A comparison of average Bitcoin prices in US dollars and average Wolf numbers for the solar cycle average for 2009–2025 allowed us to construct a model that explains 63.22% of the data variance. The author predicts a decline in the average annual Bitcoin price in 2026 and 2027.
Context: Legal contracts have served as the bedrock of business transactions for millennia. They are core to modern supply chains, and their execution can now be automated through the use of i) smart contracts, supported by blockchain technology that safeguards data integrity, and ii) Internet-of-Things technologies to support their monitoring functions. Symboleo is a specification language used to formalize legal contracts, enable property analysis, and generate smart contracts for a permissioned blockchain platform (Hyperledger Fabric). However, automation around resulting smart contracts poses security challenges, particularly regarding who should have access to operate on contract elements. Additionally, how such smart contract should interact with their Cyber-Physical System (CPS) environment, including IoT devices, remains challenging. Purpose: The thesis proposes an architecture to integrate smart contracts, Complex Event Processing (CEP), message brokers, and a blockchain platform (namely Hyperledger Fabric) to support end-to-end Cyber-Physical Smart Contracts (CPSCs). This architecture makes it possible to connect IoT devices with smart contracts (generated using Symboleo) through a CEP engine and a message broker. Additionally, this thesis proposes an access control model, treating all contract elements as resources and ensuring regulated access by designated parties. This model extends the Symboleo ontology and language for legal contracts with new modeling concepts inspired by Role-Based Access Control (RBAC), tailored for the legal contract domain, resulting in SymboleoAC (Symboleo Access Control). SymboleoAC also extends the Symboleo language to handle dynamic contract execution scenario. Methodology: This research follows a Design Science Research methodology, which guides the development and evaluation of the research artifacts. This research is conducted in several iterative steps that are divided into two main phases, one that focuses on theoretical aspects and the other on the design, demonstration, and evaluation of the research artifacts. Contributions: The contributions of this thesis are: • An architectural framework for CPSCs that leverages complementary aspects of CPS and smart contracts; • SymboleoAC, an access control ontology for Symboleo; • An extension of the current Symboleo specification language (syntax and semantics) that supports smart contract requirements, including automation and control actions, access control, and CPS components; • An implementation of the SymboleoAC ontology and semantics into a reusable JavaScript library (SymboleoACJS), together with a tool, SymboleoAC2SC, that generates JavaScript smart contract code with security aspects for a designated platform (Hyperledger Fabric); and • A secure and event-driven SymboleoAC Application Programming Interface (API) that orchestrates the runtime ecosystem connecting IoT sensors, the message broker, the CEP engine, and the blockchain platform. Through extensive and the evaluation of multiple variations of two contract case studies, SymboleoAC (architecture, ontology, and language), along with its associated tools, is shown to be an effective environment for CPSCs, simplifying the design of secure smart contracts and their connections to message brokers, CEP engines, and IoT devices.
Abstract Crypto-asset services without governance mechanisms maximize transparency and censorship resistance through automation but may sacrifice adaptability to changing market conditions, depending on their institutional design. This study examines the consequences of user adoption for a fully automated stablecoin bank that offers zero-interest loans: Liquity Protocol. Using 1586 daily observations from April 2021 to August 2025, this paper investigates whether user decline stems from portfolio allocation rationale or internal design constraints, under heightened competitive pressure and a tight monetary policy environment. We employ probit specifications to analyze the relationship between stablecoin (LUSD) peg deviations and three behavioral outcomes: collateralization adjustments, loan position closures, and capital withdrawals. Results provide strong evidence that negative peg deviations predict defensive position management, with marginal effects that are 4–6 times larger during post-competitive shock periods. The closure of loan positions exhibits the greatest sensitivity, with 8.7 percentage points across the pre-shock period versus 51.8 percentage points post-shock. In comparison, collateralization ratios increased significantly by 6.0 percentage points, versus 38.8 percentage points in the same periods, indicating a systematic deterioration in capital efficiency. By contrast, the directional probability of capital flight during the post-shock period remains comparatively insignificant. An extension analysis incorporating yield differentials from major competing services is implemented using both probit and OLS specifications. The OLS results show that yield differentials predict larger capital outflows in the pre-shock period ( $$p = 0.023$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>p</mml:mi> <mml:mo>=</mml:mo> <mml:mn>0.023</mml:mn> </mml:mrow> </mml:math> ), while full-sample and post-shock specifications are not significant. Concurrently, the probit results reveal significant links with the direction of capital withdrawal in the pre-shock period ( $$p = 0.007$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>p</mml:mi> <mml:mo>=</mml:mo> <mml:mn>0.007</mml:mn> </mml:mrow> </mml:math> ), with no further significant associations in the post-shock period. However, yield differentials show no significant predictive power for the magnitude or direction of position management or collateralization behavior in any specification. The evidence points to a coexistence of mechanisms throughout different temporal periods: yield competition acts as a magnitude amplifier for capital flows prior to 2024, when competitive pressure had not reached its peak. In contrast, as competition reaches a high point for stablecoin saving instruments by early 2024, the systematic day-to-day behavioral dynamics of position management (loan positions and collateral) becomes more consistent with protocol-internal design frictions. Regime-based robustness checks examining Federal Reserve tightening and major crypto market shock periods reveal distinct temporal patterns, with macro stress periods leading to capital flight, whereas active position management in the subsequent period of increasing competitive stress does not. These findings provide insight into the critical design trade-offs between deterministic automation and adaptive governance in the decentralized finance industry, particularly for decentralized banks, with implications for protocol developers and researchers studying the viability of governance-free design subject to alternating external market conditions.
The impact of digital transformation within the legal domain has extended to the theory of contract. This evolution has altered traditional contracting patterns towards electronic contracts and, ultimately, to smart contracts integrated into blockchain technology. These modern mechanisms, built on the foundations of automation and autonomy, have been effective in accelerating contractual transactions. However, these advantages have created challenges in view of their incompatibility with some of the established principles of contract theory. Therefore, this necessitates a legislative intervention to establish a comprehensive legal framework that ensures the compatibility of this technique, safeguards the safety of its use, and provides the necessary protection for its underlying data attaining contractual security. امتد تأثير التحول الرقمي في المجال القانوني ليشمل نظرية العقود. وقد غيّر هذا التطور أنماط التعاقد التقليدية نحو العقود الإلكترونية، وفي نهاية المطاف، نحو العقود الذكية المُدمجة في تقنية البلوك تشين. وقد أثبتت هذه الآليات الحديثة، القائمة على أسس الأتمتة والإستقلالية، فعاليتها في تسريع المعاملات التعاقدية. إلا أن هذه المزايا طرحت عددا من التحديات نظرًا لعدم توافقها مع بعض المبادئ الراسخة لنظرية العقود. لذا، كان لا بد من التدخل التشريعي لوضع إطار قانوني شامل يضمن توافق هذه التقنية، ويؤمن سلامة استخدامها، كما يوفر الحماية اللازمة للبيانات التي تستند إليها، مما يحقق الأمن التعاقدي. Keywords
# Proof-of-Information (PoI) Consensus Protocol## Complete Specification: MVP + Production > **Version:** 1(PIntegration) > **Status:** Specification Complete • MVP Ready for Testing > **License:** CC BY‑SA 4.0 > **Core Thesis:** *Consensus driven by predictive information quality, not resource expenditure.* --- > *"This proposal deserves serious study, refinement, and testing — not only for its technical merits, but for its potential to reframe our understanding of the relationship between truth, forecasting, and legitimate power in distributed systems."*>> — *Design Principle & Call to Validation* --- ## 📋 Executive Summary | Aspect | Description ||--------|-------------|| **Problem** | PoW wastes energy; PoS concentrates capital. Neither rewards *informational value*. || **Solution** | PoI secures the network through **verifiable forecasting accuracy** — validators earn influence via epistemic performance. || **Two-Track Design** | **MVP**: Minimal testable core (binary events, Brier scoring). **Full Spec**: Production-ready with ZK proofs, uncertainty layers, adaptive mechanisms. || **Output** | A blockchain that finalizes blocks *and* produces a public good: continuously updated, uncertainty-quantified global forecasts. || **Evaluation Ready** | Clear validation thresholds, implementation checklist, and progression roadmap from MVP → Production. | --- ## 🎯 Core Thesis > **Proof-of-Information** redefines blockchain security: instead of rewarding those who burn electricity or lock capital, PoI rewards those whose predictions about the external world are historically the most accurate, well-calibrated, and information-rich. By integrating strictly proper scoring rules (Brier, CRPS), zero-knowledge proofs of inference, and adaptive reputation systems directly into consensus, PoI transforms the blockchain from a transactional ledger into a **decentralized truth-seeking engine** — where the right to produce blocks is earned through epistemic merit. --- ## 📜 Epistemic Mandate > **Purpose Beyond Protocol** > Proof-of-Information is designed with a trans-temporal objective: to create a verifiable epistemic substrate that future superintelligent systems can use to anchor their understanding of reality. > > **Core Premise**: An ASI trained on data where every claim is cryptographically linked to an outcome, and every error is scored by a strictly proper rule, develops not just competence — but *calibrated humility*. > > **Design Implication**: Every technical choice in PoI (scoring rules, delay mechanisms, Cassandra bonuses) serves two masters: > 1. Immediate network security through epistemic merit > 2. Long-term value as a training environment for truth-aligned intelligence > > *This is not an aspiration. It is a constraint: if a feature does not serve at least one of these goals, it is out of scope.*
The global push for net zero emissions by mid-century is reshaping the competencies required in managing buildings. The built environment is a major front in climate mitigation, accounting for an estimated 38% of energy-related carbon emissions worldwide. This study reviews the tools, skills, and knowledge necessary to future-proof facility management (FM) professionals in alignment with evolving climate legislation, with a focus on the UK’s NetZero 2050 target. Through a critical synthesis of recent academic, industry, and policy literature, five key themes emerge: the rise of carbon literacy and regulatory fluency as core FM competencies, the urgency of digital proficiency, the importance of strategic communication, persistent gaps in training frameworks, and the uneven integration of sustainability tools in practice. Institutional barriers, such as outdated qualifications and limited upskilling opportunities are identified alongside technical challenges. A conceptual framework is proposed to guide FM upskilling, tool integration, and strategic repositioning within organizations. The findings offer actionable insights for industry bodies, training providers, and policymakers to align FM practice with national and global climate goals, highlighting that empowering FM professionals is pivotal to achieving decarbonisation targets.
Zero-knowledge proofs (ZKP) provide strong cryptographic guarantees allowing a prover to demonstrate knowledge of a property without revealing the underlying secret. These mechanisms are increasingly deployed in blockchain systems, digital identity infrastructures, web proofs, confidential finance, and privacy-preserving computation. However, many practical deployments rely on an often-overlooked component: the trusted acquisition and semantic extraction chain responsible for obtaining, interpreting, and transforming real-world data into proof witnesses. While proof verification may be cryptographically trustless, the acquisition process itself frequently depends on trusted runtimes, transport security, credential issuers, parsers, APIs, browser hooks, secure execution environments, or privileged software components. This paper argues that practical proof systems do not eliminate trust entirely, but frequently displace it toward increasingly complex acquisition infrastructures. We introduce the notions of Trusted Acquisition and Governed Disclosure, where programmable trust anchors and trusted governance platforms provide explicit, attestable, and policy-controlled acquisition boundaries. We further show that once such trusted acquisition layers already exist — as is often the case in industrial, enterprise, embedded, regulated, and digital identity systems — they may also provide simpler and more deployable alternatives to selected zero-knowledge constructions through governed disclosure mechanisms. The paper does not argue against zero-knowledge proofs. Instead, it proposes a complementary architectural perspective distinguishing between trustless verification and trustworthy acquisition.
Joseph malisaba, Barah Obinna Onyebuchi, Samuel George Onep, Emmanuel Ninsiima
<ns5:p> Background Access to safe drinking water remains a persistent challenge in low-resource settings such as Ishaka Municipality, Uganda, where surface and groundwater sources are frequently contaminated and access to reliable electricity is limited. This study presents the design, modeling, and performance evaluation of a solar-powered hybrid water treatment system integrated with a biosensor-based microbial detection unit, enabling autonomous operation and real-time water quality monitoring for decentralized applications. Methods A total of 384 water samples were collected from springs, wetlands, wells, and tap sources and analyzed for key physicochemical and microbial parameters, including turbidity, pH, and indicator organisms. The proposed system integrates sedimentation, activated carbon filtration, reverse osmosis, and solar thermal disinfection to achieve multi-barrier treatment. Hydraulic and filtration performance were modeled using fluid flow and porous media principles, while microbial inactivation was described using first-order kinetic models. The photovoltaic subsystem was evaluated through detailed loss modeling, incorporating temperature effects, partial shading, and inverter inefficiencies to assess overall system reliability. Results Baseline results indicated significant contamination, with <ns5:italic>Escherichia coli</ns5:italic> concentrations reaching 210 CFU/100 mL and turbidity values up to 146 NTU. The hybrid system achieved over 95% removal of contaminants, complete elimination of <ns5:italic>E. coli</ns5:italic> , and compliance with World Health Organization drinking water standards. Solar thermal disinfection provided a 4–6 log reduction in microbial indicators. The integrated biosensor demonstrated rapid response times (45–90 seconds) and strong correlation with laboratory biochemical oxygen demand measurements (R <ns5:sup>2</ns5:sup> = 0.89–0.94). The photovoltaic subsystem maintained a performance ratio of 0.84–0.88, consistently meeting 100% of operational energy demand under varying environmental conditions. Conclusion These results demonstrate that the proposed system provides an effective, energy-autonomous solution for decentralized water purification with real-time monitoring capability, offering significant potential for improving access to safe drinking water in rural and resource-limited environments. </ns5:p>
This study aims to analyze and synthesize prior research on navigating human resource capacity and accountability challenges in decentralized public finance through a Systematic Literature Review (SLR). The review focuses on how human resource capacity, fiscal autonomy, digital governance, and accountability mechanisms interact in shaping the effectiveness of decentralized public financial management. The SLR method was employed because it allows a structured and transparent synthesis of previous findings, identifies recurring patterns, and clarifies inconsistencies across studies. Literature was searched through the Directory of Open Access Journals (DOAJ), covering publications from 2022 to 2026, using combinations of keywords related to fiscal decentralization, human resource capacity, accountability, transparency, local government finance, and public financial management. The initial search identified 63 records, which were then screened based on title relevance, abstract suitability, research focus, publication year, full-text availability, and substantive alignment with the topic. After the selection process, 11 articles were retained for final review and analyzed through descriptive-qualitative synthesis. The findings indicate that decentralized public finance becomes more effective when supported by competent human resources, merit-based administration, strong internal control, adequate digital systems, and meaningful citizen participation. In contrast, weak technical capacity, fiscal dependence, fragmented institutions, and limited managerial autonomy repeatedly hinder accountability outcomes. This review contributes to the literature by reinforcing the capacity–accountability linkage as a central explanatory framework and by offering practical insight for policymakers and public administrators seeking to strengthen local fiscal governance in decentralized settings.
Modern company activities depend greatly on inventory management, which covers demand forecasting and inventory optimization to guarantee operational effectiveness and customer happiness. This paper presents a new method fusing blockchain technology with cutting-edge deep learning to overcome these restrictions for better inventory management. Initially, the data are preprocessed using Zmin–max normalization (ZMM), and then feature extraction follows. To extract the spatiotemporal features and capture long-term temporal dependencies in demand data, a hybrid deep learning architecture is presented, built on a Deep Convolutional Koopman Network (CKN) integrated with a Coordinate Attention-Based Gated Recurrent Unit (CKN-CGRU).Genetic Secretary Bird Optimization (GSBO) is used to further tune the model automatically. While the CKN captures complex spatial temporal correlations, the GRU effectively models sequential dependencies. Blockchain architecture with smart contracts and improved Proof-of-Stake consensus is integrated to guarantee data integrity and transparency in stock transactions. This makes it possible to securely, automatically, and in a tamper-proof way record inventory projections, orders, and stock updates. The suggested system improves the stakeholder trust in decentralized inventory management by ensuring complete traceability and real-time auditability throughout the process. Experimental outcomes show the efficiency of the proposed model strategy, with an accuracy of 99.94% and precision of 99.93%.
Blockchain technology functions as an innovative solution that allows users to obtain secure decentralized tamper-resistant data management capabilities. The traditional blockchain systems demonstrate drawbacks because they create scalability challenges and need high amounts of energy for operations together with delayed transaction times, especially when used in high-throughput and resource-constrained situations. This research designs an improved cloud-based blockchain system which combines Proof-of-Stake (PoS) protocol with Byzantine Fault Tolerance (BFT) controls, along with batch transaction handling systems, to foster better performance results. The framework delivers dynamic scalability and distributed processing efficiency because it makes use of cloud infrastructure. The implementation simulation reveals substantial increases in transaction rate upto 10,000 TPS and quicker consensus times together with enhanced validator impartialty as well as more than 60% energy efficiency improvement, as compared to standard Proof-of-Work protocols. Security and trust evaluation methods exhibit thorough testing of the system that confirms its capabilities in adverse environments. This proposed framework provides an energy-efficient solution which can serve robust real-world applications in IoT together with healthcare and supply chain and finance domains.
This paper examines the growing role of blockchain and distributed ledger technology (DLT) in transforming climate finance and global carbon markets. Drawing on twenty-six peer-reviewed studies alongside current market intelligence from financial institutions, technology firms, regulatory bodies, and multilateral organizations, the research explores how blockchain infrastructure is reshaping transparency, efficiency, and trust within carbon credit ecosystems. The study presents three primary contributions. First, it synthesizes recent 2025–2026 developments in Regenerative Finance (ReFi), Web3 systems, and Layer 2 blockchain architectures influencing modern carbon market infrastructure. Second, it introduces a Blockchain Climate Finance Readiness Matrix designed to map deployment conditions to expected institutional and regional outcomes. Third, it proposes a conceptual framework for a next-generation integrated on-chain carbon ecosystem aimed at addressing structural gaps in emerging climate finance systems. Existing literature highlights significant operational benefits from blockchain adoption in carbon markets. Prior studies report improvements in market price efficiency, major reductions in monitoring and verification timelines, and substantial decreases in administrative overhead across the carbon credit lifecycle. Current industry deployments, including J.P. Morgan's Kinexys Digital Assets platform, India's blockchain-enabled Carbon Credit Trading Scheme, and emerging ReFi infrastructure initiatives, demonstrate how blockchain integration is increasingly becoming a foundational layer for next-generation climate finance ecosystems.
Federated Learning (FL) enables collaborative model training while preserving data privacy but relies on centralized aggregation servers, leading to issues such as lack of transparency, vulnerability to malicious updates, and single points of failure. This paper proposes a decentralized federated learning framework integrating blockchain technology and the InterPlanetary File System (IPFS) to eliminate central authority and enhance trust. Smart contracts deployed on the Ethereum Sepolia testnet manage model submission, validation, incentive distribution, and reputation tracking. Model updates are stored off-chain using IPFS, while their hashes are recorded on the blockchain to ensure integrity and immutability. A staking and slashing mechanism is introduced to encourage honest participation, where valid contributions are rewarded and malicious updates are penalized. A reputation system further evaluates participant reliability over time. The system is implemented using PyTorch, Solidity, Web3.py, and React.js. Experimental results demonstrate improved security, transparency, and efficient decentralized coordination, highlighting the feasibility of integrating federated learning with blockchain and decentralized storage for scalable and trustworthy machine learning applications.
Dr. Soumya M Anakal, Sharanabasava, Vinayak S Chakki
The global creator economy exceeds $100 billion but remains dominated by platforms such as YouTube, Spotify, and Patreon. These platforms control distribution and monetization, charging high fees, delaying payments, and exercising censorship. To address these issues, a decentralized Web3 Music and Content Monetization Platform is proposed. The system uses blockchain, non-fungible tokens (NFTs), decentralized storage, and crypto wallets to enable creators to upload, mint, and sell their digital content directly to audiences. Each asset is stored on the Inter Planetary File System (IPFS) and represented on the Polygon blockchain as an ERC-721 token. Consumers purchase NFTs via MetaMask, and smart contracts automatically transfer payments to creators, enforcing royalties without intermediaries. A prototype demonstrates NFT minting, wallet-based payments, and content access control, validating the technical feasibility of the approach. Compared with centralized systems, the proposed platform offers instant payments, transparent ownership, and censorship resistance, aligning with similar advancements in decentralized music and publishing platforms. Future work includes exploring multi-chain support, decentralized governance through DAOs, and integrating metaverse applications.
Cryptocurrency pump-and-dump schemes coordinated via Telegram threaten market integrity. However, existing research addressing this specific threat has not yet produced solutions that combine reliable results with fast response. This is in part due to the absence of publicly available, message-level labeled data, as well as design choices. In this paper, we address both issues. In particular, we introduce a corpus of over 280,000 Telegram posts from 39 pump-organizing groups, all manually reviewed to identify 2,246 pump announcements and their targeted cryptocurrency and exchange. Leveraging this dataset, we define two tasks: real-time pump-announcement detection and target cryptocurrency/exchange extraction. For detection, we compare two machine-learning models: a lightweight tree-based LightGBM classifier (F1=0.79, latency=9.4 s/sample) and a transformer-based BGE-M3 (F1=0.83, latency=50 ms/sample). With our proposed approach, we show that message analysis can achieve near-instant pump detection at the level of individual Telegram message windows. Unlike prior work that relies purely on market data and typically detects pumps tens of seconds after abnormal trading activity is observed, our method operates directly on the coordination messages themselves and can be evaluated in microseconds per window on commodity hardware. To our knowledge, we also establish the first benchmark for manipulated coin and exchange extraction. We demonstrate that traditional rule-based extraction methods, widely relied upon in prior literature, are ineffective due to ticker ambiguity. In contrast, LLMs achieve the highest accuracy with a score of 0.91.
LLM guardrails face four structurally distinct barriers: algebraic blindness arising from syntactic monoid aperiodicity (unconditional); an illustrative information-theoretic lower bound (Fano-type, under a uniformity assumption); NP-hardness of instantiation verification; and structural transfer via free-category functoriality (unconditional) combined with string-level indistinguishability under a semantic-opacity assumption on symbol naming. Together these results characterize why inference-layer defenses are necessary but insufficient. We operationalize these barriers through five attack vectors. V1–V4 (homomorphic reasoning: decomposition, zero-knowledge pipelines, Tree-of-Thought solving over abstract grammars, and encoding bootstrap) exploit the information-theoretic and computational barriers against abstraction-based attacks. V5 (modular counting bypass) exploits algebraic blindness: we prove that all substring-matching regex guardrails have aperiodic syntactic monoids and are therefore provably blind to any payload encoded using modular counting. Empirically, V3 yields a mean yield of 0.466 for BFS, 0.172 for random-beam, and 0.122 for LLM-guided Tree-of-Thought (N=50, seeds 0–49, p{<}0.001); BFS dominates, as exhaustive search over small synthetic grammars outperforms LLM heuristic pruning. We extracted syntactic monoids from a corpus of 142 patterns drawn from twelve sources — 100 patterns shipped by nine third-party open-source guardrail projects and 42 patterns assembled from three author-curated pattern sets; 100\% are aperiodic, and the MOD_2 bypass construction succeeds against all aperiodic patterns. A 376-line proof-of-concept with three execution mediums validates all five vectors. We conclude that inference-layer guardrails are necessary but insufficient, and that effective defense must migrate to the execution layer where concrete artifacts become observable.
Abstract The SIS‑10 framework establishes a typed, invariant preserving safety calculus for cyber physical systems. It unifies temporal semantics, schedulability, semantic preservation, ML admissibility, cryptographic verification, and risk bounded control into a single mathematically coherent architecture. All domains and operators are fully explicit, enabling formal reasoning over system trajectories and safety envelopes. The temporal layer defines an ordered metric structure with drift aware bounded causality, interval set operators, and jitter robust event semantics. The QoS layer enforces schedulability and feasible actuation, ensuring that all control actions remain within admissible timing and load bounds. Semantic compression provides a safety preserving homomorphism that guarantees invariants survive dimensionality reduction. Multi‑modal fusion introduces cross sensor falsifiability, enabling fault detection through probabilistic disagreement. The ML layer is input validated and logic embedded, ensuring that all model outputs entail the SIS‑10 invariant set. The cryptographic layer supplies zero knowledge execution trace proofs, allowing runtime verification of transition correctness without revealing internal state. Predictive shutdown optimization is constrained by a formally defined safety envelope, ensuring that operational objectives never violate admissible safety bounds. Cyber physical risk evolves through a bounded monotone propagation model with explicit mitigation operators, while the Safety Twin provides deterministic and stochastic discrete time system dynamics. The inductive proof layer establishes global invariant preservation for all admissible executions, and the event→action mapping connects the formal calculus to real SIS triggers. SIS‑10 therefore constitutes a unified, verifiable, and implementation ready safety architecture, suitable for runtime assurance, cyber‑physical certification, and next generation functional safety systems. Further enhancements include graphical formalization, parameterized system tuning, implementation DSLs, and automated verification scripts, none of which alter the core mathematical model..
We study the Banach dual of the one-parameter stochastic integral δ_L(u) = ∫₀^T u_t dL_t for a symmetric γ-stable Lévy process with γ ∈ (1,2). The natural integrand exponent is p ∈ (1,γ): the small-jump integrability ∫|z|^p ν_γ(dz) < ∞ holds iff p < γ, so this is not an arbitrary L^p but the unique scale dictated by the singularity of the Lévy measure at the origin. On this scale, the operator-covariant derivative D_L := δ_L^* : L^q(Ω) → H_L^* is the Banach dual of the one-parameter integral. Since p < 2, the Riesz identification H_L^* ≅ H_L is unavailable, and the Banach setting is forced. The principal result is structural: D_L is strictly more restricted than the standard Malliavin add-a-point operator D_{t,z}F = F(ω + δ_(t,z)) − F(ω) on Poisson space, which is the dual of the full two-parameter compensated Poisson integral ∫∫ h(s,z) Ñ(ds,dz). By Lévy-Itô, the one-parameter integrand of δ_L has the special form h(s,z) = u(s) · z — linear in z — whereas full martingale representation on Lévy space uses general h(s,z). The representability obstruction quantifies the resulting gap precisely: centered functionals depending nonlinearly on jump sizes — canonically, the centered large-jump count #{|ΔL_s| > 1} − E[#{|ΔL_s| > 1}] — lie in ker(D_L) yet are detected by the standard add-a-point operator. The obstruction is a property of the one-parameter integral, not a feature of jump processes themselves. The factorization (Theorem A) holds on the closed proper subspace im(δ_L) ⊊ L^p_0(Ω) and characterizes precisely which functionals admit one-parameter representation. Theorem B (product rule with Leibniz defect) is a standalone duality identity: its proof uses only the definition of D_L, the Lévy-Itô formula, and Hölder's inequality, and it does not invoke (H3) or the factorization machinery. Theorem C — the strongest technical result — identifies ker(D_L) and the annihilator of im(δ_L) via L^q-L^p truncation in the jump variable, showing the annihilator is infinite-dimensional even within the first chaos. The framework has been formally verified in the Lean 4 proof assistant (2,439 lines, zero sorry, zero axioms) using Mathlib. To our knowledge, this is the first formalization of the operator-covariant derivative framework with its representability obstruction in any proof assistant. The formalization includes proved Poisson mean and variance identities, a constructed compound Poisson path, a compensated-integral interface with derived Banach-side consequences, a concrete first-chaos orthogonality model, and the full abstract theorem pipeline — all machine-checked from clearly isolated stochastic-analysis assumptions.
AI as Productive EnergyCivilization Physics — AI Economics & Human Systems Series This paper argues that AI should be understood less as a software feature embedded inside inherited workflows and more as a new form of productive energy: callable cognitive capacity that can be routed into many different tasks at low marginal cost. Like steam power and electricity before it, AI becomes economically transformative not when it exists as a tool, but when organizations and individuals reorganize production around its actual operational characteristics—rapid iteration, reusable context, broad symbolic competence, and continuous human evaluation . The analysis begins by distinguishing between adoption and reorganization. AI usage is spreading rapidly across firms and individuals, yet large-scale enterprise value remains uneven. The paper argues that this gap exists because many organizations are still attaching AI to old workflows rather than redesigning production loops around AI-native properties. AI therefore resembles earlier general-purpose technologies whose transformative impact depended on complementary organizational change rather than the technology alone. The historical analogy to steam and electricity provides the structural frame. Steam engines initially solved localized pumping problems before eventually reorganizing manufacturing and transportation systems. Electricity delivered its full productivity gains only after factories were redesigned around distributed power rather than centralized mechanical layouts. AI follows the same pattern: early deployment appears as isolated assistance or software augmentation, while deeper transformation emerges only when systems are rebuilt around AI’s strengths. To explain how this transformation occurs, the paper introduces the concept of micro-integration. A micro-integration is a bounded closure in which recurring friction is addressed through a tight loop connecting: Context retrieval. Model generation or agent action. Human evaluation and correction. Deployment or operational action. Telemetry and reusable feedback. Micro-integrations represent the primary mechanism through which AI diffuses socially and economically. Rather than following a single centralized adoption ladder, AI spreads through thousands of localized closures tailored to specific bottlenecks. The paper identifies several major routes of micro-integration: Local business arbitrage using AI-generated websites, lead extraction, and automation. Agentic product engineering through code generation, workflow automation, and autonomous tooling. Short-form content production using AI-assisted editing, generation, and localization. Personal health systems combining wearable data, coaching models, and behavioral planning. Personal knowledge systems integrating memory, search, scheduling, and persistent context. These use cases demonstrate that AI diffusion occurs not only through frontier labs or large enterprises, but through ordinary individuals and small teams building localized productive closures around recurring problems. A central theoretical contribution is the idea that AI acts as productive energy rather than as isolated intelligence. Productive energy becomes transformative when combined with complementary systems, feedback loops, and institutional structures. AI therefore does not simply automate work; it changes the feasible scale and granularity of human coordination, iteration, and cognitive outsourcing. The paper also emphasizes the importance of feedback loops in AI-native production. Anthropic’s analysis of software-development workflows illustrates how human-supervised “feedback loop” patterns dominate successful AI-assisted engineering. AI generates drafts or actions, humans evaluate and correct them, and the resulting loop stabilizes into reusable infrastructure. This pattern recurs across domains: AI succeeds where rapid feedback and bounded closures keep outputs connected to reality. At the same time, the paper recognizes important structural constraints. AI-assisted systems expand attack surfaces, increase dependency on centralized infrastructure, and may intensify concentration of compute, cloud resources, and capital. Micro-integrations can improve local productivity while still existing atop highly centralized infrastructure stacks. The paper therefore argues that governance, provenance, and accountability remain critical even in highly decentralized AI diffusion. The policy implications follow directly. Governance frameworks should focus less on generalized AI ethics rhetoric and more on preserving traceability, responsibility assignment, and operational accountability within AI-native closures. Public policy should support domain-specific AI literacy, micro-specialization pathways, and transparent feedback systems rather than only large-scale centralized deployment strategies. The paper concludes that AI diffusion is fundamentally plural rather than linear. AI spreads not through a single “leveling-up” ladder, but through countless small closures where callable intelligence removes recurring friction from work, culture, health, and everyday life. Within the Civilization Physics framework, this work establishes a broader principle: AI becomes economically transformative when human systems reorganize around its productive properties rather than merely embedding it inside inherited industrial structures. The future AI-native economy therefore emerges through distributed closures, continuous human evaluation, and increasingly dense networks of AI-assisted productive energy. Keywords: AI Economics · Productive Energy · Micro-Integration · AI-Native Economy · Human-AI Interaction · Workflow Redesign · General-Purpose Technology · Cognitive Infrastructure · Organizational Change · Civilization Physics
Abhinav Goel, Agostino Capponi, Alfio Gliozzo, Chaitya Shah
We introduce SmartEval, a benchmark for systematically evaluating the quality of Solidity smart contracts generated by large language models (LLMs) from natural language specifications. SmartEval provides a corpus of 9,000 generated contracts paired with expert-written ground-truth implementations drawn from the FSMSCG dataset, a five-dimensional evaluation rubric covering functional completeness, variable fidelity, state-machine correctness, business-logic fidelity, and code quality, and a reproducible generation-and-evaluation pipeline. To validate the benchmark's reliability, we conduct three independent empirical studies: a five-condition ablation study (N=300 per condition) isolating the contribution of each pipeline component, a human expert evaluation by three Columbia University PhD researchers confirming automated scores align with expert judgment to within 0.34 points, and external security analysis via the Slither static analyzer confirming 79.4% agreement between the LLM auditor and a non-LLM rule-based tool. Systematic analysis of 9,000 generated contracts reveals characteristic failure modes (logic omissions at 35.3%, state transition errors at 23.4%, and complexity-driven degradation) and quantifies a +8.29 composite-score advantage of generated contracts over ground-truth implementations, attributable to LLMs' literal specification-following behavior. SmartEval establishes a reproducible, validated foundation for empirical research on LLM smart contract synthesis quality, with all data, evaluation code, and generated contracts publicly released.
Gabriela Dobrita, Simona-Vasilica Oprea, Adela Bara
Smart-contract exploits have caused billions of USD in cumulative losses, yet audits remain expensive and slow. Automated tools have emerged to close this gap, but each class has a characteristic failure mode. Static analyzers report findings that frequently fail manual triage at high rates, while large language models (LLMs) hallucinate findings that contradict the source code. Thus, we propose Chaintrix, an end-to-end auditing framework whose central architectural commitment is that every LLM-generated claim must be discharged against a deterministic structural contract representation. We introduce a Cross-Contract Interaction Model (CCIM) that parses Solidity into a structured map of function-level reads, writes, modifiers and resolved cross-contract calls. CCIM serves as the substrate against which all 12 of Chaintrix's deterministic signal engines and the parallel LLM audit pipelines operate. A staged false-positive-reduction pipeline, terminating in a Structural Verdict Engine (SVE) that applies deterministic structural checks against parsed code, filters the merged finding set, with selected high-confidence findings further validated through symbolic execution and fuzz testing. We evaluate Chaintrix on EVMbench, the smart-contract security benchmark by OpenAI, Paradigm, OtterSec. Chaintrix detects 86 of 120 high-severity vulnerabilities (71.7% recall), with 25 audits scoring 100% recall, placing Chaintrix 26 percentage points above the strongest frontier-model baseline.
Umar . Abubakar, Hamza Itopa Sanni, Abdulsalam Aliyu
Conventional attendance management approaches in academic and organisational settings face persistent challenges that include susceptibility to proxy attendance, inadequate protection of biometric credentials, and the absence of privacy-preserving verification mechanisms in cloud-hosted deployments. This paper presents SecureAttend, a cloud-based attendance management framework that addresses these deficiencies through the integration of Zero-Knowledge Proof (ZKP) cryptographic authentication with biometric capture via a ZKTeco K40 Pro fingerprint terminal. The proposed framework employs a challenge-response ZKP protocol that enables users to demonstrate possession of valid authentication credentials without disclosing underlying private keys or biometric templates to the server. Attendance records are encrypted using AES-256 prior to storage in a MongoDB cloud database, while SHA-256 hashing provides tamper-evidence for each record. Session integrity is maintained through JWT-based token management, and access boundaries are enforced via a Role-Based Access Control (RBAC) policy. Functional evaluation across eighteen test scenarios confirmed complete compliance with stated requirements. Security assessment validated correct operation of cryptographic mechanisms, access controls, and audit logging subsystems. Performance benchmarks recorded average API response latencies of approximately 85 milliseconds for authentication requests and 120 milliseconds for attendance marking operations. The results demonstrate that ZKP authentication can be deployed effectively in real-world attendance management contexts, offering measurable improvements in privacy, integrity, and resistance to credential-based attacks compared with conventional approaches.
LLM-based financial agents increasingly rely on both numerical market data and textual signals for sequential trading and stock prediction. However, financial misinformation often appears as subtle textual perturbations rather than explicit falsehoods, making it difficult to detect while still capable of significantly altering agent reasoning and decisions. To study this risk, we propose AutoRedTrader, an autonomous red-teaming framework that generates finance-specific misinformation through behavioral bias manipulation, minor textual perturbations, and rewriting strategies, with agent feedback used to strengthen attacks over time. We evaluate AutoRedTrader in a POMDP-based financial agent simulation environment, and further examine a time-series-informed grounding setting for robustness analysis. The framework enables systematic evaluation of how subtle misinformation affects financial agents and whether historical market evidence can stabilize decisions under misleading textual signals. We evaluate the framework on Bitcoin transaction data. The results show that AutoRedTrader achieves the strongest attack performance with 69.00% misinformation exposure rate and 26.67% attack success rate, outperforming general-purpose misinformation and red-teaming baselines. Ablation studies further show that all modules contribute to generating retrievable and decision-effective financial misinformation.
Bowei Su, Mingxi Ye, Yuhong Na, Peilin Zheng · 5 authors
The Solidity smart contract ecosystem has rapidly grown, leading to multiple compilers targeting different blockchain platforms or improving compilation efficiency. Although many compilers aim to be compatible with the primary Solidity compiler (Solc), significant inconsistencies in compilation and execution remain. These inconsistencies hinder contract migration, mislead developers during debugging, and may introduce exploitable vulnerabilities, causing financial losses. Existing testing techniques mainly focus on bugs within a single compiler or perform differential testing in the same execution environment. However, they are insufficient for detecting cross-compiler inconsistencies, as they lack mechanisms to explore triggering conditions and compare bytecode across environments. We propose ParityFuzz, a cross-compiler differential testing framework for Solidity. It operates in three stages. First, it derives mutation rules, including syntax- and boundary-oriented rules, by analyzing compilers and execution environments. Second, it uses reinforcement learning to select effective mutation rules for test generation. Third, it compiles and executes programs across multiple compilers, then normalizes and compares results to detect inconsistencies. Our evaluation shows ParityFuzz is efficient and effective. It achieves up to 18x higher compilation success rate and 1.8x higher code coverage than state-of-the-art fuzzers. It uncovers 64 previously unknown inconsistencies across six compilers. Notably, 11 issues have been fixed, and our findings received a bounty from the Polkadot community.