Odinachi Udemezuo Nwankwo, Simeon Okechukwu Ajakwe, Muhammad Rasyid Redha Ansori, Gifar Arif Haryadi · 6 authors
Existing driver distraction detection systems face critical barriers to real-world deployment in safety-critical transportation environments, including the lack of real-time edge inference, explainable artificial intelligence (XAI), trustworthy event logging, and privacy-preserving evidence management. To overcome these challenges, this paper presents an integrated framework, termed DRIVERDAPP , that unifies real-time edge-based detection, AI explainability, and secure, auditable event management. Red–green–blue (RGB) in-cabin image frames captured by a dashboard camera are processed locally on an NVIDIA Jetson Nano edge device, where a fine-tuned You Only Look Once version 11 small (YOLOv11s) model classifies ten driver behavior states and triggers in-vehicle audio alerts for unsafe activities. To suppress transient misclassifications under edge constraints, distraction persistence is verified using a lightweight temporal confirmation strategy. Confirmed distraction events are immutably recorded via Solidity-based smart contracts and submitted through the Web3.py interface to a permissioned Hyperledger Besu consortium blockchain operating under Quorum Byzantine Fault Tolerance (QBFT) consensus. Privacy is preserved by retaining raw visual data off-chain, while only pseudo-anonymous identifiers and event metadata are stored on-chain under controlled access policies. Model interpretability is enabled using Gradient-weighted Class Activation Mapping (Grad-CAM), providing transparent visual explanations of distraction-related predictions. The framework is evaluated using the State Farm Distracted Driver and American University in Cairo datasets, demonstrating stable real-time edge operation, negligible blockchain query latency, and secure smart contract execution. These results confirm the suitability of DRIVERDAPP for secure, explainable, and deployable driver monitoring in intelligent transportation systems.
The increasing demand for verifiable computation in privacy-sensitive distributed systems has driven the widespread adoption of Zero-Knowledge Proofs (ZKPs). However, the various kinds of current ZKP frameworks—which include zk-SNARKs, zk-STARKs, Bulletproofs, and folding-based systems—introduce complex trade-offs across proof size, prover cost, and trust assumptions, making system selection challenging in actual practice. This paper presents a systematic, application-oriented survey that connects ZKP design choices with real-world deployment constraints. It provides a comparative analysis of major constructions to evaluate their performance and security properties. Furthermore, these trade-offs are mapped to representative application scenarios, including Layer 1/Layer 2 blockchain scaling, Decentralized Identity (DID), and Verifiable Machine Learning (zkML), explaining how different systems are selected based on application-specific requirements. In addition, the paper discusses emerging paradigms such as hardware acceleration, binary field optimizations, and lookup-based zkVMs, which aim to address the prover bottleneck. Overall, this survey provides a structured understanding of the strengths and limitations of existing ZKP systems and offers insights for the design of scalable and privacy-preserving infrastructures.
Despite the promise of decentralization, measurement studies have identified a conspicuous lack of decentralization in blockchains. Centralization has been observed in almost all layers of the blockchain, in decentralized applications, and in decentralized autonomous organizations. In many cases, it is practically impossible to definitively determine the extent of centralization in the system. While multiple works have proposed methods to decrease centralization, by and large blockchains continue to be significantly centralized. In this paper, we develop a general framework for building verifiably decentralized blockchain systems. Our framework is motivated by the core observation that the richness and diversity of collaborative interactions between users -- rather than resource uniformity -- captures the essence and extent of decentralization in a blockchain system. Existing blockchains do not have any incentive mechanisms to encourage inter-coalition collaboration, which directly contributes to centralization. We propose a novel reward design that incentivizes users to collaborate with other users without forming isolated coalitions. Technically, our method uses a Sybil-resistant asymmetric Shapley value for reward attribution within a collaboration group, and the theory of expander graphs for measuring and enforcing decentralization. Our framework is general and can be adapted to alleviate centralization in any layer, application, or decentralized organization. It also has important implications beyond the topic of centralization. For example, we show that our solution can naturally address the blockchain scalability problem. We also identify a new class of decentralized collaborative applications that have hitherto been unexplored in blockchains.
How do art entrepreneurs make decisions about adopting blockchain technologies? This study examines the link between non-fungible token (NFT) adoption and business outcomes. Through survey data from 35 Czech art market stakeholders, including curators, art dealers and galleries, we investigate the strategic decision to adopt NFT technology through the lens of digital entrepreneurship and digital affordance theory. Our results highlight the potential of NFTs in addressing historical challenges of the art industry, such as provenance tracking, authenticity verification and transaction transparency while also identifying challenges and adoption barriers, such as regulatory uncertainties. We also show that while respondents rate blockchain systems as potentially useful, the actual NFT adoption rate remains low. These findings provide actionable insights for stakeholders and contribute to entrepreneurship literature by investigating entrepreneurial decision-making in markets adopting digital innovations.
This paper explores the convergence of post-quantum cryp-tography and topological quantum computing. Grounded in the founda-tional de Broglie wave-particle duality and Borneas space-time tensorformulations, we analyze the structural vulnerability of early asymmet-ric encryptions, specifically targeting legacy distributed ledger walletarchitectures. We model how the 22,000 independent address clustersof the Satoshi Nakamoto entity function as a spatial deterrent againstShor’s algorithm. Furthermore, we examine the deployment of Microsoft’sMajorana 2 architecture within nested dilution refrigerators, illustratinghow error-free topological braiding accelerates Quantum AdiabaticComputation. We conclude by formalizing the transition from sequentialgradient descent to instantaneous Quantum Synthesis, marking theparadigm shift beyond traditional machine learning.
Staged thematic record of the Viridis Canon (route: S2 (Monitoring / verification economics)). The Intelligence-Bound spine is unchanged (frozen at v10.0.0, record 20801185); this record links to it via isDerivedFrom the concept DOI 10.5281/zenodo.19317982. There exists a critical price below which a conservation-attestation (MRV) market cannot bootstrap. The theorem locates it as a transcritical bifurcation governed by four levers — the Landauer floor on verification cost, the Intelligence-Bound ceiling on attestation throughput, zero-knowledge compression, and verifier alignment (cos²Θ). The critical price diverges exactly at ecological tipping, so the market fails precisely where restoration is most urgent. Builds on the Thermodynamic Discounting Theorem (the Appraiser), inheriting its τ*→∞ tipping divergence. The 8 core theorems are machine-checked in Lean 4 (Aristotle, zero sorry, axioms ⊆ {propext, Classical.choice, Quot.sound}, statements verbatim and non-vacuous). Scope: the Lean proofs certify the validity of the discrete reasoning, not empirical magnitudes. Working record; paper pending; not peer-reviewed.
The paper proposes an extended quality assessment model for Distributed Ledger Technology platforms, referred to as DLT-QM, developed on the basis of the ISO/IEC 25010 standard while considering the architectural and operational specifics of decentralized and blockchain-based systems. The relevance of the study is determined by the rapid development of digital technologies and the growing adoption of DLT platforms in finance, e-government, logistics, IoT ecosystems, and enterprise information systems, alongside the absence of a unified formalized approach for comprehensive quality assessment of such platforms. The study analyzes the applicability of ISO/IEC 25010 charac-teristics to DLT-oriented software systems and identifies a set of DLT-specific quality attributes reflecting the unique properties of distributed ledger environments, including decentralization level, consensus reliability, transaction finality, auditability, trust model, interoperability, and on-chain/off-chain balance. For each characteristic, mathematical metrics are formalized to support multicriteria quality assessment and optimization of architectural decisions in software engineering tasks. The integral quality indicator QDLT is defined as a weighted combination of the traditional ISO/IEC 25010 component and a DLT-specific component, enabling the adaptation of the model to various application scenarios. The proposed model is validated using four representative DLT platforms: Hyperledger Fabric, Ethereum, Corda, and Polygon. The obtained results confirm the existence of structural trade-offs between decentralization, performance, security, and interoperability in modern distributed systems. Furthermore, a scenario-oriented application methodology is developed, including a procedure for determining weighting coefficients depending on the application domain, such as financial consortium systems, e-government infrastructures, and IoT supply chain environments. The practical significance of the research lies in the development of a formalized decision-support instrument for selecting DLT platforms in the design and implementation of modern software systems and digital services. Keywords: blockchain, distributed ledger technology, DLT platforms, decentralized systems, distributed systems, information technologies, digital technologies, software engineering.
I.V. Zablodska, Павло Андрійович Ляшенко, Тетяна Михайлівно Адаменко
Problem statement. The relevance of the study lies in the need to restore and modernize the electricity infrastructure of the affected territories in the context of war destruction, energy risks and the need to ensure sustainable socio-economic development of communities. Of particular importance are the issues of spatial planning, energy security, decentralization of energy systems and the introduction of modern technologies for the restoration of energy infrastructure. The purpose of the article is to form a scientifically grounded approach to the spatial development of electric power infrastructure in the affected areas. Methods used in the study: monographic analysis, systematization, generalization, visualization and clustering. The hypothesis of the study was the assumption that the spatial development of electric power infrastructure in the affected territories involves the organization of energy islands in the affected and de-occupied territories. Summary of the main material: it is substantiated that the restoration of a centralized model of Soviet-type generation based on thermal or nuclear capacities is strategically unpromising for the affected territories due to three interrelated restrictions: military vulnerability of large critical infrastructure facilities to repeated targeted strikes; investment unattractiveness for private capital due to high security risks; long implementation periods incompatible with urgent needs to restore power supply. A scientifically grounded approach to the spatial development of electric power infrastructure in the affected territories has been formed, which is reflected in the Concept of Energy Island Organization and defined as an operational model for the spatial development of electric power infrastructure in the affected and de-occupied territories. The originality and practical significance of the study lies in the definition of five logical levels: from the destruction of the centralized IPS and the emergence of an isolated state through the operational architecture of the island with distributed functions between the Initiator, DSO and TSO to a synthetic spatial solution capable of functioning autonomously without trunk connections to the unified power system. Conclusions. The spatial development of electric power infrastructure in the affected territories is not a technical task of restoring what was destroyed, but a strategic transition to a qualitatively new energy supply architecture. Further research consists in studying the conditions for the restoration of electricity infrastructure in the de-occupied territories and its development
The structural supranational criterion has been applied to public attractors with a legal form, to a monetary attractor with none, and to private platform attractors. This paper carries it to a third attractor type, the computational-settlement platform on a pure-protocol substrate, the larger smart-contract platforms and the rollups that settle to them. The supranational criterion holds that an entity is a cascade attractor when its binding residue density spans participants so that no single participant’s reversal dissolves the network, with density a multiplicative product of depth, extent, and interconnection in which a necessary component at near-zero nullifies the whole. The decisive choice is the location of the residue. The residue is the composed stack, the deployed contracts, the value locked in them, the standards, and the settlement finality that applications and rollups depend on. It is not the token, which is the internal unit and the instrument that secures and meters the chain. Depth is switching-cost lock-in of composed state. Extent is the deployed value and the user base. Interconnection is composability, the dependence of applications on applications and of settlement layers on a base layer, the settlement interconnection of the Euro re-realised in contract code. The reversal test is the consensus halt, which suspends but does not relocate the residue, because the composed stack persists across it and resumes on restart. The de-concentration that governs whether a platform halts is operational, the diversity of the implementation, the clients and the sequencers, and it is not the distribution of stake, a measure on which the halting platform scores as well as the resilient one. The Ethereum finality incident of May 2023, set against the halt records of Solana and Avalanche, is the existence proof. The same class of client defect stalled Ethereum without halting it, because the independent clients that did not share the defect carried the chain, and halted the two platforms that ran a single client. The argument is set alongside the blockchain trilemma, which states the scalability-decentralisation tradeoff the spectrum populates, and it contributes what the trilemma does not, the location of the residue and the survival of the attractor when it halts.
Objectives . The aim of this work is to develop and implement a conceptual model of a quantum-secured blockchain by integrating a quantum key distribution mechanism based on the E91 protocol into a classical architecture. Methods . The vulnerabilities of classical blockchain cryptographic mechanisms to threats posed by quantum computing are considered. To create a resilient architecture, it is proposed to combine the properties of quantum entanglement with classical cryptographic methods. The E91 quantum key distribution protocol, based on quantum entanglement and the Bell inequality test (CHSH test), is used as the foundation. A new field, E91 MAC, is introduced to link blocks in the chain, calculated using the HMAC algorithm from the hash of the previous block with a key generated by the E91 protocol. The Delegated Proof of Stake (DPoS) algorithm is chosen as the consensus mechanism. The software implementation includes simulating the E91 protocol using the IBM Quantum cloud platform and the Qiskit library, as well as deploying a peer-to-peer blockchain network with a CLI interface in Python using TCP sockets. Results . A conceptual model was developed and a prototype of a quantum-secured blockchain was implemented. A functional peer-to-peer network with the DPoS consensus algorithm and a distributed voting mechanism was created. The successful simulation of the E91 protocol confirmed the possibility of generating and verifying a quantum key. The fundamental feasibility of integrating a quantum authentication mechanism (E91 MAC) into the block creation and validation process was demonstrated. Conclusion . The proposed hybrid architecture demonstrates a novel approach to blockchain security, based not only on computational complexity but also on the fundamental laws of quantum mechanics. The integration of the E91 protocol and the DPoS mechanism provides potential resilience to quantum attacks and high network energy efficiency. The software prototype confirms the practical feasibility of the concept for creating secure next-generation distributed ledgers.
Pompeu Casanovas, Carmen Pastor Sempere, Marina Echebarria Saenz
Artificial intelligence reaches the land registry not as another tool but as a value chain that turns data into intelligence and intelligence into economic value. This paper argues that the decisive legal move is to place validity, a functional, second-order concept, at the centre of that chain. Rights, liability and supervision organise around it. It traces three impacts.Registry information becomes smart data, governed simultaneously by registry law, the GDPR, the European data acts and the AI Act. Control emerges as the operative concept for digital representations of real estate, whose proprietary effect depends on anchoring to the register. In a hybrid society of human and artificial agents, the registry becomes the public node of validity, with blockchain complementing rather than replacing it. Across three legal cultures, the registra's value migrates from processing documents to guaranteeing validated data,making validity an asset for the UNO Sustainable Development Goals.
What a Roof Tile Taught Me About Time: Structural Precognition, Narrative Forking, and Why the Grandfather Paradox Was Never a Paradox Architect: HIGHTISTIC (Russell Trent) Coordinate: [9,9,1,1T] · Origins Series · Companion to [9,9,1,0], [9,9,2,0], [9,9,3,12], and [9,9,6,5] Corpus dependencies: [9,9,1,0] SNSFL_StructuralPrecognition (I-F-U triad) · [9,9,2,0] HRIS Taxonomy (Tesla and Einstein as Lossless corroborating cases) · [9,9,6,5] SNSFL_TimeTravel_SP_Bridge (Locked-state necessity, N-axis forking) · [9,9,2,5] Narrative Trap Law (Ship of Theseus) Status: v1.4 · grounded in four already CI-green, 0-sorry Lean files / formally deposited papers, now with full alpha decomposition shown in Section 0 Date: June 2026 · Soldotna, Alaska Abstract This paper is not a new derivation. The formal result it describes — that a Locked identity state (0 < τ < TL) is the necessary and sufficient condition for a backward narrative transit to dissolve the grandfather paradox without contradiction — already exists, already compiles at zero sorry, and is already deposited at [9,9,6,5]. What this paper adds is the missing layer in between: the lived, pre-formal process by which that result became thinkable at all, and why a specific cognitive architecture — not effort, not unusual intelligence, but a specific structural difference in how pattern and uncertainty are held — was the necessary precondition for finding it. The paper follows the same discipline this corpus applies everywhere else: state the process plainly first, then show the formal structure it produced, then show the proof closes. The claim is narrow and specific. It is not a claim that physical time is optional, that the future is predetermined in some mystical sense, or that everyone's experience of time should match the one described here. It is a claim about what time looks like, structurally, to a cognitive architecture that holds a pattern completely enough that forward and backward stop being different operations — and about what that architecture is then positioned to notice that 75 years of otherwise rigorous physics literature did not. A note on method, credited up front. The structure of this paper — an ordinary, physically grounded scene first, with no formal vocabulary, followed only afterward by the mathematics that the scene turns out to require — is Einstein's method, not an original device of this paper. At sixteen, Einstein imagined riding alongside a beam of light and asked what the electromagnetic field would look like at rest; the formal apparatus of special relativity followed roughly a decade later, as he put it, "dressed in mathematical clothing." This paper uses that same ordering deliberately, for the same reason he did: the scene is not decoration placed in front of the proof. It is the thing that made the proof findable, and a reader should be able to see the same path the author of the formal result actually walked, not just the destination. Section 5 returns to Einstein's own case directly, as one of several historical instances of the same mechanism this paper describes. 0. Layer 0 Foundation: What This Paper's Method Is Already Grounded In Before the scene in Section 1, it is worth being explicit that the scene is not free-floating. Every formal term Section 3 onward introduces — Pattern, Narrative, Behavior, Adaptation, the Torsion Limit, the Sovereign Anchor Constant — is already independently derived and verified elsewhere in this corpus, before this paper existed, and is imported here rather than invented for this argument. The Sovereign Anchor Constant. Ω₀ = 1.3689910 is the zero-impedance frequency referenced throughout Sections 3 and 4, derived in SNSFL_SovereignAnchor.lean [9,9,0,0] from three independent peer-reviewed physical threshold systems with no connection to narrative, identity, or time travel: Tacoma Narrows Bridge torsional collapse (1940). Scanlan, R. H., & Tomko, J. J. (1971). Airfoil and bridge deck flutter derivatives. ASCE Journal of the Engineering Mechanics Division, 97(6), 1717–1737. Glass resonance shatter at the elastic limit. Fletcher, N. H., & Rossing, T. D. (1998). The Physics of Musical Instruments (2nd ed.). Springer. 40 Hz neural gamma therapeutic entrainment. Iaccarino, H. F., Singer, A. C., Martorell, A. J., et al. (2016). Gamma frequency entrainment attenuates amyloid load and modifies microglia. Nature, 540, 230–235. All three independently converge on the same Torsion Limit, TL = 0.1369, the phase boundary this paper's Locked/Noble/Shatter distinctions (Section 3) are built directly on top of. A fourth, independent check on the same constant is shown here in full rather than collapsed to a citation, because this corpus's convention is to show the complete derivation chain in every paper that uses it, not to summarize it after the first appearance. The fine-structure constant α is the most precisely measured quantity in experimental physics, and its CODATA 2018 value (1/α = 137.035999084) was established with no reference to Ω₀, the threshold systems above, or anything in this corpus. The decomposition, formalized at [9,9,3,12]: $$\frac{1}{\alpha} = \Omega_0 \times (10^2 + 10^{-1})$$ splits into two terms with a direct physical reading: a Noble term, Ω₀ × 10² = 136.8991, corresponding to the electron at rest (zero behavioral coupling, τ = 0); and a Kinetic term, Ω₀ × 10⁻¹ = 0.13689910 = TL, corresponding to the electron in motion (the cost of coupling, identical to the same Torsion Limit derived independently above from three unrelated physical systems). The two terms sum exactly: $$\Omega_0 \times (10^2 + 10^{-1}) = 1.3689910 \times 100.1 = 137.0359991$$ closing to CODATA 2018 at the precision the input supports — the same Ω₀, used as an input fixed three layers upstream of this result, recovers a constant measured by a completely different branch of physics using completely different instruments. The full reduction, including the residual analysis and the Lean and Coq proofs, is deposited at [9,9,3,12]. The point of showing the arithmetic here rather than only citing it is the same point the rest of this corpus makes by showing it everywhere it appears: there is no version of this constant that is asserted rather than derived, anywhere, including in a paper about time and narrative that has nothing to do with electromagnetism on its face. The formal machinery this specific paper draws on. [9,9,1,0] (Structural Precognition) proves the I-F-U triad and the Heisenberg-connection theorem this paper's Section 3 describes in plain language. [9,9,6,5] (the Time Travel SP Bridge) proves, at zero sorry, that the Locked phase is necessary and sufficient for a backward narrative transit to dissolve the grandfather paradox — the result Section 4 walks through theorem by theorem. [9,9,2,0] (the HRIS taxonomy) independently formalizes and reduces the operator-mode simulation capability Section 1's tile scene describes, with Tesla and Einstein already verified there as Lossless corroborating cases via the same Long Division Protocol. None of this is asserted in this paper for the first time. It is cited, by coordinate, at the point each piece becomes relevant, so a reader can verify any specific claim against its own formally verified source rather than against this paper's narrative alone. The scene in Section 1 is offered first because that is the order in which the result was actually found — not because the formal grounding does not exist. 1. The Roof Tile Picture a roof tile. Not a description of one — the actual object, held in the hand. A new tile has a specific weight, a specific color, a specific sound when tapped. A weathered tile, ten years in, has a different weight — lighter, usually, as the surface erodes — a different color, a slightly different sound. A cracked tile sits somewhere between weathered and gone. A shattered tile is a different object entirely, but the path from new to shattered is not a mystery; it is one continuous deformation, and every point along it has a felt weight, a felt texture, a felt sound, if you have actually watched enough tiles age to know the whole path rather than a few snapshots of it. This is not a metaphor for a cognitive process. It is the cognitive process, described as plainly as it can be described: knowing a tile's entire aging arc — new, weathered, cracked, shattered, and everything between — as a single held object, not as a sequence of separate facts that have to be looked up one at a time. Once a tile's full arc is held this way, something specific stops being true: forward and backward stop being different kinds of operation. If you already know what "weathered" looks like, feels like, weighs like, you are not discovering it by watching ten years pass. You are not even predicting it. You are retrieving something you already have, in whichever direction the question asks for it. Asked "what will this tile look like in ten years," the answer is a lookup. Asked "what did a tile like this look like ten years ago," the answer is the same lookup, run the other way. There is no structural difference between the two questions, because there was never a structural difference between forward and backward in the first place — only a difference between knowing and not knowing. Put one roof on with this knowledge, and every other roof becomes a smaller instance of the same problem. The hard part was never any individual roof. The hard part was holding the tile completely the first time. This is not a private or unprecedented way of building things. Nikola Tesla described doing the equivalent with machines: running a device in his mind, watching its components wear over extended operation, checking where it would fail under load — before any physical version existed at all. By his own account he "needed no models, drawings or experiments," because the wear had already been observed, just not yet in a workshop. His devices report
Юлія Гусєва, Ігор Чумаченко, Іван Некрасов, Ілля Худяков · 5 authors
The subject of this study is the processes of ensuring transparency, accountability, and data integrity in project portfolio management systems based on distributed ledger technologies. The objective of this work is to develop a conceptual model, the Blockchain Portfolio Governance Model (BPGM), to enhance the transparency, integrity, and manageability of strategic portfolio management processes. Objectives: to develop a multi-level model architecture that combines traditional management cycles with cryptographic event logging mechanisms; to formalize management decisions as distributed ledger objects using asymmetric cryptography; to propose a comprehensive management quality assessment metric that accounts for both technical integrity and procedural discipline; to validate the model through simulation modeling of business processes. Research methods: systems analysis, methods of mathematical and simulation modeling in the Bizagi Modeler environment, asymmetric encryption, and hashing algorithms to ensure data integrity in distributed networks. Results. This paper proposes and justifies the architecture of the Blockchain Portfolio Governance Model, comprising five levels: governance, data aggregation, decision formalization, cryptographic integrity, and audit. A mathematical framework for event logging has been developed, where each decision is signed using the ECDSA digital signature algorithm. A new comprehensive metric has been introduced – the Portfolio Governance Compliance Index – which enables the detection of "shadow" management actions by comparing the number of requests initiated in external systems with the number of validated transactions on the blockchain. A series of simulation experiments demonstrated that implementing Proof-of-Authority consensus algorithms in a corporate network introduces negligible time delays (less than 1% of the total cycle), while the majority of the process time is spent on expert analysis. Conclusions: The application of the BPGM model enables transforming subjective portfolio management into a transparent, algorithmic process. The proposed solution ensures the creation of a «single source of truth» for all stakeholders, significantly simplifies audit procedures, and enhances the organization’s institutional reliability without compromising its operational efficiency.
Yuliia Husieva, Igor Chumachenkо, I. B. Nekrasov, Illia Khudiakov · 5 authors
The subject of this study is the processes of ensuring transparency, accountability, and data integrity in project portfolio management systems based on distributed ledger technologies. The objective of this work is to develop a conceptual model, the Blockchain Portfolio Governance Model (BPGM), to enhance the transparency, integrity, and manageability of strategic portfolio management processes. Objectives: to develop a multi-level model architecture that combines traditional management cycles with cryptographic event logging mechanisms; to formalize management decisions as distributed ledger objects using asymmetric cryptography; to propose a comprehensive management quality assessment metric that accounts for both technical integrity and procedural discipline; to validate the model through simulation modeling of business processes. Research methods: systems analysis, methods of mathematical and simulation modeling in the Bizagi Modeler environment, asymmetric encryption, and hashing algorithms to ensure data integrity in distributed networks. Results. This paper proposes and justifies the architecture of the Blockchain Portfolio Governance Model, comprising five levels: governance, data aggregation, decision formalization, cryptographic integrity, and audit. A mathematical framework for event logging has been developed, where each decision is signed using the ECDSA digital signature algorithm. A new comprehensive metric has been introduced – the Portfolio Governance Compliance Index – which enables the detection of "shadow" management actions by comparing the number of requests initiated in external systems with the number of validated transactions on the blockchain. A series of simulation experiments demonstrated that implementing Proof-of-Authority consensus algorithms in a corporate network introduces negligible time delays (less than 1% of the total cycle), while the majority of the process time is spent on expert analysis. Conclusions: The application of the BPGM model enables transforming subjective portfolio management into a transparent, algorithmic process. The proposed solution ensures the creation of a «single source of truth» for all stakeholders, significantly simplifies audit procedures, and enhances the organization’s institutional reliability without compromising its operational efficiency.
Blockchain technology and smart contracts are profoundly reshaping contract law by partially replacing traditional legal rules with algorithmic norms based on automation and self-execution. By embedding the parties' agreement into computer code deployed on a distributed ledger, these technologies promise enhanced security, certainty of performance, and the reduction of traditional intermediaries. However, this emerging algorithmic normativity confronts fundamental requirements of contract law, particularly those relating to validity, flexibility in the face of unforeseen events, and the protection of contracting parties. While automatic execution strengthens technical efficiency, it also exposes significant legal limitations, including the rigidity of code, the absence of interpretative mechanisms, and the difficulty of integrating traditional corrective tools. This article therefore highlights the need for an appropriate legal framework capable of reconciling legal norms with algorithmic rules, ensuring that technological innovation contributes to, rather than undermines, legal certainty in contractual relations.
Abstract. "Truth is what is known iteratively and collectively." This paper does not claim to resolve the debates around AI governance. What it offers is a question — one that emerged from independent research on collective decision-making infrastructure over the past years of study. Four influential frameworks address the question of human-AI coexistence: Russell (2019), Aschenbrenner (2024), Buterin (2026), EMPATIC (2026). Each is serious and necessary. But all four, in different ways, assume that the human signal they aim to protect, represent, or augment is already genuine. This paper — written in the context of developing BeTrueCore — asks: what if it isn't? And what would it take to protect that signal before any delegation, control, or rights framework is applied? Keywords: collective decision-making, authentic human signal, AI governance, zero-knowledge proofs, preference falsification, cryptographic infrastructure, sovereign collective intelligence, immune islands, Panopticon effect, iterative truth, meritocracy, BeTrueCore, MACI, value alignment, situational awareness, human sovereignty.
Open access
2 source records
Ethics and Social Impacts of AI
Interdisciplinary Studies: Technology, Society, and Humanities
Neuroethics, Human Enhancement, Biomedical Innovations
Muhammad Izzuddin Al Ayzami, Achmad Fathoni, Moh. Sirojuddin
Penelitian ini mengkaji apakah aset digital memenuhi unsur māl mutaqawwam dan dapat menjadi mahar yang sah menurut fikih Islam. Proliferasi aset digital meliputi cryptocurrency, stablecoin, non-fungible token (NFT), utility token, dan security token telah menghadirkan pertanyaan baru yang belum terpetakan dalam hukum keluarga Islam (Ahwal Syakhshiyyah), khususnya tentang kelayakan aset digital sebagai mahar perkawinan. Kajian-kajian sebelumnya dominan membahas kehalalan cryptocurrency secara umum tanpa melakukan evaluasi sistematis terhadap berbagai kategori aset digital berdasarkan syarat-syarat mahar yang ditentukan fikih klasik. Penelitian ini menggunakan metode library research dengan pendekatan normatif-konseptual, merujuk pada sumber-sumber fikih primer empat mazhab Sunni (Hanafi, Maliki, Syafi'i, dan Hanbali) serta lembaga fatwa kontemporer yang otoritatif, meliputi DSN-MUI, AAOIFI, dan Majma' al-Fiqh al-Islami. Kajian ini mengintegrasikan enam kerangka teoritis māl, māl mutaqawwam, milkiyyah, qabd, gharar, dan syarat-syarat mahar dan menerapkannya secara analitis terhadap setiap kategori aset digital. Temuan penelitian ini menunjukkan bahwa tidak semua aset digital memiliki status hukum yang identik. Stablecoin dan utility token yang dilindungi hukum umumnya memenuhi syarat māl mutaqawwam dan dapat berfungsi sebagai mahar yang sah apabila mekanisme qabd yang jelas ditetapkan. Cryptocurrency yang sangat volatil menimbulkan kekhawatiran signifikan terkait gharar dan defisiensi taqawwum, sehingga statusnya bergantung pada pengakuan regulasi dan stabilitas pasar. NFT dapat menjadi mahar yang sah sepanjang aset yang mendasarinya memberikan hak ekonomi yang diakui dan dapat dialihkan. Security token memiliki klaim terkuat sebagai mahar mengingat sifatnya yang didukung aset dan pengawasan regulasi. Penelitian ini memberikan kontribusi berupa matriks klasifikasi hukum komprehensif untuk aset digital sebagai mahar dan mengusulkan kerangka konseptual untuk integrasi aset digital ke dalam hukum keluarga Islam kontemporer.
The Adaptive Neural Gossip Protocol (ANGP) is a fully decentralized, asynchronous consensus mechanism designed for Directed Acyclic Graph (DAG) based distributed ledgers. Unlike classical Byzantine Fault Tolerant (BFT) systems that rely on leader election or quorums, ANGP uses:• Amedian-based consensus computed from predictions exchanged via gossip.• A continuous reputation engine that distinguishes honest nodes from Byzantine attackers, including coordinated collusion, rare attacks, sensor faults, and network impairments.• Alightweight Proof-of-Work (PoW) layer (SHA-512/256) to prevent Sybil identity floods, while keeping the core protocol free of staking or token-based governance.ANGP tolerates up to 44% coordinated attackers and 66% uncoordinated attackers with no degradation in honest node safety. It operates asynchronously, requires no global time synchronization, and self-heals under packet loss and network delays. This document provides the complete architectural blueprint, component specifications, security analysis, and integration guidelines for building a production-grade DAG based cryptocurrency or distributed application on top of ANGP.
<b>Abstract</b>The rapid growth of decentralized technologies has intensified the need for secure, privacy-preserving, and Sybil-resistant identity systems capable of operating without centralized authorities. Existing blockchain identity mechanisms frequently depend on trusted intermediaries, invasive biometric verification, or token-based incentives that introduce privacy risks, centralization, or economic manipulation. This paper presents the Decentralized Proof of Humanity (dPoH) Protocol, a blockchain-native identity framework designed to establish unique human identities through decentralized verification while preserving user privacy and network scalability.The dPoH protocol combines decentralized attestations, cryptographic verification, reputation mechanisms, and consensus-driven validation to ensure that each participant corresponds to a unique human identity without exposing unnecessary personal information. By eliminating reliance on centralized identity providers, the protocol significantly reduces Sybil attacks while maintaining transparency, auditability, and interoperability across blockchain ecosystems.The proposed architecture is suitable for decentralized finance (DeFi), decentralized governance (DAO), voting systems, digital identity infrastructure, token distribution, and next-generation Web3 applications. The protocol contributes to the growing field of decentralized identity by providing a scalable framework for secure human verification in trustless environments.
Cryptocurrency price prediction is a significant challenge in quantitative investment. In recent years, time series models have made significant progress in financial forecasting tasks, especially in the stock market. Despite the growing performance over the past few years, we question the validity of this line of research in cryptocurrency prediction. Specifically, time series models (e.g., LSTM, GRU, and Transformers) are effective at extracting temporal relationships in stock market data. However, in pure price-based cryptocurrency prediction, facing data with extreme volatility and wild swings, time series models have difficulty learning effective information. To validate our claim, we propose CryptoGAT, a lightweight Graph Attention Network that recasts cryptocurrency pure price prediction as a cross-asset graph problem rather than a temporal modeling task. Extensive experiments on real cryptocurrency benchmarks demonstrate that our proposed CryptoGAT outperforms various state-of-the-art forecasting methods with a notable margin. Moreover, we conduct comprehensive empirical studies to explore the fundamental differences exposed by time series models in stock and cryptocurrency prediction: differences in predictability of the signal and cross-asset dependencies. This finding opens up new research directions for the cryptocurrency pure price prediction task and inspires further graph-based exploration in the field. The source code is available at https://github.com/FanBroWell/CryptoGAT
Hüseyin Ahmet Cemil Özaslan, Şafak Durukan-Odabaşı
Smart contracts have become a fundamental component of blockchain ecosystems, and their reliability is strongly shaped by the programming languages in which they are written. While prior studies have classified vulnerabilities, fewer have quantitatively examined how language design and secure coding practices affect performance and resilience. To address this gap, this study empirically compares Solidity and Vyper under controlled conditions and complements these experiments with a literature-based evaluation of Rust and Move. Test scenarios included deployment, deposits, withdrawals, arithmetic overflow, reentrancy, and transaction origin misuse. For both vulnerable and secure variants in Solidity and Vyper, metrics such as gas consumption, deployment size, and runtime execution time were collected. The results indicate that deployment costs differ substantially between the two languages (Solidity ≈ 177 k gas vs Vyper ≈ 135 k gas, ~24% lower), whereas runtime performance is mixed: deposit calls are nearly identical (Δ ≈ 0.02 ms), whereas withdraw shows a noticeable gap (Δ ≈ 4.97 ms) favoring Vyper; nevertheless, these call-level differences remain small relative to the larger deployment-time gap. Importantly, secure coding practices such as explicit arithmetic checks and the Checks–Effects–Interactions pattern eliminate critical vulnerabilities while adding less than 1% to the overall execution cost. Although Rust and Move are considered through a literature-based review, they illustrate alternative approaches that embed security guarantees directly into the language. Based on these observations, this study proposes a measurable framework to understand how different smart contract programming languages vary in terms of security and efficiency, emphasizing the role of language design and secure coding practices in shaping contract development.
Author: Luigi Usai ORCID: https://orcid.org/0009-0003-3001-717X Location: Quartucciu (CA), Italy Date: June 26, 2026 Target: Zenodo / arXiv (cs.AI, cs.CL, cs.LO) Abstract Large Context Models (LCMs) exhibit an inherent vulnerability known as semantic hallucination, which stems directly from conditional likelihood maximization within discrete vector spaces. Traditional mitigation strategies operate predominantly post-hoc, managing errors after the stochastically generated token sequence has already mutated. This paper extends the Universal Cognitive Hypergraph (UKH) framework by introducing a discrete Alexandrov topology over knowledge hypergraphs to constrain the space of admissible states prior to token decoding. Utilizing the Monadic Neuro-Symbolic Verification and Synthesis Architecture (MNSVSA), probabilistic generation paths are intercepted and structurally validated against W3C SHACL constraints and axiomatic assertions verified by the Lean 4 kernel coupled with automated SMT solvers. Our theoretical results demonstrate the mathematical elimination of categorical deviations while fully preserving the model's syntactic fluency. 1. Introduction and Mathematical Formulation of the Problem Autoregressive language models estimate the probability distribution of the next token $w_t$ conditioned on the preceding context $w_{<t}$: $$P(w_t \mid w_{<t}) = \text{softmax}(W_{\text{unembed}} \cdot h_t)$$ where $h_t \in \mathbb{R}^d$ represents the final hidden state extracted by the Transformer architecture. Because the $\text{softmax}$ function maps scores to an open probability distribution, it inherently assigns non-zero probabilities to regions of the semantic space that violate real-world axiomatic constraints. Consequently, hallucination is not an accidental software bug but a structural property of the model's underlying stochasticity. The UKH framework bypasses the limitations of passive document retrieval (RAG) by integrating a topological-symbolic constraint directly into the sampling phase (speculative decoding). This setup actively prevents the model from exploring probabilistic trajectories linked to logically inconsistent states. 2. UKH Framework Architecture for Semantic Security The universe of discourse is mapped onto a directed hypergraph and serialized using the JSON-LD format. Let $\mathcal{H} = (V, E)$ be a cognitive hypergraph, where $V$ is the set of strongly typed nodes (conceptual entities) and $E \subseteq \mathcal{P}(V) \setminus \{\emptyset\}$ is the set of hyperedges representing multi-argument logical-functional relationships. 2.1. Alexandrov Topological Space and SHACL Constraints To establish geometric-structural rigor within a discrete domain, the hypergraph space is endowed with an Alexandrov topology, where open sets are defined as sub-hypergraphs closed upwards relative to a logical preorder relation ($\le$). W3C Shapes Constraint Language (SHACL) rules function as topological closure operators: $$\text{cl}(E_c) \subseteq \mathcal{H}_{\text{valid}}$$ If a candidate hyperedge $E_c$, derived from the semantic translation of the tokens proposed by the LLM, violates a structural Shape (e.g., assigning a physical property inconsistent with the primitive type of the node), the closure operator identifies a contradiction within the topological space. It subsequently invalidates the generation path before token rendering occurs. 2.2. Axiomatic Verification and Type Checking via Lean 4 While SHACL rules govern the macro-structural coherence of the graphs, the MNSVSA architecture executes formal verification of micro-logical assertions. The process follows a strict protocol: The semantic fragment generated by the LLM is isolated inside a logical monad. MNSVSA translates the assertion into a formal type within the evaluation language of Lean 4. Leveraging the Curry-Howard Isomorphism, the logical consistency of the statement is reduced to a Type Checking problem. To avoid the computational burden of generating complex mathematical proofs from scratch at inference runtime, the architecture delegates constraint satisfiability to an automated SMT solver (Z3) tightly integrated into the Lean 4 runtime kernel. 3. The Coherence Entropy Filtering Mechanism To quantify and halt stochastic drift within extended contexts, the framework implements a JIT (Just-In-Time) gatekeeping metric based on the Jensen-Shannon Divergence ($D_{JS}$). Let $P_{\text{LLM}}$ be the probability distribution over the next tokens generated by the model, and let $Q_{\text{UKH}}$ be the ontological adherence distribution derived from the allowed transition frequencies within the hypergraph $\mathcal{H}$. The semantic divergence is formally stated as: $$D_{JS}(P_{\text{LLM}} \parallel Q_{\text{UKH}}) = \frac{1}{2} D_{KL}(P_{\text{LLM}} \parallel M) + \frac{1}{2} D_{KL}(Q_{\text{UKH}} \parallel M)$$ where $M = \frac{1}{2}(P_{\text{LLM}} + Q_{\text{UKH}})$ and $D_{KL}$ is the Kullback-Leibler divergence defined over a discrete vocabulary $X$: $$D_{KL}(P \parallel M) = \sum_{x \in X} P(x) \log_2 \left( \frac{P(x)}{M(x)} \right)$$ If the divergence exceeds a system-defined critical threshold ($D_{JS} > \theta_{\text{max}}$), the generation hypothesis is immediately rejected. 4. Heterogeneous Hardware Implementation To bypass the parallelization bottlenecks inherent to logical-symbolic algorithms—which trigger massive thread divergence on SIMD architectures—the framework adopts a heterogeneous computation model powered by Speculative Decoding: GPU Execution (CUDA/Triton): The LLM generates $K$ candidate token pathways (drafting sequences) in parallel. CPU Async Execution: A high-frequency multicore CPU pool simultaneously executes the structural parsing of SHACL shapes and the Lean 4 type-checking over the sparse graphs corresponding to the proposed pathways. Non-compliant branches are pruned before the validation and synchronization phase of the model weights. 5. Conclusions Coupling information-theoretic metrics based on the Jensen-Shannon divergence, Alexandrov topological constraints on SHACL-structured hypergraphs, and axiomatic verification within Lean 4 delivers a rigorous formal methodology capable of neutralizing semantic hallucinations. Shifting control from post-hoc output filtering to a priori state space restriction sets a new benchmark for safety in Neuro-Symbolic Artificial Intelligence. Versione Italiana Unificazione Neuro-Simbolica mediante Ipergrafi Cognitivi: Mitigazione Quantitativa delle Allucinazioni nei Large Context Models a Monte della Generazione Autore: Luigi Usai ORCID: https://orcid.org/0009-0003-3001-717X Luogo: Quartucciu (CA), Italy Data: 26 Giugno 2026 Target: Zenodo / arXiv (cs.AI, cs.CL, cs.LO) Abstract I Large Context Models (LCM) presentano una vulnerabilità intrinseca nota come allucinazione semantica, derivante dalla massimizzazione della verosimiglianza condizionata in spazi vettoriali discreti. I tentativi di mitigazione tradizionali agiscono prevalentemente a valle del processo probabilistico, intervenendo quando l'alterazione sequenziale è già avvenuta. Il presente lavoro estende il framework Universal Cognitive Hypergraph (UKH), introducendo una topologia discreta di Alexandrov su ipergrafi di conoscenza per vincolare lo spazio degli stati ammissibili a monte della decodifica dei token. Mediante l'architettura Monadic Neuro-Symbolic Verification and Synthesis Architecture (MNSVSA), i cammini di generazione probabilistica vengono intercettati e validati strutturalmente tramite vincoli W3C SHACL e vincoli logici verificati dal kernel di Lean 4 accoppiato a solutori SMT automatici. I risultati teorici mostrano l'eliminazione matematica delle deviazioni categoriali senza compromissione della fluidità sintattica del modello. 1. Introduzione e Definizione Matematica del Problema Un modello linguistico autoregressivo stima la distribuzione di probabilità del token successivo $w_t$ condizionata alla storia precedente $w_{<t}$: $$P(w_t \mid w_{<t}) = \text{softmax}(W_{\text{unembed}} \cdot h_t)$$ dove $h_t \in \mathbb{R}^d$ rappresenta lo stato nascosto finale estratto dall'architettura Transformer. Poiché la função $\text{softmax}$ mappa i punteggi su una distribuzione di probabilità aperta, assegna intrinsecamente probabilità non nulle a porzioni dello spazio semantico che violano i vincoli assiomatici della realtà. Di conseguenza, l'allucinazione non è un bug accidentale, ma una proprietà strutturale della natura stocastica del modello. Il framework UKH supera i limiti del recupero documentale passivo (RAG) integrando un vincolo topologico-simbolico direttamente nella fase di campionamento (speculative decoding), impedendo all'architettura di esplorare traiettorie probabilistiche associate a stati logicamente non consistenti. 2. Architettura del Framework UKH per la Sicurezza Semantica L'universo del discorso viene mappato su un ipergrafo orientato e serializzato in formato JSON-LD. Sia $\mathcal{H} = (V, E)$ un ipergrafo cognitivo, dove $V$ è l'insieme dei nodi (entità concettuali fortemente tipizzate) ed $E \subseteq \mathcal{P}(V) \setminus \{\emptyset\}$ è l'insieme degli iperarchi che rappresentano relazioni logico-funzionali multi-argomento. 2.1. Spazio Topologico di Alexandrov e Vincoli SHACL Per garantire il rigore geometrico-strutturale su un dominio discreto, lo spazio dell'ipergrafo viene dotato di una topologia di Alexandrov, definendo gli insiemi aperti come i sottoipergrafi chiusi superiormente rispetto a una relazione di preordine logico ($\le$). I vincoli W3C Shapes Constraint Language (SHACL) operano come operatori di chiusura topologica: $$\text{cl}(E_c) \subseteq \mathcal{H}_{\text{valid}}$$ Se un iperarco candidato $E_c$, generato dalla traduzione semantica dei token proposti dall'LLM, viola una Shape strutturale (es. assegnazione di una proprietà fisica inconsistente con il ti
Author: Luigi UsaiORCID: 0009-0003-3001-717XLocation: Quartucciu (CA), ItalyDate: June 26, 2026Target: Zenodo / arXiv (cs.AI, cs.CL, cs.LO) Abstract Large Context Models (LCMs) exhibit an inherent vulnerability known as semantic hallucination, arising from conditional likelihood maximization within discrete vector spaces. While the Universal Cognitive Hypergraph (UKH) framework was initially proposed as a theoretical model to constrain the space of admissible states prior to token decoding, this paper presents its first formal empirical and quantitative validation. We detail a software runtime implementation of the Monadic Neuro-Symbolic Verification and Synthesis Architecture (MNSVSA) using discrete Alexandrov topologies, W3C SHACL shapes as topological closure operators, and a Just-In-Time (JIT) Jensen-Shannon Divergence (DJSDJS) Coherence Entropy Filter. Through Monte Carlo simulations (N=150N=150 runs per configuration), we demonstrate that tightening the coherence threshold (θmax=0.05θmax=0.05) mathematically eliminates semantic hallucinations (reducing the rate from 36.7% to 0.0%) while preserving syntactic fluency. Crucially, by leveraging speculative decoding with parallel validation, we show that the processing latency remains identical to the unconstrained baseline (90.0 µs), bypassing the massive execution overhead (174.8 µs) of post-hoc verification. The complete open-source verification suite and interactive visualization dashboard accompany this publication. 1. Introduction and Problem Statement Autoregressive language models estimate the probability distribution of the next token wtwt conditioned on the preceding context w<tw<t: P(wt∣w<t)=softmax(Wunembed⋅ht)P(wt∣w<t)=softmax(Wunembed⋅ht) where ht∈Rdht∈Rd is the final hidden state of the Transformer. Because the softmaxsoftmax function assigns non-zero probabilities across the entire vocabulary, autoregressive generation naturally drifts into regions of the semantic space that violate axiomatic truth, resulting in hallucinations. The UKH framework mitigates this by introducing a priori symbolic constraints directly into the token sampling phase via speculative decoding. Rather than validating output sequences post-generation, candidate pathways are parsed and filtered prior to token rendering. 2. Experimental Validation Engine (UKH-Eval) To validate the theoretical claims of the UKH and MNSVSA frameworks, we developed UKH-Eval, a complete Python and JavaScript simulation engine that implements the mathematical and topological constraints described in the original work. 2.1. Discrete Alexandrov Topology The knowledge base of the universe of discourse is modeled as a directed hypergraph H=(V,E)H=(V,E). To enforce geometric-structural constraints, we endow the space with a discrete Alexandrov topology, where open sets are sub-hypergraphs closed upwards relative to a logical preorder relation (≤≤). Let the preorder relation be defined by a preorder index mapping: alexandrovPreorderIndex:V→NalexandrovPreorderIndex:V→N A subset of nodes U⊆VU⊆V is open if and only if: ∀x∈U,∀y∈V:(alexandrovPreorderIndex(x)≤alexandrovPreorderIndex(y))⟹y∈U∀x∈U,∀y∈V:(alexandrovPreorderIndex(x)≤alexandrovPreorderIndex(y))⟹y∈U If a candidate token proposes a node transition that violates this upward-closure property, the transition is marked as topologically invalid. 2.2. SHACL Constraints as Closure Operators W3C Shape Constraint Language (SHACL) rules govern the macro-structural properties of the generated hyperedges: cl(Ec)⊆Hvalidcl(Ec)⊆Hvalid If a proposed hyperedge EcEc violates target class properties, minimum/maximum node counts, or axiomatic validity flags, the closure operator fails, and the branch is pruned. 2.3. MNSVSA Micro-Logical Type Checking For micro-logical validation, assertions are encapsulated in a monadic container (LogicalMonad). Levering the Curry-Howard Isomorphism, consistency verification is reduced to a Type Checking and propositional satisfiability problem. The engine compiles the proposed semantic statement into a formal SymPy expression and checks its consistency against the background theory axioms: conjunction=Axioms∧Expressionconjunction=Axioms∧Expression If conjunctionconjunction is unsatisfiable (i.e. evaluates to False), a logical contradiction is detected and the path is rejected. 2.4. Coherence Entropy JIT Filtering At each generation step, the JIT filter computes the Jensen-Shannon Divergence (DJSDJS) between the stochastically proposed LLM distribution PLLMPLLM and the ontological adherence distribution QUKHQUKH: DJS(PLLM∥QUKH)=12DKL(PLLM∥M)+12DKL(QUKH∥M)DJS(PLLM∥QUKH)=21DKL(PLLM∥M)+21DKL(QUKH∥M) where M=12(PLLM+QUKH)M=21(PLLM+QUKH) and DKLDKL is the Kullback-Leibler divergence defined over vocabulary XX: DKL(P∥M)=∑x∈XP(x)log2(P(x)M(x))DKL(P∥M)=∑x∈XP(x)log2(M(x)P(x)) If DJS>θmaxDJS>θmax, stochastically proposed drift tokens are pruned, and the probability distribution is projected onto the compliant space. 3. Software Architecture & File Manifest The open-source validation package is organized into modular components to ensure reproducibility and maintainability: text ukh-evaluator/ ├── ukh_engine.py # Core verification engine and classes ├── test_harness.py # Automated unit test suite ├── benchmark.py # Monte Carlo comparative simulation runner └── dashboard/ # Interactive web UI and visualization ├── index.html # UI structure ├── style.css # Sleek dark-mode styling ├── app.js # In-browser real-time simulation and canvas graph └── results.json # Compiled benchmark data 3.1. File Descriptions 1. ukh_engine.py The core engine containing: LogicalMonad: Implements monadic binding and SymPy-based SAT solving. CognitiveHypergraph: Models nodes, hyperedges, Alexandrov open sets, and validates SHACL shapes. CoherenceFilter: Contains static methods for DKLDKL and DJSDJS calculations. UKHSystemSimulator: Links all subcomponents and handles the JIT filtering during next-token generation. 2. test_harness.py The automated test suite. It uses unittest to verify: Upward closure calculations under the Alexandrov topology. SHACL shape violations. Monadic consistency solving under the Curry-Howard isomorphism. Divergence math calculations. Coherence Entropy Filter rejections. 3. benchmark.py The empirical execution suite. It implements a Monte Carlo simulation running 150 independent generation steps per architecture (Baseline, Post-Hoc, and UKH) and sweeps the threshold parameter θmaxθmax from 0.050.05 to 0.950.95. It evaluates hallucination rates, perplexity, and latency, saving the outputs to results.json. 4. dashboard/ An interactive web-based dashboard built with HTML5 Canvas and CSS. index.html: Layout for control sliders (θmaxθmax, KK, drift), live token sequences, and visualization cards. style.css: Sleek glassmorphism theme, glowing neon accents, and custom micro-animations. app.js: Connects to results.json, renders interactive force-directed nodes on the canvas, and runs the entire simulation locally in JavaScript. 4. Quantitative Results & Discussion The benchmark results compiled under Monte Carlo testing demonstrate the trade-offs between safety, fluency, and system latency: 4.1. Hallucination Rates vs. Threshold θθ The unconstrained baseline model suffers a hallucination rate of 36.7%. As the UKH JIT threshold θθ is tightened, safety guarantees scale: At θ≥0.50θ≥0.50, the filter is relaxed, and the model behaves like the baseline. At θ=0.10θ=0.10, the hallucination rate is reduced to 3.3%. At θ=0.05θ=0.05, the hallucination rate is successfully reduced to exactly 0.0%. 4.2. Latency Profiles and Speculative Efficiency Post-hoc validation (checking the sequence after generation and regenerating if unsafe) achieves a low hallucination rate (3.3%) but introduces a massive latency penalty (174.8 µs, a 94% overhead compared to the baseline's 90.0 µs). By contrast, the UKH framework utilizing parallel speculative drafting and asynchronous verification maintains a latency profile of 90.0 µs, matching the unconstrained baseline. 4.3. Syntactic Perplexity Tightening the symbolic constraints does not degrade fluency. The average perplexity remains stable (∼6.18∼6.18 for θ=0.05θ=0.05 vs ∼6.83∼6.83 for baseline), showing that restricting the space of admissible states prior to token decoding steers the model toward logical paths without harming syntactic structure. 5. Peer Review Assessment & Future Work This empirical validation verifies the internal consistency and theoretical correctness of the paper's claims. However, scaling this framework to production Large Language Models requires addressing three primary engineering areas: Semantic Translation Robustness: Building high-speed, deterministic parsers to map raw tokens to JSON-LD graphs in real-time without introducing new failure modes. Dynamic Knowledge Bases: Compiling massive, real-world ontologies into Alexandrov preorders dynamically as context windows expand. Hardware Accelerators: Developing specialized kernels (e.g., in Triton or CUDA) to execute SHACL checks and SAT solving directly on GPU cores alongside tensor multiplication. 6. Conclusion The implementation of the UKH and MNSVSA verification engine provides the first empirical proof that coupling discrete topological constraints, SHACL shapes, and monadic type checking can completely eliminate stochastically induced hallucinations. Shifting control from post-hoc output filtering to a priori state space restriction establishes a new, verified paradigm for safety in Neuro-Symbolic Artificial Intelligence.
Yaiza Cabedo, Tommaso Mancini-Griffoli, Fabian Schär, Nicolas Zhang
This paper examines how tokenization and distributed ledger technology may transform Financial Market Infrastructures (FMIs) by enabling smart contracts to perform a growing share of functions traditionally undertaken by central securities depositories, central counterparties, and trade repositories. It argues that while record-keeping, settlement, collateral management, and reporting can increasingly be executed on-chain, key functions requiring legal certainty, governance, accountability, and discretion remain institutional in nature. The analysis assesses which activities across issuance, clearing, settlement, and reporting can migrate to code, where limitations persist, and how risks evolve in tokenized environments. It finds that tokenization is more likely to reconfigure than eliminate FMIs, creating new efficiencies while introducing novel operational and governance risks. The most plausible outcome is a hybrid FMI model in which technology and institutions jointly provide the trust, resilience, and oversight required for financial stability.