Paper 14 gives an analytical model of the Qoin economy as a dynamic network: node balances that rise and fall with local creation and consumption events, a physical de- livery graph those events populate, and closed-form results for adoption, topology, and resilience under stated assumptions. Those results are the model’s skeleton. This pa- per is discursive rather than mathematical: it asks what would actually need to be built to give that skeleton stochastic life, test its assumptions, and check its closed-form predictions against simulated behaviour — before any of it touches a real deployment. Five requirements follow directly from Paper 14’s own structure, not from any new modelling choice. The event log is not an implementation detail but the correct primary data structure, because Paper 14 already defines node balance as a derived quantity rather than stored state — the model specifies event sourcing whether or not the word is used. The simulation engine should be discrete-event rather than continuous, because every quantity in the model changes at a point in time, not continuously. Node arrival, edge formation, and lifecycle-window realisation are three distinct stochastic processes, each with its own calibration target, and should not be collapsed into one undifferentiated source of randomness. Calibrating the model against reality requires specific, nameable data that does not yet exist, and the paper says exactly what that data would need to be. And nothing built should be trusted beyond what Paper 14 already proves analytically until it reproduces those proofs first. This paper does not specify the real distributed ledger of Paper 1, does not perform any calibration (no pilot data exists), and does not address deployment, production, or user-facing engineering. It specifies a research instrument for studying the dynamics, nothing more.
This paper develops BU76 AAI-08|Institutional Real-Time Closure Operations as the eighth file in the B_U-based Agentic AI series. Its central claim is that the next stage of Agentic AI should not be limited to single-enterprise automation, departmental coordination, or workflow orchestration. The decisive transition is toward a same settlement surface for enterprises, institutions, industrial clusters, infrastructure systems, and multi-flow real-world operations. In this frame, Agentic AI becomes a real-time closure interface for social-scale coordination, not merely a productivity layer inside software. The paper begins by reframing institutional operation as a multi-flow reality system. Enterprises and institutions do not operate through isolated tasks. They continuously coordinate people, goods, places, capital, information, time, permissions, responsibilities, risks, and feedback. Meetings, medical services, dining, travel, procurement, production, logistics, finance, legal review, customer service, and public services are not separate events. They are scenario windows in which multiple flows must enter the same state ledger and settlement window. When these flows remain fragmented across departments, firms, platforms, or infrastructure layers, the system generates hidden residuals: timing mismatch, resource conflict, responsibility ambiguity, logistics delay, budget misalignment, and operational bottlenecks. BU76 upgrades this analysis from a single enterprise to enterprise clusters, industrial clusters, and social infrastructure. A firm usually cannot see its future throughput capacity clearly because its real production chain is distributed across multiple companies, suppliers, logistics nodes, financial windows, labor pools, public services, and spatial infrastructures. Therefore, the true settlement surface is not inside one company. It emerges when enterprise clusters, industrial clusters, infrastructure networks, financial systems, logistics systems, public-service systems, and social demand enter a shared settlement window. This is the level at which future capacity, bottlenecks, risks, and deployment gaps become visible. The paper introduces all-factor co-temporality as the operating condition of this settlement surface. All-factor co-temporality means that people, goods, places, capital, information, time, permissions, responsibilities, risks, and feedback enter the same state ledger and settlement window within a shared time range. This condition applies at multiple nested scales: an individual user, a single enterprise, enterprise-to-enterprise coordination, industry-to-industry coordination, and the alignment between enterprise or industrial capacity and social demand. These layers form a multi-respiratory-system structure, in which demand flow acts as oxygen, production flow supplies output, logistics flow transports, capital flow circulates, information flow signals, human flow provides meaning and service interaction, responsibility flow identifies boundaries, infrastructure forms organ-like carrying capacity, and the same settlement window records the metabolic rhythm. BU76 further defines pre-feedback and preloading as institutional operating capacities. Preloading is not completed settlement. It is the feasibility loading of future demand matrices into the same settlement surface. It produces feasible-throughput readouts, bottleneck exposure, and pre-deployment signals before action occurs. Pre-feedback therefore differs from real-time feedback: real-time feedback corrects ongoing deviation, while pre-feedback exposes future capacity pressure under current constraints, resources, time windows, spatial capacity, responsibilities, and risks. Its confidence interval must be assessed through the B_U development chain: background clearing, admissible carrier, directional amplification, unified settlement, and resolution ascent. The final judgment is that institutional Agentic AI must evolve into a social-scale closure operation system. Its value lies in aligning demand and production at higher granularity, synchronizing multiple real-world flows, exposing bottlenecks before failure, stabilizing resource deployment, and enabling higher-order amplification and civilizational development through a shared settlement surface.
Maneesha Papireddygari, Xintong Wang, Bo Waggoner, David M. Pennock
Automated Market Makers (AMMs) are used to provide liquidity for combinatorial prediction markets that would otherwise be too thinly traded. They offer both buy and sell prices for any of the doubly exponential many possible securities that the market can offer. The problem of setting those prices is known to be #P-hard for the original and most well-known AMM, the logarithmic market scoring rule (LMSR) market maker [Chen et al., 2008]. We focus on another natural AMM, the Constant Log Utility Market Maker (CLUM). Unlike LMSR, whose worst-case loss bound grows with the number of outcomes, CLUM has constant worst-case loss, allowing the market to add outcomes on the fly and even operate over countably infinite many outcomes, among other features. Simpler versions of CLUM underpin several Decentralized Finance (DeFi) mechanisms including the Uniswap protocol that handles billions of dollars of cryptocurrency trades daily. We first establish the computational complexity of the problem: we prove that pricing securities is #P-hard for CLUM, via a reduction from the model counting 2-SAT problem. In order to make CLUM more practically viable, we propose an approximation algorithm for pricing securities that works with high probability. This algorithm assumes access to an oracle capable of determining the maximum shares purchased of any one outcome and the total number of outcomes that has that maximum amount purchased. We then show that this oracle can be implemented in polynomial time when restricted to interval securities, which are used in designing financial options.
Nawaz Abdullah Malla, Rumyana Neykova, Giuseppe Destefanis, Francesco Tiezzi
Blockchain platforms have revolutionized decentralized computing, with smart contracts enabling trustless and autonomous applications. However, the diversity of blockchain ecosystems—such as Bitcoin, Ethereum, Solana and Algorand creates significant challenges for developers seeking cross-platform compatibility. Algorand, known for its scalability and security, transaction finality and transaction costs has a distinct smart contract model that differs from Ethereum’s Solidity-based approach, making migration and adoption difficult. This study explores the potential of Large Language Models (LLMs) in addressing this challenge by assisting in the translation of Solidity smart contracts into Algorand’s Python-based smart contract language. We evaluate four LLMs—Claude, ChatGPT, Qwen, and DeepSeek—using various prompting strategies across 10 contracts. Our findings reveal that while LLMs can accelerate the learning process and provide useful code suggestions, they introduce translation errors and require careful validation. These insights contribute to understanding how AI-powered tools can support blockchain development, reduce manual effort, and lower barriers to entry for developers transitioning to Algorand.
Smart contracts play a pivotal role in blockchain ecosystems, and fuzzing remains a critical approach to securing them. However, existing smart contract fuzzers often optimize either seed generation or mutation scheduling in isolation and rely on narrow, fragmented feedback signals, leaving multi-transaction reasoning and stagnation recovery under-explored. In this work, we propose aLarge Language Models(LLMs)-based Multi-feedback Smart Contract Fuzzing framework (LLAMA). Key components of the proposed LLAMA include: (i) a hierarchical prompting strategy that guides LLMs to generate structurally valid, context-aware multi-transaction initial seeds, together with a lightweight pre-fuzzing phase that validates and prioritizes high-potential LLM-generated candidates; (ii) a multi-feedback-guided evolutionary optimization module that jointly optimizes seed selection and mutation scheduling by a group of constraints for driving an LLM-bootstrapped bandit scheduler. (iii) an LLM-guided hybrid fuzzing module that integrates evolutionary fuzzing with a dual-channel recovery mechanism, which concurrently employs asynchronous coverage-stagnation- based LLM reseeding and selective symbolic execution to resolve complex path constraints. Our extensive experiments demonstrate that LLAMA outperforms state-of-the-art fuzzers in both coverage and vulnerability detection. Specifically, it achieves 92% instruction coverage on small contracts and 81% on large contracts, while detecting 132 out of 148 known vulnerabilities across diverse categories. Ablation studies further evidence that the proposed multi-feedback and hybrid recovery strategies have strong impact on LLAMA’s performance. The results explain LLAMA’s effectiveness, adaptability, and practicality in complex smart contract scenarios.
In this paper, we propose an analytical method to compute the collateral liquidation probability in decentralized finance (DeFi) stablecoin single-collateral lending. Our approach models the collateral exchange rate as a zero-drift geometric Brownian motion, and derives the probability of it crossing the liquidation threshold. Unlike most existing methods that rely on computationally intensive simulations such as Monte Carlo, our formula provides a lightweight, exact solution. This advancement offers a more efficient alternative for risk assessment in DeFi platforms.
Romario Desouza Daniel Mangiwa, Revina Siregar, Sri Anum Sari, Neli Agustina
Ethereum is one of the leading cryptocurrencies utilizing blockchain technology for peer-to-peer financial transactions. This study aims to forecast Ethereum's price using the Autoregressive Integrated Moving Average (ARIMA)model. Historical price data from January 1, 2023, to January 15, 2025, covering 534 periods, was analyzed. The ARIMA (0,1,9) model was selected based on AIC, SC, and Adjusted R-squared criteria, with forecast evaluation showing a Mean Absolute PercentageError (MAPE) of 15.01% and a Root Mean Squared Error (RMSE) of 649.702. Forecast results indicate an upward trend in Ethereum's price over the next 30 periods, with fluctuations being less pronounced compared to historical data. The study concludes that ARIMA provides reasonably accurate short-term predictions, although forecasting errors increase with longer prediction periods. These findings can serve as a reference for investors in developing short-term investment strategies for Ethereum.
DeFi (Decentralized Finance) is one of the most important applications of today's cryptocurrencies and smart contracts. It manages hundreds of billions in Total Value Locked (TVL) on-chain, yet it remains susceptible to common DeFi price manipulation attacks. Despite state-of-the-art (SOTA) systems like DeFiRanger and DeFort, we found that they are less effective to non-standard price models in custom DeFi protocols, which account for 44.2% of the 95 DeFi price manipulation attacks reported over the past three years. In this paper, we introduce the first LLM-based approach, DeFiScope, for detecting DeFi price manipulation attacks in both standard and custom price models. Our insight is that large language models (LLMs) have certain intelligence to abstract price calculation from smart contract source code and infer the trend of token price changes based on the extracted price models. To further strengthen LLMs in this aspect, we leverage Foundry to synthesize on-chain data and use it to fine-tune a DeFi price-specific LLM. Together with the high-level DeFi operations recovered from low-level transaction data, DeFiScope detects various DeFi price manipulations according to systematically mined patterns. Experimental results show that DeFiScope achieves a high recall of 80% on real-world attacks, a precision of 96% on suspicious transactions, and zero false alarms on benign transactions, significantly outperforming SOTA approaches. Moreover, we evaluate DeFiScope's cost-effectiveness and demonstrate its practicality by helping our industry partner confirm 147 real-world price manipulation attacks, including discovering 81 previously unknown historical incidents.
Due to the inherent immutability of blockchain technology, smart contract updates require their deployment at new addresses rather than modifying existing ones, thus fragmenting version histories and creating critical blind spots for analyses. Indeed, for example, this fragmentation severely hinders security researchers ability to track vulnerability lifecycles across contract versions. While platforms like Etherscan provide detailed information about Ethereum smart contracts, they lack crucial functionality to trace predecessor-successor relationships within smart contract lineages, preventing systematic analysis of how vulnerabilities emerge, propagate, and potentially remain unresolved across versions.To address the challenge of tracing smart contract lineages, we adopt a Design Science Research (DSR) approach and introduce ContractTrace, an automated infrastructure that accurately identifies and links versions of smart contracts into coherent lineages. This tool enables the construction of lineageSet, an up-to-date, open-source dataset specifically designed to support security research on vulnerability, defect or any other property evolution patterns in smart contracts. Through a security-focused case study we demonstrate how ContractTrace reveals previously obscured vulnerability life-cycles within smart contract lineages, tracking whether critical security flaws persist or get resolved across versions. This capability is essential for understanding vulnerability propagation patterns and evaluating the effectiveness of security patches in blockchain environments. In the evaluation phase of our DSR approach, we validated our lineage detection methodology against an alternative approach using Locality-Sensitive Hashing (LSH) to cluster contract versions, confirming the security relevance and accuracy of our technique.
As a key enabler of Web3, Ethereum has long faced scalability challenges. The recent EIP-4844 upgrade aims to alleviate the scalability issue by introducing the ''blob'', a new data structure for Layer-2 rollups that enables off-chain storage with much reduced costs. Yet, this new mechanism's impact on Ethereum, and the wider Web3 ecosystem, remains largely underexplored. In this paper, we conduct the first large-scale empirical analysis of the post-EIP-4844 ecosystem, leveraging a dataset of 319.5 million transactions, out of which 1.3 million are blob-carrying. Our analysis reveals two major trends: (1) average block size has increased 2.5 times, from 150 KB to 400 KB, while the share of conventional transactions has shrunk from over $150$ KB to around 80 KB; (2) rollups are rapidly migrating from expensive calldata, falling from approximately 7,500 to nearly zero, toward cheap blobs, rising from zero to about 10,000. These shifts introduce a new economic game between block builders and rollups. Thus, we develop a game-theoretic model to characterize their equilibrium strategies: a profit-maximizing inclusion rule for builders, and a cost-minimizing blob batching strategy for rollups. Empirically, however, we find notable economic inefficiencies: for example, 29.48% of blob-containing blocks are built sub-optimally, yielding less revenue than available alternatives. These findings highlight the intricacies of the blob marketplace, and our work has established both methodological and empirical foundations to understand the evolving post-EIP4844 Ethereum ecosystem.
Asset securitization is an important financial derivative involving complicated asset transfer operations. Therefore, digitizing traditional asset securitization contracts will improve efficiency and facilitate reliability verification. Furthermore, accurate and verifiable requirement description is essential for collaborative development between financial professionals and software engineers. A domain specific language for writing asset securitization contract has been proposed. This solves the problem of difficulty for financial professionals to directly write smart contract by simplifying writing rules. However, due to existing design of the language focused on some simple scenarios, it is insufficient and informal to describe various detailed scenarios. What is more, there are still many reliability issues, such as verifying the correctness of the logical properties of the contract and ensuring the consistency between the contract text and the contract code, within the language in the generation and execution of smart contracts. To overcome the challenges stated above, we extend, simplify and innovate the syntax subset of the domain specific language and name it AS-SC (Asset Securitization – Smart Contract), which can be used by financial professionals to accurately describe requirements. Besides, because formal methods are math-based techniques that describe system properties and can generate programs in a more formal and reliable manner, we propose a semantic consistent code conversion method, named AS2EB, for converting from AS-SC to Event-B, a common and useful formal language. AS2EB method can be used by software engineers to verify requirements. The combination of AS-SC and AS2EB ensures consistency and reliability of the requirements, and reduces the cost of repeated communications and later testing. Taking the credit asset securitization contract as case study, the feasibility and rationality of AS-SC and AS2EB are validated. In addition, by carrying out experiments on three randomly selected real cases in different classic scenarios, we show high-efficiency and reliability of AS2EB method.
In recent years, particularly with the Ethereum blockchain’s advent, smart contracts have gained significant interest as a means of regulating exchanges among multiple parties via code. This surge has prompted the emergence of various smart contract (SC) programming languages, each possessing distinct philosophies, grammatical structures, and components. Conse-quently, developers are increasingly involved in SC programming. However, these languages are platform specific, implying that a transition to another platform necessitates the use of different languages. Additionally, developers require a certain level of control over SCs to address encountered bugs and ensure maintenance. To address these developer-centric challenges, this paper presents SCEditor, a novel Eclipse Sirius-based prototype editor designed for the visualization, design, and creation of SCs. The editor proposes a means of standardizing the usage of SC programming languages through the incorporation of graphical syntax and a metamodel conforming to Model-Driven Engineering (MDE) principles and SC construction rules to generate an abstract SC model. The efficacy of this editor is demonstrated through testing on a voting SC written in Vyper and Solidity languages. Furthermore, the editor holds potential for future exploitation in model transformation and code generation for various SC languages.
Zibin Zheng, Jianzhong Su, Jiachi Chen, David Lo · 6 authors
The Smart Contract Weakness Classification Registry (SWC Registry) is a widely recognized list of smart contract weaknesses specific to the Ethereum platform. Despite the SWC Registry not being updated with new entries since 2020, the sustained development of smart contract analysis tools for detecting SWC-listed weaknesses highlights their ongoing significance in the field. However, evaluating these tools has proven challenging due to the absence of a large, unbiased, real-world dataset. To address this problem, we aim to build a large-scale SWC weakness dataset from real-world DApp projects. We recruited 22 participants and spent 44 person-months analyzing 1,199 open-source audit reports from 29 security teams. In total, we identified 9,154 weaknesses and developed two distinct datasets, i.e., DAPPSCAN-SOURCE and DAPPSCAN-BYTECODE. The DAPPSCAN-SOURCE dataset comprises 39,904 Solidity files, featuring 1,618 SWC weaknesses sourced from 682 real-world DApp projects. However, the Solidity files in this dataset may not be directly compilable for further analysis. To facilitate automated analysis, we developed a tool capable of automatically identifying dependency relationships within DApp projects and completing missing public libraries. Using this tool, we created DAPPSCAN-BYTECODE dataset, which consists of 6,665 compiled smart contract with 888 SWC weaknesses. Based on DAPPSCAN-BYTECODE, we conducted an empirical study to evaluate the performance of state-of-the-art smart contract weakness detection tools. The evaluation results revealed sub-par performance for these tools in terms of both effectiveness and success detection rate, indicating that future development should prioritize real-world datasets over simplistic toy contracts.
A proof of balance plus transaction fees block-chain cash system as well as a proof of logarithmic stake block-chain system are proposed. Securities of both systems are analysed.
Blockchain technology has provided a platform for the decentralized execution of smart contracts. A smart contract is an agreement that is automatically executed when certain conditions have been met. The immutability, decentral nature, and consensus mechanisms that are characteristic to blockchain technology make the smart contract and its development cycle a new field of study in software engineering. A novel economic and defensive thinking is needed to develop workable, secure smart contracts. Motivated by the need for a novel approach to development, this thesis proposes a model-driven approach to smart contract development.\nModel-Driven Engineering (MDE) is an approach to information system development in which models and model technologies are applied to raise the level of abstraction at which developers create and evolve software, with the goal of both simplifying and formalizing the various activities and tasks that comprise the Software Development Life Cycle (SDLC). Model-Driven Architecture (MDA) is a framework for this approach. This thesis aims to apply this framework to create a method which describes the development phase from domain knowledge to smart contract foundation.\nThe creation of a method has two main aims, namely (i) to bridge the semantic gap between domain knowledge and smart contract by lowering the threshold for domain experts, and (ii) support developers in creating less vulnerable smart contracts that accurately represent the problem domain. This is done by constructing a model-driven method based on existing research that applies MDE to smart contract development. A literature study into this field yields the requirements and techniques for the method, which is consequently constructed based on these requirements and techniques.\nThe method is evaluated in twofold. First, the value is assessed through a case study, which shows that the developer benefits from a structured approach and the reduction of manual programming. Second, by an experiment which shows that people are better able to comprehend and communicate about models containing functional aspects of the smart contract if a computational independent model is included. By doing so it fulfills the aim of lowering the threshold for domain experts to participate in the smart contract development cycle.
For any industry or company, to be a competent in market needs increase in performance in all possible ways. Quick response to market and sufficient production as per requirement is the only way to increase the returns, which require critical planning and production systems. For this co-ordination between business units or workstations is essential. Many of the executive managers in industries has to instruct only the task flows to its subordinates, that is a single straightforward production planning process is followed as executed from top level of organization; their capacity as per their education level is not utilized more than 10%. The use of multi-agent system allows physical distribution of the decisional system and procures a hierarchical organization structure with decentralized control that guarantees the autonomy of each entity and flexibility of network. Our study focuses on managers/partners that adapt together their local planning process to face different requirements of supply chain environment using different planning strategies, when decisions are supported by distributed planning systems. The agent based system has the advantage of making collaborative management of disturbances in supply chain as the agents has the advantage of making autonomous decisions in a distributed network. Because each partner can choose different behavior and all behavior has an impact on the overall performance, it is difficult to know which is preferable for each partner to increase their performance. Thus, in this paper study of Multi-behavior planning agent model is done using different planning strategies when decisions are supported by distributed planning system.