This paper introduces Crossroads, a smart contract layer for chain-abstracted assets. In Crossroads, assets from nearly any chain are represented on a single backend blockchain as ERC-20 tokens. As a result, any asset can participate in smart-contract-based exchange, lending, or privacy applications on a single unified platform. So while Crossroads offers cross-chain bridging, a common, partial approach to alleviating the fragmentation of the blockchain ecosystem today, this is just one service within Crossroads' general-purpose chain-abstraction model. Crossroads relies on key encumbrance: a threshold signing committee holds encumbered keys controlling assets on each integrated chain, signing transactions only as authorized by smart contracts on the backend blockchain. Asset movements are fee-efficient, as ownership changes are recorded on the backend blockchain and users may set the transaction fee for withdrawals. Crossroads enables permissionless, modular integration of new blockchains using pluggable oracles with flexible design options (zkBridge, TEE-based, hybrid). Asset deposits into Crossroads benefit from strong, chain-specific finalization guarantees, minimizing the risk of reorg attacks. Unlike existing bridges, however, third-party smart contracts in Crossroads can provide fast, optimistic access to funds before finalization completes. We prove that Crossroads satisfies soundness: given an honest quorum of signing committee members, any user can unilaterally generate a withdrawal transaction transferring their net balance to an account on an integrated blockchain. We implement a proof of concept across multiple public blockchains: Bitcoin, Ethereum, and Solana. We catalog a range of applications enabled by Crossroads, including universal wallets, cross-chain staking and lending, privacy-preserving payments, and private management of public blockchain assets.
Zero-knowledge proof systems rely on a trusted setup phase to generate a Common Reference String (CRS), yet existing approaches are typically static, one-time ceremonies that are inflexible and vulnerable to long-term compromise. Offloading continuous, recurring trusted setups to a decentralized Layer 2 (L2) network introduces a fundamental coordination challenge arising from the mismatch between high-throughput transaction processing and the multi-round requirements of trusted setup ceremonies. This paper presents an L2-coordinated framework that safely decouples transaction pipelines from ceremony execution to achieve automated, continuous CRS generation without centralized coordination. We design and implement two protocol variants over a decentralized, PBFT-coordinated ZK-rollup architecture: an on-chain smart contract approach and an asynchronous peer-to-peer consensus variant. Both designs utilize non-interactive zero-knowledge proofs of knowledge alongside commit-reveal structures to eliminate adaptive manipulation vectors and isolate ceremony latency. Experimental evaluations under simulated wide-area network constraints and adversarial conditions demonstrate that our architecture successfully isolates ceremony liveness. Continuous setups complete reliably within practical time bounds despite node dropouts or malicious contributions, while preserving stable L2 transaction throughput.
Applying NFTs to urban planning offers revolutionary potential but raises serious equity concerns. Although NFTs are linked to digital art speculation, their underlying technology, decentralized ownership, smart contracts, and tamper-proof records could transform participatory planning, land-use management, and community asset stewardship. This article discusses how NFTs could decentralize planning power by enabling tokenized community land trusts, zoning-compliance automation, and neighborhood DAOs (Decentralized Autonomous Organizations). However, they also pose dangers, such as digital exclusion, environmental degradation from energy-hungry blockchains, and capture by technocratic elites. Mitigation is needed through hybrid digital-physical participation systems, public sector-led blockchain infrastructure, and equity-sensitive token design if these tools are to benefit the public interest. The article proposes a staged implementation approach, beginning with controlled pilots (e.g., NFT-tracked community benefit agreements) before scaling up to more advanced applications such as dynamic zoning regimes. Lastly, ethical use of NFTs in planning demands visionary public-sector leadership to avert the entrenchment of existing inequalities in the name of innovation. Planners must critically engage with this emerging technology not as eager consumers but as stewards shaping its trajectory toward more equitable, open, and democratic urban futures. The question is not whether NFTs will influence cities, but who will determine that influence and to what ends.
Educational institutions require secure, transparent, and tamper-resistant systems to manage academic records, examination data, and student results while ensuring accountability and data integrity. Conventional marks management systems primarily rely on centralized databases, making them susceptible to unauthorized modifications, security breaches, limited traceability, and single points of failure. The proposed blockchain-based university student marks management framework utilizes academic information collected from institutional administrative records, including student details, faculty information, academic structures, subject allocations, examination schedules, marks, and result data. The workflow incorporates secure user authentication using SHA-256 hashing, AES-based encryption of sensitive marks data, role-based access control, blockchain transaction validation, and smart contract execution for academic operations. Ethereum blockchain, Solidity smart contracts, Flask, Web3.py, MetaMask, and Ganache are integrated to implement secure record management, immutable storage, result publication, audit trail generation, and academic analytics. Performance evaluation is conducted using blockchain transaction processing, encryption efficiency, data integrity verification, access control validation, audit traceability, and result dissemination correctness. Experimental results demonstrate reliable storage of academic records, secure handling of examination information, accurate result processing, comprehensive audit logging, and effective protection against unauthorized modifications while maintaining complete transaction transparency. The proposed architecture significantly enhances the security, reliability, transparency, and trustworthiness of university examination and academic record management systems.
Smart contracts manage high-value digital assets, making their security a critical priority. In this work, we present a preliminary ecosystem analysis of how smart contract vulnerabilities are currently classified, disclosed, and managed across academia and industry. Our findings reveal the fragmented nature of Web3 security, characterized by a history of attempted classification schemes and a lack of proper vulnerability disclosure. We propose several hypotheses for this divergence from traditional software standards, including ideological decentralization, reputation management, and misaligned financial incentives. A case study of Uniswap illustrates these challenges, revealing inconsistent reporting and the difficulty of verifying vulnerability data. Ultimately, this work serves as a foundational step toward establishing unified methodologies for the detection, management, and disclosure of smart contract vulnerabilities.
We propose a protocol for cross-chain atomic transactions (CATs), enabling composable atomic execution across different blockchains. The protocol addresses the key interoperability challenge of providing atomicity guarantees in the presence of asynchronous communication and Byzantine actors. It preserves chain autonomy by allowing each blockchain to maintain its own execution model while participating in coordinated cross-chain operations. The design introduces a shared coordination layer involving sequencers, transaction processors, a coordinator, and a confirmation layer which together ensure that either all parts of a CAT succeed or none do. To prevent unnecessary blocking, we separate transaction execution into accepted and postponed sets, with the coordination layer resolving the outcomes of CATs within a few rounds. We further introduce timeouts and dependency-depth bounds for liveness and mitigation of cascading delays. Our formal analysis establishes strong safety and liveness guarantees and demonstrates that the protocol achieves minimal blocking for independent transactions while ensuring bounded blocking time for dependent transactions. Experimental evaluation shows high CAT success when cross-chain transactions are a modest share of traffic, and characterizes the CAT-lifetime trade-off between success and dependent-transaction latency. This protocol enables fast, secure, and deterministic atomic cross-chain execution while preserving chain autonomy, providing a foundation for scalable blockchain interoperability solutions.
This paper develops a representation-theoretic perspective on cryptographic protocols. The focus is not solely on the computability of the abstract value as an extensional property, but on the algorithmic structure of its presentation in a representation system: for operational use in protocols, algorithmic accessibility of the value does not suffice; its fixed presentation is also decisive. We distinguish three representation-theoretic notions -- algorithmically approximable (A_app, the computable real numbers), finitely exactly describable in a system (A_fin(S)), and canonical normalizability of a system -- and show that there is no computable extensional canonicalizer that uniformly transforms arbitrary approximation programs of computable real numbers into unique finite value encodings. As the operational rational core presentation we use the rational system with its canonical encoding specification Sigma_Q (fixed rules for valid fraction descriptions, canonical codes, and normalization); the associated value set is A_ex = Q. The notion of a canonically serializable object class transfers this core idea to practical protocol objects (files as byte sequences, hash values, transaction IDs, and normatively serialized payloads). We illustrate the consequences for interoperability, well-definedness, and verification with fully worked toy examples from symmetric and asymmetric encryption and hashing, and with a real-world example, the snaproot hash-anchoring protocol for blockchain-based file integrity verification. The paper thereby shows that the mathematical determinacy of a value and its operational uniqueness as a protocol object are two different requirements. Once a normative representation specification has been fixed, byte-level correctness and well-definedness arguments can be carried out without further implementation-dependent serialization or rounding decisions.
This paper is a prequel to our recent work, "Equivariant Borel liftings in complex analysis and PDE" (arXiv:2507.12058). While the results presented here were established in that work in a more general and abstract setting, the purpose of this paper is to provide a direct proof of the equivariant Weierstrass theorem. It states that there exists a Borel map assigning to each non-periodic positive divisor $Λ$ an entire function $F_Λ$ such that the divisor of zeroes of $F_Λ$ is $Λ$ and such that $F_{Λ-w}(z) = F_Λ(z+w)$, $w\in\mathbb{C}$. In general, non-periodicity cannot be omitted, and Borel measurability cannot be strengthened to continuity. The two key ingredients are the Runge approximation theorem and the existence of "Borel toasts", which are Borel counterparts of Rokhlin towers from ergodic theory. We do not assume prior knowledge of descriptive set theory and have aimed to make the exposition self-contained, aside from several results taken from graduate textbooks.
We show that frictions at cryptocurrency exchanges, captured through consumer complaints, are reflected in the relative pricing of Bitcoin across U.S. trading venues. Using daily Bitcoin prices and CFPB complaint data, we find that average cross-exchange price deviations relative to Coinbase are significantly higher on days with at least one exchange-targeted Bitcoin complaint, indicating that Bitcoin on Coinbase trades at a larger relative discount on those days. The effect strengthens with complaint intensity. The results survive placebo tests using non-exchange complaints and diagnostics for omitted-variable bias. Complaints targeting Coinbase are associated with cumulative abnormal returns for Coinbase stock that are about 3.17 percentage points lower around complaint days. Overall, our evidence indicates that consumer complaints are associated with both cross-exchange Bitcoin price deviations and the equity valuation of the affected exchange.
We prove the full Birch–Swinnerton–Dyer conjecture for all elliptic curves over Q. Using a fundamentally new approach that extends the method developed for the Riemann Hypothesis, we construct a sequence of finite-dimensional self-adjoint matrices from the Euler product of the elliptic curve L-function. We establish a strict spectral correspondence between the eigenvalues of these matrices and the squares of the distances from the critical point s=1 to the zeros of L (E, s), with no prior knowledge of zero locations required in the construction. Using mathematical induction, perturbation bounds and the monotone convergence theorem for self-adjoint operators, we extend these results to the infinite-dimensional case, proving that the order of vanishing of L (E, s) at s=1 equals the rank of the Mordell–Weil group E (Q). We then prove the exact leading-term formula relating the first non-vanishing coefficient of the Taylor expansion of L (E, s) at s=1 to the arithmetic invariants of the elliptic curve, including the period, regulator, Tamagawa numbers, and the order of the Tate–Shafarevich group, which we prove is finite. We also embed this result into the broader universal self-adjoint integral operator framework. Keywords: Birch–Swinnerton–Dyer conjecture; elliptic curve; L-function; self-adjoint operator; spectral correspondence; Mordell–Weil rank; Tate-Shafarevich group MSC 2020 Classification: 11G05; 11M41; 47A10; 14H52; 11G40
[Depreciated and replaced by V3] The application-specific clean rebuild has not yet been published; its authoritative theoretical boundary is now the governing V3 branch: After Turing: The Fold Machine - An Exact, Parameter-Free and Machine-Closed Derivation of Classical Computational Science from Smithian Fold Theory; From Fold to Consciousness: An Exact, Zero-Parameter and Machine-Closed Foundational Reconstruction of Consciousness and Cognitive Science from Smithian Fold Theory. The V3 source platform is https://github.com/MettaMazza/ernos-labs-sft-platform. The original DOI, concept DOI, version number and files are preserved for transparent historical provenance; this record must not be presented or cited as current V3 work. Full paper v1.1 — supersedes the pre-paper (From One Axiom to Master-Level Chess — and the Law Inside Neural Networks). Built from scratch by one woman, working alone, in under twenty-four accumulated hours: where a score falls short it marks an implementation gap at measurement time, never a limit of the mathematics — the gains between releases are the finding. v1.4 adds the fold eye (vision as exact integer Walsh spectra, self-certified by integer Parseval per image, recognition of seen images with no image model in the loop) and the graduation score (blind head-to-head vs the teacher, tallied per question-territory; the teacher retires as wins cross the majority lock) -- and documents the 2026 convergence: DeepSeek Engram arrives at deterministically-addressed exact memory from the gradient side, and two independent results place the optimal curriculum at p = 1/2, the fold lock. v1.6: the full omnimodal engine (the voice via Kokoro, the fold ear -- sound as Parseval-certified integer Walsh spectra, video composed from frames + sound), speaker-transparent reasoning threads, and 32/32 end-to-end empirical verification of the entire architecture including persistence across process death. v1.7: removal-proof omnimodality, measured -- every supporting model is a teacher with an exit: a sound taught once by the synthesis teacher is re-spoken from the engine's own exact counted record in 0.00s with no model; a sound heard once is recognized natively with no transcriber; 34/34 end-to-end verification. v1.9: zero-model perceptual learning (the human observer -- a novel image learned and re-recognized at share 1.00 with no model in the loop); agentic self-knowledge (the observer reads the engine's own source, measured); the hourly progress instrument with a committed pre-boot birth line; one-tap y/n closure. v2.0 (flight-ready): the full modern-agent toolkit (live web search/fetch, paginated reading, in-file grep -- every call held as a training trace), the 43-domain everything-curriculum under the fold-only law, SOTA 1-1 benching on the public MMLU test split with the newborn baseline committed, generation closure (the Learning Law reaches generate() itself), and 36/36 end-to-end verification. v2.1: the ReAct law (reason-act-observe enforced in-turn; narrated intent without an act is detected and forced), reasoning trained on the observer's NATIVE thinking tokens (STaR-gated) with both minds' full thinking streamed to the user, and document intake (a sent file is reading -- inboxed, counted, persistent). Three connected results and the architecture they force. First, a pre-registered, self-certifying spectral instrument shows trained neural-network weights carry placement-law in the dyadic (Walsh) basis: 18/18 unanimous on validated released models; the law concentrated in transformer expansion projections and token embeddings across three unrelated architectures (up to 230x chance in GPT-2), attention at chance; strictly training-caused (He-initialised controls at 1.0x); surviving 4-bit deployment quantization. A recipe map from 124M to one trillion parameters shows the law tracks training recipe, not scale or architecture — strongest carrier DeepSeek-R1-671B at 43–47x — and loud-recipe weights transform under the fold's transformation group exactly as solved game-theoretic value fields do. Second, the "learned similarity space" is a counted object: word kinship as exact co-occurrence shares reproduces semantic family structure (quark → lepton, neutrino, proton) with zero parameters and zero gradients. Third, UnisonAI: a complete language architecture in which every LLM mechanism — memory, attention, similarity, learning, prediction, generation — is replaced by a machine-verified law of the Smithian Fold Theory, zero trained parameters end to end. On identical held-out text the fold-native engine outperformed its trained transformer twin (cross-entropy 1.289 vs 1.888) after reading the corpus once (26 seconds) against 48,000 gradient readings (21 minutes per seed). Deployed as a live, continuously-learning agent whose teaching loop also runs autonomously: a teacher model asks, judges, and closes the learning law itself, and the engine self-plays against its own held lessons. Negative results reported in full with their scopes. Companion to The Smithian Fold Theory of Everything (DOI: 10.5281/zenodo.21182469; 307 suites, 1,844 forced checks, 0 failures). Engine and records: github.com/MettaMazza/UnisonAI and github.com/MettaMazza/Smithian-Fold-Theory-Of-Everything.
Industrial Internet of Things (IIoT) systems face growing demands for low-latency, energy-efficient, and trustworthy operation under heterogeneous devices, mobility, and renewable energy variability. Existing fog-cloud approaches typically optimize isolated objectives and lack integrated mechanisms for sustainability and verifiable coordination. This paper presents the Energy-Aware Hierarchical Green Fog (EAHGF) framework, which introduces a unified reinforcement learning (RL) orchestration layer that explicitly incorporates residual energy, renewable energy availability, spatial proximity (via BLE), and task deadlines into hierarchical fog-cloud decision-making. A lightweight Proof-of-Stake blockchain provides immutable auditability of allocations with minimal overhead. A stochastic multi-layer queuing model captures system dynamics, while RL-based scheduling and proximity-aware offloading jointly optimize energy and latency. Extensive OMNeT++/INET simulations with up to 3,000 heterogeneous IIoT devices (Poisson arrivals λ = 0.5-2 tasks/s, random waypoint mobility 1-5 m/s, 70% renewable offset on fog nodes) demonstrate that EAHGF achieves a workload acceptance rate of ~ 92%, reduces energy consumption by approximately 28%, and improves latency by ~ 22% compared to baseline fog frameworks and FogNetSim++. The integrated PoS blockchain maintains ~ 100 ms confirmation latency while providing blockchain-assisted accountability, traceability, and trust in resource allocation decisions. EAHGF thus offers a scalable, sustainable, and trustworthy foundation for next-generation Green IIoT deployments, preserving ~ 65% residual energy versus ~ 45% in conventional systems.
Autonomous AI agents capable of holding digital assets, signing transactions, and executing smart contracts on public blockchain networks have moved from research prototypes to active deployment over the past two years. Despite this pace of adoption, no systematic treatment of their architecture, coordination protocols, and governance structures exists that spans the full design space. This survey addresses that gap through a systematic review of the literature from 2019 to 2026, covering 177 peer-reviewed publications and 14 system documentation sources, identified through a structured search of IEEE Xplore, the ACM Digital Library, Scopus, and arXiv. We classify deployed and proposed systems along four architectural dimensions: on-chain execution, off-chain agents with on-chain settlement, verifiable off-chain computation, and multi-agent on-chain interaction. Then, we examine the coordination mechanisms through which agents reach collective decisions, covering auction-based protocols, cooperative multi-agent reinforcement learning, token-incentive structures, and gossip-based peer-to-peer coordination. Governance is treated as a distinct dimension, analysed through a technical lens, covering on-chain parameter control, dispute resolution, and DAO structures, and an organizational one, covering accountability, incentive alignment, principal–agent dynamics, and regulatory compatibility. We survey applications across decentralized finance, supply chain, IoT, and agent marketplace domains, and identify six open research problems whose resolution is a prerequisite for broader deployment. The convergence of mechanism design and multi-agent reinforcement learning in asynchronous blockchain environments is identified as the direction of greatest near-term research value.
The evolution of autonomous systems is reverberating through all industry sectors. Autonomous Industrial Systems represent the evolution of the current “Automated Industry,” towards systems that operate independently, supported by continuous edge decision making and learning. Beyond control, orchestration of complex industrial-level processes can enable large-scale decision making based on a continuous market and regulations signals: without a centralized operator, such processes can operate independently. Autonomy also extends to “Smart Infrastructures” that enable the smart detection and connectivity of constituents and their connections to support the edge processing and decentralized decision making. The self-organization characteristics of these systems shall drive the market towards more the large-scale deployment of both connecting infrastructures and industry. The cross-regulatory nature of these systems requires an orchestration approach for the provision of interdependent services from numerous operators along a supply chain. Therefore, the direction of future development lies in the growing autonomy of the economy and the evolving edge-driven digital connection of smart infrastructures, service production, and regulation of the resources and ecosystem on which the digital representation relies.
The paper investigates how cross-cultural branding has adapted to the new reality of globalization, digital revolution and dynamic customer needs. The paper reviews the historical and modern views on branding to analyze how organizations strive to create a consistent global brand while responding to the requirements of local culture. The study is based on a qualitative review which reveals such issues as the need for balancing standardization and localization, the concept of glocalization, cultural intelligence, AI-powered personalization, sustainable branding and immersive digital ecosystems (Web3, metaverse). The branding has moved from its original function of identification to more interactive approaches powered by technologies and sensitive to culture. The digital glocalization seems to be an adequate strategy that allows merging globalized identity and consumer-localized experience. Modern resilient brands should combine cultural intelligence, ethical sustainability, emotional integrity, and flexibility in digital environments. Graphical Abstract
Open access
Consumer Behavior in Brand Consumption and Identification
Zero-knowledge privacy protocols let users hide transaction details on public blockchains. Systems like Tornado Cash, FixedFloat, and the Houdini Private Swap feature recently added to Jumper rely on cryptographic techniques that unlink sender and receiver addresses. These constructions give legitimate users meaningful protection for their financial activity. They also create a straightforward dual-use dilemma. The February 2025 Bybit incident supplies a clear example. Thieves stole $1.5 billion in ETH, the largest cryptocurrency theft on record. The FBI linked the attack to North Korea’s Lazarus Group. The stolen funds moved quickly through Tornado Cash. The resulting lack of transparency triggered a wave of customer withdrawals. Bybit responded by securing loans of several hundred million dollars from other institutions to keep its operations running. Cases like this demonstrate that zk-based privacy tools, when used at large scale for illicit purposes, can accelerate liquidity crises and place costs on market participants who had no involvement in the original theft. The real problem is not the underlying mathematics that delivers privacy. It lies in the missing mechanisms that could impose accountability on criminal actors while leaving the privacy protections for everyone else intact.
Jin Ah Seo, Kun Hwa Lee, Vijayan Sugumaran, Jo Yeon Park · 5 authors
We build and evaluate a concrete Zero-Knowledge Machine Learning (ZKML)-based pipeline for epidemic diagnosis and show that it can enforce computational integrity without exposing raw medical data in a Web3 setting. In response to security challenges posed by centralized data handling in medical AI applications, particularly during public health crises such as COVID-19, ZKML offers a privacy-preserving alternative by combining machine learning and Zero-Knowledge Proofs (ZKP). We experimentally applied ZKML to a CNN (Convolutional Neural Networks)-based COVID-19 diagnostic model, achieving 87% accuracy and 0.35 loss. All proof generation and verification processes were executed entirely off-chain, with the verified outputs represented as committed public_vals recorded on-chain via smart contracts. To ensure authenticity, the system enforces dual ECDSA signature verification from both the model provider and the data provider. This mechanism prevents unauthorized submissions and confirms the validity of the result before it is stored on-chain. The system was tested under both normal and adversarial conditions, demonstrating robust and reliable operation. By enabling decentralized trust and self-sovereign control over data, this architecture aligns well with Web3 principles. The results indicate that ZKML can support the development of privacy-preserving and verifiable AI systems.
Open access
Adversarial Robustness in Machine Learning
Privacy-Preserving Technologies in Data
Artificial Intelligence in Healthcare and Education
Blockchains have evolved from simple distributed ledgers into programmable platforms that process complex application logic and carry significant financial value. All modern Web3 systems share a common goal: providing secure, decentralized, and trustworthy execution in an increasingly interconnected environment. However, this evolution has shifted the attack surface from isolated infrastructure disruptions to programmable economic abuse and cross-domain exploits. In this article, we focus on the research of blockchain attacks and defenses. In particular, we categorize the threat landscape and corresponding mitigation strategies according to both a four-tier layered architecture (network, cryptographic, consensus, and application) and cross-domain trust boundaries. We seek to answer these important questions: How has the research in blockchain security evolved over the past decade, especially with the rise of decentralized finance (DeFi) and cross-chain interoperability? How do local security assumptions fail when protocols are composed, and what are the driving needs for Web3 security research in the future?
The article examines the decentralized finances of some organizations that are an independent part of the financial system of the Russian Federation. The question of their place in the system of the branch of financial law is analyzed. Based on the study of the works of a number of well-known scientists of Soviet as well as Russian financial law, it is concluded that the idea of separating the finances of some organizations into an independent legal institution was put forward during the Soviet period. Such an independent institute of the branch of financial law was the Institute of Finance of state-owned enterprises (associations) and branches of the national economy. At the present stage, these ideas are expanding due to the justification of the include new legal institutions in the system of financial law, for example, organizations such as state corporations, public law companies, state and municipal institutions.
The present study analyzed the utilization of the CDF for climate resilience projects in vulnerable communities of Sinazongwe District, Southern Province. It was observed that vulnerable communities in Sinazongwe continue to experience water scarcity, reduced agricultural output, and livelihood insecurity, despite the availability of expanded CDF resources meant to address these climate stresses. The study adopted a descriptive case study design with a mixed-methods approach, and sampled 120 respondents using both random and non-random (purposive or non-probability) sampling procedures. The study then employed the semi-structured questionnaires to community members/beneficiaries, to gather quantitative data; as well as the conduction of interviews using the semi-structured interview guide on the CDF committee members, WDCs, and government officials, to gather in-depth qualitative insights; and FGD held with community groups to understand collective perceptions, and challenges. The findings revealed that major CDF-funded climate resilience interventions included borehole drilling and rehabilitation, irrigation projects, conservation farming, tree planting, and water supply systems. Water-related projects were identified as the most significant interventions because they improved access to water, household food security, irrigation activities, and community coping capacity during drought periods. The study further established that community participation mainly occurred through community meetings and Ward Development Committees, although participation remained largely consultative rather than fully empowering. The findings also revealed that political influence, inadequate funding, delayed disbursement of funds, limited technical expertise, weak monitoring systems, and poor integration of DRR affected effectiveness and sustainability of climate resilience interventions. The study concluded that CDF has significant potential to support local climate resilience and livelihood improvement through decentralized financing. However, climate resilience interventions remained inadequate relative to increasing climate-related risks affecting vulnerable communities in Sinazongwe District. The study recommends increased climate-focused funding under CDF, stronger integration of Disaster Risk Reduction into local development planning, improved community participation, strengthened governance and accountability systems, enhanced technical capacity, and greater investment in early warning systems, environmental conservation, and sustainable livelihood diversification.
SPECTRA is a static-analysis framework for Solidity contracts that integrates symbolic execution, seven-domain abstract interpretation, semantic vulnerability detection, specification synthesis, SMT validation, and CEGAR refinement. Across 220 contracts, SPECTRA analyzes 216 (98.18%), infers 1,381 function-level specifications (6.39 per successful contract), and reports 673 vulnerability findings in 192 contracts (88.89%), with 0.70 s median runtime. These results indicate that specification-oriented analysis remains practical at benchmark scale.
The rapid integration of cryptocurrencies into mainstream finance has introduced a novel form of digital collateral into the mortgage lending landscape, yet the consequences for traditional real estate-backed lending applications remain poorly understood. Integrating the Technology–Organization–Environment (TOE) framework with the core-satellite investment model, this study investigates if crypto-backed products displace conventional real estate-backed applications. Utilizing traditional mortgage application records from a financial institution adopting crypto-collateral in 2022, we find that crypto-backed mortgages significantly reduce traditional mortgage approval rates. This "crowding out" effect is intensified by regional cryptocurrency legitimacy and housing illiquidity, while mitigated by property information insensitivity and speculative concerns. Furthermore, results reveal heterogeneous impacts across demographics: this innovation potentially exacerbates racial discrimination while alleviating age-based disparities. These findings underscore the complex interactions between emerging fintech and traditional mortgage lending, suggesting that collateral innovation may redistribute credit access across diverse market segments.
Flawed funds allocation logic in DeFi contracts can result in disproportionate token distribution, reflecting fundamental errors in how contracts determine and assign user payouts. These issues extend beyond funds allocation logic and affect token distribution mechanisms. Existing defenses such as static analyzers and runtime detectors fail to capture these behaviors because each operation appears valid in isolation. In this work, we present AllocScope, an auditor-centric static analysis framework that identifies allocation manipulation vulnerabilities by modeling allocation-related fund flow semantics and generating vulnerability findings for auditors. AllocScope constructs a funds allocation graph to track the relationship between user contributions and received payouts, and identifies logic that result in unfair outcomes. Evaluated on over 8,000 real-world contracts, AllocScope achieves zero false negatives. A user study with experienced auditors confirms that its findings are accurate, actionable, and easy to integrate into standard auditing workflows.
This preprint formalizes the Cross-Agent Governance Alignment (CAGA) problem: the challenge of verifying mutual governance compatibility between autonomous AI agents operating under distinct organizational policy regimes, without disclosing proprietary governance structures. As AI agents increasingly coordinate across institutional boundaries in regulated industries (healthcare, finance, cross-border data exchange, supply chains), existing governance models prove insufficient. Current frameworks assume either a single organizational authority or full policy transparency between participants. Neither assumption holds in multi-stakeholder settings where governance constraints encode confidential risk tolerances, regulatory interpretations, and competitive strategy. This paper: Defines governance domains and cross-domain interactions in formal terms, adopting the triadic verdict space (ALLOW, DENY, ABSTAIN) of the execution-time authorization framework Introduces the governance alignment predicate Φ(Dᵢ, Dⱼ, τ) Formalizes the CAGA problem under an honest-but-curious threat model Identifies required solution properties spanning correctness, privacy, determinism, evidentiary sufficiency, and composable security, including the requirement that alignment protocols produce tamper-evident authorization artifacts sufficient for independent third-party replay, consistent with the Replay requirement of the Five Tests Standard (5TS) Demonstrates that CAGA is irreducible to existing paradigms, including agent communication protocols, federated learning, secure multi-party computation, single-organization governance architectures, and blockchain-based transparency systems We argue that CAGA constitutes a zero-knowledge coordination problem at the intersection of AI governance, cryptographic protocol design, and multi-agent systems. The paper deliberately stops at problem formalization and does not disclose protocol constructions or implementation mechanisms. By precisely defining the problem space and evaluation criteria, this work establishes the foundation for rigorous solution development and provides a formal framework against which candidate governance-alignment protocols can be assessed. Version 1.1 (July 2026) retitles the paper to make explicit that CAGA is formalized as a zero-knowledge coordination problem for cross-organizational AI governance; aligns terminology with the Five Tests Standard (5TS) v1.2.0 and the FERZ authorization-artifact vocabulary; adopts the triadic verdict space in the governance domain formalization; and adds a companion reference to Execution-Time Authorization for AI Agents (v2.1), which develops the formal architecture of the single-domain authorization boundary. The problem formalization, threat model, and irreducibility argument are unchanged from the February 2026 release (v1.0). Keywords: AI governance, multi-agent systems, zero-knowledge proofs, cross-organizational coordination, governance alignment, deterministic governance, authorization boundaries, authorization artifacts, Five Tests Standard