Este artigo apresenta um esboço estruturado sobre “Migração de Lógica de Negócio para Layer‑2: Desafios de Compatibilidade.”. O objetivo é analisar os fundamentos técnicos e econômicos relacionados à migração de contratos e aplicações da camada base (L1) para soluções de escalabilidade em Layer‑2, discutindo implicações para o ecossistema Web3 e tendências de mercado. A metodologia baseia‑se em revisão bibliográfica e análise de casos práticos, com foco em diferenças entre soluções L2 EVM‑equivalentes e apenas EVM‑compatíveis, modelos de segurança de bridges, padrões de liquidez multi‑chain e impactos em tooling, UX e governança. Argumenta‑se que a migração não é apenas um “lift‑and‑shift” de bytecode, mas um processo que envolve reavaliação de suposições de segurança, dependências de infraestrutura (oráculos, indexadores, sequencers) e design de incentivos em ambientes com finalização e custos distintos da L1. Casos práticos de migração de protocolos de DeFi e indexação evidenciam trade‑offs entre custo por transação, fragmentação de liquidez e complexidade operacional, bem como a importância de padrões de bridging, mensageria cross‑chain e governança multi‑domínio para manter coerência de lógica e de risco entre instâncias L1/L2. Conclui‑se que migrar lógica de negócio para Layer‑2 exige abordagem incremental e consciente de compatibilidade, com atenção especial à equivalência de EVM, à segurança de rollups e bridges, e à coordenação de liquidez e governança em um ecossistema crescentemente modular e multi‑chain.<br>
O presente artigo analisa o estado atual da verificação formal de contratos inteligentes, com ênfase em ferramentas, métodos e limitações práticas para o ecossistema Web3. A verificação formal é compreendida como o emprego de técnicas matemáticas – entre as quais model checking, verificação baseada em SMT (Satisfiability Modulo Theories) e lógica de Hoare – para provar que propriedades especificadas são válidas para todas as execuções possíveis de um contrato, oferecendo garantias de segurança mais fortes do que aquelas proporcionadas por testes e auditorias manuais. Pesquisas recentes comparam ferramentas líderes voltadas à linguagem Solidity, a exemplo de solc-verify, SMTChecker, VeriSmart, ESBMC-Solidity e Certora Prover, destacando diferenças em expressividade de especificações, grau de automação, desempenho e taxas de falsos positivos e negativos. Surveys sistemáticos revelam ainda que, entre mais de duzentas ferramentas de análise e detecção de vulnerabilidades desenvolvidas entre 2018 e 2024, fração relevante adota métodos de verificação formal – especialmente model checking em nível de design e SMT em nível de implementação – mas que a adoção em esteiras industriais permanece limitada por fatores como complexidade de uso, custo e expertise especializada exigida. Casos práticos em protocolos de finanças descentralizadas (DeFi) demonstram que a verificação formal é capaz de detectar erros sutis, como bugs de arredondamento (rounding errors) que podem ensejar perdas de milhões de dólares, desde que invariantes de negócio e propriedades de segurança sejam corretamente especificados nas linguagens próprias de cada ferramenta. Conclui■se que, embora a verificação formal constitua peça crucial para elevar o patamar de segurança de contratos inteligentes críticos, ela enfrenta limitações de escalabilidade, cobertura de propriedades, dependência de especificações precisas e integração com ciclos ágeis de desenvolvimento, o que aponta para tendência de emprego combinado com auditoria manual, fuzzing e análise estática tradicional.
As Distributed Ledger Technology and smart contracts continue to grow in popularity, there is increasing interest in developing more expressive programming abstractions for digital asset management, along with verification tools that ensure safety and correctness before deployment on blockchain platforms. Addressing this challenge, we introduce AlgoMove , a framework designed to improve smart contract development on the Algorand blockchain. While Algorand is widely recognized for its high performance, scalability, and secure consensus protocol, it still lacks high-level programming abstractions and strong language-based verification mechanisms. AlgoMove brings the Move language, renowned for its robust support for secure digital asset management, to the Algorand platform, adapting its abstractions to the underlying execution model. The result is a high-level, resource-oriented programming model that preserves the core principles of Move while adapting them to Algorand’s unique environment. We present a formal specification of AlgoMove and its encoding into TEAL, Algorand’s native assembly-level language, along with a proof of the soundness of this encoding. To demonstrate the practical value and expressive power of the framework, we provide a prototype implementation consisting of a Move-to-TEAL compilation system and an accompanying library for writing smart contracts. Beyond enhancing the Algorand smart contract ecosystem, AlgoMove is significant in its own right as part of a broader effort to bring advances in programming language theory and formal verification into the blockchain space. By balancing expressiveness, ease of use, and strong compile-time guarantees, we seek to meet the distinctive requirements of secure and reliable blockchain applications.
P Praveen Kumar, Dudimetla Pravalika, B. S. Dileep Kumar, Gattu Akshitha · 5 authors
The rapid advancement of blockchain technology has introduced new possibilities for secure digital ownership and transparent fundraising through Non-Fungible Tokens (NFTs).However, most existing charity platforms remain centralized, limiting transparency, accountability, and verifiable proof of donations.Donors often lack visibility into how funds are utilized, while reliance on intermediaries increases risks such as data manipulation, reduced auditability, and decreased trust.To address these issues, this work proposes a decentralized charity auction framework that leverages blockchain technology and NFT-based asset representation.The system is developed using the Django web framework integrated with Web3 infrastructure and smart contracts.In this model, each auction item is tokenized as a unique NFT, ensuring authenticity, traceability, and immutable ownership.The platform allows users to act as donors or auctioneers, enabling participation in NFT-based charity auctions.Users can place bids or contribute funds, with all transactions securely recorded on a blockchain ledger.At the end of each auction, NFT ownership is automatically transferred to the highest bidder or contributor, providing verifiable proof of participation.By removing intermediaries and incorporating a transparent, incentive-driven mechanism, the proposed system enhances donor trust and engagement.It ensures tamper-proof record-keeping and clear fund flow, strengthening accountability within charitable ecosystems.This framework demonstrates a scalable and efficient approach to modern fundraising, showcasing the potential of blockchain and NFTs in improving trust and transparency in charity applications.
We study binary decision-making in governance councils of Decentralized Autonomous Organizations (DAOs), where experts choose between two alternatives on behalf of the organization. We introduce an information structure model for such councils and formalize desired properties in blockchain governance. We propose a mechanism assuming an evaluation tool that ex-post returns a boolean indicating success or failure, implementable via smart contracts. Experts hold two types of private information: idiosyncratic preferences over alternatives and subjective beliefs about which is more likely to benefit the organization. The designer's objective is to select the best alternative by aggregating expert beliefs, framed as a classification problem. The mechanism collects preferences and computes monetary transfers accordingly, then applies additional transfers contingent on the boolean outcome. For aligned experts, the mechanism is dominant strategy incentive compatible. For unaligned experts, we prove a Safe Deviation property: no expert can profitably deviate toward an alternative they believe is less likely to succeed. Our main result decomposes the sum of reports into idiosyncratic noise and a linearly pooled belief signal whose sign matches the designer's optimal decision. The pooling weights arise endogenously from equilibrium strategies, and correct classification is achieved whenever the per-expert budget exceeds a threshold that decreases as experts' beliefs converge.
P. Saranya, A. W. Ali, Meesala Shobha Rani, H. Shaheen
The current land transfer system in India is beset with inefficiencies, delays and increased costs due to its reliance on manual processes, extensive paperwork and involvement of multiple intermediaries such as real estate agents and government officials. Discrepancies and verification challenges arise from land records maintained in physical ledgers or decentralized digital formats across various government departments, often leading to fraudulent transactions, disputed ownership claims and unauthorized land sales. The limited transparency and access to land records, further exacerbate corruption and undermine trust in the system. This chapter explores how blockchain technology and smart contracts can revolutionize the land transfer system in India by addressing these inherent challenges. The emergence and growing popularity of blockchain technology is mainly due to the success and influence of cryptocurrencies like Bitcoin and Ethereum. Ethereum has become the backbone of the decentralized finance sector, further driving its adoption and market perception. Blockchain&s;s decentralized and immutable ledger ensures the authenticity and security of ownership data, while smart contracts automate the entire land transfer process, reducing the need for intermediaries and minimizing human error. The integration of these technologies fosters transparency by providing real-time access to unified land records for all stakeholders, significantly reducing the risk of fraud. Additionally, the streamlined process can greatly reduce the cost and time associated with land transactions. By providing a clear, tamper-proof chain of ownership, blockchain technology also aids in dispute resolution, offering a transformative solution for modernizing land management in India. The proposed work aims to enhance property ownership by creating smart contracts with the terms of the land sale, including the agreed price, property details and conditions for transfer. It verifies the ownership of the seller and checks for any encumbrances or legal issues with the property. This can be done automatically by querying the blockchain ledger. Once the verification is done, the payment can be transferred and the ownership records would be updated on the blockchain ledger,transferring the ownership rights to the buyer and providing a tamper proof and transparent record of the transaction.
S. Sridevi, RIYAZULLA RAHMAN J, Jobin Thomas, Komalavalli C · 6 authors
The rapidly evolving NFT (Non-Fungible Tokens) ecosystem has brought about a wealth of opportunities for investors and creators alike. However, the allure of this burgeoning market has also attracted a host of malicious actors, who have exploited vulnerabilities to perpetrate rug pull incidents, a form of exit scam where project developers abruptly abandon their project, absconding with investors' funds. To safeguard oneself against these pernicious schemes, it is crucial to understand the mechanisms underlying rug pulls, as well as the strategies for detecting and avoiding them. In this paper, we discuss the anatomy of an NFT rug-pull and a detailed investigation and study on how these rug-pulls are executed. In this study, we have made an attempt to investigate the common signs along with the cautionary steps to identify and avoid them. Finally, we have also designed and proposed an effective mitigation strategy for a rug pull with deterministic mathematical modelling.
Website: https://manual.warondisease.org/knowledge/appendix/wishocracy-paper.html Abstract: Politicians' votes have near-zero correlation with citizen preferences (Gilens and Page, 2014). Elite preferences predict policy outcomes. No mechanism connects citizen preferences to electoral consequences for representatives. RAPPA: Millions of citizens answer simple pairwise questions ("How would you split \$100 between these two budget categories?"). Geometric mean aggregation produces population-level preference weights from sparse individual responses. Unlike approval voting or ranked choice, RAPPA captures preference *intensity*, not just what people want, but how much they care. Compare aggregated preferences to each legislator's voting record. Publish Citizen Alignment Scores. Channel campaign resources to high-alignment candidates through Incentive Alignment Bonds. The mechanism achieves three properties no prior system combines: minimal cognitive load (~20 comparisons per participant yields statistical convergence), preference intensity capture, and approximate strategy-proofness. At system scale, the Optimal Governance Trajectory reaches 56.7x (95% CI: 19.3x-304x) the Earth baseline after 20 years, raises average income to \$1.16 million (95% CI: \$395,118-\$6.22 million) versus \$20,483 on the status-quo path, reaches \$10.7 quadrillion (95% CI: \$3.64 quadrillion-\$57.2 quadrillion) in total output, and recovers roughly \$101 trillion (95% CI: \$83.3 trillion-\$191 trillion)/year in suppressed value ([The Political Dysfunction Tax](https://political-dysfunction-tax.warondisease.org)). Summary: Representative democracy suffers from an inescapable principal-agent problem where elected officials' incentives diverge from citizen welfare. Wishocracy introduces RAPPA (Randomized Aggregated Pairwise Preference Allocation), which aggregates citizen preferences through cognitively tractable pairwise comparisons and creates accountability via Citizen Alignment Scores that channel electoral resources toward politicians who actually represent what citizens want.
Traditional selling systems often limit products to local markets and rely heavily on intermediaries, resulting in reduced profit margins, inconsistent quality, and limited market reach. Maintaining consistent quality and ensuring market transparency remain significant challenges in these legacy frameworks. To address these issues, this project proposes a secure and efficient Double Auction System for multi-category product trading. To enhance security, privacy, and trust, the project integrates advanced cryptographic mechanisms. zk-SNARKs (Zero-Knowledge Succinct Non- Interactive Arguments of Knowledge) are employed for sealed bidding, ensuring that both bidder identities and bid values remain hidden while maintaining mathematical verifiability. Conversely, Linked Ring Signatures are used for open bidding, allowing bid values to remain transparent while masking the identities of the bidders. A Commit-Reveal Scheme is implemented to prevent bid manipulation and ensure fairness during the submission phase. Additionally, a Reputation Score Algorithm incentivizes honest participation by rewarding users with a trust score based on their historical behavior. Finally, Blockchain technology is integrated via a private blockchain to record all auction data and reports in an immutable and tamper-proof manner. This multi-layered approach ensures a fair, secure, and sustainable trading ecosystem, benefiting both producers and buyers across diverse sectors.
Using transaction cost economics (TCE) and agency theory, this paper examines how blockchain, smart contracts, and decentralized autonomous organizations (DAOs) reconfigure financial services across payments, wealth management, real estate, and corporate governance. Three research questions are addressed: (1) What are the quantifiable efficiency gains from blockchain-based real-time settlement compared with legacy systems? (2) How do blockchain technologies reduce intermediation and agency costs in wealth management and real estate? (3) Finally, to what extent do DAOs resolve or transform traditional corporate governance problems? By combining a present-value model calibrated to U.S. Automated Clearing House (ACH) data ($86.2 trillion in annual volume), comparative institutional analysis, and synthesis of empirical evidence from pilot implementations and on-chain governance metrics, this paper makes three principal contributions. First, real-time settlement yields approximately $12 billion in annual opportunity cost savings at the baseline 7.5% discount rate, with sensitivity analysis producing a range of $8–15 billion. The majority of gains accrue from moving to same-day or within-hour settlement. Second, tokenization and smart contract escrow substantially reduce real estate intermediation costs, blockchain-based digital identity streamlines wealth management onboarding, and a stablecoin taxonomy classifies fiat-collateralized, crypto-collateralized, and algorithmic designs by risk profile. Third, on-chain data reveal persistent governance token concentration (Gini > 0.98) and low voter participation (typically below 10%), exposing a gap between DAO theory and practice. Blockchain-specific risks are mapped to National Institute of Standards and Technology (NIST) Cybersecurity Framework 2.0, and mechanism design solutions, such as quadratic voting and AI-assisted proposal evaluation, are proposed to address whale dominance. Effective adoption requires hybrid architecture combining on-chain automation with off-chain structures for accountability and regulatory compliance.
Decentralized finance (DeFi) systems currently rely on static parameters and reactive mechanisms that fail to adapt to rapidly changing market conditions. These limitations contribute to systemic inefficiencies including yield instability, capital fragmentation, and the extraction of value through adversarial mechanisms such as maximal extractable value (MEV). This paper introduces The Aeon Protocol, a control-theoretic framework for adaptive financial infrastructure. The protocol models decentralized liquidity management as a closed-loop control system in which economic variables are continuously monitored, predicted, and regulated through feedback mechanisms derived from classical control theory. The Aeon architecture integrates four primary system layers: • KENDRA — predictive forecasting and regime detection from on-chain data streams• NOEMA — model predictive control for economic orchestration• AURA — ethical routing layer that captures and redistributes MEV through sealed-bid auctions• LEIA — liquidity management engine governing protocol-owned liquidity across decentralized markets At the core of the system is a PID-controlled adaptive yield mechanism designed to regulate total value locked (TVL) and stabilize protocol yield within bounded ranges. A complementary Burn-and-Mint Equilibrium (BME) mechanism dynamically adjusts token supply to maintain long-term economic balance. A central implication of the Aeon architecture is the emergence of a self-reinforcing liquidity ecosystem. By integrating predictive forecasting, control optimization, and ethical MEV capture into a closed-loop economic system, the protocol continuously identifies inefficiencies in decentralized markets and redirects the associated value back into the protocol’s liquidity layer. This process transforms otherwise extractive market dynamics into a productive feedback cycle, where captured value is redistributed through liquidity provisioning, treasury reserves, and reflection mechanisms. Empirical simulations and historical replay experiments demonstrate that this feedback architecture materially increases capital utilization across the system. In controlled Monte Carlo simulations spanning 10,000 market scenarios, the protocol achieved improvements of 50–180% in capital efficiency, while redirecting approximately 68% of extractable value to protocol participants rather than external arbitrage actors. These results suggest that adaptive control systems can convert structural market inefficiencies into a persistent source of liquidity and yield generation, enabling decentralized financial networks to operate as self-regulating economic environments rather than static rule-based infrastructures. Formal analysis establishes asymptotic stability conditions for the controller using the Routh–Hurwitz criterion and Lyapunov stability methods, providing theoretical guarantees that the system converges toward equilibrium under defined parameter constraints. Collectively, the results demonstrate that control-theoretic economic architectures can provide a principled foundation for designing stable, transparent, and adaptive decentralized financial infrastructure. The Aeon Protocol represents a broader research direction toward autonomous economic systems, where financial networks operate as self-regulating feedback environments capable of maintaining equilibrium under dynamic market conditions.
Fatemeh Erfan, Mohammad Yahyatabar, Martine Bellaïche, Talal Halabi
• Created a refined, expanded, and precisely labeled dataset with explanations, risk assessments, and fixes for each vulnerability • Fine-tuned an open-source LLM: LLaMA-3.1-8B using parameter-efficient techniques (LoRA) for smart contract vulnerability detection • Fine-tuned GPT-4o-mini on the same corpus for comparative analysis • Developed a real-time Visual Studio Code (VSCode) plugin integrating GPT-4o-mini for smart contract auditing • Released the datasets, tool, and fine-tuned model to advance research in smart contract security Since the advent of Ethereum, ensuring the security of smart contracts has become imperative. Integer overflow and underflow, reentrancy, and timestamp dependency remain the three most prevalent vulnerabilities in deployed contracts. Existing static-analysis tools often yield insufficient accuracy, and datasets derived from them inherit the same shortcomings. Moreover, the smart contract ecosystem lacks a dependable, real-time auditing aid for developers and a fine-tuned model trained on a truly comprehensive corpus. In this paper, we present three main contributions. (1) Dataset curation: the state-of-the-art vulnerability datasets are aggregated and harmonized, producing a clean, fully labeled dataset that integrates detailed explanations, potential security risks, vulnerable line ranges, code snippets, and corresponding fixes. The dataset is publicly available via our GitHub repository. (2) Model fine-tuning: the LLaMA-3.1-8B model as well as GPT-4o-mini are fine-tuned on this corpus and evaluated with both standard classification metrics and text-quality measures. The fine-tuned LLaMA-3.1 model achieves a precision of 93.55%, an average semantic similarity of 77.48%, and a code similarity of 87.25%. (3) IDE integration: We implement a real-time Visual Studio Code extension, backed by the GPT-4o API, that highlights, explains, and automatically patches vulnerabilities as the developer writes. Together, these contributions deliver a rigorously validated model and a practical developer toolchain that markedly advance the state of smart contract security research and practice.
Smart contracts (SCs) implemented on blockchain represent a breakthrough in decentralized applications, enabling a range of functions such as managing supply chains and handling elections. As the adoption of SCs increases, the need to detect flaws and vulnerabilities in their execution grows. To address this challenge, we present Branch Reinforcement Learning Fuzzer (BRLF), a deep reinforcement learning-based solution for the detection of vulnerabilities in SCs. The novelty of our method is threefold: first, our deep model uses text-based embeddings of conditional branches to enhance its adaptability and flexibility. Secondly, we propose a reward function that considers multiple aspects of fuzzing, such as opcode analysis and gas usage. Finally, we incorporate evolutionary algorithms into our approach, which significantly bolsters its ability to produce varied outputs. Extensive evaluation on three datasets of Ethereum-based SCs shows that BRLF outperforms state-of-the-art methods, detecting more vulnerabilities and achieving higher code coverage than existing solutions. Our code and data are available at: https://zenodo.org/records/15022152
Decentralized finance (DeFi) applications have attracted a recent surge in popularity. Token contracts underpin DeFi applications by managing liquidity. To regulate the interactions between token contracts and DeFi applications, token standards have been proposed to ensure predictable execution semantics and out-comes, thereby enabling reliable interoperability. However, there is no mechanism to prevent developers from customizing token contracts in ways that violate these standards. Even without malicious intent, such customizations pose severe risks to DeFi applications. Therefore, a comprehensive understanding of financially risky behaviors in token contracts is essential to better safeguard DeFi applications. To this end, we conduct the first systematic study that uncovers these behaviors and their concrete threats to DeFi applications. Specifically, we begin by constructing a taxonomy of nine financially risky behaviors in ERC20 token contracts. We then recognize the financial risks these behaviors pose to DeFi applications, which can result in significant financial losses, through a rigorous open-coding process on the real-world incidents of DeFi applications. To enable a large-scale study, we develop FRBScan, a novel tool that automatically identifies financially risky behaviors in ERC20 token contracts by combining Datalog analysis with token behavior inference heuristics. Our evaluation on a manually labeled dataset shows that FRBScan achieves 98.7% accuracy in identifying financially risky behaviors, with an average analysis time of just 4.73 seconds per contract. In contrast, the baseline tool Pied-Piper achieves only 72.5% accuracy while taking approximately 27.2 times longer, highlighting FRBScan's superior efficiency and effectiveness. Leveraging FRBScan, we conduct a large-scale study of ERC20 token contracts in Ethereum, and find that each type of financially risky behavior is present in practice, with 65.8% of token contracts exhibiting at least one such behavior. These findings underscore the widespread prevalence of financially risky behaviors in practice, and highlight the substantial threats they pose to DeFi applications.
Autonomous software agents on blockchains solve distributed-coordination problems by reading shared ledger state instead of exchanging direct messages. Liquidation keepers, arbitrage bots, and other autonomous on-chain agents watch balances, contract storage, and event logs; when conditions change, they act. The ledger therefore functions as a replicated shared-state medium through which decentralized agents coordinate indirectly. This form of indirect coordination mirrors what Grassé called stigmergy in 1959: organisms coordinating through traces left in a shared environment, with no central plan. Stigmergy has mature formalizations in swarm intelligence and multi-agent systems, and on-chain agents already behave stigmergically in practice, but no prior application-layer framework cleanly bridges the two. We introduce Indirect coordination grounded in ledger state (Coordinación indirecta basada en el estado del registro contable) as a ledger-specific applied definition that maps Grassé's mechanism onto distributed ledger technology. We operationalize this with a state-transition formalism, identify three recurring base on-chain coordination patterns (State-Flag, Event-Signal, Threshold- Trigger) together with a Commit-Reveal sequencing overlay, and work through a State-Flag task-board example to compare ledger-state coordination analytically with off-chain messaging and centralized orchestration. The contribution is a reusable vocabulary, a ledger-specific formal mapping, and design guidance for decentralized coordination over replicated shared state at the application layer.
Rosa Pericàs Gornals, M. Magdalena Payeras Capellà, Victor Garcia Font, M. Puigserver · 5 authors
The exchange of non-fungible tokens (NFTs) traditionally occurs on centralized NFT marketplaces, which often wield significant control over transactions, as the imposition of restrictions and fees. Additionally, the current NFT standard (ERC-721) lacks certain functionalities that hinder the seamless direct exchange of NFTs between owners. This article introduces a novel protocol that eliminates the need for an intermediary marketplace, as well as their intermediary smart contract or escrow contract, facilitating a fair exchange of NFTs as collectibles. The protocol is represented by an extension of the ERC-721 called Exchangeable NFTs (ExNFTs), which introduces the necessary functionalities to enable direct NFT exchanges. We analyze the limitations of the ERC-721 standard and build upon our previously proposed protocol of Rejectable NFTs (RejNFTs), which introduces a rejectable functionality empowering the receiver to reject the transfer of an NFT. Additionally, we discuss the reasons why achieving a fair direct exchange of NFTs is currently unattainable with the prevailing standards. We present the key functionalities of the ExNFT approach, along with a Solidity implementation of the ExNFTs. Furthermore, we evaluate the performance of the protocol, considering costs and security.
First-generation Decentralized Autonomous Organizations (DAOs) rely predominantly on "Coin-Voting" schemes (1 Token = 1 Vote). While permissionless, this model inevitably degrades into plutocracy, where capital centralization allows a minority of "Whales" to capture protocol utility. Furthermore, the liquidity of voting tokens exposes governance to "Flash Loan Attacks" and short-term rent-seeking. This paper introduces a novel governance primitive: Time-Decayed Stake-Weighted (TDSW) Voting. We propose a dual-class structure that weighs voting power not merely by capital magnitude, but by Capital Lock-Duration (Time) and Epistemic History (Merit). By introducing "Reputation Entropy"—where governance power decays automatically if not exercised - we solve the "Zombie Stake" problem and ensure that control remains with active, long-term contributors rather than passive capital aggregators. Author's Note: This paper is a foundational pillar of the Klyrox Protocol architecture, expanding upon the core framework published in The Klyrox Protocol: A Decentralized Framework for Optimistic Content Verification and Epistemic Reputation (available at: https://doi.org/10.5281/zenodo.18729968). It outlines the specific mechanics underpinning the concept of "Epistemic Capital," as explored in the complete five-volume series, The Algorithmic Monographs (The Algorithmic Invisible Hand, The Republic of Code, The Market for Truth, The Heavy Metal Intelligence, and The Synthetic C-Suite).
Sealed-bid auctions are a fundamental mechanism for pricing across a wide range of online applications, but their deployment in online and decentralized environments raises significant security and privacy challenges. This paper presents a comprehensive survey of secure online sealed-bid auction schemes, whose primary contribution lies in a unified taxonomy, a structured comparative analysis, and an explicit identification of design trade-offs and research gaps in secure sealed-bid auction systems. We organize existing work into a taxonomy covering trusted, verifiable, confidential, fully decentralized, and identity-privacy–preserving auctions and systematically analyze their cryptographic foundations and trust assumptions. Through this comparative evaluation, we highlight fundamental trade-offs between privacy guarantees, public verifiability, and efficiency, particularly the computational cost imposed by zero-knowledge proofs and secure computation techniques. Finally, we identify key research gaps and outline future directions toward scalable, verifiable, and privacy-preserving auction designs for real-world online applications.
The past decade has witnessed the burgeoning and continuous development of blockchain and its applications. Besides various cryptocurrencies, an industry that has quickly embraced this trend is gaming. Thanks to the support of blockchain, games have started to incorporate non-fungible tokens (NFTs) that can enable a new gaming model, play-to-earn (P2E), which incentivizes users to participate and play. While recent studies looked at several NFT games qualitatively and individually, an in-depth understanding is still missing, particularly on how the P2E model has transformed traditional games. In this work, we set to conduct a measurement study of NFT games, aiming to gain a comprehensive understanding of the effectiveness of P2E in practice. For this purpose, we collect and analyze relevant NFT transaction data from the underlying blockchain (e.g., Ethereum) of 12 games, supplemented with various data scraped from their websites. Our study shows that (1) a few top wallets control unproportionally high percentage of NFTs, and the majority of wallets own only one or two NFTs and do not actively trade; (2) promotion events do boost the trade amount and the NFT price for some games, but their effect does not sustain; and (3) few players actually earned a profit, and players in 9 out of 12 games who traded NFTs have a negative profit on average. Motivated by these findings, we further investigate effective incentive mechanisms based on game theory to improve the trading profits that players can earn from these NFT games. Both modeling and simulation results confirm the effectiveness of the proposed incentive mechanism.
Liquidation of collateral are the primary safeguard for solvency of lending protocols in decentralized finance. However, the mechanics of liquidations expose these protocols to predatory price manipulations and other forms of Maximal Extractable Value (MEV). In this paper, we characterize the optimal liquidation strategy, via a dynamic program, from the perspective of a profit-maximizing liquidator when the spot oracle is given by a Constant Product Market Maker (CPMM). We explicitly model Oracle Extractable Value (OEV) where liquidators manipulate the CPMM with sandwich attacks to trigger profitable liquidation events. We derive closed-form liquidation bounds and prove that CPMM transaction fees act as a critical security parameter. Crucially, we demonstrate that fees do not merely reduce attacker profits, but can make such manipulations unprofitable for an attacker. Our findings suggest that CPMM transaction fees serve a dual purpose: compensating liquidity providers and endogenously hardening CPMM oracles against manipulation without the latency of time-weighted averages or medianization.
We present SPEAR, a multi-agent coordination framework for smart contract auditing that applies established MAS patterns in a realistic security analysis workflow. SPEAR models auditing as a coordinated mission carried out by specialized agents: a Planning Agent prioritizes contracts using risk-aware heuristics, an Execution Agent allocates tasks via the Contract Net protocol, and a Repair Agent autonomously recovers from brittle generated artifacts using a programmatic-first repair policy. Agents maintain local beliefs updated through AGM-compliant revision, coordinate via negotiation and auction protocols, and revise plans as new information becomes available. An empirical study compares the multi-agent design with centralized and pipeline-based alternatives under controlled failure scenarios, focusing on coordination, recovery behavior, and resource use.