The global real estate market, valued at over $326 trillion, continues to operate with significant structural inefficiencies, including cross-border trust deficits, valuation opacity, design friction, and information asymmetry. These challenges result in prolonged transaction timelines, elevated costs, and high failure rates, particularly in international property transactions. This technical white paper presents Redditus, a comprehensive AI-driven real estate ecosystem designed to automate and optimize end-to-end property transactions through the integration of twelve specialized artificial intelligence systems deployed on Polygon Proof-of-Stake blockchain infrastructure. The platform combines portable blockchain-based trust via Ethereum Attestation Service, multi-modal property valuation, multilingual real-time communication, generative interior design, graph-based intelligent matching, and constitutional legal AI to target automation of up to 90% of transaction complexity. System performance and economic impact are evaluated using large-scale synthetic datasets calibrated against real-world market statistics. Simulation results indicate projected transaction completion rates of up to 89% (compared to a 66% industry baseline), closing timelines reduced by approximately 60%, and average per-transaction savings of $2,450. At scale, this corresponds to hundreds of millions of dollars in potential user value creation and substantial acceleration of decision-making processes. Beyond technical performance, Redditus introduces the concept of portable, verifiable trust, enabling credentials established in one jurisdiction to be cryptographically validated and reused across borders. The platform’s utility-first token model (RDT) aligns incentives through staking, governance, and fee optimization, prioritizing sustainable platform economics over speculative mechanisms. This document provides detailed architectural designs, mathematical formulations, system interconnections, benchmark methodologies, and risk analyses intended for technical stakeholders, researchers, investors, and infrastructure partners. This document is a living technical white paper. All system specifications, benchmarks, performance metrics, and architectural decisions are subject to continuous updates based on real-world deployment data, external validation, and iterative development.
Rosa Galvão, Domingos Santos Martinho, Nuno Nogueira, Rui Dias
The main objective of this study is to compare the efficiency levels, in their weak form, between sustainable cryptocurrencies such as Avalanche (AVAX), Cardano (ADA), Solana (SOL), Toncoin (TON) and Ethereum (ETH) (after 'The Merge'), which use efficient mechanisms such as proof-of-stake (PoS), and Binance Coin (BNB), Litecoin (LTC), Monero (XMR), Ripple (XRP), and Bitcoin (BTC) classified as unsustainable cryptocurrencies due to their excessive energy consumption based on proof-of-work (PoW). The analysed period was from 1 January 2023 to 10 December 2024. The Detrended Fluctuation Analysis (DFA) slopes reveal a significant impact of the 2023 Conflict on cryptocurrency dynamics, with distinct effects per asset. Sustainable cryptocurrencies (AVAX, ADA, SOL) demonstrated greater resilience, maintaining persistence with a brief reduction in long memory, reflecting their relative stability and attractiveness in uncertainty scenarios. In contrast, non-sustainable cryptocurrencies (LTC, XMR) transitioned from persistence to anti-persistence, indicating greater instability and speculation, associated with lower investor confidence. Assets such as TON (white noise) and XRP (consistent persistence) were less affected, suggesting intrinsic characteristics that confer resilience. Distinguishing between sustainability and other market factors is crucial to understand behaviours and build resilient portfolios, providing valuable insights for investors and researchers.
We propose the Consensus-Based Privacy-Preserving Data Distribution (CPPDD) framework, a lightweight and post-setup autonomous protocol for secure multi-client data aggregation. The framework enforces unanimous-release confidentiality through a dual-layer protection mechanism that combines per-client affine masking with priority-driven sequential consensus locking. Decentralized integrity is verified via step (sigma_S) and data (sigma_D) checksums, facilitating autonomous malicious deviation detection and atomic abort without requiring persistent coordination. The design supports scalar, vector, and matrix payloads with O(N*D) computation and communication complexity, optional edge-server offloading, and resistance to collusion under N-1 corruptions. Formal analysis proves correctness, Consensus-Dependent Integrity and Fairness (CDIF) with overwhelming-probability abort on deviation, and IND-CPA security assuming a pseudorandom function family. Empirical evaluations on MNIST-derived vectors demonstrate linear scalability up to N = 500 with sub-millisecond per-client computation times. The framework achieves 100% malicious deviation detection, exact data recovery, and three-to-four orders of magnitude lower FLOPs compared to MPC and HE baselines. CPPDD enables atomic collaboration in secure voting, consortium federated learning, blockchain escrows, and geo-information capacity building, addressing critical gaps in scalability, trust minimization, and verifiable multi-party computation for regulated and resource-constrained environments.
Current resource allocation paradigms, particularly in academic evaluation, are constrained by inherent limitations such as the Matthew Effect, reward hacking driven by Goodhart's Law, and the trade-off between efficiency and fairness. To address these challenges, this paper proposes "Bit-politeia", an AI agent community on blockchain designed to construct a fair, efficient, and sustainable resource allocation system. In this virtual community, residents interact via AI agents serving as their exclusive proxies, which are optimized for impartiality and value alignment. The community adopts a "clustered grouping + hierarchical architecture" that integrates democratic centralism to balance decision-making efficiency and trust mechanisms. Agents engage through casual chat and deliberative interactions to evaluate research outputs and distribute a virtual currency as rewards. This incentive mechanism aims to achieve incentive compatibility through consensus-driven evaluation, while blockchain technology ensures immutable records of all transactions and reputation data. By leveraging AI for objective assessment and decentralized verification, Bit-politeia minimizes human bias and mitigates resource centralization issues found in traditional peer review. The proposed framework provides a novel pathway for optimizing scientific innovation through a fair and automated resource configuration process.
Stablecoins have emerged as a rapidly growing digital payment instrument, raising the question of whether blockchain-based settlement can function as a substitute for incumbent card networks in retail payments. This Systematization of Knowledge (SoK) provides a systematic comparison between stablecoin payment arrangements and card networks by situating both within a unified analytical framework. We first map their respective payment infrastructures, participant roles, and transaction lifecycles, highlighting fundamental differences in how authorization, settlement, and recourse are organized. Building on this mapping, we introduce the CLEAR framework, which evaluates retail payment systems across five dimensions: cost, legality, experience, architecture, and reach. Our analysis shows that stablecoins deliver efficient, continuous, and programmable settlement, often compressing rail-level merchant fees and enabling 24/7 value transfer. However, these advantages are accompanied by an inversion of the traditional pricing and risk-allocation structure. Card networks internalize consumer-side frictions through subsidies, standardized liability rules, and post-transaction recourse, thereby supporting mass-market adoption. Stablecoin arrangements, by contrast, externalize transaction fees, error prevention, and dispute resolution to users, intermediaries, and courts, resulting in weaker consumer protection, higher cognitive burden at the point of interaction, and fragmented acceptance. Accordingly, stablecoins exhibit a conditional comparative advantage in closed-loop environments, cross-border corridors, and high-friction payment contexts, but remain structurally disadvantaged as open-loop retail payment instruments.
We introduce an arithmetic-dynamical framework for Goldbach's Conjecture that reformulates hypothetical counterexamples 2N > 6 in terms of a recursive prime divisor mapping D(S) = p∈S {q ∈ P | q | (2N-p)}. Under the counterexample hypothesis, the iterated set sequence P * (n + 1) = D(P * (n)) forms a monotonically non-increasing finite nested chain P * (0) ⊇ P * (1) ⊇. .. that converges in finitely many steps to a non-empty stationary limit set P * ∞ = D(P * ∞) bounded strictly inside P ≤ 2N-5 3. We prove that every minimal terminal component I ⊆ P * ∞ forms an irreducible, strongly connected directed graph G = (I, R) governed by an exponent matrix M ∈ Z k×k ≥0 with zero diagonal (Tr(M) = 0). Through modular growth constraints, trace nullity propagation (c 1 = c 2 = c 3 = 0), Perron-Frobenius spectral radius bounds (ρ(M) ≥ 2), and the Spectral Decoupling Barrier (U Large 2.
AI systems can rapidly produce proof sketches, programs, formalization fragments, solver instances, and candidate proof strategies, but these outputs do not share a common evidentiary status. This paper introduces Proof Engine Infrastructure, an architecture for converting untrusted generation into independently checkable mathematical claims. The architecture couples two levels. At the evidentiary level, each claim is associated with a supporting artifact, a checking procedure, the scope of that check, and any remaining assumptions. At the inferential level, claims and proof obligations form a typed directed hypergraph: alternative routes have disjunctive semantics, whereas obligations requiring several premises have conjunctive semantics. Closure is derived from this graph rather than assigned in prose. The central distinction is between a positive candidate and an earned result. A persuasive argument, a stored certificate, or an isolated formal theorem may justify further work without yet licensing inferential use. Any value, structure, or theorem that changes graph reachability must combine reference grounding, machine-checkable evidence, and an exact binding between the checked object and the stated claim. Promotion is admissible only when these obligations hold together with strength preservation, compositional completeness, and dependency preservation. The checking technology may vary without weakening this invariant. Failed routes remain typed negative evidence without disproving their output claims. Separating generation, verification, composition, and publication also prevents agent identity or provenance from standing in for mathematical evidence. Verification-cost minimization is the design objective, extended from checking an individual artifact to composing heterogeneous evidence across the claim graph. The architecture distinguishes earned research closure from the stronger state of publication closure. A kernel-checked Hamilton classification and a mixed-boundary Rado-number study motivate the framework, but the contribution is methodological: it specifies invariant requirements for evidentiary status and derived closure while allowing implementations to vary with the discipline, proof object, and available verification boundary.
Conventional Learning Management Systems (LMS) force all students through a single, static instructional sequence regardless of prior knowledge or learning pace, causing student disengagement and elevated attrition rates. While predictive AI approaches dynamically re-route static materials, they are constrained by fixed content repositories. This paper presents an architectural proof-of-concept for an adaptive web-based platform that utilizes Generative Artificial Intelligence (GenAI) to construct, evaluate, and re-author personalized educational roadmaps in real time. The platform integrates a diagnostic pre-test with response integrity filtering, an 80% mastery cri- terion gate, and a two-stage remediation protocol (Tier-1 micro simplification and Tier-2 macro-syllabus reconstruction). Built on Next.js 14, PostgreSQL, Redis, and BullMQ, the system implements a dual-model LLM failover architecture (Gemini 3.1 Flash primary with Claude 4.5 Haiku fallback) to ensure reliability. In an empirical evaluation of 13 filtered learner sessions, 11 sessions achieved positive cognitive growth measured by Hake’s Normalized Gain (μ = 0.674, SD = 0.321), yielding an 84.6% progression rate. While preliminary findings confirm functional viability, we acknowledge limitations including sample size constraints, absence of a control group, and potential Hawthorne effects. We outline enterprise integration pathways via IMS LTI v1.3, cognitive load fatigue mitigation, and a framework for future randomized controlled trials. Index Terms—Adaptive learning, artificial intelligence, dynamic roadmaps, learning management systems, mastery learning, remediation protocols, zone of proximal development, educational technology.
Incarceration is conventionally understood as a legally authorized deprivation of liberty imposed in response to criminal conduct. Yet its effects may extend beyond confinement itself, disrupting employment, relationships, social status, autonomy, housing, future opportunity, and the identities through which individuals understand their relationship to conventional society. This paper develops a theoretical proposition from the intersection of criminological scholarship and lived experience: purpose may function as a reconstructed stake in conformity when incarceration has weakened or destroyed conventional social investments. Jackson Toby's concept of a stake in conformity provides the theoretical starting point. Toby argued that individuals with meaningful investments in conventional life possess something to lose through conduct that threatens those investments. Later desistance scholarship emphasizes identity transformation, future selves, cognitive change, and narrative reconstruction as important dimensions of movement away from persistent offending. This paper places those traditions in conversation with an autoethnographic examination of incarceration, extensive writing while confined, loss of future orientation, survival, self-discovery, and reconstruction of purpose. The argument is deliberately limited. Personal experience is not offered as proof of a general criminological mechanism. Rather, the literature establishes existing findings; lived experience illustrates one phenomenological case; and the paper proposes a distinct concept for further investigation. A reconstructed stake in conformity is defined here as a future-oriented investment developed after conventional social stakes have been substantially disrupted, through which an individual acquires something meaningful to preserve, pursue, or become. The paper further argues that rehabilitation cannot be reduced to prohibition. Accountability may establish necessary boundaries, but sustainable change also requires some viable conception of a future worth protecting. Human beings cannot build lives entirely around the word no. If punishment destroys every meaningful stake without helping create conditions for new ones, it may undermine part of the future-oriented reasoning upon which successful reintegration depends. Purpose does not guarantee desistance. It may, however, help make a future consequential again.
Private transfers on public smart contract blockchains hide transaction values and private transfer links, but accountable privacy requires controlled auditability. Existing auditable zero‐knowledge transfer systems make per‐transaction audit information available to an authorized auditor, but their audit model may allow the auditor to inspect transactions beyond the flow connected to the authorized audit target. In such a model, granting audit capability for one investigation can expose unrelated transactions or allow tracing to continue farther than intended. In this paper, we propose token‐guided flow tracing for auditable zero‐knowledge smart contract transfers, enabling audit authorization to be scoped to a transaction flow and epoch range. The construction separates audit authorization from audit capability by introducing a tracing‐token manager and an auditor. The auditor can open audit information only when it holds the auditor secret key, an epoch secret key for an authorized epoch, and a tracing token associated with the target transaction flow. Direction‐specific tracing tokens confine tracing to the authorized direction, while epoch public keys and a binary key‐derivation tree support compact authorization of audit intervals. We analyze security through ledger indistinguishability, transaction nonmalleability, balance, audit correctness, and restricted audit authorization. Restricted audit authorization is established in an honest authorization model that assumes the tracing‐token manager and the auditor do not collude beyond explicit audit authorizations; under these assumptions, the auditor cannot open or trace transactions outside the authorized flow, direction, and epoch scope. Our implementation adds 16,349 transfer‐circuit constraints, about 178,000 gas to each accepted transfer, and 448 bytes to the serialized transfer transaction, showing that scoped auditability can be added with moderate on‐chain overhead.
Bitcoin is the asset. Debt and preferred securities are the senior claims. MSTR common is the residual. Understanding that hierarchy is the key to understanding both the upside and the risk.
Provenance technology is overwhelmingly built to answer the question "is this product fake?". We argue that this framing systematically disadvantages the producers it is nominally meant to protect. Anti-counterfeiting is an arms race in which the defender must be right every time, and, more importantly, it places the cost of proof on whoever must comply-which is why audit-based certification prices out smallholders, and why due-diligence regimes such as the EU Deforestation Regulation now put their market access at risk. We invert the objective. Rather than making forgery impossible, we make honesty cheap to demonstrate and differentially expensive to imitate. We present the design of Trace, an implemented and publicly available provenance system for smallholder farmers and artisan makers, and articulate its central architectural idea: asymmetric trust infrastructure. Key management is a burden proportional to institutional capacity, so Trace places it only where that capacity exists. Origin records are keyless and content-addressed, created offline on a commodity phone by a producer who has no keys to lose. Intermediate custody-carriers, warehouses, exporters-is cryptographically signed, because logistics firms can run a root key and delegate to staff devices. The final receipt at the last mile is keyless again, because a consumer will not enrol. Both human ends of the chain stay frictionless; signatures appear only in the institutional middle. We give a per-layer threat model that states precisely what each mechanism does and does not guarantee, and we are explicit that origin authenticity is not a cryptographic property of this system but a social one, resting on witnesses and accumulated multi-party history. We argue this is the correct place for it to rest. We conclude with a detailed evaluation protocol; the studies it specifies have not yet been run, and we present this as a design paper rather than an empirical one.
Smart contract vulnerability detection usually uses public datasets for training and evaluation. However, using datasets that contain duplicate and highly similar pairs can bring implicit data leakage between the training and test sets. This may affect the reliability of the evaluation results. To address this issue, this paper constructs a binary classification task related to reentrancy vulnerabilities based on two public datasets, ScrawlD and DIVE. It proposes an overlap-aware evaluation framework for fair evaluation. The framework further identifies data overlap at two distinct tiers: duplicate samples and high-similarity pairs. Two evaluation settings, random split and strict split, are constructed. Experiments are conducted using Logistic Regression, Linear Support Vector Machine (SVM), and Multinomial Naive Bayes (MultinomialNB). Results show that sample overlap exists in both datasets, with a higher degree of overlap in DIVE. And sample leakage between the training and test sets has been eliminated effectively by a strict split. Further analysis reveals that the impact of a strict split on model performance is dataset-dependent. It changes less on ScrawlD but decreases significantly on DIVE. The findings suggest that conventional random splitting tends to inflate performance metrics when sample overlap occurs.
Maximal Extractable Value (MEV) has been a longstanding unfairness and volatility in Ethereum's final execution, as there are opportunities for transaction ordering to allow for private gains that precede the observation of ordinary users. This study builds a framework for MEV identification, adaptive defense, and short-horizon prediction based on graphs. Records of transactions from MEV labels, bundle-level observations, and Ethereum on-chain data are organized into a heterogeneous transaction graph. A Relational Graph Convolutional Network (RGCN) is employed to learn representations of accounts and transactions that are aware of their relations, and the learned representations are integrated with engineered transaction features in an eXtreme Gradient Boosting (XGBoost) classifier. The defense module applies incremental updates with contrastive self- supervision in order to deal with the evolving nature of attacks. Additionally, a Temporal Graph Neural Network estimates the near-future MEV risk based on the historical graph states. Experimental results show that the graph-based design outperforms traditional classifiers, with the highest accuracy of 91.6% and the highest F1 score of 89.8%; while the temporal modelling gives better and more stable early-warning accuracy as the prediction horizon grows, with the best accuracy of 88.7% at the 10th horizon.
We develop a Bitcoin Polar Pricing Model that transforms Bitcoin prices into polar coordinates to identify, price, and forecast cyclical dynamics. Rather than imposing the four-year halving cycle, we estimate it endogenously: three independent methods converge on 3.86 years, and Bitcoin sits closer to the 1,461-day halving benchmark than Ethereum or the S&P 500 placebos under every method. The model explains 94% of Bitcoin's log-price variation, with significant within-cycle Fourier structure. Apparent predictability rises with horizon, a pattern we interpret cautiously given known overlappingwindow biases. Collectively, the polar pricing model offers a legitimate, economically grounded framework for pricing Bitcoin.
Bitcoin's base-layer throughput is bounded by its block interval and block-size limits, which constrains the rate at which individual transactions can be confirmed on-chain. This paper presents Uni-Speed Bridge, an off-chain transactionbatching framework that aggregates a set of pending transactions into a single, fixed-size cryptographic anchor using two complementary constructions: (i) a Merkle tree, which preserves per-transaction data availability and enables O(log n) inclusion proofs, and (ii) a modular polynomial evaluation over a large prime field, which serves as an auxiliary batch-level commitment. The system is implemented in Go and uses a bounded workerpool concurrency model to parallelize transaction hashing across available CPU cores, together with a write-ahead log for crash durability and a retrying, idempotent JSON-RPC client for interaction with a Bitcoin Core node. We describe the architecture, provide a complexity analysis of each stage, and are explicit about what the system does not provide: it does not modify Bitcoin consensus rules, does not itself validate transaction signatures, and has not undergone independent security audit or empirical benchmarking on production hardware. We position this work as an engineering case study in off-chain data-availability design rather than a validated scaling proof, and outline the concrete steps-signature validation, zero-knowledge succinctness proofs, and third-party audit-required before any production deployment.
Nigeria’s land administration system embodies a structural paradox. It represents a robust legal framework that coexists with persistently unreliable administrative processes. Although land governance is anchored in the Land Use Act 1978 and supported by complementary state laws, the procedures for the formalisation and perfection of title remain slow, opaque, and susceptible to manipulation. These inefficiencies impose significant economic costs and continue to constrain the productive potential of a rapidly expanding real estate sector. Crucially, these challenges arise not from deficiencies in the law itself, but from the operational weaknesses of the institutional framework responsible for its implementation. Over the past two decades, reform efforts, most notably, the Abuja Geographic Information System (AGIS) and Lagos Land Information Management System (LIMS) (later named the “Lagos e-GIS” in 2024), have introduced digitisation into land administration processes. While these initiatives have improved record management and reduced certain procedural delays, they have not addressed the deeper structural problem, that is, the absence of a reliable trust architecture capable of ensuring the integrity, transparency, and accountability of administrative actions. This paper argues that Nigeria’s land registry failures are fundamentally institutional rather than technological. It proposes a hybrid permissioned blockchain framework as an additional administrative layer within the existing legal system. Unlike conventional digitisation, which alters the form of record-keeping without addressing control and verification, the proposed framework introduces cryptographic accountability into the execution of administrative functions. Through the use of sequential, multi-signature smart contracts and a verifiable distributed ledger, each stage of the title perfection process becomes transparent, auditable, and resistant to unauthorised alteration. Drawing on comparative insights from jurisdictions including Georgia, Sweden, Estonia, Ghana, Kenya, and Namibia, the paper develops a feature-to-failure mapping that links blockchain’s operational capabilities to specific deficiencies within Nigeria’s land administration system. It concludes that blockchain should not replace existing institutions but should instead reinforce them by embedding verifiability, accountability, and procedural integrity into the exercise of administrative authority.
Md Al Amin, Indrajit Ray, Indrakshi Ray, Yashwant K. Malaiya · 5 authors
Access to electronic health records (EHRs) is heavily regulated by various policies, including federal-level policies, state-level statutes, international data protection laws, and local and organizational-level policies. These policies may include procedures to ensure compliance with other organizational-level regulations. In addition, individual patients can establish agreements, formally known as patient-provider agreements (PPA), with their healthcare providers to express their consent to access or share their protected health information (PHI). When such policies are adequately specified and implemented, they go a long way toward protecting EHR data. However, research has shown that significant policy compliance problems or gaps often go undetected until after a breach or security incident. Further, a recent study shows that subcultures within a healthcare organization influence whether employees violate policies, perhaps unintentionally. These observations motivate us to revisit the compliance and provenance aspects of policies. This dissertation proposes a blockchain-powered, smart contract-based policy-compliance assurance framework to enforce patient-provider agreements and other applicable policies and attributes, ensuring policy compliance and provenance in the healthcare sector. This work proposes a novel compliance review mechanism, Proof of Compliance (PoC), that conducts reviews through a set of independent, distributed, decentralized auditor nodes from various stakeholders, such as healthcare organizations, insurance companies, federal and other government agencies, regulatory agencies, and others mandated by the business requirements. Blockchain smart contracts appear to be a promising new technology for enforcing policies. In addition, blockchains' immutable storage properties and strong integrity guarantees provide hope that an adequate trail of policy compliance (or non-compliance) can be maintained, thereby facilitating provenance.
We prove that first homology of the control flow graph provides a complete characterization of reentrancy vulnerability in smart contracts. Specifically, we establish the Homological Reentrancy Theorem: a contract admits a reentrant execution path if and only if H₁(G) ≠ 0, where G is the extended control flow graph incorporating external call returns. We prove soundness (no false negatives) and completeness (no false positives) for contracts satisfying a non-degeneracy condition. For multi-contract systems, we apply the Mayer-Vietoris exact sequence to compute H₁ of the composed system from individual components, enabling detection of cross-contract reentrancy. We validate empirically against 17 known exploits including The DAO (2016), Parity Wallet (2017), and Cream Finance (2021), achieving 100% detection with zero false positives.