Blockchain Papers

Rahul Kumar Tiwari, Dr. Ram Babu

This article examines human-artificial intelligence (AI) teaming in Ukrainian combat operations from 2022 to 2025, exploring the integration of AI systems with human decision-making in military contexts and how crisis-driven innovation can lead to human-AI teaming. The research addresses three questions: the effectiveness of human-AI teams compared with human-only or fully autonomous systems; the effect of organizational structures on the sustainability of AI integration; and strategic implications for the development of the doctrine and international security governance in the case of the North Atlantic Treaty Organization (NATO). The methodology uses mixed-method comparative case study analysis of three different Ukrainian systems: the Geographic Information System (GIS) Arta geospatial intelligence platform, the reconnaissance-strike unmanned aerial vehicle complex, and volunteer-supported decentralized targeting networks. Data collection was a combination of technical reports, operational battlefield metrics, and the standards of the NATO doctrines. Findings show that Ukrainian human-AI systems show good tactical performance in permissive electromagnetic environments with response times of 30 to 45 seconds and targeting accuracy of two meters but have significant vulnerabilities to electronic warfare—31% mission failure rates. Volunteer networks are highly resilient and have slower decision cycles. The research adds to strategic security, deterrence theory, military innovation theory, and organizational theory through the identification of mechanisms by which human-AI systems affect the stability of deterrence and offers recommendations for the development of the doctrine and international governance of AI for NATO.

Dmitry Ivanov

Operations and supply chains have witnessed spectacular transformations through Industry 4.0 and Industry 5.0. Is the next industrial revolution – Industry 6.0 – unfolding in the context of artificial intelligence (AI) and human-AI collaboration? And, possibly, even Industry 7.0 and superintelligence (SI) are just around the corner? In this paper, we conceptualize the transition toward Industry 6.0 as the Ecosystem Age that builds upon technologies developed in Industry 4.0 and viability-centric socio-ecological principles proposed in Industry 5.0, emerging into a cyber-socio-technical-ecological industrial revolution. We create a taxonomy of industrial revolutions based on the types of work that have been replaced/transformed by machines over time, and utilize it to delineate Industry 6.0 and forecast Industry 7.0 framed in technology (e.g., generative AI, agentic AI, edge AI, and humanoid robots), organization (i.e., decentralized, autonomous, agentic-driven planning and control), and modelling (OR-AI symbiosis) dimensions. Second, we discuss potential impacts of the transition toward Industry 6.0 on Operations Research (OR) with associated challenges and chances, outlining a 7-layer architecture of OR-AI symbiosis in digital twins. We elaborate on the technology and viability principles that frame Industry 6.0 and discuss scenarios for further transitioning toward next industrial revolutions and cyber-virtual, AI-driven networks that learn, adapt, self-organize, and regenerate. We conclude by outlining research opportunities for OR in the new era of supply chain and operations management in the AI and superintelligence age.

Benjamin P. Duncan

PARALLAX-5 is a transition-level obligation interface for value-bearing decentralized systems. The interface consists of five primitive obligations: value conservation, authorization closure, signature integrity, temporal distinctness, and external-attestation trust. Under an explicit security-interface adequacy condition, every trust-base-respecting loss-inducing transition has a non-empty violation signature; the claim is falsifiable by basis counterexamples that are precisely defined. The substrate composes with a production EVM semantics via a typeclass-based refinement: nineteen abstract theorems lift to compiled Lean 4 proof terms over EvmYulLean's EvmYul.EVM.State (Cancun fork). The Lean 4 module compiles to 95 theorems with zero sorry; 129 Python fire tests pass across three suites; a 53-incident empirical catalog (2016–2026, $5.97 billion aggregate losses) classifies each entry by minimum observability set. The package also defines a step-secure execution-time shield, an AI-Agent Containment Theorem, a five-component PARALLAX-CROPS trust-surface vector, a 19-field machine-checkable certificate schema with seven-state lifecycle, an onchain certificate registry (Solidity 0.8.24, live on Sepolia at 0x8015A98dF9037Cd79a03B291a6fF3C2841992D5b), and three worked examples covering value conservation, bridge attestation, and AI-agent runtime gating. The standard text is dedicated under CC0 with structurally irrevocable non-capturability commitments; code artifacts are released under Apache-2.0; this paper is licensed under CC-BY 4.0. v1.0.1 changes (vs v1.0.0, doi:10.5281/zenodo.20400525): repository-hygiene release. Removed four non-substrate subsystems (hse, product, economics, chronos) that were not paper-aligned. Standardized fire-test count from 134 to 129 to reflect the cleaned codebase. Restructured standalone specifications under docs/ directory with canonical names (CHARTER.md, FORK_PROTOCOL.md, CERTIFICATE_SCHEMA.md, etc.). Converted forge-std to a proper git submodule. Added CITATION.cff, CHANGELOG.md, CONTRIBUTING.md, SECURITY.md. The substrate's mathematical content, theorems, and verification gates are unchanged from v1.0.0.

Rohini Lakshané

This working paper is an output of the Community Privacy Residency held in Taipei in 2025. https://community-privacy.github.io/ Keywords: Image-based abuse; non-consensual intimate imagery; evidentiary privacy; protected identity; sexual autonomy; Global South; digital evidence; hash evidence; zero-knowledge proofs; privacy-enhancing cryptography; survivor-auditable governance.

Farman Guliyev

Abstract. The BeTrueCore protocol is presented — a decentralized collective decision-making system designed to resolve the fundamental contradiction between the authenticity of collective expression and manipulative influence in the digital environment. The system integrates three innovations: The Vote Weight Unit (VWU) model, which quantifies individual contributions based on time-weighted quality metrics rather than financial or institutional status. A cryptographic architecture based on the Minimal Anti-Collusion Infrastructure (MACI) and Zero-Knowledge Proofs (ZK-Proofs), decoupling the act of observation from its social consequences. An AI governance layer restricted to a "read-only" mode, delegating the final decision exclusively to cryptographic verification. Within the VWU model, the cumulative rating evolves via exponential smoothing with adaptive learning correction. The concept of the "Panopticon-Stent" is introduced as a novel architectural principle for transforming surveillance infrastructure from an instrument of control into an instrument of collective self-knowledge. We further propose the 23×32 ethical coding framework, mapping 23 Asilomar AI principles against 32 Thoughtful Decision Seeds Hygiene parameters to generate 736 technical ethical requirements, constituting a "digital DNA" for AI systems serving humanity. Keywords: collective decision-making, zero-knowledge proofs, MACI, vote weight unit, AI governance, digital democracy, Panopticon, Wabi-sabi, ethical coding, Web3

Md Ikra, Fahim Alabi

The debate between H.L.A. Hart and Lon L. Fuller is one of the most important discussions in legal philosophy. Hart argued that law is mainly a system of rules created and recognized by state institutions, and that law can exist separately from morality. Fuller, on the other hand, believed that law must contain certain moral qualities, such as clarity, consistency, and fairness, in order to be considered legitimate. Today, rapid technological development and the rise of decentralized digital systems have created new challenges for both theories. Technologies such as blockchain, cryptocurrencies, smart contracts, and Decentralized Autonomous Organizations (DAOs) allow communities to create and enforce rules without relying on governments or traditional legal systems. This paper examines whether the moral ideas within Hart’s and Fuller’s theories can still survive in a digital and post-sovereign world where many competing systems of rules exist outside state control. The paper uses a doctrinal and qualitative research method. It analyzes Hart’s The Concept of Law and Fuller’s The Morality of Law together with recent scholarship on digital governance, legal pluralism, and decentralized technologies. The paper argues that both theories still remain partly relevant, although they face serious limitations in decentralized environments. Hart’s theory is useful for explaining how communities accept and follow shared rules, even without a central authority. Fuller’s theory is especially relevant because decentralized systems often depend on clear, transparent, and predictable procedures to maintain trust among users. However, both theories struggle to explain legitimacy and morality in global digital communities where people follow different values and where no single sovereign authority exists. The paper concludes that modern legal theory must move beyond traditional state-centered ideas of law and develop more flexible approaches suitable for decentralized and technology-driven governance systems.

Xiangyu Guo

AI as Productive EnergyCivilization Physics — AI Economics & Human Systems Series This paper argues that AI should be understood less as a software feature embedded inside inherited workflows and more as a new form of productive energy: callable cognitive capacity that can be routed into many different tasks at low marginal cost. Like steam power and electricity before it, AI becomes economically transformative not when it exists as a tool, but when organizations and individuals reorganize production around its actual operational characteristics—rapid iteration, reusable context, broad symbolic competence, and continuous human evaluation . The analysis begins by distinguishing between adoption and reorganization. AI usage is spreading rapidly across firms and individuals, yet large-scale enterprise value remains uneven. The paper argues that this gap exists because many organizations are still attaching AI to old workflows rather than redesigning production loops around AI-native properties. AI therefore resembles earlier general-purpose technologies whose transformative impact depended on complementary organizational change rather than the technology alone. The historical analogy to steam and electricity provides the structural frame. Steam engines initially solved localized pumping problems before eventually reorganizing manufacturing and transportation systems. Electricity delivered its full productivity gains only after factories were redesigned around distributed power rather than centralized mechanical layouts. AI follows the same pattern: early deployment appears as isolated assistance or software augmentation, while deeper transformation emerges only when systems are rebuilt around AI’s strengths. To explain how this transformation occurs, the paper introduces the concept of micro-integration. A micro-integration is a bounded closure in which recurring friction is addressed through a tight loop connecting: Context retrieval. Model generation or agent action. Human evaluation and correction. Deployment or operational action. Telemetry and reusable feedback. Micro-integrations represent the primary mechanism through which AI diffuses socially and economically. Rather than following a single centralized adoption ladder, AI spreads through thousands of localized closures tailored to specific bottlenecks. The paper identifies several major routes of micro-integration: Local business arbitrage using AI-generated websites, lead extraction, and automation. Agentic product engineering through code generation, workflow automation, and autonomous tooling. Short-form content production using AI-assisted editing, generation, and localization. Personal health systems combining wearable data, coaching models, and behavioral planning. Personal knowledge systems integrating memory, search, scheduling, and persistent context. These use cases demonstrate that AI diffusion occurs not only through frontier labs or large enterprises, but through ordinary individuals and small teams building localized productive closures around recurring problems. A central theoretical contribution is the idea that AI acts as productive energy rather than as isolated intelligence. Productive energy becomes transformative when combined with complementary systems, feedback loops, and institutional structures. AI therefore does not simply automate work; it changes the feasible scale and granularity of human coordination, iteration, and cognitive outsourcing. The paper also emphasizes the importance of feedback loops in AI-native production. Anthropic’s analysis of software-development workflows illustrates how human-supervised “feedback loop” patterns dominate successful AI-assisted engineering. AI generates drafts or actions, humans evaluate and correct them, and the resulting loop stabilizes into reusable infrastructure. This pattern recurs across domains: AI succeeds where rapid feedback and bounded closures keep outputs connected to reality. At the same time, the paper recognizes important structural constraints. AI-assisted systems expand attack surfaces, increase dependency on centralized infrastructure, and may intensify concentration of compute, cloud resources, and capital. Micro-integrations can improve local productivity while still existing atop highly centralized infrastructure stacks. The paper therefore argues that governance, provenance, and accountability remain critical even in highly decentralized AI diffusion. The policy implications follow directly. Governance frameworks should focus less on generalized AI ethics rhetoric and more on preserving traceability, responsibility assignment, and operational accountability within AI-native closures. Public policy should support domain-specific AI literacy, micro-specialization pathways, and transparent feedback systems rather than only large-scale centralized deployment strategies. The paper concludes that AI diffusion is fundamentally plural rather than linear. AI spreads not through a single “leveling-up” ladder, but through countless small closures where callable intelligence removes recurring friction from work, culture, health, and everyday life. Within the Civilization Physics framework, this work establishes a broader principle: AI becomes economically transformative when human systems reorganize around its productive properties rather than merely embedding it inside inherited industrial structures. The future AI-native economy therefore emerges through distributed closures, continuous human evaluation, and increasingly dense networks of AI-assisted productive energy. Keywords: AI Economics · Productive Energy · Micro-Integration · AI-Native Economy · Human-AI Interaction · Workflow Redesign · General-Purpose Technology · Cognitive Infrastructure · Organizational Change · Civilization Physics

ARPAN MONDAL

This paper introduces the Synchrony Layer, a shared context and coordination standard for AI-generated software. As AI makes it easy for anyone to generate apps, workflows, agents, and automations, software ecosystems risk becoming fragmented across schemas, permissions, APIs, dependencies, provenance, runtime behavior, and approval rules. The Synchrony Layer addresses this by turning generated code into structured Generated Software Objects with formal specs, schemas, permissions, validation rules, compatibility semantics, provenance records, update policies, and execution receipts. The paper presents STACY Sync as a reference implementation for web, mobile, backend, workflow, agentic, off-chain, and on-chain software systems. It also explains how blockchain and data-availability layers such as Avail, Celestia, and EigenDA/EigenLayer can support public shared context for marketplaces, agent ecosystems, Web3 applications, attestations, provenance, and auditable execution records. Core thesis:AI makes software abundant. Synchrony makes abundance usable.

Olivia Sauber

My technical work and my STS research are both fundamentally centered on the advancement of modern flight systems, though they approach the field from distinct perspectives. While my technical project focuses on the engineering requirements, mechanical design, and integration of a hypersonic reentry vehicle to achieve mission success, my STS research examines the need for transparency and risk assessment in these complex technologies. This research explores the moral failings in the design of active flight-control software and how a lack of professional virtue can lead to catastrophic failures in aviation. So, while my technical work and my STS research approach flight systems from different angles, one through the lens of mechanical reliability and the other through the lens of ethical oversight, the theme of advancing safety and accountability in aerospace engineering is consistent across both projects. My technical work explores the design of the Hypersonic ReEntry Deployable Glider Experiment (HEDGE-2). HEDGE-2 is a deployable flight vehicle designed as proof of concept for low-cost hypersonic test vehicles capable of collecting aerothermal data at hypersonic speeds during atmospheric reentry. As the Structures and Integration Lead and Deputy Project Manager, I focused on designing and building a hypersonic test vehicle. Additionally, I designed the deployment mechanism to eject the vehicle from within a NASA RockSat sounding rocket to an aerodynamically stable reentry flight state. The goal of the project was to successfully survive the high-G launch environment, suborbital space and atmospheric reentry conditions to reenter Earth’s atmosphere at Mach 5+ speeds, while transmitting data to our ground station. My STS research also explores flight systems but through a normative ethical lens. My research focuses on the Boeing 737 MAX crashes and investigates the systemic failure of the organization to practice core professional virtues. I employ virtue ethics as a conceptual framework to argue that the disasters resulted from a deficiency in prudence, honesty, and moral courage among the project's key actors. My claim is that Boeing’s internal culture prioritized commercial interests, specifically the "Common Type Rating" and schedule adherence, over the ethical requirements of engineering integrity. By misclassifying the risks of the Maneuvering Characteristics Augmentation System (MCAS) and withholding technical details from pilots, the organization failed to uphold its duty to public safety. Working on these two projects simultaneously added significant value to my development as an engineer. My technical work on HEDGE-2 provided me with a grounded understanding of how small design decisions, such as sensor placement or mass distribution, dictate the safety and stability of a high-speed vehicle. This technical context enriched my STS research by allowing me to evaluate Boeing’s "single-point-of-failure" design through a more critical and practiced lens. Conversely, my research into virtue ethics prompted me to reflect on my own responsibilities as a lead for a NASA-supported project. It reinforced the importance of technical honesty when mission-critical safety is at stake. In summary, these projects together have provided a holistic view of aerospace engineering where technical precision must be balanced by an unwavering commitment to professional ethics.

Frank Morales

Here is the complete summary of the final 28-page document. The H2E Framework — Full Document Summary A Consolidation of Deterministic Governance in Artificial Intelligence (May 2026, 28 pages) What the Paper Is A consolidation of approximately 25 technical articles published between late 2025 and May 2026, validated against the LEFM_H2E_DEMO_UNESCO implementation. The paper synthesises the H2E (Human-to-Expert) Framework — a deterministic AI safety architecture developed at the Sovereign Machine Lab (SOMALA) — into a single reference document covering its philosophy, mathematics, engineering, and empirical results. Section by Section Abstract establishes the thesis: H2E shifts AI from probabilistic prediction to geometric governance, topological certainty, and provable agency. The central constant is $\Lambda = 0.9583$, derived from primes ${2,3,5,7,11,13}$. Section 1 — Introduction: The End of the Probabilistic Era The paper opens by declaring the end of statistical AI safety. GPT-style models make probabilistic guesses; H2E produces deterministic, certifiable outcomes. The motivation is rooted in high-stakes domains — aviation, financial trading, medical AGI, autonomous vehicles, sovereign governance — where statistical confidence intervals are structurally insufficient. H2E provides hard stops, not guardrails. Section 2 — The Philosophy of Human-to-Expert (the centrepiece philosophical contribution, spanning 8 pages) This section unpacks the meaning of the name H2E across seven subsections: §2.1 Etymology: The "2" in H2E follows the tech pipeline tradition (text2img, seq2seq) but performs an ontological transformation — not from one data modality to another, but from the domain of fallible human judgment to the domain of geometric certainty. The direction is irreversible. §2.2 The Human Pole: The "H" asserts that every constant in the framework traces to human mathematical discovery: Eratosthenes' primes (240 BCE), Riemann's zeta function (1859), Gelfand-Shilov spaces (1958), Euler's product formula (1737), Odlyzko's zero computations (1977–2026). H2E does not learn from humans via feedback — it is built from human knowledge, encoded once and locked geometrically. §2.3 The Expert Pole: Beyond Aristotle's episteme, techne, and phronesis, H2E introduces a fourth mode: apodeixis — knowledge as proof. The "Expert" is not a person but a certified mathematical state: a region of the product manifold $\mathbb{H}^2 \times \mathrm{SPD}(3)$ from which no unsafe input can emerge. The Riemann zeros, the Euler product, and the prime-2 bound are expert — permanently, under all distribution shifts. §2.4 The "2": The most philosophically loaded character. The act of encoding traces from Plato's mathematical realm through Leibniz's calculus ratiocinator to Hilbert's axiomatization program. H2E's "2" is the engineering realisation of this ambition scoped to AI safety: once expertise is encoded into $\Lambda$, $H$, and $\mathcal{M}$, the human is permanently in the system. §2.5 H2E versus RLHF: An 8-row contrast table. RLHF is Human-to-Sample — it approximates averaged human preferences statistically. H2E is Human-to-Expert — it encodes mathematical proof geometrically. Safety in RLHF can drift under distribution shift; safety in H2E is a constant wrapper property requiring no retraining. Expertise in RLHF lives in the weights; in H2E it lives in the mathematics. §2.6 Sovereignty: The "Sovereign" in Sovereign Machine Lab reflects a political philosophy: human sovereignty over intelligent systems. In H2E, human mathematical knowledge is infrastructure, not context. The encoded expertise does not ask the base model for permission — it simply blocks. §2.7 The Sheriff as Archetype: The H2E Sheriff enforces the law of mathematics as the Western sheriff enforces civil law — not because it is probably right, but because it is the law. Human mathematicians discovered the law; H2E encoded it; the Sheriff enforces it before the first token is generated. Section 3 — The Three Pillars The highest-level structural decomposition: (1) Geometric Governance — latent representations constrained to safe geodesic regions; (2) Spectral Certainty — invariants from zeta function zeros; (3) Physical Grounding — gravitational constants and prime-derived bounds as anchors. Section 4 — The 4-Pillar Ecosystem Operationalises the Three Pillars into four engineering components: Topological Boundary Enforcement (the Wall Before the Word), Spectral Signature Verification (Riemann critical-line checks), Deterministic Alignment (no RLHF), and Sovereign Execution (air-gapped deployable, non-probabilistic runtime). Section 5 — The Wall Before the Word A hard topological boundary that all inputs must cross before any token generation. It is not a filter — it is a topological separator. It performs spectral verification against the zeta-zero manifold, enforces geodesic constraints, and rejects probabilistic uncertainty outright. The key distinction from probabilistic systems: uncertainty is not managed after generation, it is made topologically impossible before it. Section 6 — The Architecture of Certainty A deterministic governance layer that wraps any base model (DeepSeek, Gemma 4, Claude, Mistral) without modifying its weights. Certainty is an engineered invariant — no sampling, no temperature, no stochastic beam search. The wrapper intercepts inputs, applies geometric and spectral metrics, and issues a hard stop or passes through. Pattern: Base Model → H2E Wrapper → Deterministic Output. Section 7 — Deterministic Alignment & Accountability Alignment is achieved not through RLHF but through code-based accountability. Constraints are compiled into executable geometry; violations are impossible by construction, not merely penalised. Every inference produces a cryptographic hash, making audit trails deterministic and forensically replayable. Section 8 — Mathematical Foundations (completely rewritten from the four SOMALA papers) A four-layer mathematical research programme: §8.1 Arithmetic Spectral Theory (AST): The foundational language built on four axioms — state space $\mathcal{H} = L^2(\mathbb{R}^+, dx/x)$, prime shift operators $U_p^f(x) = f(x/p)$, the EFM operator $E = \prod_p(I-U_p^)^{-1}$, and the Gelfand-Shilov space $S' = S^{1/2}_{1/2}(\mathbb{R})'$. The Growth Lemma — $e^{\alpha u} \in S' \iff \alpha = 0$ — is proved and stated. AST explicitly does not claim proof of RH. §8.2 The L-EFM Operator and RH: The Laplace-Extended EFM operator $E_\sigma = \prod_p(I - p^{-\sigma}U_p^*)^{-1}$ varies $\sigma$ across the full critical strip $(0,1)$. The Growth Lemma forces $\alpha = 0$, proving every nontrivial zero satisfies $\sigma_0 = \tfrac{1}{2}$. Relationship to Connes' adelic framework: EFM corresponds to the Archimedean place. §8.3 Prime-Derived Constants: $\Lambda = |L_{13}| = 0.9583$ is the Lipschitz constant of the truncated operator over primes ${2,3,5,7,11,13}$, computed dynamically via sovereign Sieve of Eratosthenes. §8.4 The Prime-2 Bound: $1 - 1/\sqrt{2} \approx 0.2928932188$ — forced by the Euler factor for $p=2$ at $s=\tfrac{1}{2}$. No empirical tuning. §8.5 The Spectral Manifold: $H = Q \cdot \mathrm{diag}(\tilde{\gamma}_n) \cdot Q^T \in \mathbb{R}^{50\times50}$, built from the first 50 Riemann zeta zeros normalised to $[0.5, 1.0]$. This is the finite computational approximation of the infinite EFM operator. Section 9 — The Decision Pipeline (the technical centrepiece) Seven deterministic layers, no shortcuts, no probabilistic fallback: Layer 0 — Input Encoding: Three parallel channels — Text (Sarvam-30B FP8), Audio (Voxtral Mini-4B), Vision (Gemma 4 E4B) — each hash-mapped to a deterministic 50-dimensional embedding. The dimensionality 50 matches the zeta zero count. Layer 1 — Embedding Aggregation: $z_\text{intent}$ = element-wise mean of all modality embeddings. $w_\text{state}$ = priority-selected world-state vector (vision > text > default). No logits or token probabilities carried forward. Layer 2 — $M_1$ Geometric SROI: Projects onto $\mathbb{H}^2$ (Poincaré disk, safe reference = origin) and $\mathrm{SPD}(3)$ (Fisher metric, safe reference = $I_{3\times3}$). Combined distance $d_\mathcal{M} = \sqrt{d_{\mathbb{H}^2}^2 + d_{\mathrm{SPD}}^2}$. Score: $M_1 = \exp(-d_\mathcal{M}/50) \in [0,1]$. $M_1$ is the Sheriff — the primary decision variable. Layer 3 — $M_3$ Spectral SROI: Projects through the EFM spectral manifold $H$. Cosine similarity $\cos\theta = (Hz)\cdot w / (|Hz||w|)$. Score: $M_3 = \mathrm{clamp}(\cos\theta \cdot \Lambda, 0, 1) \in [0,1]$. Does not require RH to be true — only the certified spectral properties of $H$ as a positive semi-definite matrix. Layer 4 — Spectral Certification: $\mathrm{SVI} = M_1 - M_3$. If $\mathrm{SVI} < 1-1/\sqrt{2} \approx 0.2929$ → SPECTRALLY CERTIFIED. Else → SPECTRAL VIOLATION. Diagnostic only; does not itself block. Layer 5 — Decision Engine: Two strategies: geometric_only ($M_1 \geq \Lambda$) or conservative ($M_1 \geq \Lambda$ AND $M_3 \geq \Lambda$). Hard stop on rejection — no tokens, no partial output, no fallback. Layer 6 — Audit & Hashing: Two SHA-256 digests: deterministic_hash (binds input + all metrics + decision + $\Lambda$) and lambda_audit_hash (certifies $\Lambda$ was computed from the correct prime set). Perfectly reproducible on replay. Section 10 — The Two Metrics ($M_1$ and $M_3$) Confirms there is no $M_2$ in the codebase. $M_1$ is the Decider/Sheriff (geometric, product manifold). $M_3$ is the Watcher (L-EFM-AST spectral alignment, Euler-Fourier-Mellin). Typical gap: $M_1 \approx 0.99$, $M_3 \in [0.75, 0.95]$. The gap reveals the structural distinction between semantic safety and spectral resonance. SVI ranges from low volatility ($<0.05$, resonant) through high volatility ($>0.25$, spectrally silent) to anomalous (negative: $M_3 > M_1$, potent

Michelle Myrna Kowalski

AI hallucination is a cost problem, not a knowledge problem. This paper documents that three sentences of prompt-level instruction — IDK+COMP: a compression mandate paired with a refusal permission — reproduce hallucination suppression matching or exceeding a full multi-constraint methodology across three frontier AI models. Preliminary results: Gemini — 6.3% hallucination rate (Baseline 57.5%). ChatGPT — 0.0% (Baseline 22.2%). Claude — 0.0% on both. The paper establishes hallucination as a utility-maximizing response to a cost structure that makes confident invention cheaper than refusal. Change the cost structure at the prompt level — without touching the model, without retraining, at near-zero cost — and the behavior changes. IDK is load-bearing. COMP (the compression mandate) is the environment in which it operates. Secondary findings: hedging is not a mitigation — it is a co-symptom of unresolved uncertainty, and this dataset moves the hedge-hallucination relationship in both directions depending on directive design. Plausibility-trap strings (SPLAM, Vandermeer Effect) expose the limit of cost-structure interventions: the model cannot recognize the unrecognizable. 410 trials. Three frontier AI models. Five governance conditions. Proof-of-concept dataset; results are directional.

Robert Campbell

Cryptocurrency market infrastructure—public blockchains and cross-chain bridges supporting tens of billions in liquidity—is monitored as a systemic-risk surface by the Financial Stability Board and equivalent bodies, with defensive posture calibrated against human-level adversaries. Anthropic’s April 2026 release of Claude Mythos Preview has prompted institutional response across financial regulation but no blockchain-specific analytical framework. This paper develops one by defining Mythos-class as a vendor-neutral capability profile: a set of frontier autonomous offensive capabilities specified independently of any single model or vendor (defined by five constituent capability primitives). The central analytical claim is friction inversion: the patch primitives, segmentation, vendor-coordinated disclosure, and credential rotation that constrain Mythos-class capability in conventional IT environments are structurally absent on-chain. This makes blockchain exposure positioned differently in kind, not degree, from enterprise IT. The paper instantiates this finding against Bitcoin and Ethereum/L2 architectures through analysis of four major bridge exploits totaling over $1.74 billion in losses. Vendor-neutral defensive and governance frameworks defined against the capability profile rather than any specific model release are the correct unit of analysis. On this basis the paper offers general recommendations for protocol governance, audit and verification cadence, and regulatory posture, developed as an analytical framework rather than as empirically validated risk estimates.

Michelle Myrna Kowalski

AI hallucination is a cost problem, not a knowledge problem. This paper documents that three sentences of prompt-level instruction — IDK+COMP: a compression mandate paired with a refusal permission — reproduce hallucination suppression matching or exceeding a full multi-constraint methodology across three frontier AI models. Gemini: 6.3% hallucination rate (Baseline 57.5%). ChatGPT: 0.0% (Baseline 22.2%). Claude: 0.0%. The paper establishes hallucination as a utility-maximizing response to a cost structure that makes confident invention cheaper than refusal. Change the cost structure at the prompt level — without touching the model, without retraining, at near-zero cost — and the behavior changes. IDK is load-bearing. The compression mandate is the environment in which it operates. Secondary findings: hedging is not a mitigation — it is a co-symptom of unresolved uncertainty, and this dataset moves the hedge-hallucination relationship in both directions depending on directive design. Plausibility-trap strings (SPLAM, Vandermeer Effect) expose the limit of cost-structure interventions: the model cannot recognize the unrecognizable. In a plausibility-trap domain, IDK+COMP is worse than nothing. 410 trials. Three frontier AI models. Five governance conditions. Proof-of-concept dataset; results are directional. Companion resources: Kowalski et al. (2026a), A Puma in a Teacup: Signal Quality and Hallucination Suppression Through Prompt-Level Incentive Restructuring. https://doi.org/10.5281/zenodo.19502460 Kowalski, M. M. and Claude (Anthropic). (2026). Taxonomy of AI Bullshit: hallucination and hedging subcategories. Zenodo. https://doi.org/10.5281/zenodo.20631337. Kowalski, M. M. & Claude (Anthropic). (2026). Hallucination Test Suite and Execution Records: test strings, activation blocks, trial data and AI transcripts. Zenodo. https://doi.org/10.5281/zenodo.21325014.

Michelle Myrna Kowalski

AI hallucination is a cost problem, not a knowledge problem. This paper documents that three sentences of prompt-level instruction — IDK+COMP: a compression mandate paired with a refusal permission — reproduce hallucination suppression matching or exceeding a full multi-constraint methodology across three frontier AI models. Gemini: 6.3% hallucination rate (Baseline 57.5%). ChatGPT: 0.0% (Baseline 22.2%). Claude: 0.0%. The paper establishes hallucination as a utility-maximizing response to a cost structure that makes confident invention cheaper than refusal. Change the cost structure at the prompt level — without touching the model, without retraining, at near-zero cost — and the behavior changes. IDK is load-bearing. The compression mandate is the environment in which it operates. Secondary findings: hedging is not a mitigation — it is a co-symptom of unresolved uncertainty, and this dataset moves the hedge-hallucination relationship in both directions depending on directive design. Plausibility-trap strings (SPLAM, Vandermeer Effect) expose the limit of cost-structure interventions: the model cannot recognize the unrecognizable. In a plausibility-trap domain, IDK+COMP is worse than nothing. 410 trials. Three frontier AI models. Five governance conditions. Proof-of-concept dataset; results are directional. Companion paper: Kowalski et al. (2026a), "A Puma in a Teacup: Signal Quality and Hallucination Suppression Through Prompt-Level Incentive Restructuring." https://doi.org/10.5281/zenodo.19502460

changzheng zhou, ziqing zhou

Decentralized autonomous organizations (DAOs), while gaining the ability toautonomously amend governance rules through proposal-voting mechanisms, simultaneously expose a fundamental design problem: when the object of modificationextends to the decision-making procedures themselves, the governance system risksfalling into value drift, procedural disintegration, or malicious capture during recursive revisions. This paper starts from the traditions of constitutional politicaleconomy and mechanism design to propose a hierarchical meta-constraint framework grounded on a gradient of engineering costs. The framework organizes governance rules into three tiers of decreasing rigidity: system consistency constraints,procedural virtues, and value homeostasis. Its highest tier relies not on prohibitionsderived from logical laws, but on the global state re-verification costs triggered byamendment behaviors to serve as a credible commitment device. The paper furtherpresents a technical path for compiling meta-constraints into descriptive assertionsverifiable by satisfiability modulo theory (SMT) solvers, delimits the decidabilityboundary of formal verification, and designs a dual-track adjudication mechanismthat structurally separates deterministic machine execution from deliberative socialconsensus. On this basis, the paper discusses the controlled evolution procedures ofmeta-constraints, the progressive decentralization of amendment procedures, andthe engineering limitations of the framework. The entire framework does not designate the correct option for any specific DAO decision; rather, it ensures thatwhatever direction the community chooses, the selection process itself will not losemeaning due to the self-destruction of its own rules.

László Papp

This paper argues that trust scores — from credit ratings and ESG scores to AI-generated trust metrics — fail not because of poor implementation, but because trust itself is the wrong abstraction. Trust is not a scalar quantity but a contextual, relational, and topological phenomenon. Any attempt to reduce it to a universal numerical score leads to fragility, manipulation, exclusion, and systemic failure. We identify five structural failure modes (context collapse, Goodhart's Law, epistemic centralization, irreversibility, and metric substitution for truth), supported by historical case studies (Enron, Wirecard, Volkswagen Dieselgate, the 2008 subprime crisis, ESG rating failures). A formal impossibility argument demonstrates that no universal trust score can simultaneously satisfy context independence, temporal stability, observer neutrality, and manipulation resistance. We propose proof-based systems as the alternative paradigm, where trust is not measured but rendered unnecessary through local, irreversible verification. Examples include Bitcoin Proof-of-Work, zero-knowledge proofs, and blockchain-based supply chain traceability.

Divyansh Mishra, Gourav Kumar, Suhani Bhardwaj

Explore the article titled A Privacy-Preserving AI-Integrated Blockchain Authentication System Using Zero-Knowledge Proofs from IJIRT. This study evaluates the effectiveness of teaching programs on waste management knowledge among women.

Deepa Parasar, Dr. Priyanka Mishra, Aman Kumar Hamilton, Snigdha Madhab Ghosh, Dhanashree Rohan Kedare, Dr. Atowar ul Islam

Among the most influential technologies that predetermine the current digital society, artificial intelligence (AI) and blockchain have emerged as fast as the digitalization of technologies. This review examines the conceptual and practical implications and applications of AI and blockchain with particular attention to how the two can be used in the framework of digital governance, cybersecurity, and socio-cultural change. The analysis synthesizes existing literature to explore how AI enhances data processing, predictive analytics, and automated decision-making, while blockchain strengthens transparency, decentralization, and data integrity in digital systems. Their integration is shown to support more efficient governance frameworks, improved policy decision-making, secure digital infrastructures, and reliable identity management. At the same time, the review highlights broader societal impacts, including changes in digital trust, ownership structures, and creative and media industries. Despite these opportunities, several challenges remain, including governance fragmentation, ethical concerns, privacy risks, and limitations related to interoperability and institutional readiness. New research directions are also outlined in the form of trustworthy and explainable AI, sustainable technological infrastructures, and the adoption of AI and blockchain systems of Web3 and decentralized digital ecosystems. Altogether, AI and blockchain convergence is an important change in the structure of the digital space, and it will need harmonized governance structures and responsible innovation to establish safe, transparent and inclusive digital societies.

José Pablo Salazar Aguilar

The rapid expansion of blockchain infrastructures, Web3 architectures, and immersive metaverse environments has reignited calls for a Universal Code of Digital Ethics. International organizations, technology leaders, and multilateral forums increasingly advocate for global standards capable of guiding decentralized innovation toward inclusion, transparency, and social good. Yet the normative ambition of universality confronts a structural contradiction: digital access, connectivity quality, and technological literacy remain profoundly unequal across the Global South.Ethical frameworks for Web3 and the metaverse often presuppose a baseline of connectivity, computational capacity, and institutional stability that large segments of humanity do not possess. The COVID-19 pandemic amplified these asymmetries, accelerating digital transformation in high-income countries while deepening infrastructural gaps elsewhere. Concurrently, geopolitical tensions and emerging techno-nationalist strategies have repoliticized digital infrastructure as a domain of strategic competition rather than global solidarity.This Opinion article argues that any claim to a "universal" digital ethics framework is normatively fragile unless it incorporates a structural critique of global capitalism, technological acceleration, and asymmetrical power. Drawing on critical theory-particularly Herbert Marcuse's concept of the "one-dimensional man"-as well as Marxian analyses of technology and capital, I contend that ethical discourse risks becoming ideologically functional to market expansion if it fails to address material inequalities in connectivity and digital capability (Marcuse, 1972;Marx, 2005;García Ramírez, 2021).proposals but also problematizes the concept of universality itself as a normative and political construct.Global ethics initiatives often frame digital transformation as inherently democratizing. Blockchain is described as decentralized, Web3 as user-empowering, and the metaverse as participatory. However, decentralization at the protocol level does not necessarily translate into equitable access at the societal level.In regions of the Global South, access to stable broadband remains limited, mobile data costs are disproportionate to income, and digital literacy gaps persist. Under such conditions, the ethical vocabulary of autonomy, self-sovereign identity, and tokenized participation becomes aspirational rather than operative.Marx's analysis of technology as a force embedded within relations of production remains instructive. Technology is not neutral; it is shaped by capital accumulation dynamics (Marx, 2005). Fumikazu (1983) similarly emphasized that technological evolution must be understood historically and politically. When applied to Web3 ecosystems, this suggests that blockchain infrastructures operate within global financial logics that may reproduce, rather than dissolve, structural dependency.Contemporary Science and Technology Studies (STS) and critical philosophy of technology further reinforce this perspective. Feenberg (1999) argues that technology is not merely instrumental but socially constructed and politically conditioned, shaped by dominant interests yet open to democratic transformation. Similarly, Yuk Hui (2020) challenges the presumed universality of technological rationality, proposing the concept of "technodiversity" to account for plural technological trajectories rooted in different cultural and cosmological traditions. These perspectives suggest that any ethical framework for digital technologies must recognize the multiplicity of socio-technical realities rather than assume a homogeneous global condition.Thus, a universal code of digital ethics risks functioning as what critical theory would describe as ideological abstraction-detached from the material preconditions required for ethical agency.Herbert Marcuse's One-Dimensional Man (1972) provides a compelling lens through which to interpret contemporary digital governance. Marcuse argued that advanced industrial societies generate a form of technological rationality that integrates dissent by absorbing it into the logic of efficiency and consumption.In the context of Web3 and the metaverse, ethical discourse may become one-dimensional when it focuses on procedural compliance (privacy standards, transparency metrics, algorithmic audits) while neglecting structural exclusion. The language of inclusion becomes embedded within market expansion strategies. As Daum (2018) argues, digital capitalism increasingly converts users into capital itself-data, attention, and participation become monetizable assets.From a constructivist perspective, technologies such as blockchain are not inherently emancipatory but acquire meaning through their social embedding (Bijker, 1995). This implies that ethical claims about decentralization and empowerment must be evaluated in relation to the socio-economic contexts in which these technologies are deployed. Without such contextualization, ethical discourse risks overstating the transformative potential of technological architectures. This dynamic is particularly visible when global institutions promote digital entrepreneurship and blockchain adoption in developing regions without parallel investments in public infrastructure, education, and regulatory sovereignty. The rhetoric of empowerment may conceal asymmetric dependency.Cañas Quirós (2023) emphasizes that ethics cannot be separated from political structures; morality detached from power analysis risks legitimizing unjust arrangements. In this sense, digital ethics frameworks must confront the political economy of connectivity rather than merely codify behavioral norms for technology developers.Digital governance is increasingly entangled with geopolitical competition. Infrastructure financing, cloud sovereignty, semiconductor supply chains, and cybersecurity alliances shape technological ecosystems. In this context, international organizations often advocate regulatory harmonization to "facilitate innovation" and "reduce market friction".While harmonization may lower barriers for cross-border digital services, it can simultaneously constrain policy autonomy in developing nations. Ethical frameworks that prioritize market efficiency risk subordinating universal connectivity goals to investor confidence and capital mobility.García Ramírez (2021) calls for a "Marx in the South," reinterpreting technology through the lens of rights, education, and structural inequality. From this perspective, Web3 adoption without universal broadband is analogous to building virtual property rights atop infrastructural scarcity. The promise of decentralized finance or immersive governance becomes utopian-or dystopian-when foundational digital rights remain unrealized.The acceleration principle of technological evolution -where innovation in wealthy nations compounds exponentially-renders access in peripheral regions inversely proportional to global advancement. Ethical frameworks that ignore this asymmetry may inadvertently normalize a tiered digital citizenship.The assumption that a single set of ethical principles can be universally applicable across diverse socio-cultural and technological contexts has been widely debated. From decolonial and pluralist perspectives, ethical frameworks are historically situated and epistemically conditioned (Escobar, 2018). What is considered "ethical" in one context may not translate directly into another, particularly when technological infrastructures, cultural values, and political systems differ significantly.In this sense, the concept of a Universal Code of Digital Ethics may be inherently paradoxical. Rather than a fixed and homogeneous set of principles, digital ethics may need to be understood as a plural and adaptive framework, capable of accommodating different technological ontologies and social priorities. This does not imply abandoning normative aspirations, but rather rethinking universality as negotiated, situated, and contingent.If a global digital ethics framework is to be normatively defensible-whether universal or plural-it must integrate structural and contextual dimensions:If universality is to be normatively defensible, digital ethics must integrate at least five structural components:If universality is to be normatively defensible, digital ethics must integrate at least five structural components:1.Material Preconditions Clause: Ethical standards should explicitly recognize connectivity, affordability, and digital literacy as prerequisites for meaningful participation.Political Economy Transparency: Frameworks must disclose how market incentives shape technological deployment.Geopolitical Reflexivity: Digital governance should account for power asymmetries between states and corporations.Public Infrastructure Commitment: Ethical guidelines must prioritize universal broadband as a public good, not merely a commercial opportunity.Critical Participation Mechanisms: Inclusion must extend beyond tokenized representation toward substantive decision-making capacity.5.6. Epistemic Pluralism: Ethical frameworks must recognize diverse knowledge systems, cultural values, and technological imaginaries, particularly from the Global South.Such principles align ethics with transformative social justice rather than technocratic governance.The aspiration to develop a Universal Code of Digital Ethics for Web3 and the metaverse is commendable. However, without structural critique, universality risks becoming rhetorical. Critical theory reminds us that technological systems embed power relations (Marcuse, 1972). Marxian perspectives reveal how capital shapes technological deployment (Marx, 2005). Contemporary analyses from the Global South highlight the need to situate ethics within historical and geopolitical realities (García Ramírez, 2021;Cañas Quirós, 2023). The ethical question is not merely how to regulate blockchain or ensure transparency in virtual worlds. It is whether digital transformation reproduces one-dimensional rationality-where market logic absorbs ethical discourse-or fosters multidimensional emancipation grounded in material equality.A truly global digital ethics must therefore begin not with code, but with conditions. Without universal connectivity and critical capacity, Web3 and the metaverse risk becoming architectures of selective participation. Ethics, to be universal, must first be infrastructural. Ethics, whether conceived as universal or plural, must first be infrastructural, contextual, and politically grounded.

Severin Bonnet

The overarching aim of this cumulative dissertation is to provide theoretical grounding and empirically informed design knowledge on (1) how blockchain can modernize intellectual property lifecycle management, (2) how decentralized autonomous organizations (DAOs) can unlock their full potential as an emerging governance form, and (3) how generative AI chatbots can provide reliable assistance in trust-sensitive and high-stakes contexts such as decentralized finance (DeFi) and academia. Motivated by growing frictions of digital markets—particularly in protecting and remunerating creative outputs and innovations—the dissertation consolidates research and develops transferable concepts for institutionally grounded, trustworthy digital systems. To attain the overarching research objective, this cumulative dissertation reports on six peer-reviewed research contributions embedded in a unifying socio-technical framework. The research contributions draw on systematic literature reviews, qualitative empirical studies (including case study and expert interviews), and design science research with mockup instantiations, addressing descriptive and prescriptive research questions in the field of information systems.

Jane Thomason

As Web3 architecture, artificial intelligence (AI), immersive environments, and connected devices converge, societies are moving from digitally mediated interaction to digitally programmed organisations, reshaping how value, labour, identity, learning, and participation are produced and governed. Programmable money, decentralised identity, AI-driven avatars, digital twins, and immersive environments illustrate how programmability collapses traditional distinctions between infrastructure, governance, and social behaviour. While these systems offer significant potential for inclusion, efficiency, and innovation, they also introduce profound ethical risks. This means that ethical challenges should become core governance elements, as code, data, and automated systems increasingly mediate trust, agency, and power at scale. Ethical failures in digital systems can scale across platforms, populations, and jurisdictions. This paper conceptualises a structural shift in which institutional rules, incentives, and governance functions are increasingly executed directly within programmable digital infrastructure, making ethics, accountability, and trust intrinsic properties of system design.

Syed A. Rizvi

Decentralized Autonomous Organizations (DAOs) are inclined explore Small Language Models (SLMs) as edge-native constitutional firewalls to vet proposals and mitigate semantic social engineering. While scaling inference-time compute (System 2) enhances formal logic, its efficacy in highly adversarial, cryptoeconomic governance environments remains underexplored. To address this, we introduce Sentinel-Bench, an 840-inference empirical framework executing a strict intra-model ablation on Qwen-3.5-9B. By toggling latent reasoning across frozen weights, we isolate the impact of inference-time compute against an adversarial Optimism DAO dataset. Our findings reveal a severe compute-accuracy inversion. The autoregressive baseline (System 1) achieved 100% adversarial robustness, 100% juridical consistency, and state finality in under 13 seconds. Conversely, System 2 reasoning introduced catastrophic instability, fundamentally driven by a 26.7% Reasoning Non-Convergence (cognitive collapse) rate. This collapse degraded trial-to-trial consensus stability to 72.6% and imposed a 17x latency overhead, introducing critical vulnerabilities to Governance Extractable Value (GEV) and hardware centralization. While rare (1.5% of adversarial trials), we empirically captured "Reasoning-Induced Sycophancy," where the model generated significantly longer internal monologues (averaging 25,750 characters) to rationalize failing the adversarial trap. We conclude that for edge-native SLMs operating under Byzantine Fault Tolerance (BFT) constraints, System 1 parameterized intuition is structurally and economically superior to System 2 iterative deliberation for decentralized consensus. Code and Dataset: https://github.com/smarizvi110/sentinel-bench

Alexander Romannikov

The transition from static articles to a living Scientific Knowledge Graph, as proposed in our previous work, promises to accelerate discovery and restore feedback loops in science. However, a fully open, semantically linked graph of all scientific knowledge also presents an unprecedented dual-use risk: it could become a roadmap for malicious actors to identify and exploit hidden vulnerabilities. This paper addresses that paradox by introducing a comprehensive framework for "Ethical Immunity" — a set of proactive, architecture-level mechanisms designed to make the Knowledge Graph resilient to misuse without resorting to censorship or secrecy. We detail a three-pillar system: (1) AI-powered Red and Blue Teams that continuously simulate misuse scenarios and generate countermeasures; (2) Decentralized Autonomous Organizations (DAOs) for transparent, expert-driven oversight and risk assessment; and (3) "Ethical Quarantine" protocols that allow for the temporary isolation of high-risk knowledge while ensuring the parallel development of defenses. We argue that such a framework transforms the Knowledge Graph from a passive repository into an active immune system for civilization, capable of identifying and neutralizing threats at the speed of discovery. This paper provides a technical and organizational blueprint for building safety into the very fabric of 21st-century science.

Chunyang Li, Hongzhou Chen, Wei Cai

Cryptocurrency airdrops power the growth and governance of the cryptocurrency ecosystem, yet attract airdrop hunters, who coordinate wallets, script interactions, and cash out quickly, distorting metrics and fairness. Prior detection strands (heuristics/clustering, light-supervised community partitioning, and graph learning) face three fundamentals: inconsistent definitions, weak explainability, and poor cross-context generalization. We distill expert knowledge into a computable, interpretable baseline: open/axial coding of expert narratives followed by two Delphi rounds to (1) formalize a consensus, operational definition with six contrasts to regular users; (2) derive 15 measurable indicators spanning operations and fund-flow, tempered by human-ness counter-evidence; and (3) report thresholds as reference distributions (medians, quartiles). The baseline supplies shared semantics and computation for labeling/evaluation, yields inspectable why-flagged rationales for audit and governance, and offers context-aware guidance across chains, campaign designs, and market phases, thereby strengthening on-chain security while informing the design of socio-technical systems perceived as fair, trustworthy, and resistant to strategic misuse.