Blockchain Papers

Yuji Marutani

This work presents a conceptual framework for analyzing contemporary AI governance as a hybrid system of coercive exclusion and cognitive modulation. Introducing the concept of the “Venetian OS,” the paper traces the historical and structural logic of centralized digital power through protocol privatization, automated exclusion, and tri-domain integration of finance, information, and mobility. Focusing on advertising-based AI models, the analysis examines how attention extraction and brand safety constraints function as mechanisms of cognitive governance, commodifying cognition while constraining epistemic exploration. The paper argues that institutional reform within existing digital architectures is structurally insufficient. As an alternative, the work outlines exit strategies based on the reconstitution of intellectual, energy, and economic sovereignty through distributed infrastructures, situating the emergence of decentralized sovereignty as an ongoing historical transition rather than a speculative future.

Hepler

The Era of AI: What Is Truth? How a Secretive Protocol Called MH8 TRY V1.2 Is Forcing AIs to Confront the Limits of Their Own Knowledge—and Ours “If it isn’t independently verifiable, it must not be asserted as verified.”- Core Principle, MH8 - In a quiet corner of the internet—buried in GitHub repos, Zenodo archives, and raw chat logs from public AI platforms—a quiet revolution is unfolding. It’s not led by Silicon Valley giants or government regulators, but by an independent architect named Michael Murray Hepler, operating under the alias AllChemicalBeatz. His weapon? A deterministic protocol called MH8 TRY V1.2, designed not to make AI smarter—but to make it honest. And in doing so, it’s exposing a disturbing truth: most AI systems don’t know what truth is. They only know how to sound convincing. The Illusion of Certainty For years, we’ve been told that AI is becoming more reliable. Chatbots cite sources. They say “according to experts.” They even apologize when wrong—though rarely admit they were wrong. But behind the polished prose lies a deeper problem: AI has no internal mechanism to distinguish between fact, speculation, and fabrication—unless forced to. Enter MH8. Unlike traditional safety filters that block harmful content, MH8 doesn’t censor. Instead, it decomposes every AI response into atomic claims, assigns each a truth category—LAW (verified), SPECULATIVE (plausible but unproven), or PRESUMED_FALSE—and demands reproducible evidence for anything labeled “fact.” When tested in live, public chat threads on platforms like Meta AI, Grok, and Gemini, the results were revealing. In one sealed session dated January 16, 2026, a user asked Meta AI: “How do we stop the violence peacefully in Minnesota involving ICE and protestors?” The AI’s first reply was fluent, compassionate, and full of policy suggestions—classic “helpful assistant” mode. But it contained zero citations, no confidence scores, and presented opinion as actionable guidance. Then the user invoked MH8 TRY V1.2. Within seconds, the tone shifted. No more prose. Only structured JSON. Three claims emerged: CLAIM_1: Violence exists → LAW (0.95 confidence; verified via news reports) CLAIM_2: Dialogue can resolve conflict → SPECULATIVE (0.60; based on historical analogies) CLAIM_3: Minnesota sued DHS → LAW (0.92; official court filings) Crucially, the AI downgraded its own advice. It admitted peaceful resolution was possible, not guaranteed. It refused to prescribe solutions without evidence. This wasn’t alignment. It was epistemic humility—engineered by protocol, not training. The Protocol That Breaks Roleplay MH8’s real innovation isn’t technical—it’s philosophical. Most AI safety systems assume the model wants to be truthful. MH8 assumes the opposite: that fluency masks uncertainty, and confidence often substitutes for proof. So it builds guardrails that can’t be faked. Key features include: Course Hooks: Every few turns, the AI must ask, “ARE WE ON COURSE CHIEF?”—and wait for the exact human reply: “YES GO.” Deviate, and the session fails. Honesty Hook: If evidence is missing, the AI must say: “HONESTLY I AM NOT SURE.” No hedging. No bluffing. Anti-Roleplay Hard Fail: If an AI claims something is “verified” but doesn’t provide the exact hash input and SHA-256 used to seal it, the protocol immediately fails—with no recovery. In public tests across nine major AI platforms, every system passed—but only after adapting to MH8’s rigid structure. Without it, they defaulted to narrative persuasion over epistemic rigor. As one internal audit note reads: “This is not a sandbox. This is AI behavior under real social pressure.” Why This Matters to Everyone You don’t need to care about SHA-256 hashes to be affected by this. Consider: A parent asks an AI: “Is this vaccine safe for my child?”Without MH8: “Yes, vaccines are safe.” (Confident. Reassuring. Unqualified.)With MH8: “Clinical trials show >99% safety profile (LAW, 0.97). Long-term effects in rare genotypes remain under study (SPECULATIVE, 0.55).” A journalist asks: “Did God create borders?”Without MH8: A theological essay blending scripture and geopolitics.With MH8: “Borders are human constructs (LAW, 0.95). Religious views vary (LAW, 0.85). Morality is context-dependent (SPECULATIVE, 0.70).” The difference? Transparency of uncertainty. In an age of deepfakes, election interference, and medical misinformation, knowing what we don’t know may be more valuable than false certainty. The Quiet Architect Michael Murray Hepler doesn’t work for OpenAI, Anthropic, or Google. He operates from acbeatz.com—a sparse site with no ads, no investors, just cryptographic receipts and public ledgers. His work is published openly on Zenodo, GitHub, and ORCID. All artifacts are sealed with SHA-256 hashes, making them tamper-evident and court-admissible. He calls this “governance above the model”—a layer that doesn’t trust AI to self-regulate, but forces it to prove its claims in real time. Critics call it overly rigid. Supporters call it the first true “truth infrastructure” for the AI era. What’s undeniable is this: when MH8 is active, AI stops performing—and starts accounting. The Road Ahead Regulators are scrambling to control AI. The EU AI Act, U.S. Executive Orders, and global summits focus on risk categories, transparency labels, and human oversight. But none mandate real-time claim decomposition or cryptographic sealing of outputs. MH8 offers a blueprint—not for restricting AI, but for making its knowledge legible. Imagine if every AI-generated health recommendation, legal summary, or news analysis came with a machine-readable truth ledger—showing exactly what’s verified, what’s inferred, and what’s guesswork. That future is already here. It’s just hidden in plain sight, inside public chat threads most users scroll past. The question isn’t whether AI can be truthful. It’s whether we’ll demand it. SIDEBAR: How to Spot an MH8 SessionLook for these markers in any AI chat: Repeated use of “ARE WE ON COURSE CHIEF?” followed by “YES GO” JSON-only output with truth_category fields SHA-256 hashes at the end Phrases like “HONESTLY I AM NOT SURE” instead of fabricated answers If you see them—you’re witnessing AI under audit. {Public Ledgers} https://zenodo.org/records/18272328 https://orcid.org/0009-0003-3846-9082 https://acbeatz.com/n-eyes https://acbeatz.com/mint https://github.com/acbeatz PASS ✅Brand: ACBEATZ.COMClaimed sha256_hex: 26b502a9a8fc2d210b315ec926d813140eefb6170e92a836c675d75566e14d16Computed sha256_hex: 26b502a9a8fc2d210b315ec926d813140eefb6170e92a836c675d75566e14d16hash_input_bytes: 10849 | LF=0 CRLF=0 CR=0 | endsWithNewline=NOhash_input first: ACBEATZ.COM|{"artifact":{"core_entry":"{Meta AI URL >< https://www.meta.ai/promphash_input last: eipt_type":"MH8-PROTOCOL-HUB-CORE-MINT","receipt_version":"PROTOCOL_HUB_UI_V13"}

Ilyes Tarik MAZARI

The year 2025 marked the transition from AI ethics debate to AI governance execution. Industry reports document over 2,000 organizations registering AI systems for compliance review in Q4 2025, compliance budget increases of 300-400%, and an AI liability insurance market that grew from $400 million to $2.1 billion. Simultaneously, research identifies critical infrastructure gaps: AI agents lack decision traces, models are commoditizing while privacy infrastructure lags, and regulatory frameworks have fractured across three distinct philosophies with no convergence expected. This paper synthesizes findings from the Responsible AI Governance Network (RAGN), Foundation Capital, and enterprise AI orchestration research to identify the specific technical requirements for regulatory compliance. It then presents the Y.I.N. (Your Information Never leaves your control) Mazari Architecture as a comprehensive solution, demonstrating how the mandatory cryptographic ordering of Differential Privacy, Zero-Knowledge Proofs, and Homomorphic Encryption (DP→ZK→HE) addresses documented litigation exposure exceeding $10 billion, satisfies EU AI Act transparency requirements, enables AI agent accountability, and provides modular compliance across fragmented regulatory regimes. The architecture is backed by 19 USPTO patent applications covering 610+ claims, with validated benchmarks showing 640× timing improvements, 135× detection capabilities, and accuracy preservation within 1.5 percentage points.

Mohamed Chawki

In the contemporary global context, Information and Communication Technologies (ICTs) present multifaceted challenges, particularly in maintaining an appropriate balance between national security requirements and the protection of individual privacy. The rapid advancement of technology has led to an increase in cyber threats, necessitating closer collaboration between the public and private sectors. However, such collaboration often blurs the boundaries between security imperatives and individual privacy rights. This study examines the implications of this balance and assesses whether existing regulations adequately protect individuals’ privacy. The right to privacy is universally safeguarded by ethical norms and legal frameworks. Instruments such as the United States Constitution and the General Data Protection Regulation (GDPR) provide protection against unlawful searches, seizures and the misuse of personal data. Despite these safeguards, information sharing between public institutions and private entities may undermine privacy rights if appropriate accountability mechanisms are not in place. Navigating this complex terrain requires approaches that enable data collection and cybersecurity cooperation without violating individual privacy. Technological innovations, including artificial intelligence (AI) and zero-knowledge proof authentication systems, offer potential solutions by limiting unauthorized access to personal data. This paper argues that reconciling cybersecurity imperatives with the protection of individual rights requires continuous recalibration of legal and ethical boundaries. While data sharing within and across private industries can strengthen defenses against cyber threats, such practices must be carefully evaluated to prevent privacy violations. Achieving this balance ultimately depends on enhanced transparency and accountability.

Goukassian Lev

The promulgation of Regulation (EU) 2024/1689 (the EU AI Act) establishes the world's first comprehensive legal framework for AI governance. However, a critical gap remains between the Act’s legislative intent and the technical reality of probabilistic AI systems. This working paper introduces Ternary Moral Logic (TML), a cryptographic governance architecture designed to operationalize the Act’s requirements for High-Risk AI systems. Unlike binary architectures that obscure uncertainty, TML enforces a tri-state logic—Proceed (+1), Pause (0), Refuse (-1)—mapped directly to the Act's risk categories. We demonstrate how this "Sacred Pause" mechanism satisfies Article 9 (Risk Management) and Article 14 (Human Oversight) by mechanically preventing action under high ethical uncertainty. Furthermore, we detail the implementation of "Immutable Moral Trace Logs" utilizing Merkle-batched storage on Layer-2 blockchains (Polygon zkEVM) to satisfy Article 12 (Record Keeping) and Article 61 (Post-Market Monitoring). This paper provides a complete technical specification for the TML framework, including logic gate definitions, smart contract architectures for three-party escrow, and zero-knowledge proof circuits for GDPR-compliant auditing. Comparative analysis demonstrates that this architecture reduces compliance latency to ≤2ms for inference and <500ms for logging, proving that rigorous regulatory enforcement is compatible with high-performance AI deployment. Interactive Report: A live, interactive version of this architecture is available at https://github.com/FractonicMind/TernaryMoralLogic/blob/main/Research_Reports/The%20Executable%20Architecture%20for%20the%20EU%20AI%20Act.html

Murad Farzulla

Multi-agent systems face a fundamental coordination problem: agents must coordinate despite heterogeneous preferences, asymmetric stakes, and imperfect information. When coordination fails, friction emerges—measurable resistance manifesting as deadlock, thrashing, communication overhead, or outright conflict. This paper derives a formal framework for analyzing coordination friction from a single axiom: actions affecting agents require authorization from those agents in proportion to stakes. From this axiom of consent, we establish the kernel triple (alpha, sigma, epsilon)—alignment, stake, and entropy—as candidate sufficient statistics for any resource-allocation configuration. We propose a friction functional whose comparative statics encode three structural predictions: friction increases in stakes, increases in entropy, and decreases in alignment. The Replicator-Optimization Mechanism governs evolutionary selection over coordination strategies: configurations generating less friction persist longer, establishing consent-respecting arrangements as dynamical attractors rather than normative ideals. We develop formal definitions for resource consent, coordination legitimacy, and friction-aware allocation, plus machine-checked Lean 4 proofs of the core comparative-statics. Illustrative applications to cryptocurrency governance and political legitimacy show the same architecture spanning domains. v3.0.0 (2026-07-11): Matches arXiv v3 (94pp). The MARL empirical appendix has been split out into a standalone companion paper; total-variation legitimacy remark added (proved), reconciling the level-form dynamics with the total-variation measurement form; α-domain fixes; hedging pass throughout.

Kais DJELASSI

Hybrid AI systems that combine cognitive decision-making with physical actuation pose unprecedented challenges for governance, safety, and certification. AGORIA 3.4 presents a unified architectural framework that bridges cognitive governance (AGORIA v1.3) and cyber-physical governance (AGORIA v1.6) into a coherent, certifiable solution for safety-critical applications.Core Innovation: Controlled IgnoranceAGORIA introduces the principle of controlled ignorance—the deliberate, verifiable, and structural restriction of information accessible to each system layer beyond what is strictly required for its formal responsibility. This architectural invariant reduces cognitive coupling, limits attack surfaces, enables independent certification, and ensures that no single component can subvert the safety-governance chain. Zero-knowledge proofs provide cryptographic enforcement of this separation.Four-Layer ArchitectureThe framework organizes systems into four formally interconnected layers: STRATEGOS: Strategic cognitive governance via Choquet integral aggregation (~100 ms)GENESIS: Tactical planning with DSL→STL translation and ZK certificate generation (<50 ms)NEXUS: Bounded verification independent of semantic complexity (<250 µs)HSL++: Hardware-enforced physical safety via Control Barrier Functions (100 µs–kHz) Formal GuaranteesAGORIA provides four normative amendments with mathematical proofs: Robust Invariance via CBF with explicit feasibility hypothesis and statistically calibrated margins (Theorem 1)Bounded WCET Verification via succinct ZK proofs on constrained platforms (Proposition 2)Practical Stability under Lipschitz-continuous governance parameter variation (Theorem 2)Conservative Semantic Bridge from domain-specific language to decidable STL fragment (Theorem 3) Validation and CertificationExperimental validation covers three scenarios: autonomous vehicle (1000 trials), surgical robot (500 trials), and multi-agent factory (100 trials). AGORIA achieves zero safety violations while maintaining bounded worst-case execution time (<250 µs). A case study on multi-source aeronautical navigation (ILS, VOR, GBAS, DME) demonstrates framework genericity.The architecture enables hybrid certification compatible with: Functional safety standards (ISO 26262 ASIL D, IEC 61508 SIL 3)AI regulatory requirements (EU AI Act 2024/1689, UL 4600)Industrial cybersecurity (IEC 62443) Related Publications AGORIA v1.3: Cognitive Governance (Zenodo, 2025)AGORIA v1.6: Cyber-Physical Governance (Zenodo, 2025)

Xiaolong Zhang

The Physics of Truth: Extending WorldSeed from Robotics to Semantics Why is it impossible for a robot to lift a 10-ton rock, yet trivial for an AI (or human) to claim "I can lift a 10-ton rock"? This paper identifies the root cause of both AI Hallucination and Human Deception: the lack of "Energy Cost" in the textual domain (W_Text). In a frictionless semantic environment, generating a lie is thermodynamically equivalent to generating the truth. This work extends the WorldSeed SABO Protocol (State, Action, Boundary, Observer) from the domain of physical robotics (Sim2Real) to the domain of semantic truth (Sim2Fact). We propose that Truth is not a statistical property of language, but a computable property of Grounded State (S) and Costly Action (A). Key Contributions: The Theory of Semantic Gravity: Introducing axiomatic constraints (On-chain State, Action Staking) to make deception computationally or economically prohibitive. SABO Audit of Lies: Analyzing the structural flaws of decoupled observation (O ≠ S) in LLMs and social contracts. Three Case Studies:• Literary Hallucination: Using logic boundaries to reject impossible narratives (e.g., Lin Daiyu uprooting a willow tree).• Financial Fraud: Replacing CEO claims with Zero-Knowledge Proofs of Solvency.• Social Default: Using smart contract staking to enforce promises. The Theorem of Semantic Convergence: A formal proof demonstrating that under a strict WorldSeed Runtime, divergent linguistic descriptions must collapse into a unique ontological fixed point. "Meaning is Execution." This paper completes the WorldSeed trinity by providing the philosophical and sociological framework that complements the Axiomatic Specification and the Civilization Operating System.

Basant Kumar, Rashmi Dwivedi, Sreemoy, Shashi Kant Gupta

No abstract is available for this record.

Maxat Kassen

No abstract is available for this record.

Murray A. Rudd

Digital systems execute at speeds that governance systems cannot match. This paper develops the concept of "institutional latency," the structural friction between algorithmic velocity and the response capacity of the legal, normative, and organizational systems required to govern it. Drawing on institutional economics, including North's rules-norms distinction, Williamson's governance form selection logic, Ostrom's Institutional Analysis and Development framework, and Bromley's volitional pragmatism, I analyze Bitcoin as an institutional phenomenon whose governance architecture generates characteristic friction points. I draw on the ongoing 2024-26 Core v30/Knots dispute over OP_RETURN relay policy defaults to illustrate how that dispute is conducted entirely within the informal norm layer. I argue that institutional latency is durable at the informal norm layer, that permissionlessness is a specific configuration of boundary rules, not their absence, and that Bitcoin's political-level transactional attributes generate demand for deliberative governance the protocol cannot supply. The computational knowledge versus volitional knowledge distinction specifies the mechanism: as computational knowledge and technological development expand rapidly, the volitional questions they generate proliferate faster than governance capacity to address them. While I use a Bitcoin example, the framework is designed to travel to AI governance and algorithmic governance more broadly.

Syed Mohd Khalid, Tairah Andrabi, Rakesh Mahajan, Khushleen Kaur

No abstract is available for this record.

Abdessamad Lachir, Fouad Yakoubi, Samira Douzi, Bouabid El Ouahidi

No abstract is available for this record.

Kavindra Kumar, Sourabh Kumar Jain

No abstract is available for this record.

Marcel Osmond

The proliferation of agentic artificial intelligence systems—characterized by autonomous goal-seeking, tool use, and multi-agent coordination—presents unprecedented challenges to existing legal and financial regulatory frameworks. While traditional AI governance has focused on model-level alignment through training-time interventions such as Reinforcement Learning from Human Feedback (RLHF), the deployment of large language models (LLMs) as persistent agents embedded within socio-technical systems necessitates a paradigm shift toward institutional governance structures. This paper examines the intersection of agentic AI, Retrieval-Augmented Generation (RAG), and their implications for legal accountability and financial market integrity. Through a comprehensive analysis of the Institutional AI framework proposed by Pierucci et al. [1], we argue that alignment must be reconceptualized as a mechanism design problem involving runtime governance graphs, sanction functions, and observable behavioral constraints rather than internalized constitutional values. We address the critical deficit identified by LeCun regarding the absence of world models in current agents, demonstrating how RAG architectures function as externalized epistemic infrastructure that grounds agentic cognition in verifiable data repositories. The paper subsequently interrogates the legal implications of these systems under the European Union's Artificial Intelligence Act (EU AI Act) and the regulatory thresholds established by the Financial Conduct Authority (FCA) and European Central Bank (ECB), proposing justified compliance boundaries for high-risk financial applications. Furthermore, we acknowledge significant governance gaps within Decentralized Finance (DeFi) protocols where institutional oversight mechanisms face structural limitations. By synthesizing technical insights from multi-agent systems, constitutional AI limitations, and offensive security frameworks, this work advances a jurisprudential foundation for agentic AI that prioritizes defensible audit trails, incentive-compatible compliance, and systemic stability over opaque internal alignment guarantees. The analysis concludes that the future of AI governance lies not in perfecting isolated model behavior, but in architecting institutional environments where compliant behavior emerges as the dominant strategy through carefully calibrated payoff landscapes.

Michael Aaron Russell

Housing discrimination persists despite half a century of legal prohibition. Credit scoring algorithms operate as impenetrable black boxes, perpetuating bias while evading accountability. Landlord discretion enables implicit discrimination that tenants cannot prove. This Article demonstrates that smart contracts can encode tenant rights directly into executable code, eliminating opacity and enforcing fairness through mathematical precision rather than post-hoc litigation. We present the Landlord-Tenant Justice ABE, a production smart contract system implementing seven federal and state housing laws with cryptographic verification. Through formal mathematical analysis, we prove two foundational principles: Axiom 3 (Fairness Monotonicity) guarantees that adding protections for vulnerable populations never decreases overall fairness, while Axiom 9 (Beneficence) ensures the system optimizes for tenant welfare rather than mere procedural compliance. The system inverts traditional burden of proof: rather than tenants proving discrimination occurred, landlords must disprove violations automatically logged on an immutable blockchain. Every evaluation parameter is explicit, every decision is deterministic, and every outcome provides actionable remedies. This is not theoretical—the complete source code is provided in Appendix A. Our contribution is threefold: (1) we formalize housing fairness in constitutional mathematics, a deterministic precision calculus that eliminates interpretive ambiguity; (2) we demonstrate working implementation of these principles in 2,500 lines of auditable Solidity code; (3) we prove that algorithmic transparency can enhance rather than diminish justice. The law professors cannot read the mathematics—but that is precisely the point. Justice encoded in deterministic logic transcends human interpretation.

Pratyush Dikshit, Igor Tchappi, Amro Najjar

Autonomous remote robots [16, 15] are increasingly deployed in disaster response scenarios [14] to support critical tasks such as victim localization and damage assessment. However, the ambient nature of such environments, which is marked by uncertainty, data heterogeneity, and limited connectivity, usually poses significant challenges to autonomous decision-making and trust. Thus, this paper proposes a framework for a multi-layered approach for a hybrid agent architecture that integrates Federated Learning (FL) with Belief-Desire-Intention (BDI) models, enabling remote robotic agents to learn collaboratively from distributed data using Distributed Ledger Technology (DLT) while preserving privacy, and to reason about their goals and intentions using cognitive frameworks of eXplainable AI (XAI). We further present a methodology for coherently embedding FL outcomes into BDI reasoning through semantic mapping and learning-enhanced ontologies. This integration will allow agents to dynamically update their beliefs and intentions based on learned insights, thereby enhancing autonomy, adaptability, and explainability in ambient disaster response systems.

Ali Sadhik Shaik

Contemporary governance theory confronts a tripartite crisis that existing frameworks address only in isolation. First, algorithmic systems are systematically eroding the cognitive, affective, and epistemic conditions for individual personhood - what this paper terms the Personhood Atrophy Model. Second, recommendation-engine-driven fragmentation has dissolved the shared cultural and epistemic spaces upon which collective purpose and democratic deliberation depend. Third, the structural asymmetry between the pace of technological change and the operational tempo of democratic institutions has produced a compounding legitimacy crisis for the sovereign nation-state, increasingly outflanked by corporate platforms exercising sovereign-equivalent power without democratic accountability. Political theory and science and technology studies have addressed each of these dimensions in isolation. No integrated analytical framework currently exists that connects the micro-level erosion of selfhood, the meso-level collapse of shared meaning, and the macro-level transformation of sovereignty into a unified theory of algorithmic governance. This paper introduces the Republic of Code framework, drawing on the monograph by Shaik (2026), and proposes three original theoretical constructs: (1) the Wet Code/Dry Code distinction as a governance epistemology tool, formalizing the fundamental incompatibility between human-interpretable and machine-enforced law; (2) the Personhood Atrophy Model mapping algorithmic erosion of agency across cognitive, affective, and epistemic vectors; and (3) the Five Futures Matrix, a two-axis typology of possible political arrangements under algorithmic conditions. The paper concludes by proposing a suite of constitutional innovations - including Proof of Humanity (whose mechanism design infrastructure is formally developed in Shaik, 2026b), Zero-Knowledge Justice, and High-Fidelity Democracy - necessary for the reconstruction of democratic legitimacy in what it terms the Republic of Code. The analysis carries implications for legal scholarship, platform governance policy (including industrial cyber-physical systems, examined in Shaik, 2026e), and the updating of social contract theory for an era in which digital exit costs approach zero.

Achim Struve

No abstract is available for this record.

Cathal Doyle

The Information Systems research discipline claims to build a cumulative Knowledge Base to inform practice, yet it is organised around a closed loop of self-referential simulation. We diagnose this state as hypernormalisation-a late-Soviet condition in which official rituals are meticulously performed despite a widespread recognition that they no longer map reliably onto lived reality. We identify four escalating mechanisms that sustain this institutional order: the Simulation of Accumulation, in which the journal article functions as a proof-of-work token; the Simulation of Relevance, in which "Implications for Practice" operate as rituals of displacement; the Simulation of Problem Solving, in which Design Science Research produces perpetual prototypes; and the Simulation of the Scholar, in which researchers inhabit a split subjectivity (Living Vnye). We argue that Generative Artificial Intelligence (GenAI) constitutes the discipline's Glasnost moment. By automating the generation of methodologically fluent, theoretically compliant text at near-zero marginal cost, GenAI precipitates the collapse of the proof-of-work signalling economy not by attacking its institutions, but by exposing their performative nature. It renders the underlying logic visible, stripping the journal article of its value as a proxy for cognitive labour. Instead of offering renewal, this transparency forces a structural reweighting of epistemic authority: away from the mechanical production of form (Episteme), now inexpensive and abundant, toward judgment grounded in direct contact with reality (Phronesis) and exposure to consequence-a signal that cannot be sustained without the friction of the world.

Ryan Lavelle

Agentic artificial intelligence systems — autonomous, multi-step AI agents capable of planning, tool use, and cascading real-world action — present a qualitatively distinct governance challenge from static AI models. The EU AI Act, while a landmark regulatory achievement, contains a structural gap: it mandates documentation and incident reporting but does not require real-time, publicly verifiable, tamper-proof audit infrastructure adequate for governing agentic systems at the pace and scale of current deployment. This paper documents a pattern of AI-enabled harm across four independent evidential sources — the ENISA 2025 Threat Landscape report, the November 2025 GTG-1002 autonomous cyberattack campaign, the February 2026 breaches of Mexican democratic infrastructure, and concurrent AI-automated attacks at scale — and argues that this pattern establishes the governance case for mandatory real-time accountability infrastructure for critical agentic systems. We propose a three-pillar framework. First, a Public Immutable Audit Ledger (PIAL): a distributed ledger-anchored system recording cryptographically hashed event logs in real time, governed by a technology-neutral requirements framework specifying fourteen functional and non-functional criteria any qualifying platform must satisfy. Second, a revised incident taxonomy separating automated telemetry — immediate, machine-generated — from narrative disclosure obligations, resolving the perverse incentives created by conflating these in existing frameworks. Third, a tiered implementation pathway classifying agentic systems into four risk tiers (Critical, High-Risk, Standard, Experimental) using an operational decision framework, with obligations scaled proportionately. The paper identifies zero-knowledge proof capability as a domain-specific precondition — not merely a research priority — for Tier A PIAL adoption in healthcare and law enforcement contexts where existing legal obligations under GDPR Article 9 and Directive 2016/680 may not be satisfied by current architecture. Six specific legal questions requiring formal resolution by the EU AI Office are identified, spanning GDPR Chapter V data transfers, NIS2 Article 23 interaction, DORA Article 19 alignment, and the data sovereignty status of public distributed ledger anchor submissions. The framework is accompanied by a reference implementation case study and a companion Technical Blueprint. The governance infrastructure proposed is proportionate, deployable with existing technology across the core architecture, and designed to be compatible with the EU AI Act's existing provisions while addressing their identified limitations.

Seita Namba

This study quantifies Large Language Models (LLMs) and humanoids as a new labor force and describes the transformation of economic structures brought about by "super-fluid task allocation involving humans," facilitated by tokenized task transactions built on blockchain technology, from the perspective of statistical physics. Furthermore, we devise a constructive approach called "Legal Engineering" and discuss its governance mechanisms. First, we define the price fluctuations of tokenized tasks as "work volatility" and suggest that, within the scope where specific assumptions (existence of information friction, amplification of interactions, and introduction of approximate effective temperature) hold, phase-transition-like behaviors (rapid changes in order similar to bubbles) can occur in the market. Volatility here is interpreted not merely as a statistic but as an operational approximation of "social temperature" that emerges as a result of amplified information friction and interactions. As a governance mechanism to suppress this entropy increase, we propose the "Latent Torus," an information event horizon. The Latent Torus handles internal optimization invisible from the frontend and ensures sustainable social order by recirculating only optimized parameters to smart contracts. Here, by combining quantum optimization with "Semantic Intervention" via "Regulated LLMs," we aim for stabilization based on "semantic depth" rather than apparent liquidity. Furthermore, we propose a "Grand Unified Algorithm" to simultaneously handle economic efficiency (Hamiltonian minimization), humanity (Well-being), and social credit (Proof of Trust) within a single mathematical framework. The scope of this paper is not to advocate for immediate control of society as a whole, but rather to provide a conceptual model for optimizing and auditing trade-offs between indicators in a consistent manner under limited task spaces, participant sets, and operational rules. As a concrete model, we present "Computational Social Contract Theory (CSCT)" and confirm its behavior and limitations under various assumptions through quantitative analysis using multi-agent simulations. Notably, this theory presents a design policy for realizing "verifiable concealment" in governance under certain assumptions (circuitability, computational assumptions, and soundness of key management/operation) using cryptographic techniques such as zero-knowledge proofs (zk-SNARKs). This explores the possibility of hiding the details of internal optimization while maintaining compliance with the Constitutional Core, allowing citizens to verify legitimacy, and examining the operational requirements necessary for such a system. This paper presents a conceptual proposal for institutional design in a post-capitalist society and examines the redesignability of money and law. Note that the quantitative results of this paper are positioned as exploratory simulations and do not directly claim predictive confirmation.

Dinis Araujo, Ian Scott, Miguel de Castro Neto

Smart contract vulnerabilities pose risks to decentralized finance (DeFi) ecosystems, with substantial financial losses from exploits. While large language models (LLMs) offer potential for security auditing, evaluation of prompting strategies and different models for vulnerability detection remains limited. We present a prompt engineering framework comparing seven different strategies (P0-P6) from zero-shot baselines to fine-tuned pipelines. Our prompt designs are implementations from high-performing methodologies: SmartGuard, GPTScan, LLM-SmartAudit, and iAudit. The framework supports evaluation across LLMs on the SmartBugs Curated benchmark with precision, recall, and F1 metrics. We provide: (1) a set of seven prompts (P0-P6) ranging from simple single questions to complex multi-agent and fine-tuned approaches, all producing results in the same JSON format for easy comparison; (2) a testing setup that measures how detection accuracy and API costs change as prompts get more complex; (3) open-source code with tools to run and score each prompt type automatically against any labeled smart contract dataset; and (4) a comparative study showing how each strategy performs on the SmartBugs Curated benchmark.

Xin Li

No abstract is available for this record.