We introduce the State Twin: a typed, in-memory, replayable replica of an on-chain automated market maker (AMM) pool that serves as a substrate for agentic reasoning over decentralized finance (DeFi) protocols. Agentic DeFi stacks today couple reasoning to chain time, since every "what if?" query incurs a new RPC read or a real transaction, so the agent's effective action space is bounded by block confirmation latency and gas. We argue this coupling is a structural problem rather than a performance one, and that the missing layer is an off-chain substrate that preserves the protocol's exact mathematics while admitting the operations on-chain state cannot: forking, replay, branching, counterfactual rollout. We formalize each AMM family (Uniswap V2, V3, Balancer, Stableswap) as a discrete-time controlled dynamical system, prove a quantitative fidelity bound on the divergence between twin and chain, and give the open architecture used in DeFiPy v2, an open-source Python toolkit that ships the State Twin substrate and a reference Model Context Protocol server exposing typed analytical primitives as LLM tools. The same primitive (i.e., one Python class, one calling pattern) serves a notebook quant, a backtest, and an LLM agent without modification. We close with a fork-and-evaluate worked example: a single live RPC read seeds N independent in-memory twins under distinct price-shock scenarios, in sub-second wall-clock time. The contribution is the substrate, not a particular agent, which is what the specification of what an agentic DeFi substrate must look like
A first-order design task in blockchain-based decentralized autonomous organizations is to ensure that malicious actors are sanctioned. We show that, when voters act strategically and the system is insufficiently decentralized, payoff-matching bribes undermine the sanctioning of malicious actors under conventional governance. Our framework formalizes DAO voting mechanisms and lets us identify those that mitigate the problem. Stochastic voting decouples a tokenholder’s influence from the voting behavior of others. Thus, bribery-proofness can be restored in the presence of sufficiently centralized governance tokenholders. Alternatively, masked voting increases resilience against bribery. Our work contributes to the broader debate on the merits and pitfalls of decentralization and highlights the need to align governance mechanisms with the degree of decentralization in blockchain networks.
Automated market makers (AMMs) quote prices from pool state rather than from a limit order book. AMM pools often stay close to a reference price because arbitrageurs correct profitable mispricing. A large part of decentralized finance therefore relies on a simple economic premise: once the AMM price drifts away from the reference price, arbitrage incentives push it back. This paper studies when that premise is strong enough to guarantee block-scale stability. We model the gap between the reference price and the AMM price as a stochastic tracking error, treat arbitrage as the corrective input, and place blockchain execution inside the loop through fees, discrete blocks, transaction ordering, delays, and transaction failure. The detailed execution layer is reduced to the total successful correction confirmed in each block. Under a block-level correction condition, we prove geometric ergodicity of the tracking error and obtain explicit one-step bounds that connect tracking quality to liquidity and execution quality. We also show in a constant-product example how fees, fixed execution costs, and local liquidity map into the no-trade band and the optimal corrective trade. Finally, we build empirical proxies for the theorem quantities from realized block data and use them to organize reduced and mechanism-focused simulations whose comparative statics are consistent with the theory. The contribution is to turn a basic economic intuition behind decentralized finance into a quantitative stability statement together with a tractable calibration interface.
We study permissionless spot--perpetual basis trading in decentralized finance as a collateral control problem. The strategy holds spot inventory, hedges directional exposure with a short perpetual, and allocates capital between spot inventory and derivative margin under on-chain liquidity and execution frictions. The paper delivers three results. First, it solves a static control problem for the collateral share and shows that the risk-constrained formulation provides a more robust operating benchmark relative to the economic optimum. In comparative calibration, the required collateral rises monotonically under volatility stress. The collateral is the lowest for BTC and increases significantly for long tail assets such as LINK and DOGE. Second, the paper derives an asymmetric dynamic extension in which the lower boundary of intervention is solvency driven, and the upper boundary is determined by a trade-off between carry-loss and the cost of rebalancing. Monte Carlo simulation shows that the lower boundary remains structurally relevant, whereas meaningful interior upper triggers survive mainly in the regimes with high carry and low costs. Third, the paper validates an execution-aware implementation with live routed execution and historical backtests. The execution layer shows that the realized wedges are significant, but become worse in the case of selling the basis. This justifies a minimum effective rebalancing size and a positive execution buffer. The historical validation shows that in the case of a fixed control rule the realized performance is predominantly explained by the funding environment.
Auctions play a vital role in modern commerce by offering a transparent, structured, and competitive method for trading goods, services, and data. However, traditional in-person auctions are limited in terms of accessibility, convenience, security, and efficiency. However existing online auction platforms, while addressing some of these limitations, still face challenges such as limited transparency, centralized control, and insufficient security and privacy protections. Moreover these issues become extremely critical in case of sensitive applications like healthcare, defense and finance. To address these challenges, this article proposes a three-phase trading framework. In the first phase, data generation, anonymization, and storage are performed. In the second phase, an ensemble learning-based price forecasting approach is employed to estimate the asking and bidding prices, which depend on the volume and type of data. Finally, in the third phase, a Monte Carlo-inspired auction-based Non-Fungible Token (NFT) trading mechanism (MCiANT) is incorporated to enable efficient trading between buyers and sellers. The efficacy of the proposed MCiANT framework is compared with three distinct auction algorithms: the Vickrey auction, the Markov-Inspired Stationary Distribution Auction (MISD), and the Two-Phase English–Dutch Hybrid Auction (TPEDHA). The results demonstrate that the MCiANT framework significantly outperforms the others, achieving success-rate improvements of 5%, 5%, and 1% over the Vickrey, MISD, and TPEDHA auctions, respectively. Furthermore, the proposed framework is evaluated using health data by measuring anonymization time, encryption time, InterPlanetary File System (IPFS) upload time, and I/O performance.
Bu çalışma, 2020–2025 yılları arasında DAO (Decentralized Autonomous Organizations) yapılarıyla ilgili literatürü incelemek amacıyla SCOPUS veri tabanından elde edilen 3.113 akademik çalışma üzerinde bibliyometrik analiz gerçekleştirmiştir. “decentralized autonomous organization”, “DAO”, “smart contract”, “on-chain governance” gibi anahtar kelimelerle yapılan tarama sonucunda, literatürün blockchain ve akıllı sözleşmeler temelli teknik altyapı etrafında yoğunlaştığı; buna karşılık yönetişim modelleri, token ekonomisi, oylama süreçleri, güvenlik, veri gizliliği ve hukuki statü gibi konuların araştırmalarda öne çıktığı belirlenmiştir. Bulgular, DAO çalışmalarının çok disiplinli bir yapıya sahip olduğunu, ülke ve kurum bazlı yayın yoğunluklarının küresel olarak hızla arttığını ve kavramsal çeşitliliğin yüksek seviyede olduğunu göstermektedir. Analizler, DAO’ların yönetişim ve denetim açısından standartlaşmamış, teknik olarak karmaşık ve hukuken belirsiz bir yapı sergilediğini; bu nedenle geleneksel finansal denetim modelleriyle uyum sorunlarının bulunduğunu ortaya koymaktadır. Sonuç olarak, DAO ekosisteminin sürdürülebilir ve denetlenebilir bir yapıya kavuşması için yönetişim protokollerinin netleşmesi, teknik güvenlik standartlarının geliştirilmesi ve hukuki çerçevelerin güçlendirilmesi gerekmektedir.
Linear contracts are ubiquitous in practice, yet optimal contract theory often prescribes complex, nonlinear structures. We provide a distributional robustness justification for linear contracts. We study a principal-agent problem where the agent exerts costly effort across multiple tasks, generating a stochastic signal upon which the principal conditions payment. The principal faces distributional ambiguity: she knows the expected signal for each effort level, but not the full distribution. She seeks a contract maximizing her worst-case payoff over all distributions consistent with this partial knowledge. Our main result shows that linear contracts are optimal for such a principal. For any contract, there exists a linear contract achieving weakly higher worst-case payoff. The proof introduces the concavification approach built around the notion of self-inducing actions; these are actions where an affine contract simultaneously induces the action as optimal and supports the concave envelope of payments from above. We show that self-inducing actions always exist as maximizers of the gap between the concave envelope and agent's cost function. We extend these results to multi-party settings. In common agency with multiple principals, we show that affine contracts improve all principals' worst-case payoffs. In team production with multiple agents, we establish a complementary necessity result: if any agent's contract is non-affine, the unique ex-post robust equilibrium is zero effort. Finally, we show that homogeneous utility and cost functions yield tractable characterizations, enabling closed-form approximation ratios and a sharp boundary between computational tractability results.
Vabuk Pahari, B. Chandrasekaran, Johnnatan Messias, Krishna P. Gummadi · 5 authors
A decentralized autonomous organization (DAO) is a governing entity that empowers its stakeholders (i.e., users who hold one or more of its tokens) to manage blockchain-based protocols (i.e., smart contracts) collaboratively. The governance of a DAO is explicitly encoded in the DAO's governance contract, which defines how stakeholders participate in governance and how much influence (or voting power) they have in any decision. While decentralization and autonomy are the fundamental tenets of a DAO's design, empirical evidence suggests that in practice governance is often highly centralized. In this work, we study the designs and implementations of 48 public and actively used DAOs, with substantially large capital, deployed on Ethereum. We identify how three key governance mechanisms--token registration, staking, and delegation--originally introduced to improve security or participation, contribute to the concentration of voting power. Unlike prior work on centralization of voting power in specific DAOs, our findings reveal that these governance mechanisms of DAOs themselves systematically reinforce centralization. By elucidating the relationship between governance design and voting centralization, this work advances the understanding of DAO governance structures and highlights the inherent trade-offs between decentralization, security, and usability of DAOs.
Kunal Kumar, Mohammad Malik, Gujju Koushik, Nujetti Abinandhan
The evolution of blockchain technology has introduced innovative approaches for managing digital assets and transforming fundraising models through Non-Fungible Tokens (NFTs). However, many existing charity platforms continue to operate on centralized systems that restrict transparency, limit accountability, and fail to provide donors with verifiable proof of their contributions. In such systems, donors often have minimal visibility into fund utilization, and there is no direct linkage between their contributions and any traceable digital asset. Moreover, traditional charity and auction mechanisms rely heavily on intermediaries for transaction validation, data management, and operational control, making them susceptible to data manipulation, reduced auditability, and diminished user trust, while also lacking mechanisms to encourage active participation. To address these limitations, this research proposes a decentralized charity auction framework that integrates Blockchain technology with NFTs to ensure secure, transparent, and verifiable transactions. The system is developed using the Django Web Framework and leverages Web3 Technology along with Smart Contracts to automate and manage auction processes. Each auction item is uniquely tokenized as an NFT, guaranteeing authenticity and non-replicability. Users can register as donors or auction organizers, create NFTbased auctions, and participate through bidding or direct contributions. All transactions are permanently recorded on the blockchain, and NFT ownership is automatically transferred to the highest bidder or contributor, serving as a verifiable digital proof of participation. By removing intermediaries and ensuring immutable record-keeping, the system enhances trust, strengthens security, and introduces an incentive-driven participation model, thereby improving transparency, accountability, and efficiency in modern charity and fundraising ecosystems.
This study presents a structured dataset of blockchain-registered artificial intelligence agents under the ERC-8004 standard on Ethereum. The dataset integrates on-chain identity records, minting transactions, transfer events, reputation summaries, and individual feedback records, together with resolved off-chain metadata where available. Data were collected from Ethereum mainnet using Web3 RPC queries and processed into tabular form to enable reproducible analysis. The dataset covers 10,000 agents within a defined block range and includes both event-level records and aggregated summaries. It enables empirical research on agent identity formation, reputation systems, service exposure, and early-stage decentralized AI ecosystems. This resource supports studies in blockchain analytics, decentralized trust infrastructure, and the emerging agentic economy.
Este artigo apresenta um esboço estruturado sobre “Dinâmica de Arbitragem entre DEXs e CEXs: Velocidade e Lucratibilidade.”. O objetivo é analisar os fundamentos técnicos e econômicos da arbitragem entre <i>centralized exchanges</i> (CEXs) e <i>decentralized exchanges</i> (DEXs), com foco em como velocidade, latência e estrutura de taxas condicionam a lucratividade dessas estratégias no ecossistema Web3 contemporâneo. Estudos empíricos recentes medem, em detalhe, a economia por trás da arbitragem CEX‑DEX e do MEV associado, mostrando que a maioria dos lucros é capturada por poucos <i>searchers</i> profissionais e que as oportunidades de arbitragem desaparecem em janelas de tempo de frações de segundo. Análises de mercado indicam que, em média, operações bem‑sucedidas de arbitragem CEX‑DEX podem exibir margens brutas em torno de 30–40% sobre o capital efetivamente arriscado por trade, mas que a competição e os pagamentos a <i>block builders</i> comprimem esses retornos ao longo do tempo, caracterizando um mercado altamente monopolizado. Pesquisas teóricas sobre <i>latency arbitrage</i> e sobre o <i>timing</i> ótimo de arbitragem entre CEXs e DEXs modelam explicitamente o efeito da latência de blockchain, da ordem “first‑come, first‑served” e da vantagem de co‑location em data centers, demonstrando que a maior parte do <i>excess return</i> se concentra em janelas de 0,5 a 2 segundos após o surgimento de um desvio de preço entre venues. Trabalhos que estudam a dinâmica de preços em AMMs mostram, ainda, que taxas de swap introduzem uma banda de não‑arbitragem em torno do preço de referência em CEXs, restringindo as oportunidades de arbitragem a desvios acima de um certo limiar e conectando diretamente estrutura de taxas, liquidez e frequência de arbitragem. Conclui‑se que a arbitragem CEX‑DEX é hoje um jogo de alta frequência e forte competição, em que velocidade de execução, acesso a canais privados (MEV‑Boost) e otimização de custos determinam quem captura a maior parte das oportunidades de lucro.<br>
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.
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
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.