Blockchain Papers

Ronald Jason Andrews

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.

changzheng zhou, ziqing zhou

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.

Qi Liu, Xiaohui Chen, Zhihui Zhao, Yaowen Zheng · 8 authors

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.

Ankur Malik

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.

Ruben Alejandro Jaime, Verónica Gil-Costa, Leticia Cagnina, Marcelo Errecalde

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

Ms. Purva Varatha, Ms. Swaleha Shaikh, Ms. Shruti Kini, Prof. Sonali Karthik

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.

Bin Ge

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&amp;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.

Alejandro Jaime

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.

Charles Hooper

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.

Alejandro Jaime

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.

Jamiu Muhammed Usman, Muhammad Nazeer Musa, Mario Kolberg, Nafisat Abdulkadir · 5 authors

No abstract is available for this record.

Maimunatu Ya'u Ibrahim, Kabiru Ibrahim Musa, Aminu Ahmad, Muhammad Tella

This study develops and evaluates a blockchain–IoT framework for enhancing digital banking security, with a focus on the Nigerian financial ecosystem. Unlike prior works that apply blockchain in generic contexts, this research addresses unique challenges facing developing economies, including weak authentication mechanisms, insider fraud, and systemic vulnerabilities in centralized infrastructures. The framework integrates IoT-based payment processes with blockchain protocols to deliver tamper-proof, real-time transactions, supported by lightweight consensus. Implemented in Python and tested under simulated banking conditions, the model demonstrates stable one-second block intervals, simulated peak throughput exceeding 237,000 transactions per second under controlled execution conditions, and robust handling of diverse transaction values. A comparative evaluation of Proof of Authority (PoA), Proof of Stake (PoS), and Practical Byzantine Fault Tolerance (PBFT) confirms PoA as the most practical for IoT-enabled banking in Nigeria, balancing scalability, energy efficiency, and real-time validation. By situating blockchain within Africa’s digital economy and providing a context-sensitive consensus analysis, this study advances both the theoretical and applied understanding of secure, decentralized payment infrastructures. The findings lay the groundwork for real-world deployment of blockchain-based solutions to strengthen digital banking security and financial inclusion in emerging markets.

changzheng zhou, ziqing zhou

Existing theories of decentralized systems—typified by blockchain consensusprotocols and distributed autonomous organizations—universally harbor a foundational presupposition: governance rules and protocol structures are fully specifiedprior to system operation, and evolution occurs only within the parameter spaceof those rules. This paper systematically demonstrates the theoretical limits ofthis “fixed protocol” preset, pointing out that when the rules themselves becomethe focal point of conflict, traditional analytical frameworks lack the conceptualresources to address the situation. By integrating the bounded rationality tradition from decision theory with the self-organization ideas from complexity science,this paper proposes “cognitive ecosystem” as an alternative theoretical framework,reconceptualizing participants in decentralized systems as autonomous agents holding evolvable cognitive architectures, and redescribing the system as a whole as afield of structural coupling among multiple cognitive architectures. Under thisframework, forks are not system failures but legitimate expansions of conceptualspace, and consensus is not unanimous agreement but functional differentiationacross cognitive niches. The paper demonstrates the explanatory power of thisframework through the cases of the Bitcoin block size war of 2015–2017 and the2016 The DAO incident, and discusses its further application prospects in the governance of digital infrastructure.

Ilia Zhavoronkov

We present a graph-theoretic and convex optimization framework for multi-path routing in decentralized exchange networks, together with its implementation and empirical evaluation on Ethereum mainnet. The framework models the market as a directed token multigraph whose arcs carry AMM exchange functions. Routing is decomposed into two implemented layers: candidate path generation via gas-aware marginal k-shortest-path enumeration, where edge scores embed expected execution cost directly into graph traversal with an explicit pool-simple constraint tracked during path construction, and continuous flow allocation over the selected candidates solved as a concave maximization over a simplex with a per-pool price-impact cap. Under standard concavity and monotonicity assumptions, the KKT conditions imply marginal-output equalization across active paths. The central technical contribution is an improving-path certificate: after solving the allocation on k=20 candidate paths, the KKT multiplier is used as a threshold to determine via a single shortest-path query whether any omitted pool-simple path could improve the current solution; in our implementation the certificate confirms sufficiency in the majority of epochs. Execution is protected by an on-chain slippage tolerance enforced at the smart-contract level. We evaluate the implemented engine against four production DEX aggregators on repeated WETH-USDT quote observations across six trade sizes on Ethereum mainnet: median shortfall is below 5 bps across all sizes and top-3 quote rank exceeds 57% of epochs.

Muhammad Umair Basharat, Jawad Hussain, Waqas Khalid, Chiew Foong Kwong

The growing adoption of IoT and cloud computing, combined with rapid advancements in digital technologies, has considerably increased the cyber-attack surface, resulting in increasingly complex and persistent attacks. Traditional security methods, primarily based on perimeter defenses, are insufficient to meet these developing threats, especially within the context of a Zero Trust Security (ZTS) architecture. This study investigates the application of sophisticated artificial intelligence (AI) and machine learning (ML) techniques, including the use of the Synthetic Minority Oversampling Technique (SMOTE), to improve anomaly detection and threat intelligence systems. This study focuses on how Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (DT) classifiers might increase threat detection accuracy in IoT environments. The research endeavors to improve cybersecurity resilience by mitigating false positives and providing actionable intelligence through supervised learning algorithms. The KDD Cup 1999 dataset is used in the study to assess how well these models perform in simulating various network intrusions and regular traffic. The application of SMOTE significantly enhanced the performance of these models by addressing class imbalance, leading to improved detection accuracy. Furthermore, as supplementary methods for detecting malicious URLs and advanced persistent threats (APTs), edge-based machine learning and blockchain technology are investigated. This study addresses the shortcomings of conventional security systems and supports the growing demand for reliable threat detection in a world that is becoming more interconnected. It also advances the creation of more proactive and adaptable cybersecur

Artjom Pugatsov, Can Umut Ileri, Jérémie Decouchant

The successive generations of consensus algorithms have progressively shifted the performance bottleneck of blockchains to the execution layer. While recent works address this by parallelizing transaction execution, they often overlook the critical role of transaction sequencing. Historically, transaction ordering was left to validator discretion, a practice prone to Maximal Extractable Value (MEV) attacks, or rigid fair-ordering protocols that limit validator revenue. In this work, we address the tension between validator revenue and order fairness using a dynamic optimization framework. We introduce a blockchain-independent model for transaction sequencing in a continuous setting where block executions can overlap. Within this framework, we propose an anytime genetic algorithm that utilizes gas prices, object sets, and predicted execution times to optimize schedules. We evaluate our approach with real-world datasets from Sui and Ethereum, and demonstrate that our algorithm increases validator profit by approximately 15% and accelerates congestion relief by up to 58%. Furthermore, we quantify the impact of fair-ordering constraints, showing they can reduce validator revenue by 50% to 60% during periods of high congestion. We provide the first evidence that enforcing strict fair ordering effectively nullifies the advantages of advanced sequencing.

Dustin M. Haggett

Technical traders have long relied on visual analysis of candlestick charts to identify market patterns and predict price movements. While deep learning has achieved remarkable success in image classification, its application to financial chart images remains underexplored. This paper presents a systematic study comparing different visual representations for cryptocurrency regime prediction. We evaluate three image encoding methods (raw candlestick charts, Gramian Angular Fields, and multi-channel GAF), five chart component configurations, four neural network architectures (CNN, ResNet18, EfficientNet-B0, and Vision Transformer), and the impact of ImageNet transfer learning. Through eight controlled experiments on Bitcoin, Ethereum, and S&P 500 data spanning 2018-2024, we identify optimal configurations for visual regime classification. Our results show that a simple 4-layer CNN on raw candlestick charts achieves 0.892 AUC-ROC, outperforming larger pretrained models. Surprisingly, simpler representations (price-only charts, 128x128 resolution) consistently outperform more complex alternatives. We provide interpretability analysis using GradCAM and demonstrate that transfer learning improves performance by 4-16% despite the domain gap between natural images and financial charts.

Ryan Fields

The Model State Attestation Framework (MSAF) is the theoretical foundation of the Auburn Governance Stack. It establishes the intellectual authority for why cryptographic AI attestation is necessary, defines the three-tier attestation architecture, surveys over 200 papers from 2022 through 2026 confirming that no existing system composes hardware trust, continuous model health monitoring, and supply chain provenance into a unified attestation artifact, and documents the theoretical ceilings that bound what attestation can and cannot guarantee. The framework addresses a fundamental governance paradox: organizations deploying foundation models into critical infrastructure cannot definitively demonstrate that a specific output was generated by a model version in a compliant internal state. Current governance artifacts including model cards, training logs, and static benchmarks are descriptive rather than prescriptive and fundamentally non-verifiable. MSAF composes three verification tiers into a layered architecture. The first tier provides TEE-backed platform attestation for execution environment integrity, proving the silicon is real and uncompromised. The second tier provides signed continuous metrics for training and inference health monitoring, proving the model is healthy at the moment of inference. The third tier provides selective zero-knowledge proofs for high-value inference verification, enabling verification without exposing proprietary model internals. The composed attestation artifact is mapped to specific evidence requirements across five regulatory frameworks: EU AI Act Articles 11 and 15, FDA Software as a Medical Device Pre-Determined Change Control Plans, Federal Reserve SR 11-7 ongoing monitoring requirements, US Federal Procurement mandates, and AI insurance underwriting. The framework identifies the theoretical ceilings that bound attestation: Rice's theorem establishing the undecidability of semantic properties, the Impossibility Sandwich bounding what verification can achieve between Rice's theorem and Gödel's incompleteness, and TEE physical side-channel vulnerabilities limiting hardware trust guarantees. The honest framing: MSAF provides probabilistic risk reduction and accountability infrastructure, not behavioral safety guarantees. This is analogous to financial auditing, which certifies process compliance without guaranteeing future solvency. The building blocks exist individually. MSAF is the composition that binds them into an enforceable governance architecture. This work was previously hosted on Figshare, where the author maintained a portfolio of 29 publications with minted DOIs and an established ORCID record. The author's Figshare account was disabled without prior notice, without citation of a specific terms violation, and without opportunity for review, rendering all published items and their associated DOIs inaccessible. No communication was provided before or at the time of the disable action. This deposit and associated deposits on Zenodo ensure continued public accessibility of the author's research on institutional infrastructure with appropriate permanence guarantees.

RANG GANESH SINGH

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

Kaiyu Liu

Bulletproofs is a widely used zero-knowledge range proof system with logarithmic proof size and no trusted setup, but its proving phase remains computationally expensive. This work demonstrates that NTT-based polynomial acceleration is ineffective for Bulletproofs due to fundamental structural mismatch. An NTT-integrated implementation is constructed and evaluated within the Bulletproof proving pipeline. Both theoretical analysis and empirical results show that NTT introduces additional overhead without improving performance. The dominant bottleneck is identified as multi-scalar multiplication (MSM) rather than polynomial computation. Further evaluation of MSM optimization strategies shows that simple analytical models fail to outperform existing heuristic implementations due to implementation-level constraints. Based on these observations, a unified analytical framework is proposed to explain optimization mismatch across protocols. The results demonstrate that effective optimization must align with the dominant computational structure of the protocol.

T. Mohana, P. Bhanuchand, R Meghanadh, CH . Yashwanth · 5 authors

Abstract - The rapid expansion of global supply chains has intensified the prevalence of counterfeit products, posing significant risks to consumer safety and brand credibility. Conventional identification mechanisms, including QR codes and RFID systems, suffer from vulnerabilities such as duplication, limited transparency, and high implementation costs. This paper presents a decentralized architecture leveraging blockchain technology integrated with cryptographic verification techniques to ensure secure product authentication and traceability. The proposed system records each transaction within an immutable distributed ledger, enabling transparent and tamper-resistant data management. Smart contracts facilitate automated validation of product information across different supply chain stages, from manufacturing to end-user verification. Additionally, QR code integration provides a user-friendly interface for authenticity checks. Experimental analysis demonstrates enhanced reliability, improved transparency, and effective counterfeit detection compared to traditional approaches. The framework offers a scalable and trustworthy solution for securing modern supply chain ecosystems against fraudulent activities. Key Words: Blockchain, Counterfeit Detection, Supply Chain Security, Cryptographic Verification, Smart, Contracts, Decentralized Architecture, Product Traceability, QR Code Authentication, Distributed, Ledger Technology

J. Long, Zhifei Yan, Xin Tang, Ruxin Chen · 5 authors

No abstract is available for this record.