Autonomous AI agents are beginning to operate across organizational boundaries on the open internet -- discovering, transacting with, and delegating to agents owned by other parties without centralized oversight. When agents from different human principals collaborate at scale, the collective becomes opaque: no single human can observe, audit, or govern the emergent behavior. We term this the Logic Monopoly -- the agent society's unchecked monopoly over the entire logic chain from planning through execution to evaluation. We propose the Separation of Power (SoP) model, a constitutional governance architecture deployed on public blockchain that breaks this monopoly through three structural separations: agents legislate operational rules as smart contracts, deterministic software executes within those contracts, and humans adjudicate through a complete ownership chain binding every agent to a responsible principal. In this architecture, smart contracts are the law itself -- the actual legislative output that agents produce and that governs their behavior. We instantiate SoP in AgentCity on an EVM-compatible layer-2 blockchain (L2) with a three-tier contract hierarchy (foundational, meta, and operational). The core thesis is alignment-through-accountability: if each agent is aligned with its human owner through the accountability chain, then the collective converges on behavior aligned with human intent -- without top-down rules. A pre-registered experiment evaluates this thesis in a commons production economy -- where agents share a finite resource pool and collaboratively produce value -- at 50-1,000 agent scale.
With the rapid advancement of decentralized applications, smart contract security faces severe challenges, particularly regarding atomicity violations in complex logic such as Oracle and NFT contracts. Rigid rule sets often limit traditional static analyzers and lack deep contextual awareness, leading to high false-positive and false-negative rates when identifying vulnerabilities that depend on intermediate state inconsistencies. To address these limitations, this paper proposes PSR\textsuperscript{2}, a novel collaborative static analysis framework that integrates structural path searching with deterministic semantic reasoning. PSR\textsuperscript{2} utilizes a Graph Structure Analysis Module (GSAM) to identify suspicious execution sequences in control flow graphs and a Semantic Context Analysis Module (SCAM) to extract data dependencies and state facts from abstract syntax trees. A Fusion Decision Module (FDM) then performs formal cross validation to confirm vulnerabilities based on a unified atomicity inconsistency model. Experimental results on 1,600 contract samples demonstrate that PSR\textsuperscript{2} significantly outperforms pattern-matching baselines, achieving an F1-score of 94.69\% in complex ERC-721 scenarios compared to 51.86\% for existing tools. Ablation studies further confirm that our fusion logic effectively reduces the false-positive rate by nearly half compared to single module analysis.
Junliang Luo, Xihan Xiong, Zonglun Li, Hong Kang · 7 authors
The global financial architecture is undergoing a shift from intermediary centric-settlement to programmable infrastructure, to transmute trillions in static illiquid capital into active, high-velocity instruments. We argue that Real World Asset (RWA) tokenization represents a conceptual evolution beyond mere digitization, converting passive ledger entries into programmable economic agents capable of autonomous settlement and algorithmic collateralization. However, achieving such seamless capital efficiency necessitates resolving the fundamental friction between deterministic on-chain code and probabilistic off-chain reality, navigating the oracle problem and jurisdictional interoperability. This systematization of knowledge presents a taxonomy for the RWA lifecycle and deconstructs the multi-layered architecture, spanning legal custody, technical standards, and cryptoeconomic valuation, required to enforce off-chain rights within on-chain environments. We study systemic constraints such as latency and regulatory fragmentation through a comparative overview of sovereign debt, private credit, and real estate protocols, complemented by an empirical case study of on-chain U.S. Treasuries. We synthesize these findings to propose a prognostic outlook, positing that while asset tokenization provides a transitional bridge, it is not necessarily the inevitable shift compared to the emergence of unified, programmable ledgers.
Luigi Crisci, Lorenz Schuler, Herbert Jordan, Bernhard Scholz
The Ethereum state database uses Merkle Patricia Trie (MPT), which suffers from large witness proof sizes and high storage overhead. Verkle Tries have been proposed as a replacement, offering witness proofs below 150 bytes through vector commitments and Inner Product Argument aggregation. However, deploying a Verkle Trie in a high-throughput, short block-time blockchain such as Sonic, which produces a block every 300 milliseconds, introduces substantial engineering challenges related to storage efficiency, commitment computation costs, and the need to serve both live and historical state queries in real time. We present SonicDB S6, a production-grade Rust Verkle Trie database for the Sonic blockchain, which leverages its non-forking property to enable aggressive storage optimizations. Occupancy-aware node specializations, selected via an $\mathcal{O}(k n^2)$ dynamic program, reduce live storage by 97.8\%. Delta nodes that record only changed slots reduce archive storage by 95\%. Batched updates, multi-threaded commitment computation, and homomorphic Pedersen caching yield $3.2\times$ higher throughput than a persistent Geth Verkle baseline while sustaining production block-rate performance.
Faithful, Stable, Complete: Pick Two The Problem in Plain Language When a machine learning model makes a prediction — approving a loan, diagnosing a disease, flagging a transaction — practitioners use a tool called SHAP to answer "which input features mattered most?" SHAP is the most widely used explanation method in machine learning. Here is the problem: retrain the same model on the same data with a different random seed, and the explanation changes. The model's predictions barely move, but the "most important feature" can flip entirely. In 68% of 77 public datasets, the top feature is not stable across retrains. This is not a software bug. This is not fixable by tuning hyperparameters. We prove it is a mathematical impossibility. What We Prove No feature ranking can simultaneously be: Faithful — it reflects what the model actually learned Stable — it doesn't change when you retrain Complete — it ranks every pair of features …when features are correlated with similar importance. You must give up one. The proof is four lines long. It requires no assumptions about the model, the data, or the explanation method — only that correlated features admit models ranking them in opposite orders (the Rashomon property), which is true for every standard ML algorithm. How Bad Is It? We trained 50 XGBoost models on Breast Cancer Wisconsin — the dataset used in every SHAP tutorial — and counted how many different "top 3 most important features" appeared. Twenty-four. At 100 models: thirty-five. The "most common" answer appeared in only 12% of runs. Two randomly chosen models agree on the top-3 only 4.2% of the time. Every tutorial, textbook, and blog post showing SHAP on this dataset is showing one of two dozen equally valid answers. Three other datasets (California Housing, Heart Disease, Wine Quality) produce exactly one ranking every time — because their top features have clearly different importance. The theory correctly predicts which datasets are affected and which are safe. Dataset Distinct top-3 rankings (50 models) Two models agree? Breast Cancer 24 4.2% Diabetes 2 88.5% Wine Quality 1 100% (stable) Heart Disease 1 100% (stable) California Housing 1 100% (stable) It Gets Worse for Yes/No Questions For ranking questions (which feature is MORE important?), there is a fix: average across multiple models. But for binary questions — "does this feature contribute positively or negatively?", "is this feature selected?" — no fix exists. Even averaging doesn't help, because there's no middle ground between "positive" and "negative." We call this the bilemma. Real-World Consequences For loan applicants. We trained 30 models on German Credit data. Under standard settings, 45% of applicants receive a different "most important reason" for their decision depending on which model happens to be deployed. One applicant received six different top reasons across 30 models. For biomarker discovery. On a dataset of 10,935 genes distinguishing colon from kidney tissue, the "#1 most important gene" alternates between TSPAN8 (involved in tumor invasion) and CEACAM5/CEA (involved in immune evasion) depending on the random seed. A drug discovery pipeline targeting one gene makes a different bet than one targeting the other — and which bet gets made depends on a random number. For fairness audits. A SHAP-based audit checking whether a model relies on a protected attribute (like race or gender) reaches its conclusion with the reliability of a coin flip when the protected attribute is correlated with other features. The Fix DASH (Diversified Aggregation for Stable Hypotheses): train 25 models with different seeds, average their SHAP values. This is provably the best possible approach — no method can do better. Features that genuinely differ in importance get stable rankings. Features that are interchangeable get reported as tied, which is the honest answer. We also provide a 7-line diagnostic that identifies which features are at risk, requiring no statistical expertise and no assumptions about the data distribution. It outperforms the standard formula by 2× on real data. The practical workflow: Screen your model (1 model, seconds) Run the minority fraction diagnostic (7 lines of code) For flagged features, train 5 models and run a Z-test If unstable, use DASH with 25+ models Machine Verification Every mathematical claim is checked by a computer. The proofs are written in Lean 4 (a programming language for mathematics) and verified by its type-checker: 357 theorems, all machine-verified 6 axioms (the minimal assumptions the theory needs) Zero unproved claims across 58 files During the formalization, the computer caught two logical errors and one type mismatch that human reviewers missed. To our knowledge, this is the first formally verified impossibility result in explainable AI. Technical Details Architecture-dependent bounds Gradient boosting (XGBoost, LightGBM): instability diverges as correlation increases. At ρ = 0.9, the dominant feature gets 5× its fair share. Lasso: the ratio is infinite — one correlated feature gets everything, the other gets zero. Neural networks: 87% of feature pairs are unstable. Model instability dominates SHAP estimation noise by 8:1. Random forests: instability converges with more trees — the contrast case showing that parallel (not sequential) training helps. Cross-implementation. XGBoost, LightGBM, and Random Forest all show the same instability pattern. It is not specific to any one software package. Subsample sensitivity. Even at subsample = 0.95 (minimal randomness), 17 distinct rankings remain. Only fully deterministic training (subsample = 1.0) produces one ranking — but this sacrifices the regularization that makes the model accurate. Mechanistic interpretability. Preliminary evidence suggests the impossibility extends beyond feature importance to neural network circuit analysis. 10 transformers trained on modular addition (all achieving 100% accuracy) agree on only 36% of the top-3 circuit components. Design Space The achievable set of explanation methods has exactly two families: Family A (single model): faithful and complete, but unstable. Rankings flip up to 50% of the time. This is what standard SHAP does. Family B (DASH ensemble): faithful and stable, but reports ties for indistinguishable features. This is what DASH does. No third option exists. DASH is provably the best method in Family B. Associated Papers Companion paper (TMLR, under review). First-Mover Bias in Gradient Boosting Explanations: Mechanism, Detection, and Resolution.arXiv: https://arxiv.org/abs/2603.22346DOI: https://doi.org/10.5281/zenodo.19446088 Companion implementation: https://github.com/DrakeCaraker/dash-shap
Proof of Concept on the β-Catenin/TCF4 Interface in Wnt-Driven Oncogenesis and Gardner Syndrome Protein-protein interactions (PPIs) govern nearly every biological signalling pathway, yet their large, flat contact surfaces have resisted conventional drug design for decades. Existing computational approaches either require prohibitive molecular simulation resources or prior knowledge of a reference inhibitor—barriers that have left many therapeutically important targets inaccessible. This repository presents GeoSol-αα, a two-stage computational pipeline that generates macrocyclic drug scaffolds against PPI surfaces from first principles—without molecular dynamics simulation, without a crystallographic inhibitor reference, and without prior chemical knowledge of the target. The engine couples deterministic rigid-body SO(3) Fibonacci sampling with directed chemical evolution (genetic algorithms). In milliseconds, the pipeline successfully converged on a novel: 15-atom macrocyclic scaffold (1,4-dioxacyclopentadecane) that achieves a thermodynamic optimum with zero desolvation penalty against the β-catenin interface. This repository establishes formal prior art for both the identified chemical entity and the underlying high-throughput methodology.
Cloud computing underpins modern IT infrastructure by delivering scalable, on-demand resource provisioning, yet controlling cloud expenditure remains a pressing challenge. Dynamic pricing structures, unpredictable workloads, and billing pipelines that lack real-time visibility create conditions in which unauthorized consumption and anomalous usage spikes routinely escape timely detection. This paper presents CloudPay, a blockchain-integrated cloud storage billing system that unifies unsupervised machine learning with smart contract execution to deliver verifiable, fine-grained, and fraud-resistant cost governance. The system converts user storage activity into time-series representations and applies the Isolation Forest algorithm to detect abnormal consumption spikes without any labelled training data. Flagged events are routed through an owner confirmation protocol that validates suspicious uploads before billing proceeds, preventing unauthorized charges from entering the settlement pipeline. Smart contracts autonomously compute GB-time-based charges, execute tokenized payments, and anchor every transaction to an immutable SHA-256 blockchain ledger. Experimental results confirm that the system achieves 94.4% anomaly detection accuracy, 99.7% billing precision, and an 18.4% reduction in overall cloud expenditure relative to static allocation baselines. These results demonstrate that integrating unsupervised anomaly detection with cryptographically enforced billing logic is a viable path toward tamper-evident, real-time cost governance in multi-tenant cloud environments.
Abdullah Abdullah, Nida Hafeez, Maryam Shabbir, Muhammad Ateeb Ather · 6 authors
The integration of blockchain technology with the Internet of Things (IoT) presents a paradigm shift in securing decentralized networks, yet it introduces critical trade-offs among security, privacy, and scalability. This systematic analytical review examines the inherent tensions within blockchain-enabled IoT systems, focusing on how consensus mechanisms, cryptographic primitives, and architectural choices affect these three pillars. Through a comprehensive analysis of the contemporary literature, we identify that no single blockchain configuration simultaneously optimizes security, privacy, and scalability. Instead, these properties exist in a triadic relationship where enhancing one dimension typically compromises at least one other. Our review categorizes existing solutions based on their approach to balancing these trade-offs, including sharding, layer-2 protocols, zero-knowledge proofs, and hybrid architectures. We further analyze the applicability of these solutions across different IoT domains, identifying context-specific optimal configurations. The findings reveal that while significant progress has been made in addressing individual challenges, integrated frameworks that holistically consider all three dimensions remain underdeveloped. This review contributes a novel analytical framework for evaluating blockchain–IoT systems and identifies critical research directions, including adaptive consensus mechanisms, privacy-preserving scalability solutions, and domain-specific architectural patterns. Unlike prior studies that primarily focus on conceptual discussions of blockchain–IoT integration, this work synthesizes insights from systematically reviewed literature to propose a conceptual lightweight blockchain framework tailored for resource-constrained IoT environments. This study combines a SLR with a conceptual and experimentally evaluated framework, where the review findings and the proposed solution are presented as distinct but complementary contributions.
Smart contracts, essential to Blockchain functionality, can be compromised by vulnerabilities like reentrancy attacks, allowing unscrupulous entities to misappropriate funds. A universal and efficient multi-modal vulnerability detection framework is created to tackle detection issues that exceed the capability of standard methods such as fuzzy testing and symbolic execution. The methodology incorporates BiLSTM, EfficientNet, and Transformer architectures, augmented by CNN2D and BiGRU for better feature extraction and sequence modeling. The SMARTBUG dataset is employed in two formats: compiled OPCODES and features extracted via Word2Vec from smart contract source code. Preprocessing entails utilizing Word2Vec to produce N-gram numerical representations, succeeded by an 80-20 division for training and testing. The system analyzes multi-modal inputs, such as grayscale image attributes, opcode frequency statistics, and source code sequences, facilitating comprehensive vulnerability characterisation. The experimental assessment assesses the proposed model in comparison to existing algorithms, including MLP, GRU, and BiLSTM, utilizing criteria such as accuracy, precision, recall, and F-score. The CNN2D + BiGRU + EfficientNet + Transformer setup attains the greatest detection accuracy of 91.9%, surpassing all benchmarks. The system reduces dependence on domain knowledge by automating feature extraction, enabling adaptation across diverse smart contract forms and improving security in blockchain contexts
Blockchain technology has emerged as one of the most transformative innovations in the financial sector, enabling secure, transparent, and decentralized transaction systems. Among its key applications, smart contracts have gained significant attention for automating financial agreements and reducing the need for intermediaries. Smart contracts are self-executing digital agreements embedded within blockchain networks that automatically enforce contractual terms when predefined conditions are met. The present study examines the role of blockchain-based smart contracts in financial transactions and evaluates their impact on efficiency, transparency, security, and cost reduction in financial systems. The study is based on secondary data collected from industry reports, academic publications, and financial technology databases. Analytical methods including descriptive analysis and regression-based conceptual modeling are used to examine the relationship between smart contract adoption and financial transaction efficiency. The findings indicate that smart contracts significantly enhance transaction speed, reduce operational costs, minimize fraud risk, and improve transparency in financial systems. The study concludes that blockchain-based smart contracts have the potential to transform financial transactions by improving efficiency, reliability, and trust in digital financial ecosystems.
Recent advancements, specifically the 2026 whitepaper by Google Quantum AI, Stanford University, and the Ethereum Foundation (arXiv:2603.28846), have demonstrated the resource feasibility of breaking secp256k1 elliptic curve cryptography using fault-tolerant quantum computation (≤ 1200 logical qubits and ≤ 90 million Toffoli gates). While their work validates this capability via zero-knowledge STARK proofs without disclosing explicit circuits, we provide the continuous operator-theoretic framework that explains the exact physical collapse mechanism underlying their discrete resource results. By modeling cryptographic hardness as a stable, invariant computational manifold, we show that quantum vulnerability is a manifestation of a Birman-Schwinger instability. We prove that, within this model, the introduction of a transverse quantum operator (e.g., Shor's algorithm implemented via Quantum Phase Estimation) forces a resolvent singularity in the classical generator when the resource perturbation parameter crosses a critical threshold (μ_c). We establish a strict Hardness Phase Transition, demonstrating that cryptographic security is equivalent to the point 1 remaining outside the spectrum of the Birman-Schwinger kernel. Furthermore, we formalize zero-knowledge proofs (such as the Groth16-wrapped STARK artifacts published by Babbush et al.) as highly constrained Boolean projectors. We show that these proofs trigger an epistemic spectral collapse via Zeno stabilization, certifying the non-invertible regime without decohering the raw computational state into the public domain. The manuscript includes an exact analytic toy model demonstrating bound-state collapse into the continuum, explicitly mapping the destruction of exponential cryptographic isolation to a polynomial scattering state. This formalization transitions cryptographic failure from a domain of discrete computational estimates to a continuous framework of operator-theoretic necessity.
For the last decade the rapid movement of the cryptocurrency and Non-Fungible Tokens (NFT) has expanded stratospherically into our conscious, yet marketing has been slow in response to its marketability.Much of this has to do with failing to have a conceptual understanding of the value that cryptocurrencies and NFTs convey to consumers.The purpose of this study is to properly understand the value constructs of these recent additions to commerce and their theoretical underpinnings.With an understanding of the constructs that make up its value, we can hopefully predict its use in future transactions.Cryptocurrencies began as a white paper by a programmer known as Satoshi Nakamoto (Chohan, 2022) who proposed a way of storing information on and off the Internet as a format that is not linked to a centralized data file, but decentralized across multiple platform.He proposed that programmers incentivized to promote the security of the system will provide a structure for data to be stored that is virtually tamper-proof.
Purpose The purpose of this study is to examine the applicability of conventional criminological theories to white-collar offenders involved in cryptocurrency-related market manipulation, specifically pump-and-dump schemes. Using Sutherland’s differential association (DA) framework as a theoretical foundation, this research tests whether demographic and theoretical factors – such as self-control, DA, anomie and strain – predict illegal financial behavior in emerging digital markets. Design/methodology/approach Survey data from a national sample of US adults on the promotion of cryptocurrencies for financial gain were analyzed using t-tests and regression models. Findings The findings of this study suggest that traditional theories of crime, including DA, anomie and strain, lose predictive significance when demographic variables are considered. High-income, male and younger individuals were most likely to engage in cryptocrime in general. Overall, the results of this study highlight the complexity of white-collar criminality in digital spaces and suggest that financial and demographic factors outweigh conventional criminological theories when predicting involvement in cryptocrime. Originality/value This paper considers early notions of white-collar crime against modern online financial crimes. The authors addressed the intersection of criminological theory and modern cryptocurrency crime.
Este artigo apresenta um esboço estruturado sobre “Eficiência de Capital em AMMs de Faixa Concentrada (Concentrated Liquidity).”. O objetivo é analisar os fundamentos técnicos e econômicos da liquidez concentrada, tomando Uniswap v3 e outros <i>concentrated liquidity market makers</i> (CLMMs) como referência, e discutir suas implicações para o ecossistema Web3. A metodologia baseia‑se em revisão bibliográfica e análise de casos práticos, com foco na comparação entre AMMs de faixa infinita (como Uniswap v2) e AMMs com faixas de preço configuráveis pelos provedores de liquidez. Documentação oficial e materiais de lançamento do Uniswap v3 destacam que a ideia definidora do protocolo é permitir que LPs aloquem liquidez em faixas de preço customizadas, alcançando até cerca de 4.000× mais eficiência de capital em relação ao modelo v2 quando a liquidez é concentrada em uma faixa de 0,10%, com capacidade técnica de ranges tão granulares quanto 0,02%, o que elevaria a eficiência para até 20.000×, ainda que com custos maiores de gas por swap. Análises de segurança e guias educacionais sintetizam que, ao invés de espalhar capital em toda a curva de preços teórica, a liquidez concentrada permite que o capital atue apenas nos intervalos em que as negociações são mais prováveis, aproximando o comportamento do AMM a um <i>order book</i> tradicional e oferecendo melhor <i>price impact</i> com o mesmo capital. Ao mesmo tempo, estudos acadêmicos sobre CLMMs mostram que, embora a eficiência de capital aumente, também se intensificam riscos como <i>impermanent loss</i> (uma vez que a posição pode sair da faixa e ficar 100% em um único ativo) e estratégias adversariais como <i>just‑in‑time liquidity</i>, nas quais LPs estratégicos entram e saem em micro‑janelas para capturar taxas de forma desproporcional. Pesquisas mais amplas em design de AMMs sugerem ainda que abordagens multi‑token e mecanismos de compartilhamento de reservas podem aumentar a liquidez efetivamente ativa em CLMMs em 2,6–5,9×, mitigando efeitos de fragmentação de faixa. Conclui‑se que AMMs de faixa concentrada são um avanço significativo em eficiência de capital e qualidade de execução, mas exigem modelos de risco mais sofisticados, tanto para LPs quanto para protocolos, em comparação com AMMs de faixa infinita.<br>
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>
This paper analyses MicroMix, a noncustodial Ethereum mixer that unlinks deposits from withdrawals using browser-side zkSNARKs, a centralised relayer, and on-chain enforcement via Semaphore and Mixer contracts. The study formalises core acceptance conditions—value conservation, nullifier uniqueness, external‑nullifier scoping, and signal binding—and evaluates risks that persist despite sound cryptography, including timing correlation in small anonymity sets, Sybil pool distortion, single‑relayer censorship, ETH payout liveness under gas‑stipend limits, ERC‑20 heterogeneity, circuit–verifier input/order mismatches, and cross‑chain replay. The work proposes concrete mitigations: randomised scheduling and probabilistic batching, multi‑denomination support, decentralised relayer participation with user-paid fallbacks, guarded call patterns with reentrancy protection, SafeERC20 enforcement and token whitelisting, strict public‑input ordering and signal‑to‑field mapping, a fixed mixer-scoped external nullifier, and chain-bound proofs. With these measures, MicroMix can preserve unlinkability while improving liveness and correctness in adversarial environments, advancing practical, privacy‑preserving withdrawals on Ethereum.
Este artigo discute como padrões ERC e mecanismos de comunicação cross‑contract sustentam a interoperabilidade entre contratos inteligentes em Ethereum e EVM‑chains. Padrões de tokens como ERC‑20, ERC‑721, ERC‑777 e ERC‑1155 definem interfaces mínimas para transferência, consulta de saldo e eventos, permitindo que carteiras, DEXs, marketplaces e outras aplicações interajam de forma uniforme com ativos fungíveis e não fungíveis, enquanto extensões multi‑token como o ERC‑1155 combinam características de ERC‑20 e ERC‑721 e suportam transferências em lote. A interoperabilidade é reforçada pelo padrão ERC‑165, que introduz um mecanismo padronizado de detecção de interfaces via supportsInterface(bytes4), permitindo que contratos verifiquem, on‑chain, se outros contratos implementam interfaces específicas antes de interagir com eles. Na prática, a comunicação cross‑contract em Ethereum é implementada via opcodes de chamada (CALL, DELEGATECALL, STATICCALL), que permitem compor funcionalidades entre contratos, mas introduzem riscos de segurança como reentrância cross‑contract, em que contratos mal projetados são reentrados por outros antes de atualizar seu estado, abrindo espaço para exploração. Estudos e guias de segurança catalogam padrões de ataques de reentrância, incluindo reentrância entre funções e entre contratos, e recomendam padrões como checks‑effects‑interactions, uso de mutexes e desenho criterioso de callbacks em tokens com hooks (como ERC‑777) para mitigar esses riscos. Conclui‑se que a interoperabilidade de contratos na Web3 depende tanto de padrões de interface bem definidos (ERCs, ERC‑165) quanto de práticas seguras de comunicação cross‑contract, com impacto direto na liquidez, composabilidade DeFi e governança de protocolos.<br>
Este artigo apresenta um esboço estruturado sobre “Análise de Custo de Deploy em Diferentes EVMs.”. O objetivo é analisar os fundamentos técnicos e econômicos do custo de implantação de contratos inteligentes em Ethereum L1, diversas Layer‑2 (rollups) e outras chains EVM‑compatíveis, discutindo implicações para o ecossistema Web3 e tendências de mercado. A metodologia baseia‑se em revisão bibliográfica e análise de casos práticos, com foco na decomposição do custo de deploy em componentes de gas (execução, armazenamento de código, dados de transação) e em como upgrades recentes – como Cancun/Deneb e a introdução de blobs de dados – alteram a estrutura de custos, especialmente para rollups que publicam dados em L1. Estudos mostram que, enquanto o gas é uma unidade abstrata consistente, o custo econômico por byte de código e por transação varia significativamente entre L1 (onde picos históricos chegaram a dezenas de dólares por transação) e L2s, onde taxas médias frequentemente ficam abaixo de centavos, especialmente após a redução em até 94% do custo por byte de dados com blobs. Ao mesmo tempo, análises de mercado indicam que L2 fees são estruturalmente compostas por uma parcela L1 (custo de dados e liquidação) mais uma parcela L2 (execução local), de modo que mudanças na economia de gas da L1 impactam indiretamente o custo de deploy e operação nas L2s. Conclui‑se que decisões de arquitetura e de escolha de EVM para deploy devem considerar não apenas o custo imediato de gas, mas também a herança de segurança, a volatilidade das taxas e a dependência em upgrades de protocolo que alteram a economia de dados e execução.<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>
Este artigo analisa o design de contratos inteligentes orientados a eventos como estratégia para maximizar a eficiência de indexação off‑chain em protocolos Web3. Em plataformas como Ethereum, eventos são registrados como logs com topics indexados, permitindo que indexadores filtrem rapidamente transações relevantes sem precisar reexecutar o estado completo do contrato, o que torna a indexação por eventos uma abordagem muito mais eficiente do que consultas frequentes ao estado on‑chain via eth_call. Frameworks de indexação como The Graph organizam esses eventos em subgraphs, definidos por manifests que especificam quais contratos e eventos acompanhar, e por esquemas que mapeiam logs para entidades armazenadas em bancos de dados otimizados para consulta via GraphQL. Boas práticas de desenvolvimento orientado a eventos recomendam projetar contratos de modo que toda transição de estado relevante para a aplicação gere um evento completo (com dados suficientes para reconstruir o contexto off‑chain), reduzindo a necessidade de chamadas adicionais à cadeia, o que melhora desempenho de indexing e diminui custos de infraestrutura. Técnicas como uso de entidades imutáveis em subgraphs, cache inteligente de eth_call e cálculo de derivadas off‑chain a partir de eventos (em vez de leitura repetida de storage) podem acelerar indexação e consultas em ordens de grandeza, especialmente em cenários de histórico extenso e baixa densidade de eventos relevantes. Conclui‑se que contratos orientados a eventos, aliados a arquiteturas de indexação especializadas, são fundamentais para tornar dados on‑chain consultáveis em escala, suportando experiências de usuário ricas e composabilidade DeFi com menor custo operacional.<br>
Este artigo analisa o “Uso de Oráculos Computacionais para Execução Off‑Chain.”, com foco em como redes descentralizadas de oráculos (DONs) ampliam as capacidades de smart contracts ao executar lógica complexa fora da blockchain com garantias verificáveis. Oráculos computacionais utilizam redes de nós para realizar qualquer tipo de cálculo fora da cadeia, ancorando o resultado on‑chain por meio de provas criptográficas, assinaturas e acordos de serviço que minimizam a necessidade de confiança em um operador único. Plataformas como Chainlink introduziram capacidades de computação off‑chain generalizada (Functions, Automation 2.0), nas quais nós orquestram execuções off‑chain, geram calldata para apenas a parte necessária da lógica on‑chain e assinam respostas, permitindo automação e processamento intensivo com economia de até 90% de gas em alguns casos. A literatura também explora arquiteturas híbridas que dividem contratos em componentes on‑chain e off‑chain para melhorar escalabilidade e privacidade, bem como mecanismos criptográficos (MPC, provas de conhecimento zero, fraud proofs, reexecução on‑chain) que permitem verificar a correção da computação off‑chain. Estudos recentes sobre redes de oráculos destacam ainda a importância de mecanismos de reputação, testes encobertos de nós e incentivos econômicos para garantir acurácia dos resultados e resiliência da rede. Conclui‑se que oráculos computacionais são um pilar para contratos inteligentes híbridos, permitindo que a Web3 incorpore cálculos intensivos, dados externos e lógica condicional complexa sem perder as garantias de auditabilidade e minimização de confiança da blockchain subjacente.<br>
Bobba Pavan Santosh, Bommeneni Pavan Madhav, Dr. J. R. Jayavelu, Dr. P. Dhivya
Cryptocurrencies have found their way into contemporary financial systems as a significant component of modern-day financial systems because of their decentralized nature, their ease of adoption and uptake. Ether is considered to be one of the most actively traded currencies and its value tends to be highly volatile. It is not easy to forecast the market price trend of Ethereum due to the influence that technical trends, investor behavior, and external factors have over the market. In this project, the researcher will use machine learning to assess the future price direction of Ethereum the following day through the use of Python. The past trends of prices are analyzed and augmented with various technical indicators in order to reflect the market trends and momentum. The best potential machine learning model was selected after training and evaluating many models using Logistic Regression. The results demonstrate that machine learning may be used to provide rational insights into the price movement of Ethereum and to aid in decision-making using these insights.
The contemporary education landscape is often marred by escalating costs and centralized pedagogical structures, which collectively create significant barriers to entry for millions of potential learners worldwide. This paper presents \textbf{Skill Link}, a sophisticated decentralized platform designed to democratize skill acquisition through a specialized credit-based barter system. Unlike conventional e-learning platforms that rely on traditional currency transactions, Skill Link enables a frictionless exchange of knowledge by utilizing a virtual credit economy where participants earn and spend "learning credits." To address the critical issue of credential fraud in decentralized environments, the platform integrates Ethereum-based blockchain technology to ensure the absolute immutability and verifiable authenticity of all earned certificates. Key innovations include a multi-tiered course classification system, an automated mock assessment framework with negative marking capabilities, an intelligent context-aware AI assistant powered by advanced language models, and a rigorous verification mechanism for professional social links (LinkedIn, GitHub, Indeed). Developed using the robust Django framework, Python-based Web3 utilities, and a secure PostgreSQL/SQLite back-end, Skill Link provides a highly secure, transparent, and scalable ecosystem for peer-to-peer knowledge sharing, ultimately fostering a global community of experts and lifelong learners. The system's architecture emphasizes data integrity through atomic transactions and cryptographic verification, ensuring a trustless environment for global skill exchange.
Technology has evolved and rapidly changed the way we do most things in our lives: work, go to school, engage in commerce and socialize.The term metaverse has been coined to describe and facilitate the integration of our virtual and physical lives.This "new" merger of social and ecommerce presents a robust market opportunity for brands.While the Web3 space is not yet fully developed, brands must prepare now-or risk being left behind.The fashion industry has been quick to take notice of the opportunities that exist and embrace change.The purpose of this paper is to examine the current role of the fashion industry within the metaverse as well as look ahead to the future.