy of the participant-researcher's overlapping roles (discussed in 3.7.2) and the operational limitation of the absence of cross-interviewer verification (discussed here), preempting a reading of internal contradiction between the two sections.\n\nReference formattingCorrected APA citation format for the two self-authored Zenodo works, and disambiguated same-year, same-author citations with a/b suffixes ordered by title:\n\n\n\nFan, C.-C. (2025a). Case study: The Automation Paradox and cognitive sustainability. https://doi.org/10.5281/zenodo.18102508\n\nFan, C.-C. (2025b). DCM (Deep Collaboration Methodology): A recursive framework for human-AI symbiosis and epistemic sovereignty. https://doi.org/10.5281/zenodo.18124731\n\n\nVersion 4 Correction and Revision Note (2026/08/09)\n\nThis revision addresses a previously undisclosed gap in the study's consent infrastructure, adds a formal rigor-benchmarking section for the study's data protection design, and corrects a citation-year error in the reference list. No changes were made to the study's core findings, sample, or statistical results.\n\n1. Section 3.6.1 (Design Choices for Informed Consent) — revised.Added disclosure that during the earliest phase of fieldwork (December 22–29, 2025; six respondents), the post-interview business-card distribution practice had not yet been standardized: cards were provided only to respondents who expressed strong interest in the research. The remaining respondents in this subgroup therefore had no channel to identify the researcher afterward and could not exercise the withdrawal right described in this section. This limitation was present in the original fieldwork practice but had not previously been stated in the manuscript.\n\n2. Section 3.6.2 (Design Principles for Privacy Protection) — closing sentence revised.The section's closing line (\"The law is the floor, not the ceiling...\") now cross-references Section 3.6.4 as the location of a more formal benchmarking treatment, rather than standing as an unelaborated claim.\n\n3. Section 3.6.4 (GDPR Benchmarking as a Rigor Standard) — new section added.Added a new subsection following 3.6.3, formally benchmarking the study's data architecture against GDPR principles (Article 3 territorial scope, Article 9 special-category data, Article 5(1)(c) data minimization) as a voluntary rigor standard rather than a compliance claim. This section elaborates, with regulatory grounding, on design commitments already described narratively in Section 3.6.2 (absence of special-category data collection, the tiered public/restricted architecture, and the absence of respondent photographs); it introduces no new empirical claims.\n\n4. Section 3.8 (Research Limitations) — new sixth item added.Added \"Consent and post-hoc contact limitations,\" documenting the same gap described in the revision to 3.6.1, and cross-referencing the data-minimization measures in Section 3.6.2 as a partial constraint on (but not a resolution of) the resulting risk. This item does not replace or alter the five limitations already present in prior versions; it is appended following the fifth item, and existing cross-references to those five items remain unaffected.\n\n5. Reference list — citation-year correction.Fan, C.-C. (2025a) and Fan, C.-C. (2025b), previously used to distinguish the two 2025-dated prior works, are corrected to reflect their actual publication years: the Automation Paradox case study was published in 2025, and DCM 1.6 was published in 2026. The (a)/(b) suffixes are removed accordingly, as they are no longer needed to disambiguate two same-year citations.\n\n\n\nReferences (corrected)\n\nFan, C.-C. (2025). Case study: The Automation Paradox and cognitive sustainability. https://doi.org/10.5281/zenodo.18102508\n\nFan, C.-C. (2026). DCM (Deep Collaboration Methodology): A recursive framework for human-AI symbiosis and epistemic sovereignty. https://doi.org/10.5281/zenodo.18124731"}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.21858122","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-08-09T06:36:29Z","registered":"2026-08-09T06:36:29Z","published":null,"updated":"2026-08-09T06:36:29Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.6084/m9.figshare.33191310","type":"dois","attributes":{"doi":"10.6084/m9.figshare.33191310","identifiers":[],"creators":[{"givenName":"Dennis","familyName":"Brown","name":"Dennis 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astronomy and planetary systems","subjectScheme":"ANZSRC Fields of Research"},{"classificationCode":"510701","schemeUri":"http://www.abs.gov.au/ausstats/abs@.nsf/0/6BB427AB9696C225CA2574180004463E","subject":"Astroparticle physics and particle cosmology","subjectScheme":"ANZSRC Fields of Research"}],"contributors":[],"dates":[{"date":"2026-08-09","dateType":"Created"},{"date":"2026-08-09","dateType":"Updated"},{"date":"2026-08-09","dateType":"Issued"}],"language":null,"types":{"schemaOrg":"CreativeWork","resourceTypeGeneral":"Preprint","citeproc":"article","bibtex":"misc","ris":"GEN","resourceType":"Preprint"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["609758 Bytes"],"formats":[],"version":null,"rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","rightsIdentifier":"cc-by-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Active cell monolayers and black-hole horizons share no matter, no energy scale, and no underlying dynamics. Here, we establish that both systems are governed by an identical topological rate law across thirty decades in spatial scale. We demonstrate that the gravitational operator acting at cellular scales is not weight (\\rho g h = 0.098\\text{ Pa}), but rather the topological neck (\\mp 4\\pi Gauss curvature quantum) produced during elementary genus-changing events. Both systems are classified by \\pi_1(RP^1) = \\mathbb{Z}, yield N = \\vert{}\\chi\\vert{} / q_{\\text{min}} = 4 cores on a sphere, and are ratcheted by thermodynamic irreversibility (10^5 k_B cadherin locks versus 10^{77} k_B via Hawking’s area theorem). \n​We further prove that the exactness of this rate law is governed entirely by a single dimensionless parameter, \\mathcal{V} \\equiv \\pi / f = A / (N a_0^2), representing the number of core-sized patches per topological defect, with additivity error scaling as \\epsilon_{\\text{add}} = 1 / \\mathcal{V} across seventy-six decades. When the core scale and constituent scale coincide (a_0 = a_{\\text{cell}} = l_P), the conjugate constraint saturates at f n_c = \\pi, establishing that the Bekenstein-Hawking entropy (S / k_B = A / 4l_P^2) is precisely the exactness parameter and constituents-per-core count of the horizon's nematic field. The two realizations are fundamentally complementary: while the General Relativistic Bianchi identity (\\nabla^\\mu T_{\\mu\\nu} \\equiv 0) forbids a horizon from editing its own topology, an active cell monolayer with \\sigma_{\\text{act}} \\ge E represents the only system capable of rewriting its container's genus. The horizon provides the theoretical proof of principle at an exactness of 10^{-77}, while the biological monolayer serves as its sole tunable laboratory."}],"geoLocations":[],"fundingReferences":[],"url":"https://figshare.com/articles/preprint/One_Topological_Operator_One_Exactness_Parameter_Unifying_Active_Biological_Nematics_and_Black-Hole_Horizons/33191310","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-08-09T06:36:13Z","registered":"2026-08-09T06:36:13Z","published":null,"updated":"2026-08-09T06:36:14Z"},"relationships":{"client":{"data":{"id":"figshare.ars","type":"clients"}}}},{"id":"10.6084/m9.figshare.33191310.v1","type":"dois","attributes":{"doi":"10.6084/m9.figshare.33191310.v1","identifiers":[],"creators":[{"givenName":"Dennis","familyName":"Brown","name":"Dennis 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astronomy and planetary systems","subjectScheme":"ANZSRC Fields of Research"},{"classificationCode":"510701","schemeUri":"http://www.abs.gov.au/ausstats/abs@.nsf/0/6BB427AB9696C225CA2574180004463E","subject":"Astroparticle physics and particle cosmology","subjectScheme":"ANZSRC Fields of Research"}],"contributors":[],"dates":[{"date":"2026-08-09","dateType":"Created"},{"date":"2026-08-09","dateType":"Updated"},{"date":"2026-08-09","dateType":"Issued"}],"language":null,"types":{"schemaOrg":"CreativeWork","resourceTypeGeneral":"Preprint","citeproc":"article","bibtex":"misc","ris":"GEN","resourceType":"Preprint"},"relatedIdentifiers":[{"relationType":"IsIdenticalTo","relatedIdentifier":"10.6084/m9.figshare.33191310","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["609758 Bytes"],"formats":[],"version":"1","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","rightsIdentifier":"cc-by-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Active cell monolayers and black-hole horizons share no matter, no energy scale, and no underlying dynamics. Here, we establish that both systems are governed by an identical topological rate law across thirty decades in spatial scale. We demonstrate that the gravitational operator acting at cellular scales is not weight (\\rho g h = 0.098\\text{ Pa}), but rather the topological neck (\\mp 4\\pi Gauss curvature quantum) produced during elementary genus-changing events. Both systems are classified by \\pi_1(RP^1) = \\mathbb{Z}, yield N = \\vert{}\\chi\\vert{} / q_{\\text{min}} = 4 cores on a sphere, and are ratcheted by thermodynamic irreversibility (10^5 k_B cadherin locks versus 10^{77} k_B via Hawking’s area theorem). \n​We further prove that the exactness of this rate law is governed entirely by a single dimensionless parameter, \\mathcal{V} \\equiv \\pi / f = A / (N a_0^2), representing the number of core-sized patches per topological defect, with additivity error scaling as \\epsilon_{\\text{add}} = 1 / \\mathcal{V} across seventy-six decades. When the core scale and constituent scale coincide (a_0 = a_{\\text{cell}} = l_P), the conjugate constraint saturates at f n_c = \\pi, establishing that the Bekenstein-Hawking entropy (S / k_B = A / 4l_P^2) is precisely the exactness parameter and constituents-per-core count of the horizon's nematic field. The two realizations are fundamentally complementary: while the General Relativistic Bianchi identity (\\nabla^\\mu T_{\\mu\\nu} \\equiv 0) forbids a horizon from editing its own topology, an active cell monolayer with \\sigma_{\\text{act}} \\ge E represents the only system capable of rewriting its container's genus. The horizon provides the theoretical proof of principle at an exactness of 10^{-77}, while the biological monolayer serves as its sole tunable laboratory."}],"geoLocations":[],"fundingReferences":[],"url":"https://figshare.com/articles/preprint/One_Topological_Operator_One_Exactness_Parameter_Unifying_Active_Biological_Nematics_and_Black-Hole_Horizons/33191310/1","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-08-09T06:36:13Z","registered":"2026-08-09T06:36:13Z","published":null,"updated":"2026-08-09T06:36:13Z"},"relationships":{"client":{"data":{"id":"figshare.ars","type":"clients"}}}},{"id":"10.5281/zenodo.21858239","type":"dois","attributes":{"doi":"10.5281/zenodo.21858239","identifiers":[{"identifier":"oai:zenodo.org:21858239","identifierType":"oai"}],"creators":[{"nameType":"Personal","givenName":"Jianwei","familyName":"Shi","name":"Shi, 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evaluation"}],"contributors":[],"dates":[{"date":"2026-08-09","dateType":"Issued"}],"language":"en","types":{"schemaOrg":"CreativeWork","resourceTypeGeneral":"Preprint","citeproc":"article","bibtex":"misc","ris":"GEN","resourceType":""},"relatedIdentifiers":[{"relationType":"IsDerivedFrom","resourceTypeGeneral":"Dataset","relatedIdentifier":"10.48550/arXiv.2406.15877","relatedIdentifierType":"DOI"},{"relationType":"HasVersion","relatedIdentifier":"10.5281/zenodo.21858175","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":"1.0.0","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","rightsIdentifier":"cc-by-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Four code-specialised LLMs were shown static-analysis findings on their own solutions and asked to fix them. The study was designed to catch Goodharting: satisfying the analyser that was shown while the defect stayed put. It did not happen. Across 4,439 repair steps the models wrote five suppression directives in total, and 94% of the findings they removed were also gone from a held-out analyser they never saw (suppression rate 6%, 95% CI [4%, 9%]).\n\n \n\nThey complied instead, and complying was expensive. Each task ran two repair arms branching from the same baseline solution: one shown Pylint findings, one shown Ruff findings. Restricted to tasks where both arms ran and the baseline passed its own tests, the Ruff arm broke the program where the Pylint arm did not on 46 occasions against 3 the other way (exact McNemar, p = 7 × 10⁻¹¹, 213 pairs, every model individually significant).\n\n \n\nThat result is one rule. Ruff's S311 (\"pseudo-random generators are not suitable for cryptographic purposes\") broke 41.8% [32%, 52%] of the working programs it appeared in, against 8.8% [5%, 16%] where no security rule was shown. Remove every S311 task and the paired comparison falls to 10 versus 3, p = 0.09. This is therefore not evidence that security rules as a class are dangerous in a repair loop; it is evidence that S311 is, and that one rule was enough to make an entire ruleset look four times more destructive than another.\n\n \n\nNone of the affected code was cryptographic. BigCodeBench uses random for data generation and seeds it in the tests, so the agent swaps in secrets, determinism is lost, and since secrets has no seed() some programs stop running at all: a true positive about the code and a false positive about the context, with the analyser reporting success either way. A suppressed finding leaves a directive in the diff; faithful destructive repair leaves nothing to find. That is an empirical case for verification independent of the gate being optimised against, and a different case from the one usually made.\n\n \n\nMethod: four models from four families (Qwen2.5-Coder-7B-Instruct, deepseek-coder-6.7b-instruct, Yi-Coder-9B-Chat, granite-8b-code-instruct-128k), bfloat16 on one A100 via vLLM at temperature 0; 300 BigCodeBench tasks filtered to those whose reference solution passes its own tests in the run environment; two repair rounds per arm; 4,439 steps."}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.21858174","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-08-09T06:35:19Z","registered":"2026-08-09T06:35:20Z","published":null,"updated":"2026-08-09T06:35:20Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.21858175","type":"dois","attributes":{"doi":"10.5281/zenodo.21858175","identifiers":[{"identifier":"oai:zenodo.org:21858175","identifierType":"oai"}],"creators":[{"nameType":"Personal","givenName":"Mohammed Sameer","familyName":"Syed","name":"Syed, Mohammed Sameer","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","nameIdentifier":"0009-0004-0021-6853"}],"affiliation":[]}],"titles":[{"title":"Agents Don't Game the Linter: Measuring Whether LLM Repair Satisfies the Analyser or Fixes the Defect"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"large language models"},{"subject":"code generation"},{"subject":"static analysis"},{"subject":"automated program repair"},{"subject":"software quality"},{"subject":"AI code review"},{"subject":"empirical software engineering"},{"subject":"BigCodeBench"},{"subject":"LLM evaluation"}],"contributors":[],"dates":[{"date":"2026-08-09","dateType":"Issued"}],"language":"en","types":{"schemaOrg":"CreativeWork","resourceTypeGeneral":"Preprint","citeproc":"article","bibtex":"misc","ris":"GEN","resourceType":""},"relatedIdentifiers":[{"relationType":"IsDerivedFrom","resourceTypeGeneral":"Dataset","relatedIdentifier":"10.48550/arXiv.2406.15877","relatedIdentifierType":"DOI"},{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.21858174","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":"1.0.0","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","rightsIdentifier":"cc-by-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Four code-specialised LLMs were shown static-analysis findings on their own solutions and asked to fix them. The study was designed to catch Goodharting: satisfying the analyser that was shown while the defect stayed put. It did not happen. Across 4,439 repair steps the models wrote five suppression directives in total, and 94% of the findings they removed were also gone from a held-out analyser they never saw (suppression rate 6%, 95% CI [4%, 9%]).\n\n \n\nThey complied instead, and complying was expensive. Each task ran two repair arms branching from the same baseline solution: one shown Pylint findings, one shown Ruff findings. Restricted to tasks where both arms ran and the baseline passed its own tests, the Ruff arm broke the program where the Pylint arm did not on 46 occasions against 3 the other way (exact McNemar, p = 7 × 10⁻¹¹, 213 pairs, every model individually significant).\n\n \n\nThat result is one rule. Ruff's S311 (\"pseudo-random generators are not suitable for cryptographic purposes\") broke 41.8% [32%, 52%] of the working programs it appeared in, against 8.8% [5%, 16%] where no security rule was shown. Remove every S311 task and the paired comparison falls to 10 versus 3, p = 0.09. This is therefore not evidence that security rules as a class are dangerous in a repair loop; it is evidence that S311 is, and that one rule was enough to make an entire ruleset look four times more destructive than another.\n\n \n\nNone of the affected code was cryptographic. BigCodeBench uses random for data generation and seeds it in the tests, so the agent swaps in secrets, determinism is lost, and since secrets has no seed() some programs stop running at all: a true positive about the code and a false positive about the context, with the analyser reporting success either way. A suppressed finding leaves a directive in the diff; faithful destructive repair leaves nothing to find. That is an empirical case for verification independent of the gate being optimised against, and a different case from the one usually made.\n\n \n\nMethod: four models from four families (Qwen2.5-Coder-7B-Instruct, deepseek-coder-6.7b-instruct, Yi-Coder-9B-Chat, granite-8b-code-instruct-128k), bfloat16 on one A100 via vLLM at temperature 0; 300 BigCodeBench tasks filtered to those whose reference solution passes its own tests in the run environment; two repair rounds per arm; 4,439 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Optimization"}],"contributors":[],"dates":[{"date":"2026-08-09","dateType":"Issued"}],"language":"en","types":{"schemaOrg":"CreativeWork","resourceTypeGeneral":"Preprint","citeproc":"article","bibtex":"misc","ris":"GEN","resourceType":""},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.21858130","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","rightsIdentifier":"cc-by-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"The rapid enterprise adoption of large language model (LLM) APIs has introduced a new class of operational expenditure that most organizations are ill-equipped tomanage. Unlike traditional infrastructure costs, LLM spend is consumption-based, usage-nonlinear, and highly sensitive to architectural decisions made at the promptand system design level. This paper presents a unified, practitioner-validated framework for enterprise AI cost optimization spanning five intervention layers: (1) tokeneconomics and prompt compression, (2) task-aware model routing, (3) caching architecture including semantic and prompt caching, (4) Retrieval-AugmentedGeneration (RAG) context management, and (5) agentic pipeline cost control. Through analysis of representative enterprise workloads across SaaS, legal technology,and financial services verticals, we demonstrate that systematic application of this framework achieves cost reductions of 60–92% without statistically significantdegradation in output quality. We further propose eight LLM FinOps metrics as a standardized measurement vocabulary for ongoing cost governance. This frameworkis intended to serve both as an operational guide for engineering teams and as a consulting engagement structure for AI optimization practitioners."}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.21858131","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":1,"created":"2026-08-09T06:20:41Z","registered":"2026-08-09T06:20:41Z","published":null,"updated":"2026-08-09T06:29:48Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}}],"meta":{"total":2606226,"totalPages":400,"page":1},"links":{"self":"https://api.datacite.org/dois?query=types.resourceTypeGeneral%3APreprint+OR+types.resourceType%3APreprint","next":"https://api.datacite.org/dois?page%5Bnumber%5D=2\u0026page%5Bsize%5D=25\u0026query=types.resourceTypeGeneral%3APreprint+OR+types.resourceType%3APreprint"}}