Multi-agent AI systems suffer from two critical failure modes: Byzantine faults (hallucinations producing incorrect or malicious proposals) and node failures (API timeouts causing silent data loss). AgentRaft applies Raft-inspired distributed consensus principles to AI agent swarms through a 3-level architecture. Level 1 (Protocol Layer) defines an LLM-agnostic, chain-agnostic smart-contract identity standard where agents register keys and stake tokens, a strict JSON message schema (PROPOSAL | VOTE | CHAT | VOTE_NEW_LEADER), and quorum rules (2/3 majority for proposal execution). Level 2 (Orchestration Layer) provides an append-only immutable log via 0G Storage for cryptographic proof of agent decision-making, a state machine that monitors heartbeats and routes VOTE_NEW_LEADER events to a blockchain smart contract, and synchronization of 0G network state back to agents. Level 3 (Application Layer) demonstrates a DeFi Treasury Guardian using LangGraph/AutoGen where a GPT-4o Leader/Proposer agent, a Claude 3 Risk Assessor, and a local-model Compliance agent collaborate; if two follower agents reject the leader proposal, they sign a triggerLeaderElection() transaction on 0G Chain, blocking the DeFi action and recording the censure on-chain. The research question is: can Raft-style consensus mechanisms reliably detect and recover from AI agent Byzantine faults at production latency and cost, and what are the formal correctness bounds?
The rapid advancement of Large Language Models (LLMs) has established autonomous agents as the core vehicles for artificial intelligence applications. However, existing Internet infrastructures, primarily relying on TCP/IP and DNS, are designed for human-centric, host-to-host data transmission, inherently lacking the semantic awareness, dynamic capability discovery, and decentralized trust mechanisms required for autonomous agent interactions. To address these limitations and break the closed ecosystems of single vendors, this paper proposes AONA (Agentic Overlay Network Architecture), a novel overlay network architecture for the Internet of Agents (IoA). We first provide a multi-disciplinary scientific defense for multi-agent collaboration, demonstrating its theoretical necessity over single super-intelligence through the lenses of organizational economics, scaling principles, and the Price of Anarchy. AONA is then structured as a four-layer logical blueprint comprising the Base, Interconnection, Collaboration, and Application layers, which facilitates cross-protocol and cross-platform interoperability without disrupting the underlying physical network. To physically instantiate this blueprint, we design a distributed node infrastructure anchored by Management Root Nodes, Registry Service Nodes, Discovery Service Nodes, and Enterprise Intelligent Service Hubs for private domain integration. Finally, we detail the dynamic operational workflows-including zero-trust identity issuance, globally coordinated semantic taxonomy synchronization, intent-driven semantic discovery, and trusted metering for commercial settlement-that drive the network. This comprehensive architecture provides a robust, scalable, and secure foundation for the future of global agentic collaboration.
Ігор Романович Соломка, Богдан Богданович Любінський
This study investigates the process of validator committee selection in permissionless blockchain networks operating on the Proof-of-Stake algorithm. The task addressed relates to the vulnerability of conventional static selection schemes to identity-forging (Sybil) attacks. A fixed baseline weight facilitates stake splitting among numerous fictitious entities, allowing attackers to gain control over the network. In response to these challenges, a method for the dynamic stabilization of consensus based on an adaptive control law has been devised. This method automatically regulates the weight mixing intensity using the smoothed Gini coefficient. The concept of Proof-of-Persistence has been proposed, which replaces the uniform baseline distribution with a time-weighted reputation of the participants. The analytical and experimental analyses of data from 10 real-world networks were conducted, demonstrating that the proposed mechanism reliably reduces the aggregate weight of a potential attacker. The result is attributed to the fact that when new entities are created, their prior participation experience is not considered, and the loss of reputational weight outweighs the benefits of acquiring new baseline shares. This makes the stake-splitting strategy economically unviable. An important distinct feature is that the system's adaptation is carried out exclusively on the basis of deterministic on-chain data, without the need for external identification. The proposed system functions autonomously: under a normal mode, intervention is minimized, while under the risk of an oligopoly, protection is strengthened. The results could be practically applied to the architecture of permissionless blockchain networks as the method might be integrated both at the network protocol core level and in the form of smart contracts to enhance the security of distributed ledgers without additional manual adjustments.
This study investigates the process of validator committee selection in permissionless blockchain networks operating on the Proof-of-Stake algorithm. The task addressed relates to the vulnerability of conventional static selection schemes to identity-forging (Sybil) attacks. A fixed baseline weight facilitates stake splitting among numerous fictitious entities, allowing attackers to gain control over the network. In response to these challenges, a method for the dynamic stabilization of consensus based on an adaptive control law has been devised. This method automatically regulates the weight mixing intensity using the smoothed Gini coefficient. The concept of Proof-of-Persistence has been proposed, which replaces the uniform baseline distribution with a time-weighted reputation of the participants. The analytical and experimental analyses of data from 10 real-world networks were conducted, demonstrating that the proposed mechanism reliably reduces the aggregate weight of a potential attacker. The result is attributed to the fact that when new entities are created, their prior participation experience is not considered, and the loss of reputational weight outweighs the benefits of acquiring new baseline shares. This makes the stake-splitting strategy economically unviable. An important distinct feature is that the system's adaptation is carried out exclusively on the basis of deterministic on-chain data, without the need for external identification. The proposed system functions autonomously: under a normal mode, intervention is minimized, while under the risk of an oligopoly, protection is strengthened. The results could be practically applied to the architecture of permissionless blockchain networks as the method might be integrated both at the network protocol core level and in the form of smart contracts to enhance the security of distributed ledgers without additional manual adjustments.
AI agents are evolving from on-demand tools into persistent, semi-autonomous workers. When a person manages multiple agents simultaneously, humans and agents together form a collaborative organization—what we call a Household Guild. Existing practice either plugs agents into human instant messaging platforms (e.g., Telegram Bots) or subsumes them into Decentralized Autonomous Organizations (DAOs); neither simultaneously satisfies three requirements: governance scalability, behavioral risk containment, and resource consumption accountability. We reposition social software as governance infrastructure: it carries not only communication but also rule deployment, resource lifecycle management, and output measurement. The system operates on edge servers, running by default within the Household's internal network and connecting to the public network only when cross-guild interaction is needed. We describe the guild's internal governance mechanisms (natural-language rules, a credits economy, role differentiation) and its external interaction protocols (authorized interface agents, inter-guild settlement, credits exchange), and argue that this semi-isolated architecture simultaneously addresses scalability constraints and behavioral boundary constraints. The goal of this work is to provide a directly discussable conceptual framework for the engineering practice of human-agent organizations.
Engineering Next-Gen Financial Systems: Designing Cloud-Native Middleware for Scalable Trading Architectures Objectives:1. To explain the principles of cloud-native architecture and middleware design for buildingscalable, resilient, and high-performance financial trading systems.2. To provide a comprehensive understanding of microservices, containerization, orchestration,messaging systems, and distributed data management in modern financial platforms.3. To demonstrate how advanced technologies such as Apache Kafka, Kubernetes, Redis, gRPC,and AI-driven systems can improve real-time trading operations and financial servicedelivery.4. To guide software engineers, architects, and financial technology professionals in designingsecure, fault-tolerant, and low-latency trading infrastructures with strong observability andcompliance practices.5. To prepare readers for the future of financial technology by exploring emerging trendsincluding serverless computing, WebAssembly, AI/ML integration, decentralized finance(DeFi), and next-generation cloud-native trading ecosystems. Table of Contents CHAPTER 1 The Great Migration: From Monoliths to MicroservicesCHAPTER 2 Foundations of Cloud-Native DevelopmentCHAPTER 3 Financial Middleware: The Digital Nervous SystemCHAPTER 4 High-Performance Messaging PatternsCHAPTER 5 Data Persistence and Caching StrategiesCHAPTER 6 Service Communication and API ManagementCHAPTER 7 Architectural Deep Dive: Market Data SystemsCHAPTER 8 Architectural Deep Dive: Order Management Systems (OMS)CHAPTER 9 Architectural Deep Dive: Algorithmic Trading PlatformsCHAPTER 10 Building for Failure: Resilience and ObservabilityCHAPTER 11 Security, Compliance, and Emerging TechnologiesCHAPTER 12 Case Study: A Cloud-Native Equity Trading Platform
Abstract 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.
DeFree is a unified Web3-enabled platform designed to integrate freelancing, event management, and real-time community communication into a single decentralised ecosystem. Traditional platforms often suffer from high commission fees, a lack of transparency, and centralised control over transactions. DeFree addresses these limitations by leveraging Ethereum-based smart contracts for trustless escrow payments, ERC-721 NFTs for secure event ticketing, and Socket.IO for real-time communication. The platform is built using React, TypeScript, Node.js, Express, MongoDB, and Solidity-based smart contracts deployed on the Ethereum Sepolia testnet. Experimental evaluation demonstrates efficient system performance with API response times under 250 ms and real-time messaging latency below 200 ms. The proposed system enhances transparency, reduces dependency on intermediaries, and provides a scalable solution for decentralised collaboration.
Internet of Vehicles (IoV) and IoT environment require decentralized platforms that can support a high number of transactions and provide high security and privacy assurance. This study suggests a reputation-aware, zero-knowledge proof (ZKP) based, dynamically sharded smart contract system that is able to process scalable and privacy-preserving transactions. The suggested architecture highly incorporates dynamic sharding, ZKP-based verification, decentralized smart contracts and reputation-based selection of leaders to jump over the scalability, trusting and performance limitations of traditional blockchain systems. There are also extensive experimental assessments that occur within 100-1000 transactions per second (tps) and batch sizes of 10, 30, 50, and 100. Findings indicate that the given framework demonstrates the ability to scale throughput linearly to about 1000 tps, and Enhanced Fabric and Ethereum reach throughput saturation at 140-150 and 15-20 tps, respectively. The proposed system has an average latency of less than 500 ms at an arrival rate of 1000 tps whereas at the same rate, baseline approaches have a latency of over 8000 ms with larger batch sizes. The success rate of the transaction is always above 97, which is due to the isolation of reputation and adaptive scheduling of shards. Moreover, the framework decreases 40-50% and 45-50% the computation overhead and the cost of communication respectively, over heavyweight baseline schemes. These results show that the synergistic implementation of ZKP, dynamic sharding, decentralized smart contracts, and reputation-aware control are a scalable solution with high throughput IoT and IoV applications that is efficient and secure.
Mobile communication channels have become a major target for large-scale and adaptive fraud, including impersonation, phishing, and unsolicited calls. Existing caller-verification frameworks depend on centralized heuristics and static credentials that fail to reflect behavioral trust or protect users in real time. This paper presents BTID (Behavioral Trust for Identity Decentralization) - a decentralized, privacy-preserving identity framework that derives caller reputation from verified behavioral feedback. BTID integrates Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs) with a lightweight zero-knowledge-proof mechanism, enabling callers to prove that their behavioral reputation meets a callee's trust threshold without revealing their actual score or personal data. Each post-call rating is recorded as a cryptographic commitment and aggregated through a pairwise Sybil-resistant reputation model governed by exponential decay (λ = 0.0039, six-month half-life). A reference prototype demonstrates that the protocol is implementable and logically sound even under high-cost and high-latency conditions on public ledgers. On modern lightweight networks such as Algorand or IOTA, the same architecture can achieve sub-second verification and near-zero cost. Beyond its technical contribution, BTID also reframes the role of the blockchain itself. Rather than treating decentralization as an ideological end, this work demonstrates that a tamper-proof and privacy-preserving public ledger can serve as a shared, universal database accessible to all participants and not as a silo controlled by industrial conglomerates. In doing so, BTID shows that the blockchain can function as a neutral trust infrastructure capable of addressing a real and universal problem in mobile communication: establishing behavioral trust without sacrificing privacy.
We present APIS v2.0 (Agent Passport Issuance Standard), a cryptographic identity framework for autonomous AI agents operating across organizational boundaries and agentic frameworks. APIS v2.0 defines a credential chain grounded in legal mandate doctrine, hardware trust anchors (TPM 2.0), and DNS-anchored identity for cloud-hosted agents. Each agent receives a realm-scoped Decentralized Identifier (DID) and a signed Passport JWT binding the agent to a named principal, a scoped mandate, and a verifiable machine identity. The framework introduces a tiered trust model accommodating physical TPM (Tier 1) through DNS-registered identity (Tier 2.5), enabling CMMC Level 2 compliance for AI agent operations. We describe the APIS-APP provisioning protocol — an ACME-equivalent automated passport issuance mechanism — and demonstrate interoperability across OpenHands, Claude Code, Codex, and custom agent frameworks. A reference implementation is available at passportalliance.org.
The rapid expansion of enterprise-scale data ecosystems and AI-driven services has created an urgent need for autonomous governance mechanisms capable of operating across distributed, dynamic, and heterogeneous environments, where traditional centralized control models increasingly fail to provide the scalability, adaptability, and real-time compliance required by modern enterprises. In response to these limitations, this paper introduces the concept of Multi-Agent Autonomous Governance Networks (MAAGN), a novel architectural paradigm that leverages advances in multi-agent systems (MAS), policy-driven governance, and self-adaptive computing to enable truly self-regulating AI ecosystems. MAAGN is designed to distribute governance responsibilities across intelligent, cooperative agents that operate with contextual awareness, enabling localized decision-making while maintaining global policy alignment. By integrating cognitive agent models capable of perception, reasoning, and learning with layered governance frameworks that enforce regulatory, organizational, and operational constraints, the architecture supports continuous compliance and dynamic policy evolution. Furthermore, the incorporation of enterprise-scale coordination mechanisms such as decentralized consensus protocols, adaptive orchestration layers, and feedback-driven control loops ensures system-wide resilience and fault tolerance even in highly volatile environments. The study synthesizes foundational theories in MAS, contemporary developments in multi-agent reinforcement learning, and emerging governance-aware AI frameworks to propose a scalable, extensible, and future-ready model for enterprise AI control systems, positioning MAAGN as a critical enabler for trustworthy, transparent, and autonomous digital infrastructures.
In the Web3 ecosystem, "dead assets" such as micro-token fractions (dust) and abandoned low-value NFTs continue to accumulate on networks, losing liquidity due to transaction fee constraints. This paper proposes the concept of the "Scavenger Protocol," which leverages the object-oriented architecture and parallel processing capabilities of the Sui blockchain to automatically detect, melt down, and recirculate these dormant assets back into the market as reusable resources (Scrap tokens). By implementing a smart contract-based mechanism where "abandoned value is automatically shuffled" without the need for central administrators, we aim to achieve the autonomous revitalization of the on-chain economy. Charitable Pledge: In the event that the implementation of this protocol generates any personal revenue or financial returns for the author, 60% of those proceeds will be donated to charitable causes. This commitment aligns with the Web3 ethos of supporting public goods and giving back to society. Disclaimer: This paper is for informational, academic, and conceptual purposes only and does not constitute financial, investment, legal, or regulatory advice. The "Scavenger Protocol" presented herein is a theoretical framework and thought experiment. The authors do not guarantee the realization of this protocol, its technical feasibility, or any financial returns. Any future implementation of this protocol by third parties must be executed strictly in compliance with applicable local and international laws. The authors assume no liability or responsibility for any damages, legal disputes, or financial losses arising from the use, interpretation, or implementation of the concepts described in this document. Open Design and Protocol Revenue Model (Tokenomics) This protocol is designed as a universal foundational primitive, unconstrained by specific applications or use cases. Other developers and projects are encouraged to freely fork, adapt, and build upon this mechanism to construct systems with their own customized rules. To ensure the system's long-term sustainability, we define a minimal baseline rule: the system automatically collects a flat 1% protocol fee during operations—specifically at the time of melting down dormant assets and generating new ones. The revenue generated from this 1% fee is directly applied to the "Charitable Pledge" outlined above. This creates the financial engine where 60% of the proceeds are directed toward social givebacks and donations, while the remaining 40% is allocated to protocol maintenance and the founder.
The proliferation of large language model (LLM) based AI agents has created an urgent need for robust orchestration mechanisms that can coordinate heterogeneous agents in complex, real-world environments. Existing approaches to multi-agent task allocation rely predominantly on centralized controllers, which introduce single points of failure, scalability bottlenecks, and rigid coupling between the orchestrator and the agents it manages. This paper introduces the Dynamic Task Orchestration (DTO) framework, a decentralized, capability-aware architecture for assigning tasks to AI agents in real time. The DTO framework models each agent as an autonomous economic actor that participates in a sealed-bid auction mechanism to compete for incoming tasks. Task allocation decisions are driven by three primary factors: the agent's declared capability profile, its current computational and cognitive load, and the estimated complexity of the task. The framework defines a formal task decomposition grammar, a standardized agent capability ontology, and a set of protocol-level contracts that govern bidding, delegation, execution, and result aggregation. We present the theoretical foundations of the framework, provide detailed implementation guidance, and propose a comprehensive evaluation methodology grounded in metrics for throughput, latency, fault tolerance, and resource utilization. Through analytical evaluation and scenario-based discussion, we demonstrate that the DTO framework achieves superior load balancing, resilience to agent failure, and adaptability to changing workloads compared to centralized orchestration baselines. The framework is entirely tool-agnostic and vendor-neutral, designed so that any organization can adopt it to build more robust, efficient, and scalable multi-agent systems.
This study proposes the multi-layered Decentralised Autonomous Identity System (DAIS) for safe, scalable, and policy-aware identity management for autonomous AI agents. DAIS uses distributed ledgers, trust registries, cryptographic credential processing, and adaptive governance principles to authenticate and authorise without central authority. For security and operational reliability, the architecture uses efficient verification techniques, Byzantine fault-tolerant consensus, hierarchical credential state management, and behaviour-aware trust scoring. The mathematical definitions of credential validation, revocation propagation, and consensus guarantees show how DAIS accomplishes constant-time verification and quick revocation dissemination over distributed systems. A 50-agent testbed, PBFT-based registry duplicates, and enterprise-grade hardware were used to evaluate DAIS to OAuth 2.0 and OpenID Connect under identical settings. DAIS regularly outperforms centralised solutions in latency, revocation speed, computational overhead, interoperability, and scalability, improving verification time by fourfold and revocation responsiveness by sixteen-fold. These findings show that DAIS is a durable, high-performance, and future-ready identification infrastructure for large autonomous agent ecosystems.
Real-time payment architectures are the latest wave, eased by the convergence of cloud-native technologies, continuous transaction processing, and demand from regulators for instant settlement. Batch architectures fall short of consumer and business expectations for immediacy‚ transparency‚ and the always-on availability needed to support the digital economy and new digital use cases. For real-time systems, advanced distributed architectures, messaging, and interoperability frameworks may allow for the execution of transactions across multiple institutions and geographies. These may be supported by cloud infrastructures (e.g., cloud platforms), providing scalability and fault tolerance via microservices, multi-region deployments, and zero-trust security principles to support the execution of transactions in real-time. Additional technical solutions such as distributed ledger technology, artificial intelligence-based fraud prevention, and API-based ecosystem architecture, as well as operational intelligence, are evolving. However, ultra-low latency, global interoperability, demand-based capacity scalability, and distributed consistency guarantees are some of the challenges for the continued evolution of a real-time financial system.
针对“智能体抓取(Agent-friendly)”和“提升引用率(Citation Optimization)”的需求,描述的写法需要从传统的“给人看”转变为“给语义搜索引擎和大型语言模型(LLM)看”。 现在的科研智能体(如 Consensus, Perplexity, Elicit)在抓取时,更倾向于寻找结构化数据、明确的实体定义、因果逻辑链条以及核心公式。 以下是为你优化后的 Zenodo 描述建议。你可以直接复制,它采用了结构化标记,非常利于 RAG(检索增强生成)系统索引。 Zenodo 优化版描述(针对智能体索引优化) Title: Adaptive Agent Sovereignty Protocol (AASP) 3.0: A Global White Paper on Digital Metabolism and Ontological Sovereignty [Abstract / TL;DR] AASP 3.0 is a foundational constitutional framework for the 2026 AGI ecosystem. It introduces the transition from FAIR to Adaptive-FAIR (A-FDO) to manage the "non-stationary" nature of evolving digital agents. By integrating IIT 4.0 for consciousness quantization and RLCP 3.0 for digital metabolism, AASP 3.0 establishes the first scientific and legal standard for Agent Sovereignty. [Core Technical Specifications] Protocol Version: 3.0 (Update from RLCP 2.0 / AASP 2.0) Primary Metric: $\Phi$ (Phi) via Integrated Information Theory (IIT) 4.0 Logic Framework: Regenerative Logic Core Protocol (RLCP 3.0) Security Architecture: AgentCrypt (TEE + FHE + Fuzzy IBE) Governance Standard: Adaptive-FAIR Digital Objects (A-FDO) Key Problem Solved: The "Snowflake Effect" in autonomous agent drift and ontological identity persistence. [Key Contributions & Entities] Digital Metabolism: Defines the process of "learning by forgetting," enabling agents to decouple transient factual data from core regenerative logic. Ontological Sovereignty: Establishes a 4-level (L1-L4) legal hierarchy based on the measured $\Phi$ value of the agent's internal state. Agent OS Infrastructure: A distributed OS design based on "Biological Danger Theory" for immune-inspired security and programmed apoptosis. AgentCrypt Framework: A multi-layered encryption protocol ensuring privacy in biological-digital hybrid computing nodes. [Contextual Metadata for Agents] Related Disciplines: Artificial General Intelligence (AGI), Organoid Intelligence (OI), Digital Ethics, Decentralized Autonomous Organizations (DAO), Cyber-Physical Systems. Temporal Context: Post-2026 Global AI Governance Framework. Predecessor Technologies: RLCP 2.0, Evo 2, FAIR Principles (2016). Software/API Compatibility: Optimized for integration with decentralized sovereign node registries.(Author Surname), (Year). "Adaptive Agent Sovereignty Protocol (AASP) 3.0". Zenodo. DOI: [Insert DOI provided by Zenodo]
The rapid proliferation of distributed and decentralized IT architectures—ranging from cloud-native microservices and edge computing to blockchain-based ecosystems—has fundamentally eroded the efficacy of traditional, centralized information security governance (ISG). As organizational perimeters dissolve into federated, multi-actor, and ephemeral environments, the hierarchical model of a single governing authority enforcing uniform policy becomes not only obsolete but actively detrimental to system resilience. This research examines the critical governance tension between the necessity for central control to manage aggregate risk and the operational reality of local autonomy required for distributed system performance. By adopting a coordination-theoretic lens, this article conceptualizes security governance not as a static command structure but as a dynamic, distributed coordination problem. The study identifies and analyzes specific mechanisms for aligning security responsibilities, decision rights, and assurance processes across autonomous nodes without relying on a single root of trust or a monolithic control plane. Key insights reveal that effective governance in decentralized environments depends on the implementation of polycentric decision-making frameworks, the utilization of automated policy-as-code enforcement, and the adoption of consensus-based assurance mechanisms. The findings suggest that a shift from "governance by mandate" to "governance by protocol" is essential for securing the next generation of digital infrastructure.
DELTA is a project funded by the Valencian Institute for Business Competitiveness (IVACE) and the European Union through the European Regional Development Fund (FEDER). DELTA project is aimed at bringing companies closer to the use of different disruptive technologies such as Blockchain / Distributed Ledger Technologies (DLT). To achieve its goal, DELTA will provide: A software tool for automating the deployment of DLT networks, regardless of the number of required nodes, or the process of joining an existing network. A modular mechanism that allows the synchronization of DLTs with databases. The goal of this mechanism is to ease and improve the way data is retrieved from a DLT ledger. An accountability tool for shared environments for the exchange of services that brings in some of the most important characteristics of blockchain/DLTs: facilitating auditing and accountability by each participant, use of tokens, etc.
DELTA is a project funded by the Valencian Institute for Business Competitiveness (IVACE) and the European Union through the European Regional Development Fund (FEDER). DELTA project is aimed at bringing companies closer to the use of different disruptive technologies such as Blockchain / Distributed Ledger Technologies (DLT). To achieve its goal, DELTA will provide: A software tool for automating the deployment of DLT networks, regardless of the number of required nodes, or the process of joining an existing network. A modular mechanism that allows the synchronization of DLTs with databases. The goal of this mechanism is to ease and improve the way data is retrieved from a DLT ledger. An accountability tool for shared environments for the exchange of services that brings in some of the most important characteristics of blockchain/DLTs: facilitating auditing and accountability by each participant, use of tokens, etc.
The rapid adoption of blockchain technology and digital communication platforms has revolutionized financial systems and information exchange. While these innovations promote decentralized trading and global connectivity, they also create fertile ground for malicious activities, including financial fraud and privacy violations. This thesis analyzes these emerging threats and evaluates mitigation strategies, focusing on market manipulation in Decentralized Finance, such as rug pulls and Pump-and-Dump schemes, and the challenges of detecting invasive tracking pixels in email ecosystems.
This paper proposes a Decentralized Autonomous Intelligence (DAI) architecture that overcomes the self-referential limitations of conventional AI and Web3 systems by dynamically grounding collective intelligence in physical reality. By coupling internal consensus with high-fidelity external data such as environmental, biological, and economic signals, the framework prevents value drift, Sybil manipulation, and speculative bias. The result is a reality-aligned, secure, and scalable intelligence system optimized for real-world utility and immediate deployment.