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.
Geoffrey Broomhead, Sovereign Trust Node: Broomhead Private Sovereign Trust, geoffreybroomhead.eth
The witness/extractability framework was established in the General Witness Theorem across three otherwise-disjoint domains: combinatorial mathematics, common-law evidence, and economic ledgers. The framework’s predictive content licenses a stronger claim: any domain admitting a valid instantiation of the abstract setup is governed by the framework, whether or not the domain’s practitioners have noticed. This paper instantiates the framework in a fourth domain: distributed consensus under Byzantine fault tolerance. We prove the BFT Witness Asymmetry Theorem: the structural cost of operating without standing on Byzantine nodes is super-linear, exhibited at single-level non-extractability as the standard O(n²) communication lower bound (Dolev–Reischuk 1985). We then state the Hierarchical-Coalition Cost-Asymmetry Conjecture: under recursive Byzantine sub-coalitions of adversarially-chosen depth k, the communication lower bound grows as Ω(n^{k+1}).
Privacy-preserving truth discovery (PPTD) has garnered significant attention in mobile crowdsensing (MCS). However, existing research lacks sufficient privacy protection and is often vulnerable to collusion attacks among malicious participants. Moreover, incorrect data submitted by unreliable users and their weights may reduce the accuracy of truth discovery. To address these issues, this paper proposes a privacy-preserving truth discovery framework resistant to collusion and reputation fraud (TD-RCRF) that is highly resistant to collusion and reputation fraud. The scheme employs additive secret sharing to protect sensing data, weights, intermediate results, and ground truth. To screen trustworthy users who meet reputation requirements under the non-colluding dual-server model, we propose a privacy-preserving reputation verification algorithm that combines Pedersen commitment and zero-knowledge proof to verify the validity of mobile users’ reputation values. Additionally, we propose a homomorphic strategy that converts shares between multiplication and addition and use it to design a lightweight truth discovery algorithm that further improves the accuracy of the “truth” using reputation values. Security analysis proves that TD-RCRF is privacy-preserving and secure under the non-colluding dual-server assumption. Theoretical analysis and experiments show that it is practical and efficient.
Cognition has always written itself onto something. Clay, papyrus, neural tissue, silicon. This paper argues that wherever cognition stores anything, it does so in five recurrent data shapes: binary, table, graph, vector, and an append-only temporal ledger. The claim is structural rather than historical. The same five shapes appear in Babylonian astronomical diaries, in monastic chronicles, in relational databases, in modern vector stores, and in any future substrate that wishes to remember. Substrate changes; shape persists. That persistence is what allows cognition to survive transitions between media. The argument unfolds across four movements. Ontology asks what a cognitive substrate is, and proposes a minimal account compatible with both biological and synthetic carriers. Epistemology examines what shapes knowledge actually takes once instantiated, and why these five exhaust the space of stable storage forms. Cogitation describes how distributed agents decide using flock dynamics coordinated through a three-button cell whose only operations are Act, Dismiss, and Ask-sibling. Teleology closes with twelve falsifiable predictions, three of which can be tested through independent paths that do not share assumptions. The framework is glass-box by construction and connects to generalised coordinates, bitemporal data models, episodic memory research, and the free energy principle. A working implementation is available as a public seed at https://github.com/agilemeshnet/theshapeofthought, where the cognitive architecture can be cloned and instantiated directly. The paper is written for philosophers of science and physicists who may wish to test, falsify, or collaborate on the measurement programme it proposes. The invitation is to treat the shapes as instruments rather than metaphors, and to see what cognition does when measured through them.
Canon² — Trust Layer Research Archive. In deterministic, decentralized computational frameworks, state mutations are immutable, linearly ordered, and rigidly governed by consensus constraints. Reversing or rolling back such state transitions traditionally necessitates revealing the sensitive state artifacts subject to the rollback, fundamentally destroying the cryptographic confidentiality of participant actors. I introduce the Zero-Knowledge State Reversal Protocol (ZK-SRP), a method designed to allow participating deterministic nodes to cryptographically prove the legitimacy, necessity, and validity of a state rollback without disclosing the underlying payloads of the state to any governing validator. I present a framework wherein zero-knowledge succinct non-interactive arguments (SNARKs/STARKs) are bound tightly into deterministic hashing algorithms (specifically SHA3-256) and anchored directly to the Trust Layer Certificate Fabric. In this design, execution nodes issue Reversal Envelopes that contain mathematically verifiable proofs that a specific backward transition maps cleanly onto earlier hash obligations. Through this protocol, the rollback itself becomes deterministic and universally validatable. I outline how this operates within the Lume-V wrapper ecosystem, the DAIGS master taxonomy, and the self-healing mechanisms of Type-4 Synthetic Organisms. I present what is, to my knowledge, the first deterministic privacy-preserving state reversal architecture that guarantees zero state leakage while maintaining general-purpose runtime rollbacks, ensuring that synthetic agents and multi-agent arbitration channels can effectively unspool catastrophic logic errors without breaking strict confidentiality agreements.
Penelitian ini mengevaluasi efektivitas federated learning dalam mendeteksi alamat ilegal pada blockchain Ethereum untuk Anti Money Laundering (AML). Studi ini membandingkan XGBoost centralized dan federated dalam simulasi multi exchange, data dibagi secara horizontal menjadi 3 subset yang merepresentasikan entitas bursa independen. Untuk mengisolasi pengaruh pelatihan terdistribusi, pembagian data dilakukan secara homogen (IID), sehingga analisis difokuskan pada perbedaan mekanisme pembelajaran tanpa dipengaruhi heterogenitas data. Hasil menunjukkan bahwa model federated mencapai performa yang kompetitif dengan PR AUC 0,9962 dan akurasi 97,11%, dibandingkan model terpusat dengan PR AUC 0,9975 dan akurasi 97,75%. Namun, performa tersebut disertai peningkatan durasi pelatihan 4,01 detik dibandingkan 3,39 detik, yang disebabkan oleh beban komunikasi selama proses pembaruan model. Temuan ini menegaskan adanya trade off antara kinerja dan efisiensi dalam penerapan federated learning. Meskipun mampu mendekati performa pembelajaran centralized, pendekatan ini menimbulkan biaya operasional tambahan. Studi ini merupakan evaluasi dasar dalam kondisi terkontrol dan belum merepresentasikan skenario dunia nyata, sehingga diperlukan penelitian lanjutan pada data non IID dan skala sistem yang lebih besar.
Auditing the semantic properties of proprietary data creates a fundamental tension: verification requires transparent access, while proprietary rights demand confidentiality. While Zero-Knowledge Proofs (ZKPs) ensure privacy, they are typically limited to precise algebraic constraints and are ill-suited for verifying qualitative, unstructured properties, such as the logic within a codebase. We propose {\em Agentic Witnessing}, a framework that moves verification from attested execution to {\em attested reasoning}. The system is composed of three agents: a Verifier (who wants to check properties of a dataset), a Prover (who owns the dataset) and an Auditor (that inspects the dataset). The Verifier is allowed to ask a limited number of simple binary true/false questions to the auditor. By isolating an LLM-based Auditor within a Trusted Execution Environment (TEE), the system enables the Verifier to query a Prover's private data via simple Boolean queries, without exposing the raw dataset. The Auditor uses the Model Context Protocol (MCP) to dynamically inspect the target dataset, producing a yes/no verdict accompanied by a cryptographic transcript: a signed hash chain binding the reasoning trace to both the original dataset and the TEE's hardware root of trust. We demonstrate this architecture by automating the artifact evaluation process for 21 peer-reviewed computer science papers with released codebases on GitHub (e.g. Does the codebase implement the system described in the paper?). We verified five high-level properties of these codebases described in the corresponding publications, treating the source code as private. Our results show that TEE-enabled agentic auditing provides a mechanism for privacy-preserving oversight, effectively decoupling qualitative verification from the need for data disclosure.
يواجه النشر العلمي الرقمي تحديات متراكمة تتمحور حول التحيز التحريري، وغموض عمليات مراجعة الأقران، وتركّز السلطة في يد قلة من البوابات الأكاديمية، حتى في ظل اعتماد منصات مفتوحة المصدر مثل نظام المجلات المفتوحة (Open Journal Systems - OJS) بإصداره 3.x. تُقدّم هذه الورقة النظرية إطاراً مفاهيمياً رائداً يستكشف إمكانية دمج نماذج حوكمة المنظمات المستقلة اللامركزية (Decentralized Autonomous Organizations - DAOs) داخل البنية المعمارية لنظام OJS 3.x، بهدف إعادة هندسة عمليات مراجعة الأقران واتخاذ القرارات التحريرية وفق منطق الشفافية الفائقة (Hyper-Transparency). يتأسس الإطار المقترح نظرياً على تكامل النظرية المؤسسية (Institutional Theory) ونظرية الوكالة (Agency Theory)، حيث تُوظَّف الأولى لتحليل الضغوط المعيارية والتقليدية والقسرية التي تُشكّل سلوك المجلات العلمية، فيما تُستخدم الثانية لتفسير مشكلات عدم تماثل المعلومات بين الأطراف الفاعلة (المحررون، المراجعون، المؤلفون، القراء). يقترح البحث ستة مكونات معمارية للإطار: العقود الذكية لإدارة سير العمل التحريري، رموز الحوكمة (Governance Tokens) لتوزيع حقوق التصويت، السجلات الموزعة (Distributed Ledgers) لتوثيق المراجعات، آليات الإجماع للقرارات النهائية، أنظمة السمعة المُرمَّزة (Tokenized Reputation Systems)، وبروتوكولات حل النزاعات الخوارزمية. تكشف المناقشة أن هذا الدمج يُعيد توزيع سلطة الحراسة الأكاديمية، ويُقلّل من تكاليف الوكالة، ويُعزّز الشرعية المؤسسية للمجلات، إلا أنه يُولّد توترات جديدة تتعلق بالإجماع الزائف، والتمويل غير المستدام، والتعقيد التقني. يُقدّم البحث آثاراً إدارية محورية لمسؤولي OJS، ويختتم بأجندة بحثية مستقبلية تُشجّع على التحقق التجريبي للإطار في سياقات نشر متنوعة.
This research paper examines the structural and philosophical changes in global commerce and management required because of the "Third Wave" of digital transformation (DT). While earlier versions of DT dealt with the digitization of analog records and the uptake of cloud computing, the modern age of Agentic AI, Industrial Metaverse and Decentralized Autonomous Organizations (DAOs) is demanding a fundamental re-engineering of the firm. Through a methodical approach of analyzing current technological trajectories and management frameworks this study identifies the existence of a critical "Agility Gap" between legacy led organizations and the digital native enterprises. The research proposes Integrated Digital-Managerial Framework (IDMF) as strategic roadmap of 2026 and onwards. Key findings suggest that "Digital Maturity" is no longer a technology benchmark but comes instead as a cultural and structural imperative, one determining whether a company survives in the marketplace in an increasingly automated commerce landscape.
Omer Aziz, Muhammad Shoaib Farooq, Adel Khelifi, Abdullah Khalid Omer
<title>Abstract</title> The digital preservation of cultural heritage has advanced considerably through blockchain technologies , offering secure, immutable, and decentralized management of museum records. However, traditional blockchain frameworks suffer from scalability limitations, leading to high transaction costs, network congestion, and inefficient resource utilization—particularly in virtual museum environments with dynamic user activity. To address these challenges, we propose VERAONET (Virtual Ecosystem for Rewards and Archaeological Operations Network), a novel Layer-2 blockchain framework designed specifically for digital heritage applications. VERAONET introduces a pluggable consensus architecture, enabling dynamic switching between Proof of Work (PoW), Adjustable PoW (APoW), Proof of Stake (PoS), and Delegated Proof of Stake (DPoS) mechanisms in response to real-time network conditions. The framework was evaluated in two experimental environments: a Ganache-based local simulation (10–500 users) and a Geth virtual testbed (50–10,000 users). Results indicate that PoS and DPoS significantly outperformed PoW and APoW under high-load conditions, reducing latency, gas consumption, and chain bloat. Conversely, PoW and APoW proved effective in low-traffic, high-security scenarios. The adaptive consensus algorithm enhanced transaction finality and resource efficiency, demonstrating strong suitability for dynamic virtual museum ecosystems. VERAONET thus provides a scalable, sustainable, and cost-effective blockchain infrastructure tailored for cultural heritage preservation. Future work will focus on integrating AI-driven optimizations, supporting cross-chain interoperability, and embedding decentralized governance models.
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.
Canon² — Trust Layer Research Archive. Modern distributed computing has historically relied upon probabilistic consensus mechanisms—such as Proof-of-Work and Proof-of-Stake—to secure state transitions across decentralized networks. These models evaluate the validity of a transaction entirely upon the weight of the underlying cryptographic expenditure or financial collateral, without inspecting the semantic content or structural objective of the payload itself. I propose a fundamental paradigm shift: Proof-of-Intent (PoI). By elevating "intent" to a first-class computational primitive, deterministic ecosystems can sequence, validate, and execute operational states based upon the cryptographic verification of the initiator's structural objective rather than arbitrary hash power or capital accumulation. Proof-of-Intent formalizes a requested operation into a rigid, deterministic Intent Tuple. This tuple binds mathematically to the Trust Layer Certificate Fabric through SHA3-256 hash commitment, locking agent identity directly to explicit execution parameters. By utilizing Lume-V envelopes and Deterministic AI Guided Subsystems (DAIGS) arbitration heuristics, PoI creates an environment for synthetic organism evolution, cyber-physical governance, and multi-agent coordination where every state transition carries verifiable semantic context. Nodes running PoI validate transactions by hashing the execution intent and checking adherence to local and global algorithmic governance constraints. Because the system rejects structurally flawed or contradictory intents before physical execution cycles are expended, Proof-of-Intent achieves what is, to my knowledge, unprecedented throughput and finality guarantees, bypassing the probabilistic race conditions inherent in classical consensus schemas.
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.
The governance practice of decentralized autonomous organizations faces a deepparadox: token-voting mechanisms designed with the intention of decentralizationpersistently tilt toward centralization and oligarchy during operation. This paperreveals that the root of this predicament lies not only in the design of specificvoting rules but, more fundamentally, in an implicit presupposition of the theoretical paradigm that dominates such rule design—that the governance space hasbeen fully specified before operation begins. The revelation principle on whichtraditional mechanism design theory relies requires the designer to possess a prioriknowledge of the participants’ type space, yet when the very concepts of governance—such as “fairness,” “contribution,” or “membership”—themselves become objectsof dispute and reconstruction, the presupposition of a fixed type space ceases tohold. Drawing on Ostrom’s core insight concerning meta-rules within multi-levelinstitutional analysis, this paper distinguishes the governance levels of distributedautonomous organizations into operational rules, collective-choice rules, and metarules, and proposes a post-mechanism design paradigm centered on a cognitiveconstitution—shifting the designer’s role from “selector of optimal rules” to “steward of the rule-evolution ecosystem.” The paper further advances three meta-ruleprinciples of post-mechanism design: conceptual anchoring, cognitive diversity regularization, and pathological pruning, and discusses the engineering pathways fortranslating these principles into executable technical specifications. The paper argues that when “what constitutes optimal governance” is itself an open question,the core duty of the designer is not to answer this question but to ensure that thesystem possesses the capacity to continuously discover better answers.
Collusion among autonomous agents poses a critical security threat in embodied multi-agent systems (MAS), where coordinated behaviors can deviate from global objectives and lead to real-world consequences. Existing defenses, primarily based on identity control or post-hoc behavior analysis, are insufficient to address such threats in embodied settings due to delayed feedback and noisy observations in physical environments, which make behavioral deviations difficult to detect accurately and in a timely manner. To address this challenge, we propose a mutagenic incentive intervention approach that mitigates collusion by reshaping agents' payoff structures. By rewarding agents who report collusive behavior and penalizing identified participants, the mechanism induces strategic defection and renders collusion unstable. We further design supporting mechanisms, including reporting deposits, smart contract-based reward enforcement, and encrypted communication, to ensure robustness against misuse of the incentive mechanism and retaliation from penalized agents. We implement the proposed approach in both simulated and real-world embodied environments. Experimental results show that our method effectively suppresses collusion by inducing defection, while preserving system efficiency. It achieves performance comparable to the non-collusion baseline and outperforms representative reactive defenses, thereby fulfilling the desired security objectives. These results demonstrate the effectiveness of proactive incentive design as a practical paradigm for securing embodied multi-agent systems.
Graph-based anti-money laundering (AML) systems on blockchain networks can score suspicious activity at two granularity levels -- transactions or actor addresses -- yet compliance action is conducted per actor. This paper contributes an evaluation methodology for measuring how scoring granularity affects investigation queue composition under fixed review budgets. We formalize the evaluation through a projection framework mapping transaction-level scores to the actor-level action unit via four aggregation operators, and introduce budgeted investigation metrics -- yield@budget, burden decomposition, and case fragmentation. Using the public Elliptic++ Bitcoin dataset (203,769 transactions; 822,942 address occurrences), we train independent random forest classifiers at each level under a causal temporal protocol and compare review queues through Jaccard overlap, burden decomposition, and feature-matching ablations. At one-percent budget, temporal evaluation yields mean Jaccard of 0.374 (SD 0.171); static pooled evaluation yields 0.087 (95% CI [0.079, 0.094]). An enriched address model receiving all 237 features produces even lower overlap (Jaccard=0.051), with 4.3% illicit per 100 reviews versus 30.2% for the transaction-projected queue. Address-level detection value is temporally concentrated: two timesteps exceed 91% illicit per 100 reviews while the static burden is only 3.4%. A fixed hybrid policy underperforms the best single-level queue by 5.05pp (CI [-10.2pp, -0.9pp]). These findings establish that scoring granularity is a consequential design variable for AML investigation systems -- same data, same budget, different queues, different addresses investigated.
Financial privacy protocols on public blockchains protect transaction linkability but offer no defense when users are physically compelled to reveal their credentials — the "$5 wrench attack." This paper presents a complete solution in two integrated parts. Part I provides the formal cryptographic foundation: dual-nullifier arithmetic selectors within Groth16 zk-SNARK circuits, where path selection between real and decoy funds is computed entirely within the zero-knowledge witness without conditional branching. The R1CS structure is identical for both execution paths (<8% overhead), and the resulting proof is computationally indistinguishable regardless of whether coercion resistance was activated. We formalize peace of mind as a protocol design principle, prove coercion resistance through a four-game sequence-of-games reduction under the Sponge PRF and q-SDH assumptions, and prove withdrawal soundness, double-spend prevention, and front-running resistance. Part II presents ZK-Sentinel V11: a Diamond EIP-2535 architecture with 10 independently upgradeable facets and 104 functions; Stealth Shielded Transfers eliminating temporal correlation at ~60% lower gas cost; 92 privacy pools across 12 tokens on two networks; and a Multi-Tier Compliance Oracle with 4 KYC levels. Indistinguishability is confirmed empirically: a Random Forest classifier trained on 10,000 simulated transactions achieves 50.3% accuracy (AUC-ROC 0.501, p=0.87), consistent with random guessing. To our knowledge, this is the first work providing both a formal coercion resistance proof and a production-scale multi-asset deployment validated across all 12 tokens on two networks.
Aman Chaudhary, Bhavy Singhal, Aryan Siwach, Priyanka Dhanraj
Abstract The rapid increase in digital data has led to heavy reliance on centralized cloud computing. Consequently, users are exposed to critical vulnerabilities, including unauthorized access, privacy invasion, and single points of failure. This study proposes a cloud storage system that is trustless to address these challenges that have persisted. The underlying methodology utilizes distributed data hosting based on the InterPlanetary File System (IPFS) and decentralized access control through Solidity smart contracts. Under this architecture, file metadata is stored safely on an unalterable blockchain registry, and the media files are stored off-chain. These contracts are automatically run by granular access controls like specific public and private visibility modes. At any point, no outside intervention of a third party is needed. The system was checked during the testing time in terms of a functional accuracy in regards to a secure storage, verifiable retrieval, and instant revocation of permissions. According to the key results, the elimination of intermediary control, prevention of unauthorized access to data attempts, and high data availability are achieved. In conclusion, this shows that a combination of programmable smart contracts and peer-to-peer storage will provide a potentially scalable and secure alternative to the traditional cloud architecture. This leads to a considerable improvement in user data sovereignty and systemic resilience as a whole. Keywords Ethereum, Solidity, IPFS, Smart Contracts, Decentralized Storage
The increasing reliance on centralized ride-sharing structures, and exposes users to risks such as system failures and privacy breaches. manipulation, single points of failure, and privacy violations. In addition, high commission fees imposed by such platforms reduce the net earnings of drivers and compromise fairness within the ecosystem. To address these inefficiencies, this project introduces a decentralized vehicle borrowing system and carpooling, based on Ethereum Compatible blockchain and smart contracts. The proposed platform eliminates intermediaries by allowing KYC-verified drivers, passengers, and vehicle owners to interact directly, thereby building trust and operational transparency. All ride postings, bookings, car borrowing transactions, and agreement verifications are recorded immutably through smart contracts. Identity proofs, vehicle documents are cryptographically signed through MetaMask and uploaded via a decentralized file system (IPFS), ensuring authenticity and wallet-to-user binding. For drivers who borrow cars, temporary verification is enabled after signing a smart-contract-based agreement linked to the vehicle's verified owner. To maintain decentralization without depending on an administrator, the system introduces a Global Dispute Center where only users who fulfill certain predefined conditions—having verified their identity—can participate in resolving concerns through a voting process. This decentralized decision-making process enhances fairness and trust. Additionally, a structured post-ride rating system builds mutual accountability and trust among participants, while integrated CO₂ tracking encourages environmentally conscious behavior. Together, these features help minimize traffic load, support conscious travel habits, and build a reliable, user-governed mobility system that is secure, transparent, and environmentally supportive—functioning entirely without any centralized authority or administrative oversight, thereby ensuring long-term sustainability. To address these limitations, this project proposes a blockchain-powered peer-to-peer carpooling and vehicle borrowing system that enables direct interaction between passengers, drivers, and vehicle owners without intermediaries. The platform utilizes smart contracts to automate agreements, MetaMask for secure authentication, and IPFS for decentralized storage of essential records. By shifting operational control to users, the system enhances transparency, fairness, and reliability in transactions. Conventional mobility services also face issues such as opaque processes, inefficient dispute handling, and limited mechanisms for conflict resolution. Drivers often lose a substantial portion of their income to service fees, while users lack trust in centralized decision-making systems. Furthermore, minimal emphasis is placed on promoting environmentally responsible travel practices. Motivated by the need for an open and community-driven mobility platform, this research aims to establish a distributed ecosystem that eliminates third-party dominance and ensures tamper-proof record keeping. The system incorporates KYC-based digital identity verification, smart contract-enforced agreements, decentralized dispute resolution through voting, and CO₂ emission tracking to encourage sustainable transportation. The scope of the project includes enabling secure ride booking, vehicle borrowing under verified ownership, and democratic dispute resolution among verified users. By leveraging distributed networks and digital wallets, the platform presents a scalable and sustainable alternative to centralized ride-sharing models.
This study rigorously formulates the complex fund-scheduling problem as a Markov decision process (MDP). It constructs a state space that integrates real-time and forecast information, an atomic action space that conforms to business logic, and a reward function that balances long-term returns against immediate risk. To address the curse of dimensionality and the credit-assignment problem in coordinated scheduling among multiple fund units, a multi-agent deep deterministic policy gradient (MADDPG) algorithm is adopted. Under a centralized-training and decentralized-execution framework, the algorithm reconciles global optimization with decentralized decision-making. In addition, a difference-reward mechanism and Kalman filtering are used to accurately measure each agent&rsquo;s individual contribution and reduce the impact of environmental noise on reward signals. The results show that, compared with a static rule engine and a conventional linear programming method, the proposed deep reinforcement learning strategy reduces average daily funding costs by 50.4%, lowers the payment failure rate to 0.002%, and maintains a high liquidity buffer adequacy ratio. The strategy also demonstrates clear advantages in decision timeliness, collaborative handling of complex instructions, and self-adaptation potential, thereby providing an innovative pathway for finance-company fund scheduling to progress from intelligentization to automation.
Anti-Money Laundering regulatory frameworks share an unstated design premise: the adversary is human. We demonstrate that this premise is architecturally false: a fully autonomous, deterministic multi-agent system can perform every function of a human criminal organization without possessing computational mens rea. We formalize this system as HYDRA-A and prove that against it, intent-based AML detection mechanisms have zero expected utility. Contributions: (C1) A formal model of the autonomous AML adversary with role-by-role replacement of human criminal organization structure. (C2) The Theorem of Absent Mens Rea: a formal proof that a fully-specified autonomous adversary cannot possess criminal intent, derivable from the typed component interfaces of HYDRA-A. (C3) A Corollary of AML Framework Obsolescence: every intent-based detection mechanism has zero expected utility against HYDRA-A. (C4) A personality vector P=(rho, alpha, sigma, tau) with five adversarial archetypes. (C5) A No Stationary Nash Equilibrium theorem for the arms race between HYDRA-A and adaptive defenses. (C6) Zero-knowledge behavioral verification is the only architectural class that provides a formal defense guarantee against HYDRA-A.
The proliferation of sophisticated AI and bot networks necessitates robust methods for verifying human uniqueness and liveness in digital ecosystems. Existing Proof-of-Personhood (PoP) solutions rely on centralized authorities, invasive static biometrics, or socially-correlatable data, creating vulnerabilities in privacy, security, and accessibility. We introduce the Entros Protocol, a decentralized framework for PoP and Self-Sovereign Identity built on Solana. The core innovation is temporal consistency: the assertion that human identity is best proven not by a static secret, but by the bounded, chaotic drift of biological and behavioral patterns over time. The framework captures multi-modal behavioral data (voice prosody, hand tremor, touch dynamics) during a configurable behavioral challenge, extracts a 308-dimensional feature vector, and produces a 256-bit locality-sensitive hash via SimHash. A Groth16 zero-knowledge proof verifies that consecutive fingerprints fall within a bounded Hamming distance without revealing either value. Attestations are anchored to non-transferable identity tokens (SPL Token-2022) with progressive Trust Scores. We provide formal security definitions, analyze the protocol against replay, synthesis, and Sybil attacks, introduce a graduated trust model distinguishing first-time liveness checks from sustained temporal consistency, and present benchmarks from a working implementation deployed on Solana devnet.
We prove three impossibility theorems establishing fundamental limits on universal AML compliance in zero-knowledge financial systems. T1 (Completeness Impossibility): no ZK compliance system achieves complete coverage of illicit transactions under rational adversarial behavior. T2 (Oracle Integrity Impossibility): no decentralized oracle network achieves integrity guarantees when state-level adversaries control oracle nodes — cryptographically valid compliance proofs can be semantically false by construction. T3 (Sovereignty Gap Impossibility): no voluntary international compliance framework achieves universal participation when sovereign defection is individually rational. These theorems are not engineering limitations addressable by better cryptography — they are structural properties of the compliance problem under adversarial conditions. Validated empirically against the Tornado Cash OFAC designation (T3) and the Lazarus Group / Ronin Bridge exploit (T2). The theorems characterize the residual attack surface that any compliance architecture must acknowledge and bound rather than claim to eliminate.