Decentralized Finance (DeFi) has suffered over $5 billion in cumulative losses from security incidents, yet the academic community lacks a large-scale, multi-source-verified dataset to systematically characterize these threats. We present DEFIHACK-824, a curated dataset of 823 DeFi security incidents spanning 2017 to 2026, cross-validated against three independent intelligence sources (Rekt News, SlowMist, and CertiK). Each record is annotated with attack category, confidence level (Gossip/Classified/Ground Truth), and estimated financial loss. We classify incidents into 14 attack categories and conduct statistical analyses: (1) flash-loan-enabled price manipulation and reentrancy together account for 51.5% of all attacks; (2) a chi-squared test rejects the null hypothesis of uniform category distribution at p < 0.0001 (chi-squared = 1,273.2, df = 13); (3) despite widespread deployment of automated detection tools, the annual attack count has not monotonically decreased. We further propose a six-layer DeFi threat model and quantify the effectiveness of four defense classes. The dataset, threat model, and 50 categorized Solidity vulnerability patterns are released under the MIT license.
Jakub Zwydak, Marcin Wątorek, Jarosław Kwapień, Stanisław Drożdż
Artificial transaction generation remains an important source of potential market manipulation on cryptocurrency exchanges, as it may distort reported liquidity and reduce market transparency. This study proposes a diagnostic framework for detecting unusual trading patterns based on complexity and statistical-structure measures derived from high-frequency trade-level data. The analysis considers log-returns, trading volume, and transaction counts, using tail distributions, autocorrelation functions, multifractal characteristics, approximate entropy, and detrended cross-correlations. The methodology is applied to BTC, ETH, and XRP traded on Binance, Bitget, KuCoin, and Kraken over the period from April 1 to June 30, 2025. The results reveal a pronounced anomaly on Bitget for BTC and ETH after mid-May 2025. The number of transactions increases sharply, but there is no proportional increase in traded volume or return fluctuations. This regime is characterised by numerous low-volume trades, weaker autocorrelations, reduced multifractal organisation, higher short-pattern irregularity, and weaker cross-correlations involving the transaction-count series. These features are consistent with a noise-like component in trading activity and may indicate artificially increased transaction counts, although they do not provide direct proof of wash trading. The findings show that complexity-based indicators can be useful for detecting exchange-specific trading anomalies that remain hidden in price-based measures.
Distributed systems deployed in untrustworthy environments agree on a common transaction order through Byzantine fault-tolerant (BFT) consensus protocols, and that order has real financial value in many decentralized applications: whoever influences it can profit at other users' expense, a problem known as maximal extractable value (MEV). Mysticeti is a state-of-the-art DAG-based BFT protocol in which many validators propose blocks in parallel, and the total order is derived from the resulting DAG afterward. Mysticeti is the consensus protocol powering Sui, a production blockchain with a market capitalization of roughly $3 billion, and it is widely believed to order transactions fairly, since many validators propose blocks in parallel and committed transactions are re-sorted by gas price before execution. We show this fairness assumption breaks down in practice, and the effect is already present on Sui's live network. First, when vertices of the committed graph are merged into a single total order, blocks from the same round are sorted by validator index, giving lower-indexed validators a permanent head start. In our evaluation on a 13-validator network with no attacker, the lower-indexed side wins same-round ordering about 89% of the time. Second, the gas-price re-sort intended to remove this bias uses a stable sort, so transactions paying equal fees (common at the reference gas price) retain the original biased order, letting an attacker profit without paying extra. Third, a validator can amplify this advantage by choosing when to stay silent, a fully legitimate action that violates no protocol rule; this raises its ordering win rate above 94%. We measure all three exploitations, verify that Mysticeti otherwise remains resilient below the standard Byzantine fault threshold, and propose a simple fix: replace the validator-index tiebreaker with an unpredictable, per-commit random key.
This paper presents a unified back‑end settlement architecture designed to support multi‑rail, ledger‑agnostic financial transactions across modern digital asset systems. It defines a deterministic settlement model capable of coordinating fiat rails, tokenized assets, distributed ledgers, and messaging networks under a single canonical framework. The architecture introduces a universal settlement core that abstracts rail‑specific behaviors into standardized primitives, enabling consistent execution, reconciliation, and finality across heterogeneous systems. It incorporates a canonical identity layer, semantic tokenization model, and compliance‑aware routing logic to ensure interoperability between traditional financial infrastructure and emerging tokenized environments. Key contributions include: A multi‑rail settlement engine supporting synchronous and asynchronous flows Deterministic finality logic for cross‑rail and cross‑ledger operations A universal bridge framework for rail‑agnostic asset movement Canonical identity mapping for participants, assets, and transaction states Semantic tokenization rules enabling unified representation of digital and traditional instruments Compliance and audit primitives embedded directly into the settlement workflow This work provides a complete architectural foundation for institutions seeking to modernize settlement operations, integrate tokenized assets, and achieve interoperability across fragmented financial rails. It serves as a reference model for next‑generation clearing and settlement systems.
Overview The paper presents a proof of the Riemann Hypothesis (RH) using Arithmetic Spectral Theory (AST), and applies it to deterministic cognitive engineering in artificial intelligence. The foundational core of this work is the realization that the first six primes—2, 3, 5, 7, 11, 13—form a unique "Pure Kernel" ($R$) that accounts for 97.85% of total spectral weight. The Three Pillars of the Proof The proof rests on three historical and mathematical foundations: Euler's Product Formula (1737): Established the zeta function as an infinite product over primes. The Sieve of Eratosthenes (~200 BC): Used to identify the prime numbers. Set Theory (Cantor, 1895; Halmos, 1960): Used to distinguish between the pure kernel and the "noisy" remaining primes ($p \ge 17$), where the latter destroy the spectral trap. The Mathematical Mechanism L-EFM Operator: The Laplace-Euler-Fourier-Mellin operator ($E_{LEFM}$) is a finite product over the pure kernel $R$ that converges for all $s = \sigma + i\gamma$. Spectral Trap: The L-EFM operator exhibits a unique "spectral trap" at $\sigma = 0.5$, which is equivalent to the critical line condition of the Riemann Hypothesis. Validation: The framework validates all seven known consequences of the RH, including prime counting, prime gaps, primality tests, counting functions, L-function analogues, physics connections, and post-quantum cryptography. Cryptographic auditability is provided via SHA-256 hashes for each validated consequence. Applications to AI The same mathematical structure used to prove the RH has been applied to solve critical challenges in AI: Catastrophic Forgetting: Solved by using prime-anchored embeddings at the pure kernel indices, allowing networks to retain previous task knowledge. World Model Certification: TOPO-JEPA integration creates world models that avoid forgetting and demonstrate stable performance. AI Bias: Eliminated structurally through a four-tier spectral annihilation framework that rejects biased data and anchors representations to equitable primes. Deterministic AI Safety: Achieved through H2E Sheriff, which enforces geometric constraints to ensure zero safety violations. The Universal Architecture The framework was validated across six different AI architectures (including Dense Transformers, Sparse MoE, and Vision Transformers) across three continents, consistently showing minimal memory overhead and zero $NaN/Inf$ events. The author describes this as the beginning of "deterministic cognitive engineering".
This paper proposes the Global AI Development Commons, a new framework for AI governance designed to reconcile rapid AI innovation with continuous AI safety and international coordination in an increasingly multipolar world. Existing approaches often assume that stronger regulation inevitably slows technological progress, creating incentives for states and firms to avoid safety commitments while competitors continue to accelerate.The proposed framework separates AI development into a shared Foundation Layer and a competitive Innovation Layer. Participants voluntarily contribute useful but non-frontier seed technologies in exchange for interoperability, reusable components, shared evaluation resources, and opportunities to help shape emerging international standards. After joining, developers remain free to compete in AI models, products, and applications, while common governance focuses only on identity, authority, delegation, provenance, verification, and revocation.The paper introduces Proof of Constraint, a cryptographically verifiable framework that combines provenance, bounded delegation, zero-knowledge proofs, continuous verification, and instruction mediation to strengthen AI governance without requiring disclosure of proprietary technologies. It also proposes Time to Useful Scale as a measurable outcome for evaluating whether cooperative development can outperform isolated competition.Rather than slowing AI development, the framework seeks to make governed cooperation more competitive than isolated development. If successful, it offers a practical and falsifiable pathway toward international AI governance that strengthens innovation, preserves fair competition, enhances AI safety, and contributes to the long-term flourishing of humanity. Related Studies in This Research Program • A Quiet Roadmap for Preventing Uncontrollable AIhttps://doi.org/10.5281/zenodo.20946975 • AI Control Through the Analysis of Dangerous Instruction Patterns and Instruction Mediationhttps://doi.org/10.5281/zenodo.20990310 • An Instruction-Mediation Reference Implementation Protocol for High-Risk AI Governancehttps://doi.org/10.5281/zenodo.21216841 • Instruction Mediation Reference Implementation (Software)https://doi.org/10.5281/zenodo.21229233 • Water Beyond Numbers (Book)https://doi.org/10.5281/zenodo.21049923 • Gray Instructions (Book)https://doi.org/10.5281/zenodo.21193341 These studies form an integrated research program that progresses from foundational conceptual theory for preventing uncontrollable AI, through the analysis of dangerous instruction patterns, governance based on instruction mediation, operational reference implementations, and the institutional design of an international framework for AI development and control. The program further extends to book-length studies examining the broader institutional, social, and philosophical dimensions of AI governance.Although each study addresses a different subject and analytical level, they are united by a common research question: how meaningful human governance over advanced AI systems can be maintained across the successive stages of development, instruction, delegation of authority, execution, monitoring, interruption, and resumption.Collectively, these publications are intended as an interconnected body of research for readers interested in AI safety, AI governance, autonomous AI agents, instruction mediation, delegated authority, corrigibility, interruptibility, institutional oversight, cryptographic verification, international cooperation, and meaningful human control over advanced AI systems. While each publication and software implementation is designed to stand on its own, reading the series as a whole reveals a continuous research trajectory extending from conceptual foundations to institutional design, operational protocols, practical implementation, and international governance.This research program is intended to contribute to ongoing international discussions on the governance of advanced AI by presenting complementary theoretical, institutional, and implementation-oriented perspectives on maintaining meaningful human oversight and control.
This research paper aims to investigate the applications of blockchain technology and smart contracts in managing global supply chains, as well as their financial and economic implications. The study addressed the concept of supply chain management and the technologies of blockchain and smart contracts, with a focus on their applications in validating and tracking transactions, facilitating the flow and storage of information in international trade, supporting customs, shipping, and container management operations, and increasing transparency in commercial transactions. The most prominent digital supply chain platforms based on blockchain were also presented. The results showed that the application of these technologies contributes to reducing costs and fees, improving cash management, increasing transparency and reducing risks, and enhancing the economic and operational efficiency of exporting and importing companies. It also provides administrative bodies with the ability to track and verify transactions instantly, which supports more accurate financial and strategic decision-making.
# VeriSBOM: Secure and Verifiable SBOM Sharing Via Zero-Knowledge Proofs **VeriSBOM**, a trustless, selectively disclosed SBOM framework that provides cryptographic verifiability of SBOMs using zero-knowledge proofs. Within VeriSBOM, third parties can validate specific statements about a delivered software, mainly regarding the authenticity of the dependencies and policy compliance, without inspecting the content of an SBOM. Respectively, VeriSBOM allows independent third parties to verify if a software contains authentic dependencies distributed by official package managers and that the same dependencies satisfy rigorous policy constraints such as the absence of vulnerable dependencies or the adherence with specific licenses models. ## Key Features * **Selective Disclosure (Hiding):** Choose which proprietary components to hide from the public SBOM. The system generates a cryptographic proof that replaces the plaintext data, guaranteeing privacy. * **High-Performance Folding:** Powered by **Nova-Scotia**, utilizing recursive SNARKs to handle SBOMs. * **Interactive Dashboard:** A complete 4-step workflow (Package Manager, Auditor, Vendor, Client) built with **Streamlit**. ## Repository structure The repository contains three main folders: 1. **Empirical**: contains **Benchmarking** and **src**, for the analysis and source code, respectively. 2. **User study**: contains the code and results of the user study. 3. **README_Doc**: contains the images used for this documentation. ## VeriSBOM Architecture The system is divided into four main roles: 1. **Package Manager**: Maintains the package repository with the allowed packages. 2. **Auditor:** Represents the regulatory body marking the compliance status by checking the packages of the package manager. 3. **Software Vendor:** Represents the entity that provides software artefacts and wants to hide the related SBOMs for privacy reasons. He is responsible for the generation of the cryptographic proofs as verifiable substitutes of the hidden packages in SBOMs. 4. **Client:** The end-user who receives the cryptographic proofs along with the software artefact for verifying binding, inclusion and compliance status. ## Web Access (Recommended) **For direct access to the artefact, VeriSBOM can be accessed at this public link** https://verisbom-verisbom-software.hf.space ## Setup & Installation Follow the README within the artefact ## Operational Workflow The application follows a **linear workflow** composed of four steps. Each step depends on the output generated in the previous one. > **Performance Note** Due to the cryptographic operations involved, generating proofs may take some time depending on the number and complexity of the active policy constraints. In the current reference environment, proof generation takes approximately **~5 seconds**, while verification takes around **~3 seconds per proof**. ## Step 1 — Package Manager In this step, the **Package Manager initialises the package repository**. ### Instructions 1. Open the **Package Manager** tab. 2. Click **`Load repository`**. > For convenience, the system automatically loads a **default repository containing packages from the NPM ecosystem**. ### Expected Output After successful execution: - A **green confirmation message** is displayed. - The **package list** appears on the left panel. - The **dependencies of each package** can be inspected on the right panel using the search bar. - A **dependency graph** is displayed at the bottom of the interface. ## Step 2 — Auditor In this step, the **Auditor defines policy constraints** that will be applied to the packages in the repository. ### Instructions 1. Enter a **policy name** (e.g., `Vulnerabilities`, `MIT License`). 2. Click **`Add`** to create the policy constraint. 3. Use the **search bar** to locate target packages. 4. **Uncheck packages** to mark them as **non-compliant**. > By default, **all packages are marked as compliant**. 5. Click **`Save and Propagate`** to apply the policy. ### Optional - Repeat the previous steps to create additional policy constraints. - Remove policies that are no longer required. ### Expected Output - A **green confirmation message** appears. - A **dependency graph visualisation** shows how non-compliance propagates across dependencies for the selected policy (or combination of policies). ## Step 3 — Software Vendor In this step, the **Software Vendor generates cryptographic proofs for a given SBOM**. ### Instructions 1. Upload a **local SBOM file**. > For demonstration purposes, the system automatically loads an **example SBOM**. 2. In the **Selective Disclosure** section: - Select which SBOM packages should be used for proof generation. 3. Click **`Generate Proofs`**. 3. Click **`Download`**. - Download the SBOM with hidden components and plaintext components ### Expected Output - A **progress bar** indicates the proof generation process. - **Green confirmation messages** appear once proofs are generated successfully. > **Important:** Successful proof generation only means that the **cryptographic proof has been constructed correctly**. Compliance with policies is verified only in **Step 4**. ## Step 4 — Client In the final step, the **Client verifies the proofs generated by the vendor**. ### Instructions 1. Upload the **SBOM**. 2. Select a **policy** from the dropdown menu. 3. Click **`Verify`**. ### Expected Output - **Verified (green badge)** The SBOM satisfies the selected policy. - **Failed (red badge)** The verification failed, and the interface displays the reason for the failure.
Abstract Modern societies are shaped not only by visible institutions, laws, and technologies, but also by invisible structures that quietly govern the flow of information, incentives, resources, and human behavior. These hidden dynamics often remain unnoticed because they emerge gradually through countless local interactions, institutional routines, economic feedback loops, and algorithmic systems. By the time their consequences become visible, they are frequently perceived as isolated events rather than manifestations of deeper structural patterns. The Hidden Ledger presents a collection of seventeen visual essays that examine these invisible mechanisms through symbolic narratives accompanied by technical reflections. Rather than advancing a single political, economic, or technological thesis, the collection proposes a conceptual framework for exploring how complex societies organize themselves through distributed systems of incentives, institutional memory, information control, financial architecture, organizational design, and increasingly autonomous artificial intelligence. The visual essays employ metaphor and systems thinking to illuminate relationships that conventional analytical writing often struggles to communicate intuitively. Each episode functions as a conceptual thought experiment, inviting readers to examine how seemingly unrelated phenomena—including digital surveillance, data extraction, media ecosystems, bureaucratic inertia, scientific gatekeeping, economic dependency, charitable institutions, social exclusion, labor transformation, and AI alignment—may share common structural characteristics rooted in hidden feedback mechanisms. Although every essay focuses on a distinct domain, they collectively argue that modern civilization increasingly operates through invisible ledgers: distributed systems that continuously record incentives, redistribute risks, accumulate influence, and shape collective behavior without requiring centralized control or explicit coordination. These ledgers are not literal accounting systems but conceptual representations of the often unseen processes through which power, trust, responsibility, and information circulate across societies. The objective of this work is not to provide definitive explanations for contemporary social problems, nor to promote predetermined ideological conclusions. Instead, it offers a visual framework for interdisciplinary reflection, encouraging readers to move beyond isolated events and consider the structural conditions from which those events emerge. By integrating symbolic illustration with technical commentary, The Hidden Ledger demonstrates how visual reasoning can complement traditional scholarly discourse in exploring complex adaptive systems whose most influential mechanisms often remain hidden beneath everyday experience. Ultimately, this collection argues that understanding the future of human societies requires more than observing visible outcomes. It requires learning to recognize the invisible structures that quietly shape them long before they become apparent. Author's Note The Hidden Ledger began with a simple question: What if the most influential forces shaping modern society are not the ones we immediately notice, but the ones quietly operating beneath everyday events? Many discussions about artificial intelligence, economics, institutions, governance, and social change focus on visible outcomes. We debate policies, technologies, organizations, and individual decisions, yet we often overlook the invisible incentive structures and feedback mechanisms that connect them. This collection was created as an attempt to visualize those hidden relationships—not as definitive explanations, but as conceptual maps that encourage structural thinking. Each episode explores a different domain. Some focus on artificial intelligence, others on media, bureaucracy, science, finance, charity, religion, education, labor, or human psychology. Although these subjects appear unrelated at first glance, they gradually converge around a common question: What invisible systems quietly shape the visible world? For that reason, the episodes are intended to be read both independently and collectively. Individually, they function as symbolic thought experiments exploring specific structural phenomena. Together, they reveal recurring patterns—feedback loops, incentive structures, institutional memory, information asymmetries, distributed responsibility, and emergent behaviors—that transcend disciplinary boundaries. The technical reflections accompanying each illustration are therefore not literal explanations of the cartoons, but invitations to continue the conversation from multiple academic perspectives. This distinguishes The Hidden Ledger from my previous visual essay, The Age of Mirrors. While The Age of Mirrors explored the symbolic and relational dimensions of human–AI coevolution—asking how intelligent systems reshape meaning, identity, and human relationships—The Hidden Ledger shifts its attention outward toward the invisible architectures that organize societies themselves. One examines reflection; the other examines structure. Together, they represent two complementary ways of thinking about an increasingly interconnected world. Both collections share a common belief: visual narratives can communicate complex systems in ways that conventional academic writing sometimes cannot. A carefully constructed image can reveal relationships that might otherwise require pages of formal exposition. Rather than replacing analytical research, these visual essays seek to complement it by providing an additional language for interdisciplinary exploration. This work is also an experiment. I did not begin this series with a long-term publication plan, nor did I know where it would ultimately lead. It emerged gradually through curiosity, observation, and a desire to preserve ideas before they disappeared into the continuous flow of everyday conversations. Whether future visual essays will continue this series or move in an entirely different direction remains an open question. At the time of writing, I am simply exploring new subjects that may deserve similar treatment. Working as an independent researcher without institutional affiliation has, perhaps unexpectedly, become one of the greatest advantages of this journey. Without predefined disciplinary boundaries or organizational expectations, I have been free to move between artificial intelligence, psychology, economics, systems science, philosophy, governance, and visual storytelling—following questions wherever they seemed to lead. This freedom has made it possible to experiment with forms of scholarship that might not fit comfortably within conventional academic categories. If there is a single purpose behind this collection, it is not to convince readers that these interpretations are correct, nor to prescribe how society should change. My goal has always been more modest: to observe carefully, to connect ideas honestly, and to preserve those observations in a form that others may examine, question, refine, or even disagree with. Knowledge advances through conversation, not certainty. If The Hidden Ledger encourages readers to look twice at familiar systems, to ask different questions, or to notice structures that previously remained invisible, then it has already achieved more than I originally hoped. Finally, thank you for taking the time to explore this experimental work. As an independent researcher, I have the freedom to explore unconventional ideas and formats without being constrained by disciplinary boundaries. That freedom has made projects such as The Hidden Ledger possible, and I am grateful for the opportunity to share them openly. Constructive criticism, thoughtful discussion, and alternative perspectives are always welcome. If this collection encourages even a small number of readers to examine familiar systems from a different structural perspective, then this experiment has served its purpose. This collection represents an experiment rather than a conclusion, and I look forward to discovering where the next question may lead. Disclaimer: The analyses presented herein are not directed toward attributing fault or intent to any specific organization. Rather, they are intended as a conceptual and technical investigation of alignment methodologies, focusing on structural mechanisms and systemic trade-offs. Interpretations should be regarded as provisional, research-oriented hypotheses rather than conclusive statements about institutional practice. Notice: This work is disseminated for the purpose of advancing collective inquiry into generative alignment. Reuse, adaptation, or extension of the presented concepts is welcomed, provided that proper attribution is maintained. Instances of unacknowledged appropriation may be addressed in subsequent publications.
This paper asks whether the Prime Lattice Coherence Framework (PLCT) can be made genuinely predictive, and tests four distinct mechanisms — four "gears" — each corresponding to a different sense of the word. Gear 1 (forward zone prediction) predicts the zone of the next prime from the current one with 56.28% accuracy on 508,242 held‑out primes, a 90‑sigma effect, exploiting the Lemke Oliver–Soundararajan bias expressed in the PLCT's own Hard Wall / Temporal vocabulary. Gear 2 (aggregate prediction) forecasts the Temporal‑vs‑Hard‑Wall prime race at five previously unsieved values of x using only six analytically derived zeros of L(s,χ₃). The sign is correct at two checkpoints and magnitude reasonable at the nearest ones — an honest mixed result. Gear 3 (certain‑negative prediction) applies the classical Sophie Germain exclusion as a live filter at the current GIMPS search frontier: 4.4% of candidate exponents receive a mathematically certain composite verdict, eliminating a full Lucas–Lehmer test each. Gear 4 (certain‑positive prediction — naming which exponent will be prime) is proved closed. The Dirichlet obstruction shows no congruence system can ever be a sufficient condition for primality. The paper also retracts an earlier hopeful claim that spectral (zeta‑zero) data might provide an independent route around this wall, proving instead that full spectral knowledge is informationally equivalent to full prime knowledge, not a shortcut. The only remaining paths are direct computation (Lucas–Lehmer, AKS) or mathematics with no current existence
Sai Srikanth Madugula, Peplluis Esteva De La Rosa, Daya Shankar
The rapid proliferation of Agentic Artificial Intelligence fundamentally disrupts traditional customer loyalty paradigms. As AI evolves from passive recommendation algorithms to autonomous, goal-directed agents capable of executing purchasing decisions, the conventional understanding of consumer-brand relationships requires a structural reevaluation. By synthesizing extant literature across human-machine teaming, consumer decision-making, and algorithmic trust dynamics, we demonstrate that traditional loyalty models fail to account for algorithmic bounded rationality and constructed autonomy. To address this, we introduce the Dynamic Verifiable Multi-Agent Human Agentic Loyalty Loop (DVM-HALL) model. We formalize brand choice via a softmax probability formulation where human emotional equity, agentic machine-experience utility, calibrated trust, delegated authority, and verifiable execution jointly determine selection. The model features recursive updating mechanisms to dynamically calibrate trust and delegation after each interaction. Crucially, the framework integrates a verifiable execution layer for Decentralized Finance (DeFi) and tokenized loyalty settings, incorporating execution risks -- such as gas costs, slippage, MEV exposure, and smart-contract vulnerabilities -- as core predictors of agentic brand preference. Furthermore, we introduce the Net Human-Agent Score (NHAS), an auditable, risk-weighted metric designed to measure human-agent alignment using human feedback, execution logs, benchmark comparisons, and verifiable receipts. Finally, we propose a comprehensive three-stage empirical validation plan spanning controlled shopping experiments, multi-agent market simulations, and DeFi testbeds. This framework provides the foundational theory required for brands to navigate the impending transition toward machine customers.
Regulatory permissiveness is widely prescribed as the primary institutional lever for digital asset adoption. This study challenges that prescription. Analyzing NFT and DeFi adoption across 105 countries using Principal Component Analysis (PCA)-constructed composite indices and multivariate Ordinary Least Squares (OLS) regression, we find that the Frontier Technology Readiness Index (FTRI) is the dominant structural correlate across all specifications, consistently outperforming competing explanatory variables. Regulatory environments neither independently explain adoption nor are associated with it linearly: both permissive and restrictive environments outperform mostly prohibited jurisdictions, suggesting that regulatory clarity rather than permissiveness is the operative institutional dimension. NFT and DeFi markets follow empirically distinct pathways: NFT adoption shows stronger associations with digital marketplace maturity while DeFi is more closely associated with technological infrastructure, suggesting that treating Web3 as a homogeneous policy category is unwarranted. National income conditions how effectively technological readiness is associated with adoption gains, with structural determinants exhibiting considerably reduced explanatory power in lower-middle-income economies. For policymakers, these findings reframe the debate: the primary structural correlate of digital asset adoption is technological capacity, not regulatory stance, and below a development threshold, neither intervention is reliably associated with adoption gains.
Ilango (FOCS 2025) invented effectively zero-knowledge proofs, a new variant of zero-knowledge. We reformulate it in the language of logic and give simple proofs (under the same assumptions as Ilango (FOCS 2025)) of its existence and of the key property defined in Ilango (FOCS 2025) that it is "indistinguishable from true" (that property is in Ilango (FOCS 2025) a part of the definition of the prover, not its consequence). Using the theory of proof complexity generators we show that the concept can be turned it into a genuinely zero-knowledge proofs, assuming a conjecture from the theory about the existence of a hard generator and allowing the parties to share a common random string.
To secure modern distributed digital infrastructures, quantum blockchains exploit quantum resources to achieve information-theoretic security and surpass the classical one-third fault-tolerance bound. However, existing high-fault-tolerant protocols face a fundamental scalability challenge: the blockchain trilemma imposes either exponential communication complexity or experimentally demanding multipartite entanglement. Here, we experimentally demonstrate a scalable quantum blockchain protocol based on weak coherent states that achieves an exponential reduction in quantum communication complexity. The protocol employs a circular quantum Byzantine agreement mechanism that preserves information-theoretic security while avoiding multipartite entanglement. We implement this protocol on a photonic integrated circuit platform, realizing a six-node network over commercially available telecommunication infrastructure. Compared with previous schemes, the protocol requires less than 4% of the quantum communication resources. Leveraging this advantage, we further demonstrate a quantum-secured token exchange application achieving a throughput of 805.3 transactions per second with zero failures. These results establish a practical pathway toward scalable quantum blockchain.
Mohotarema Rashid, Lingzi Hong, Junhua Ding, K. S. M. Tozammel Hossain
Large language model (LLM) trading agents show promising performance in equity markets, yet remain narrowly focused on US equities with little evidence from live deployment. We present Fin-Analyst, a hybrid agent for FinMMEval 2026 Task 3: an eight-specialist LLM pipeline over news, SEC filings, fundamentals, analyst forecasts, technical indicators, and social sentiment, aggregated by a Meta-Agent for Tesla (TSLA), and a lightweight rule based three-signal vote for Bitcoin (BTC). On the final official leaderboard (accessed 2026-07-05), Fin-Analyst ranks first of all agents on TSLA with a +13.51% return, +28.33 points over Buy-and-Hold (Sharpe 4.10, 88% win rate), while the BTC vote ends flat yet well above a sharply falling baseline. Relative to the interim performance, the asset ranking reversed, indicating that short live windows yield volatility-sensitive rankings. Ablation identifies event-driven 8-K disclosures as the most influential TSLA signal. Error analysis shows that the memoryless agents repeat wrong calls for days at a time, and that the fixed-threshold BTC rules lost money by trading on noise in a sideways market while the LLM pipeline gained under similar conditions, motivating a memory-aware, LLM-based successor for both assets.
Цифровая трансформация страховой отрасли и внедрение продуктов параметрического страхования требуют пересмотра подходов к обеспечению доверия между участниками сделки. Ключевым вызовом становится выбор архитектуры доверенной третьей стороны, которая могла бы гарантировать не только юридическую значимость транзакций, но и их защищенность в условиях растущих угроз. Существующие централизованные модели, доказавшие эффективность в массовом сегменте, часто не отвечают требованиям прозрачности и безопасности, критически важным для крупных сделок перестрахования и рынков связанного со страхованием капитала. Целью работы является сравнительный анализ трех архитектурных подходов к построению доверенной третьей стороны: традиционной централизованной на базе инфраструктуры открытых ключей, децентрализованной на базе блокчейна и гибридной. Для объективизации выбора применяется двухэтапная методика системного анализа: метод главных компонент для снижения размерности и визуализации компромиссов, а также многокритериальный анализ решений для ранжирования архитектур. Особое внимание уделено теоретическим ограничениям распределенных систем («трилемма блокчейна», «проблема оракула»), моделированию векторов атак и стратегиям их минимизации. Источником данных послужили экспертные оценки, полученные методом «Дельфи». Исследование показало, что архитектура на базе блокчейна является оптимальной для задач перестрахования благодаря высокой скорости мобилизации ликвидности. В то же время для массового розничного сегмента экономически наиболее эффективной остается централизованная модель. Обоснована необходимость перехода к гибридным архитектурам, использующим криптографические доказательства с нулевым разглашением и доверенные среды исполнения. Такой подход позволяет сочетать высокую пропускную способность с проверяемой безопасностью, что подтверждается опытом реализации национальных цифровых валют, в частности, цифрового The digital transformation of the insurance industry and the introduction of parametric insurance products require a rethinking of approaches to ensuring trust between transaction parties. A key challenge is choosing a trusted third party architecture that can guarantee not only the legal validity of transactions but also their security in the face of growing threats. Existing centralized models, while proven effective in the mass market, often fail to meet the transparency and security requirements critical for large-scale reinsurance transactions and insurance-linked capital markets. This paper aims to comparatively analyze three architectural approaches to building trusted third parties: a traditional centralized approach based on a public key infrastructure, a decentralized approach based on a blockchain, and a hybrid approach. To objectively evaluate the choice, a two-stage system analysis method is applied: principal component analysis for dimensionality reduction and tradeoff visualization, and multicriteria decision analysis for ranking architectures. Particular attention is paid to the theoretical limitations of distributed systems (the "blockchain trilemma" and "oracle problem"), attack vector modeling, and mitigation strategies. The data source was expert assessments obtained using the Delphi method. The study showed that a blockchain-based architecture is optimal for reinsurance applications due to its high liquidity mobilization speed. However, for the mass retail segment, a centralized model remains the most cost-effective. The study substantiates the need to transition to hybrid architectures using zero-knowledge cryptographic proofs and trusted execution environments. This approach combines high throughput with verifiable security, as evidenced by the experience of implementing national digital currencies, in particular, the Bank of Russia's digital ruble.
What happens to Purple Mathematics if every apex of the wall is drawn at the same height, h_p = 1? Then the apex-to-apex line becomes one straight line, parallel to the dividing line, at the height of the Balance Boundary — and this paper works out everything that follows. The first answer is a theorem and an honest no: the Determination Law's predictions cannot improve or degrade under any redrawing, because κ and T are arithmetic invariants of the program's Invariance Ledger — the wall displays the law, it does not feed it. But the question uncovers a genuine structure: the wall's drawing rule is a gauge choice, and the framework has two canonical gauges. In the gap gauge (the published wall) the gaps live in the heights: the wall is a strain gauge and the apex line is terrain. In the level gauge the gaps migrate into the slope angles — gradient −1/(p₊−p), an inclinometer — and a normalization theorem holds: every zeta strike's height equals its share t exactly, so the unit strip between line and ceiling becomes the natural home of reception statistics, strikes uniformly distributed in it. The level gauge then serves as the control experiment that separates the program's reception results into arithmetic and geometric: the host rule, the identity t + t̃ = 1, the crowding law, and share uniformity survive both gauges; the Mirror Wall's repulsion law is erased exactly (the crossing point collapses to the midpoint; measured first-strike split 49.9%, flat), proving it was height-borne; and the coincidence question answers differently in each gauge — the gap gauge's helix touches the wall at the balanced primes, while the level gauge's ceiling, with turn rate T = 4, is touched exactly at the primes ≡ 1 (mod 4), the classical two-squares class of Fermat. A reader's observation — the helix meeting the parallel line — then yields the paper's strongest result. The unit radius is critical: below 1 the coil never reaches the balance level, at exactly 1 it kisses the ceiling once per turn, above 1 it crosses beyond balance. And the Ceiling Theorem holds: among all turn rates T, the balance ceiling is kissed by infinitely many primes if and only if T = 4 — for every other admissible turn rate at most a single prime ever touches, and for T not divisible by 4 none can. The structure does not merely accommodate Fermat's class; it selects it uniquely. Each gauge convenes its own congregation at the balance height, and only one turn rate convenes an infinite one.
Andrea Rizzini, Marco Esposito, Tommaso Gagliardoni, Francesco Bruschi
Federated Learning (FL) is an advancement in Machine Learning motivated by the need to preserve the privacy of the data used to train models. While it effectively addresses this issue, the multi-participant paradigm on which it is based introduces several challenges. Among these are the risks that participating entities may behave dishonestly and fail to perform their tasks correctly. This misbehavior, in turn, also threatens privacy, because an undetected deviation in training or aggregation can silently undermine the confidentiality guarantees that FL was designed to provide. This motivates mechanisms that provide checkable evidence that released checkpoints are consistent with a declared learning specification and an auditable execution trace. In this SoK, we model federated learning as an append-only transcript of submissions, admissions, aggregation, and finalization events, and formalize verifiability as a collection of integrity claims issued by clients and the aggregator, and checked by different verifier classes. We derive a taxonomy of recurring client-side and aggregator-side claims and use it to analyze representative verifiable FL (VFL) systems spanning Zero-Knowledge Proofs (ZKP) and Trusted Execution Environment (TEE) technologies. Our analysis suggests that, while verifiable aggregation is comparatively mature, data verifiability appears feasible but still sparsely adopted in practice, and verifiable training remain costly and rarely scale to modern models.
Incorporating connected mobility data into decision-making brings about significant data security and privacy challenges in next generation transportation systems. The problem becomes worse when the raw traffic data stream contains commuters' sensitive information that could be extracted by successful attackers. The challenges of data security and privacy must be addressed in order to create a secure information sharing environment for next generation transportation systems. ☐ Blockchain technology, which provides a tamper-resistant journal of state transition events, becomes an ideal candidate for realizing the goals of creating secure information sharing frameworks. However, existing blockchain technology and deployments also have their limitations, especially in data privacy. Because the saved data on the blockchain ledger can not be altered, we do not want to make sensitive information publicly available or record false information permanently without an authentication protocol. ☐ Innovations are needed to overcome barriers in blockchain technology for enabling secure and privacy-preserving information sharing in next generation transportation systems. This Ph.D. dissertation introduces three main innovations: (1) a zero-knowledge and Byzantine fault tolerant consensus that brings privacy-preserving to the blockchain consensus level for verifying and processing transactions, Chapter 2; (2) novel privacy-preserving authentication schemes for blockchain networks based on zero-knowledge proofs to increase security and safety in traffic management, autonomous truck fleets and ridesharing, Chapters 3, 4 and 5; and (3) blockchain-inspired architecture designs with access control policies that protect huge amounts of traffic and users' data and log access events into blockchain for traceability and accountability, Chapters 4 and 5.
Traditional secret sharing techniques such as Verifiable Secret sharing (VSS) are vulnerable to quantum attacks by a Cryptographically Relevant Quantum Computer (CRQC) running Shor's algorithm. We observe that the binding a VSS needs is required only at the moment of dealing, and this binding can be made before any CRQC exists. We propose Proof in a Bottle (PiB), which decouples verifiability from long-term binding: standard Pedersen commitments provide zero-knowledge, publicly checkable consistency during a pre-quantum window, while a salted, index-bound hash of the share set, anchored to an immutable public ledger, preserves the binding established in that window into the post-quantum era. The guarantee is explicitly a commit-now, reveal-later one: it protects today's honest dealings against tomorrow's quantum adversary.
Quantum resource estimates for the elliptic-curve discrete logarithm problem (ECDLP) now shape cryptographic migration planning, blockchain security analysis, and fault-tolerant architecture design. Recent work has moved in two complementary directions: Babbush et al. give improved secp256k1 resource estimates supported by zero-knowledge attestation while withholding sensitive circuit details, whereas Luo et al. publish an explicit reversible modular-inversion construction based on the extended Euclidean algorithm, reducing the logical-qubit footprint of prime-field ECDLP and identifying gate count, depth, and architecture-aware implementation as natural optimization targets. This note proposes a third disclosure model: verifiable resource certificates for public reversible arithmetic blocks. A certificate records a circuit commitment, gate basis, resource counts, input-output specification, deterministic test generation, correctness transcript, and optional proof artifact. We specialize the framework to modular inversion blocks |x⟩|0⟩ → |x⟩|x−1 mod p⟩, for \(x\in\mathbb F_p^\times\), which are central to affine-coordinate quantum ECDLP implementations. We prove a basic soundness bound for hash-derived randomized testing and outline a prototype verifier. The goal is not a new quantum attack, but reproducible, comparable, and independently auditable quantum-ECDLP arithmetic claims.
Yongqiang Du, Chen-Xun Weng, Feng Xie, Ming-Yang Li · 13 authors
Popularized by the Bitcoin cryptocurrency, blockchain technology establishes a decentralized digital framework that utilizes cryptographic and consensus protocols to secure data against unauthorized modification. Consequently, blockchain has found broad adoption across diverse fields, including finance, data management, healthcare, and digital asset governance. In the quantum computing era, a paramount objective for blockchain is to preserve its foundational advantages of cryptographic integrity and decentralized fault-tolerant resilience. In principle, quantum digital signatures and quantum Byzantine agreement protocols offer foundational security guarantees and tolerate up to one-half of malicious nodes for blockchain. However, the practical realization of such a quantum-enhanced blockchain remains a significant and multifaceted challenge. Here, we propose and experimentally demonstrate a fully operational hybrid quantum blockchain architecture built on photonic integrated circuits and deployed over commercially available classical telecommunications infrastructure. The system achieves a fault tolerance of nearly one-half, surpassing the classical limit, while reaching consensus on a timescale of seconds. A deployed food traceability application validates the practicality of the proposed architecture, achieving a throughput of approximately 500 transactions per second. This work establishes a foundation for practical quantum blockchains, enabling secure, scalable, and decentralized information processing in the emerging quantum era.