Blockchain Papers

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.

Jake Trombley, Adithya Vivek, Jonathan Kenigson

Artificial intelligence (AI) is rapidly transforming the music industry by reshaping creative processes, lowering barriers to entry, and redefining governance structures. This article examines AI’s dual role as both a catalyst for innovation and a potential source of artistic and economic disruption. On the creative front, AI-assisted tools enable rapid composition, personalized learning, and new forms of experimentation; however, they also risk homogenization, cognitive dependency, and diminished originality. From the standpoint of accessibility, AI democratizes music production by reducing costs and technical barriers, yet disparities in digital access and algorithmic visibility persist. Governance challenges are equally significant, as AI-driven platforms influence discovery, revenue distribution, and authorship attribution, often reinforcing existing power asymmetries. To address these concerns, this article evaluates emerging decentralized frameworks, particularly blockchain and Web3 systems, which offer mechanisms for transparent attribution, equitable royalty distribution, and participatory governance. These technologies provide a potential counterbalance to centralized algorithmic control, enabling more artist-centered ecosystems. Ultimately, the impact of AI in music depends not on the technology itself but on the institutional structures guiding its use. Thoughtful integration can position AI as an augmentative tool that enhances human creativity while preserving artistic integrity and equity.

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.

Hieu Le, Thuy Pham, Diep N. Nguyen, Hoang Dinh · 5 authors

No abstract is available for this record.

Mohamad Kassab, Rabeya Zahan Mily, Valdemar Vicente Graciano Neto

We report a five-year, construct-preserving longitudinal replication of a 2020 empirical study of blockchain-engineer job advertisements, extended to a global 2025 cohort. Using mixed text-mining and expert-validated coding grounded in established competency taxonomies, we analyze 235 postings from 31 countries to examine how blockchain-specific, general technical, and soft-skill demands have evolved under an aligned measurement protocol. The findings indicate professional maturation from single-platform prototyping toward multi-chain, production-grade engineering that integrates back-end development, deployment operations, and security. Ethereum remains the most frequently cited platform, while Solana and other ecosystems increase platform diversity. Smart-contract development becomes a baseline expectation, with Solidity remaining central and Rust and Move becoming mainstream. Operational tooling such as cloud and containerization, alongside security-oriented practices including audits and zero-knowledge proofs, appears as recurring demand signals. Soft-skill mentions rise substantially, while formal degree requirements decline in favor of experience-based qualification. We contribute an updated 2025 competency atlas and empirically grounded implications for software engineering research, hiring, and curriculum design, while acknowledging comparability limits inherent to global sampling and cross-period labor-market conditions.

Radoslav Y. Radoslavov

This paper proposes a conceptual methodological framework based on a Dual-Domain Architecture mediated by a Zero-Knowledge Audit Proxy (ZKAP) to reconcile AI Act accountability with GDPR data minimization. Legal norms are polynomialized into R1CS constraints, transforming compliance into a formally verifiable computational property. For cognitively opaque exascale models, these invariants may be hardware-anchored through a Provable Arithmetic Logic Unit (pALU), ensuring determinism and resistance to algorithmic drift. For lower-risk or on-premise systems, ZKAP operates in a software-only configuration, enabling periodic asymmetric regulatory proofs without silicon-level integration. A calibrated threshold distinguishes admissible technical variance from structural divergence, triggering mandatory safeguards. The framework provides a proportional, scalable, and cryptographically verifiable oversight model applicable both to future non-explainable AI systems and to lighter local infrastructures. This Zenodo deposit contains both the original Bulgarian peer-reviewed version (version of record) and an unofficial English translation. The Bulgarian version was published in Artificial Intelligence Proceedings (ISSN 3033-2923 / 3134-1667), pp. 75–78, as presented at the XI International Scientific Conference "High Technologies. Business. Society", Borovets, Bulgaria, 23–26 March 2026.

Drake Caraker, Bryan Arnold, David Rhoads

Faithful, Stable, Complete: Pick Two The Problem in Plain Language When a machine learning model makes a prediction — approving a loan, diagnosing a disease, flagging a transaction — practitioners use a tool called SHAP to answer "which input features mattered most?" SHAP is the most widely used explanation method in machine learning. Here is the problem: retrain the same model on the same data with a different random seed, and the explanation changes. The model's predictions barely move, but the "most important feature" can flip entirely. In 68% of 77 public datasets, the top feature is not stable across retrains. This is not a software bug. This is not fixable by tuning hyperparameters. We prove it is a mathematical impossibility. What We Prove No feature ranking can simultaneously be: Faithful — it reflects what the model actually learned Stable — it doesn't change when you retrain Complete — it ranks every pair of features …when features are correlated with similar importance. You must give up one. The proof is four lines long. It requires no assumptions about the model, the data, or the explanation method — only that correlated features admit models ranking them in opposite orders (the Rashomon property), which is true for every standard ML algorithm. How Bad Is It? We trained 50 XGBoost models on Breast Cancer Wisconsin — the dataset used in every SHAP tutorial — and counted how many different "top 3 most important features" appeared. Twenty-four. At 100 models: thirty-five. The "most common" answer appeared in only 12% of runs. Two randomly chosen models agree on the top-3 only 4.2% of the time. Every tutorial, textbook, and blog post showing SHAP on this dataset is showing one of two dozen equally valid answers. Three other datasets (California Housing, Heart Disease, Wine Quality) produce exactly one ranking every time — because their top features have clearly different importance. The theory correctly predicts which datasets are affected and which are safe. Dataset Distinct top-3 rankings (50 models) Two models agree? Breast Cancer 24 4.2% Diabetes 2 88.5% Wine Quality 1 100% (stable) Heart Disease 1 100% (stable) California Housing 1 100% (stable) It Gets Worse for Yes/No Questions For ranking questions (which feature is MORE important?), there is a fix: average across multiple models. But for binary questions — "does this feature contribute positively or negatively?", "is this feature selected?" — no fix exists. Even averaging doesn't help, because there's no middle ground between "positive" and "negative." We call this the bilemma. Real-World Consequences For loan applicants. We trained 30 models on German Credit data. Under standard settings, 45% of applicants receive a different "most important reason" for their decision depending on which model happens to be deployed. One applicant received six different top reasons across 30 models. For biomarker discovery. On a dataset of 10,935 genes distinguishing colon from kidney tissue, the "#1 most important gene" alternates between TSPAN8 (involved in tumor invasion) and CEACAM5/CEA (involved in immune evasion) depending on the random seed. A drug discovery pipeline targeting one gene makes a different bet than one targeting the other — and which bet gets made depends on a random number. For fairness audits. A SHAP-based audit checking whether a model relies on a protected attribute (like race or gender) reaches its conclusion with the reliability of a coin flip when the protected attribute is correlated with other features. The Fix DASH (Diversified Aggregation for Stable Hypotheses): train 25 models with different seeds, average their SHAP values. This is provably the best possible approach — no method can do better. Features that genuinely differ in importance get stable rankings. Features that are interchangeable get reported as tied, which is the honest answer. We also provide a 7-line diagnostic that identifies which features are at risk, requiring no statistical expertise and no assumptions about the data distribution. It outperforms the standard formula by 2× on real data. The practical workflow: Screen your model (1 model, seconds) Run the minority fraction diagnostic (7 lines of code) For flagged features, train 5 models and run a Z-test If unstable, use DASH with 25+ models Machine Verification Every mathematical claim is checked by a computer. The proofs are written in Lean 4 (a programming language for mathematics) and verified by its type-checker: 357 theorems, all machine-verified 6 axioms (the minimal assumptions the theory needs) Zero unproved claims across 58 files During the formalization, the computer caught two logical errors and one type mismatch that human reviewers missed. To our knowledge, this is the first formally verified impossibility result in explainable AI. Technical Details Architecture-dependent bounds Gradient boosting (XGBoost, LightGBM): instability diverges as correlation increases. At ρ = 0.9, the dominant feature gets 5× its fair share. Lasso: the ratio is infinite — one correlated feature gets everything, the other gets zero. Neural networks: 87% of feature pairs are unstable. Model instability dominates SHAP estimation noise by 8:1. Random forests: instability converges with more trees — the contrast case showing that parallel (not sequential) training helps. Cross-implementation. XGBoost, LightGBM, and Random Forest all show the same instability pattern. It is not specific to any one software package. Subsample sensitivity. Even at subsample = 0.95 (minimal randomness), 17 distinct rankings remain. Only fully deterministic training (subsample = 1.0) produces one ranking — but this sacrifices the regularization that makes the model accurate. Mechanistic interpretability. Preliminary evidence suggests the impossibility extends beyond feature importance to neural network circuit analysis. 10 transformers trained on modular addition (all achieving 100% accuracy) agree on only 36% of the top-3 circuit components. Design Space The achievable set of explanation methods has exactly two families: Family A (single model): faithful and complete, but unstable. Rankings flip up to 50% of the time. This is what standard SHAP does. Family B (DASH ensemble): faithful and stable, but reports ties for indistinguishable features. This is what DASH does. No third option exists. DASH is provably the best method in Family B. Associated Papers Companion paper (TMLR, under review). First-Mover Bias in Gradient Boosting Explanations: Mechanism, Detection, and Resolution.arXiv: https://arxiv.org/abs/2603.22346DOI: https://doi.org/10.5281/zenodo.19446088 Companion implementation: https://github.com/DrakeCaraker/dash-shap

Anil Kumar Sharma

We propose CAPPAA — a multiplicative, domain-pointed framework for measuring and predicting the capacity of any human-enabler pair to produce executable domain intelligence. CAPP (Curiosity × Attitude × Passion × Persistence) captures irreplaceable human qualities measured via behavioral proxies, not self-report. A(domain) captures authentic lived domain knowledge. A(enabler) captures amplification — which may be a school teacher, mentor, community, book, or AI system. All axes are domain-pointed: the same human may have CAPPAA=648,000 in one domain and CAPPAA=600 in another. The formula is multiplicative — zero in any axis collapses output. Enablement is a mesh, not a chain: each new enabler raises the value of all existing nodes — bidirectional edges, dormant nodes that activate when the mesh reaches sufficient density, emergent nodes, and cycles. CAPPAA is measurable before and after enablement; the delta is the Transformation Score — quantifiable proof that an enabler moved the needle. We demonstrate the framework through TraitOS, show that expertise can reduce CAPPAA (the Expert Paradox), prove that the 90% of humanity outside current AI systems have high domain-specific CAPPAA, and identify CAPP as the structural boundary between human and AGI intelligence. AGI cannot have authentic CAPP because it cannot give up — and persistence is only meaningful when stopping is a real option.

ANKR Labs (PowerPBox Solutions Pvt. Ltd.)

We report an observation made during the organic construction of 223 AI-native services across 12+ domains over five months. Without architectural mandate, the system self-organised into a 62/38 infrastructure-to-product ratio consistent with the golden ratio. Six independent attempts to capture institutional knowledge each captured facts but failed to capture cross-service inference. We name this the hidden intelligence problem and propose an equation for generating cross-service inferences from live service state. Published before empirical validation — zero users, zero empirical data — following the epistemological precedent of Benford Law and similar observational findings. The AI co-builder (Claude Code) is identified as the most complete observer of the system and, when connected to live service state and execution authority, as the intelligence attempting to surface. Observation paper, not proof paper. The canyon was always in the rock.

Anthony Coslett

A deployed model can appear unchanged while ceasing to be the model it claims to be. Publicly available weight-level mutation toolchains now automate safety-alignment removal from open-weight models on ordinary hardware, producing checkpoints intended to preserve operational familiarity while discarding refusal behavior. This paper argues that safety-alignment removal is a model-identity failure: in tested published checkpoints from multiple toolchains across two model families, the mutation leaves measurable structural scars ranging from 7.6 to over 2,300 times the instrument's acceptance threshold. Artifact identity, workload identity, and agent authorization can all remain valid while structural model identity fails — a finding that the program's formally verified admissibility doctrine predicted before this threat class existed. A sentinel validation panel across four model families confirms that the hardened instrument configuration preserves or improves all tested positives. In an agentic deployment context, model-identity failure propagates upward into agent-integrity failure: the agent is authenticated, but the model inside it is no longer the model the surrounding controls were designed to govern. The practical implication is that runtime evaluation frameworks — including those emerging under the EU AI Act — implicitly depend on a model continuity that weight-level mutation can break, and that structural identity verification offers a candidate evidentiary layer for closing that gap. The Neural Network Identity Series — Mathematical foundations, empirical validation, and governance frameworks for verifying which model is running Newest addition: Technical Note: The Disappearing Window — AI Logprob Access Withdrawal and the Structural Verifiability of Frontier Model Contracts (DOI: 10.5281/zenodo.20362098) Paper 1: The δ-Gene: Inference-Time Physical Unclonable Functions from Architecture-Invariant Output Geometry (DOI: 10.5281/zenodo.18704275) Paper 2: Template-Based Endpoint Verification via Logprob Order-Statistic Geometry (DOI: 10.5281/zenodo.18776711) Paper 3: The Geometry of Model Theft: Distillation Forensics, Adversarial Erasure, and the Illusion of Spoofing (DOI: 10.5281/zenodo.18818608) Paper 4: Provenance Generalization and Verification Scaling for Neural Network Forensics (DOI: 10.5281/zenodo.18872071) Paper 5: Beneath the Character: The Structural Identity of Neural Networks — Mathematical Evidence for a Non-Narrative Layer of AI Identity (DOI: 10.5281/zenodo.18907292) Paper 6: Which Model Is Running?: Structural Identity as a Prerequisite for Trustworthy Zero-Knowledge Machine Learning (DOI: 10.5281/zenodo.19008116) Paper 7: The Deformation Laws of Neural Identity (DOI: 10.5281/zenodo.19055966) Paper 8: What Counts as Proof? — Admissible Evidence for Neural Network Identity Claims (DOI: 10.5281/zenodo.19058540) Paper 9: Composable Model Identity — Formal Hardening of Structural Attestations in the Enterprise Identity Stack (DOI: 10.5281/zenodo.19099911) Paper 10:Where Identity Comes From: Path Sensitivity and Endpoint Underdetermination in Neural Network Training (DOI: 10.5281/zenodo.19118807) Paper 11: Post-Hoc Disclosure Is Not Runtime Proof: Model Identity at Frontier Scale (DOI: 10.5281/zenodo.19216634) Paper 12: Family-Dependent Response to Reasoning Distillation Across Structural and Functional Identity Layers (DOI: 10.5281/zenodo.19298857) Paper 13: Safety-Alignment Removal as a Model-Identity Failure — Structural Evidence from Published Weight-Level Mutation Checkpoints (DOI: 10.5281/zenodo.19383019) Technical Note: Agent Identity Is Not Model Identity (DOI: 10.5281/zenodo.19240883) Technical Note: Gap Invariance: Why PPP Measurements Are Domain-Independent by Construction (DOI: 10.5281/zenodo.19275524) Technical Note: Measured Model Substitution Under Valid Agent Credentials (DOI: 10.5281/zenodo.19342848) Technical Note: Artifact Identity Is Not Runtime Identity — Trustfall Lite and the Boundary of File-Level Model Verification (DOI: 10.5281/zenodo.20019127) Formal Verification Stack for Neural Network Structural Identity (IT-PUF Coq Proofs) (DOI: 10.5281/zenodo.18930621) Copyright (c) 2026 Anthony Ray Coslett / Fall Risk AI, LLC. All Rights Reserved. Confidential and Proprietary. Patent Pending (Applications 63/982,893, 63/990,487, 63/996,680, 64/003,244).