Disentangle introduces Topological Mass Consensus (TMC), a permissionless consensus mechanism that derives Sybil resistance from discrete curvature on transaction DAGs rather than proof-of-work or proof-of-stake. Edges connecting attack clusters to the honest network exhibit negative Jaccard curvature due to low ancestor overlap, enabling geometric throttling without trusted seeds or economic incentives. The protocol uses exclusively post-quantum cryptography (ML-DSA, ML-KEM, SHA3-256, Plonky3 STARKs) and derives all temporal properties from topological depth. We also present the Capability-Coherence Identity Protocol (CCIP), unifying DID-based identity, object capabilities, and petname naming under the same curvature analysis. Implementation: 9 Rust crates, 349+ tests.
Blockchain technology has revolutionized real-time applications with its decentralized, secure, and immutable framework, wherein the consensus mechanisms play a principal role in deciding transaction speed, security, and scalability. Traditional consensus mechanisms like Proof of Work (PoW) were affected by latency and energy inefficiency, while modern alternatives such as Proof of Stake (PoS), Practical Byzantine FaultTolerance (PBFT), and Delegated Proof-of-Stake (DPoS) realize faster and scalable solutions to real-time applications for Finance, Supply Chain, Healthcare, and IoT. This survey conducts a systematic analysis of the various consensus algorithms, including PoW, PoS, PBFT, and some upcoming models like Proof of History (PoH), in regard to throughput, latency, and security and finds that PoS-based systems and DAG (Directed Acyclic Graph) systems such as Solana and Ethereum 2.0 excel over PoW for low-latency applications with thousands of transactions per second (TPS). Despite these improvements, present-day blockchain technologies are encumbered with challenges like scalability bottlenecks, interoperability challenges, and regulatory restrictions, which prompt the search for future solutions such as hybrid consensus methods (PoS + sharding), Layer-2 scaling approaches (including rollups and sidechains), and AI-based optimizations that could benefit real-time operations of blockchains without compromising security and decentralization.
Permissionless Proof-of-Stake (PoS) blockchain networks must demonstrate security and dependability as blockchain technology evolves. This research analyzes the ongoing need for a way to identify and eradicate rogue nodes inside such networks, also to advocate for the real-time rogue nodeâs identification in PoS blockchain networks by the application of neural network methodologies, particularly random neural networks. Experimental results indicate the ability of the developed model in differentiating legitimate blockchain nodes from malicious ones. The dataset in this paper is divided into two groups- malicious (Permissionless Proof-Of-Stake Blockchains) and non-malicious; this dataset is crucial for anyone interested in blockchain. The dataset includes details of the creation and validation of the PoS blocks. The paper aims to detect malicious nodes, analyze node behavior, and enhance security on PoS permissionless blockchains through data visualization. This paper shows the performance and process of the random neural network to refine and learn and then recognize the permissionless blockchain (malicious nodes) from the dataset of Proof-of-Stake Blockchain. We selected 100 records from the original dataset to examine them with our proposal. After analyzing the results, we found clearly how the proposal algorithm works properly with the proposed dataset to achieve fine accuracy and efficiency in the work to distinguish benign nodes from malicious (permission-less blockchain) nodes. This is the result of refining and learning the dataset using the random neural network for 340 instances, coming from the neural learning of 100 instances and 21 variables: [15 features: BlockHeight, UnixTimestamp, TxnFee (ETH), Block Generation Rate, TxnFee (Binary), Status (Tags), Stake Reward, Txnsize, Coin Days, Coin Age, Coin Stake, Stake Distribution Rate, Block Density, Block Score, Coin Day Weight) + 5 meta (node label, neural network, neural network0, neural network1, fold, selected) + no missing value), by two attributes: (node label, block score), two classes(0 non-malicious nodes,1 malicious nodes).
Bitcoin's design promises resilience through decentralization, yet the physical infrastructure supporting the network creates hidden dependencies. We present the first longitudinal study of Bitcoin's resilience to submarine cable failures, using 11 years of P2P network data (2014--2025) and 68 verified cable fault events. Applying a Buldyrev-style cascade model at country level, we find that Bitcoin's clearnet (non-TOR) critical failure threshold $p_c \approx 0.72$--$0.92$ for random failures, meaning the vast majority of inter-country cables must fail before significant node disconnection. Targeted attacks are an order of magnitude more effective ($p_c = 0.05$--$0.20$). To address the majority of nodes now using TOR with unobservable locations, we develop a 4-layer multiplex model incorporating TOR relay infrastructure. Because relay bandwidth concentrates in well-connected European countries, TOR adoption increases resilience under current relay geography ($Îp_c \approx +0.02$--$+0.10$) rather than introducing hidden fragility. Empirical validation confirms weak physical-layer coupling: 87% of historical cable faults caused less than 5% node impact. We contribute: (1) a multiplex percolation framework for overlay-underlay coupling, including a 4-layer TOR relay model; (2) the first empirical measurement of Bitcoin's physical-layer resilience over a decade; and (3) evidence that TOR adoption amplifies resilience, with distributional bounds quantifying uncertainty under partial observability.
The dissertation examines statistical arbitrage methods in the cryptocurrency markets using cointegration analysis on Bitcoin, ethereum, Litecoin, Ripple using daily price data of the cryptocurrencies between January 2022 and October 2024. The research deploys strict econometric procedures, such as the Engle-Granger two-step process and Johansen test, to uncover and take advantage of the mean-reverting relationships between the key cryptocurrencies. Findings indicate that there are strong relationships of cointegration especially between Bitcoin-Ether and Ethereum-Litecoin with the relationship between Bitcoin-Ether and Ethereum being very stable in many market regimes. The statistically arbitrage strategies depending on such cointegrated pairs led to large risk-adjusted returns whose Sharpe ratios of 1.58 to 2.45 were markedly higher than buy-and-hold standards. The Bitcoin-Etherer pairs trading strategy had an annualized return of 16.34 evidenced by a volatility of just 8.45 against the volatility of Bitcoin on buy and hold at 54.67. These strategies had low beta (0.09-0.18), which was an affirmative of their market-neutral qualities and their positive alpha generation of between 11-15% per annum.
Abstract This chapter describes how the institutional design of finance governance matters for picking winners. In the United States, responsibility for devising and implementing consumer financial protections is fragmentedâboth within and across levels of government. This decentralized and fluid system of finance governance shapes the degree to which different actors can influence US consumer financial protection by raising the costs of engaging with policymakers, decreasing the visibility of regulatory actors, and allowing industry interests to engage in venue shopping for favorable treatment. The result is that industry actors can exert greater power over regulatory outcomes at the expense of wage earners or consumers. The chapter also explores how the Consumer Financial Protection Bureau reshapes the landscape of finance governance by centralizing a greater degree of policymaking authority, generating the conditions for more robust financial protection even in the absence of underlying legislative changes to the system of financial regulation.
This paper formalizes a mathematical physics framework for redefining the âchargeâ entity within physical plasma settings using the Hala-SCC (Successive Controlled Collapse) protocol. Traditionally viewed as a static dipole, we re-model charge as a dynamic informational inheritance that manifests in three distinct physical phases: Discrete (species), Wave (EM fields), and Continuum (current flow). By integrating fuzzy logic with the Hala Operator ( ËH), we introduce the concept of Gray Entropyâa stabilized transitional state that prevents âtopological tearingâ during the transition from high-entropy chaotic inheritance to zero-entropy epistemological truth. Through a 23 factorial Design of Experiments (DoE) conducted on a quiescent multi-dipole thermionic plasma source, we demonstrate that the synergy between Human, Artificial, and Protocol intelligence operators allows for a âManaged Viscosityâ of knowledge. This framework provides the first deterministic proof that the âchargeâ carrier can be distilled into a stable industrial logic gate, bridging the Reality Gap (Ďľ) between abstract plasma theory and engineering utility.
This article examines how accounting standards shape the taxation of cryptoassets, focusing on key differences under the International Financial Reporting Standards (IFRS), US Generally Accepted Accounting Principles (US GAAP), and selected offshore jurisdictions. Fragmented accounting and tax frameworks create substantial obstacles to cross-border compliance despite their growing economic significance. The article draws on a comparative regulatory analysis and corporate case studies (MicroStrategy, Coinbase, and Tesla) and identifies three persistent frictions at the book-tax interface. First, classification friction arises because jurisdictions treat the same asset as intangible property, a financial instrument, or a commodity thereby creating uncertainty for fiat-backed stablecoins and security-like tokens. Second, timing friction stems from mismatches between accrual-based financial reporting and realization-based tax rules especially for staking rewards, crypto lending, decentralized finance (DeFi), and derivatives. Third, valuation friction reflects tension between historical cost and fair value compounded by volatility and fragmented liquidity which disproportionately affects complex instruments and international structures. The article proposes the tax-accounting alignment framework (TAAF) as a conceptual roadmap to address these challenges. It prioritizes economic substance over legal form using functional classification, blockchain finality as an objective recognition trigger and adaptive valuation thresholds. The framework illustrates how these principles can simplify compliance and enhance tax transparency in cross-border and arbitrage-sensitive settings.
Bitcoinâs sixteenth anniversary in 2025 provides an important juncture to reflect on how tax law has responded to the emergence of crypto assets. Initially hailed as âthe monetary experiment of our timeâ, Bitcoin and its successors have challenged fundamental tax concepts and exposed divergences in domestic and international tax systems. This article first revisits the debate on whether crypto assets should be characterized as âmoneyâ, demonstrating that classification matters only to the extent that tax consequences diverge across regimes. It then examines four unresolved issues that continue to occupy scholars and policymakers: the characterization of mining rewards as entrepreneurial income or windfall gains; the treatment of staking rewards as active or passive income; the tax consequences of blockchain hard forks; and the classification of collateral use of crypto assets in decentralized finance. Each issue reveals tensions between traditional tax analogies and the novel features of blockchain-based activities, highlighting trade-offs between theoretical purity and administrability. While global convergence is unlikely due to foundational differences in tax systems, the analysis underscores the importance of clarity, consistency, and functional approaches to ensure that taxation keeps pace with technological innovation.
Disha Pardeshi, Sujata Sathe, Viha Bakshi, Ananya Mary Sebastian
AbstractThe growing need for secure and transparent electoral systems highlights the challenges faced by Non-Resident Indian (NRI) voters. Current rules requiring physical presence at polling stations limit participation, despite rising registrations. In our proposed blockchain-based voting framework, votes are transmitted through a secure virtual private network (VPN) and authenticated at the Election Commission of India (ECI) gateway node. After authentication, the votes are verified across multiple blockchain nodes using a consensus mechanism. Once validation is completed, the votes are permanently recorded in the distributed ledger, ensuring that they cannot be altered or removed. Smart contracts are employed to automate the vote-counting process, reducing manual intervention and minimizing the possibility of human error. The final election results are then made available through the ECI dashboard, enabling transparency and easy verification by authorized stakeholders.The proposed framework aims to improve accessibility for Non-Resident Indian (NRI) voters by enabling secure remote participation while preserving voter anonymity. By strengthening trust in the electoral system and encouraging wider participation, the solution supports improved electoral integrity. Overall, the integration of blockchain technology into the voting process contributes toward building a more transparent, secure, and inclusive democratic system in India.Keywords: Blockchain, NRI Voting, Distributed Ledger, Electoral Integrity, Consensus Mechanism, Immutability, Voter Anonymity.
Within the context of digital forensics, the integrity and authenticity of digital evidence are crucial for its legal admissibility within a courtroom setting. Chain of Custody (CoC) processes ensure that digital evidence is meticulously managed and documented from its point of origin until its use in legal proceedings. As the importance of digital forensics increases, especially with cybercrime investigations, the traditional processes used in traditional Chain of Custody have challenges in terms of transparency, security, and efficiency. This paper highlights some of the recent developments in Chain of Custody processes, particularly with the adoption of blockchain and Artificial Intelligence technologies. Blockchain technology, known for its impenetrable and distributed properties, introduces a new paradigm for Chain of Custody processes, enhancing security and traceability for digital evidence management. Additionally, AI-based algorithms for anomaly detection have the potential for increasing the reliability of Chain of Custody processes. Moreover, we will explore the decentralized evidence storage approaches and privacy-preserving mechanisms, such as zero-knowledge proofs. These are important in ensuring that more secure yet transparent approaches in managing distributed forensic investigation systems are achieved. The effectiveness of currently used CoC approaches presents lessons in understanding the future of improving the integrity of this process. Such innovations have the potential of revolutionizing the field of digital forensic investigation processes while ensuring that the handling of such evidence is of the highest integrity.
Yescha Nuradisa Ekarachmi Danandjojo, Samira Ramezani, Johan Woltjer, Taede Tillema
⢠Policies both enable and constrain LVC, requiring flexible regulatory alignment. ⢠Limited local fiscal authority weakens LVC use for transport infrastructure funding. ⢠MRT Jakarta shows transit agencies need clear mandates and institutional support. ⢠Intergovernmental collaboration is essential for effective LVC in multi-level systems. ⢠Effective LVC needs risk sharing, incentives, and non-fiscal tools for private actors. Discussions of stakeholder relationships in land value capture (LVC) for transport infrastructure development remain limited, particularly within decentralized systems in the Global South and in multi-level government contexts, where strong government control is present. This paper examines the factors affecting stakeholder relationships and how these relationships influence the implementation of LVC. The case study focuses on Jakartaâs Mass Rapid Transit (MRT) in Indonesia, where LVC is considered a promising financing tool. The findings highlight that in the context of Jakarta, policy and regulations, institutional arrangements, and risk mitigation are the most influential factors. First, while policies and regulations are essential in defining stakeholder responsibilities, they also create rigid boundaries that can limit flexibility for local innovation in exploring LVC instruments. Second, the limited authority of the transit agency indicates the need for more explicit mandates and greater support from governing bodies. Third, public agencies need to take a more proactive role in risk mitigation by developing mutually beneficial partnerships with private entities. Overall, this study bridges theory and practice by placing LVC within a multi-level governance framework that links the governance of transport infrastructure development and land-use management. It shows that successful LVC implementation depends on collaboration among stakeholders from different sectors and requires institutional flexibility and adaptive governance that balance national policy coherence with local discretion. By highlighting these cross-sector and governance dynamics, the study contributes to wider discussions on urban development, transport infrastructure governance, and publicâprivate collaboration, making it relevant to both scholars and practitioners across multiple disciplines.
Smart Contracts are stored and executed on a Blockchain network, thereby automatically enforcing the predefined rules once the execution conditions are satisfied. Hence, if the contract incorporates contradictory design rules, it may result in unforeseen outcomes within the blockchain environment. Accordingly, this proposal models the rules embedded in a Smart Contract through the Web Ontology Language (OWL), by applying the formal definition of consistency within a verification framework grounded in Description Logics. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1258Dimensions.Open Alex.
Purpose Do individuals take more financial risks when faced with a health crisis? This study examines the impact of COVID-19 on individuals' propensity to invest in cryptocurrencies. Design/methodology/approach We applied a probit model to the restricted version of 2021 data from the National Financial Capability Study (NFCS). We then combined propensity score matching (PSM) with an instrumental variable (IV) approach to address potential endogeneity concerns. Findings We found that individuals experiencing a health crisis, proxied by COVID-19 infection, demonstrate a significant tendency to take financial risks, proxied by investment in cryptocurrencies. Furthermore, the established link between exposure to a health risk and investing in high-risk financial products is more pronounced among individuals without financial education. Originality/value To the best of our knowledge, this investigation is the first to show how consumer health status affects the propensity to invest in cryptocurrency. We provide timely insights into how external mortality reminders drive risky financial decisions. Our main finding runs contrary to the traditional economic literature, which suggests that people maintain a certain level of risk tolerance and therefore adjust their financial investment strategies to mitigate, not exacerbate, increased risk.
Semantic communication is a paradigm shift in wireless systems that transmits semantic information, such as intent, context, and meaning, instead of raw data to reduce redundant data. At its core, semantic knowledge bases (SKBs) store and organize the contextual knowledge required for accurate encoding, decoding, and reasoning over semantic information. Recently, large language models (LLMs), pretrained on massive and diverse text corpora, have been integrated into SKBs to generate high-quality semantic embeddings, enable zero-shot retrieval of relevant knowledge, and support complex inference tasks across a wide range of domains. However, since the training corpus of LLM may include outdated, malicious, or privacy-sensitive content, LLM-enabled SKBs should be updated efficiently and verifiably to remove specific data without retraining from scratch. In this article, we first conduct a survey on related works and then propose a model-agnostic proof of unlearning framework for LLM-driven SKBs in semantic communications. Specifically, we track the evolution of the unlearning process by measuring drifts in the LoRA adapter subspace. We then execute successive reverse steps and generate the proof trace that a verifier can compare to provide a quantitative and verifiable unlearning guarantee. Finally, experimental results demonstrate the effectiveness of our proposed framework.
The rapid growth of cryptocurrency markets has created new challenges in understanding and predicting the structural dynamics of digital asset prices. Bitcoin, as the most traded blockchain-based currency, exhibits extreme volatility, nonlinear patterns, and complex regime shifts that traditional financial models cannot adequately capture. This study proposes a hybrid analytical framework that integrates K Means clustering with the Hidden Markov Model to identify and model multiple market regimes in Bitcoin time series data. The Bitcoin dataset used in this research contains minute-level records that were preprocessed to extract key indicators, namely logarithmic returns and rolling volatility, which represent the short-term dynamics of market behavior. The K Means algorithm was first employed to segment the data into three distinct clusters that correspond to bullish, bearish, and sideways regimes, followed by the application of the Hidden Markov Model to estimate probabilistic transitions between these regimes over time. The results reveal that the hybrid K Means and Hidden Markov Model approach achieves superior performance compared to a standalone model, as indicated by a higher log likelihood and a lower Bayesian Information Criterion value. The transition probability matrix shows that bullish and bearish regimes are highly persistent, while the sideways regime acts as a transitional buffer that connects both market extremes. The empirical findings confirm that Bitcoin prices evolve through persistent and probabilistically determined regimes rather than random fluctuations. The proposed framework provides a more comprehensive understanding of cryptocurrency market dynamics and offers practical value for investors, risk analysts, and policymakers in designing adaptive trading and risk management strategies within blockchain-based financial ecosystems.
This study investigates the volatility behaviour of Ethereum (Coinbase) returns using the Generalized Autoregressive Heteroskedasticity GARCH (1,1) model under three distributional assumptions: Normal, Student-t, and the Generalized Error Distribution (GED). Cryptocurrency markets are characterized by extreme price swings, heavy-tailed behaviour, and persistent volatility, making traditional constant-variance models ineffective. Descriptive statistics reveal strong deviations from normality in Ethereum returns, with high kurtosis (7.8454) and an extremely large JarqueâBera statistic (1797.182 with its p-value less than 5%), indicating excess tail risk and frequent extreme movements. Preliminary analysis reveal that the return series is stationary, free from serial correlation, but exhibits significant ARCH effects, justifying the use of conditional heteroskedasticity models. Empirical results show highly persistent volatility across all models, with Îą + β values close to unity: approximately 0.99 under the Normal distribution, 1.01 under the Student-t specification, and 0.994 under GED distribution. Model comparison reveals that heavy-tailed error structures outperform the Normal model, with GED achieving the lowest AIC (â3.781), SIC (â3.7629), HQC (â3.7743), and the lowest MAPE (114.6606). These findings demonstrate that flexible distributional assumptions greatly enhance the modelling of extreme and persistent volatility in Ethereum returns. The study emphasizes the importance of adopting heavy-tailed GARCH frameworks when analysing cryptocurrency risk and forecasting volatility.
Wansen Wang, P. F. Zhang, Renjie Ji, Wenchao Huang ¡ 8 authors
Some smart contracts violate decentralization principles by defining privileged accounts that manage other users' assets without permission, introducing centralized risks that have caused financial losses. Existing methods, however, face challenges in accurately detecting diverse centralized risks due to their dependence on predefined behavior patterns. In this paper, we propose JANUS, an automated analyzer for Solidity smart contracts that detects financial centralized risks independently of their specific behaviors. JANUS identifies differences between states reached by privileged and ordinary accounts, and analyzes whether these differences are finance-related. Focusing on the impact of risks rather than behaviors, JANUS achieves improved accuracy compared to existing tools and can uncover centralized risks with unknown patterns. To evaluate JANUS's performance, we compare it with other tools using a dataset of 540 contracts. Our evaluation demonstrates that JANUS outperforms representative tools in terms of detection accuracy for financial centralized risks. Additionally, we evaluate JANUS on a real-world dataset of 33,151 contracts, successfully identifying two types of risks that other tools fail to detect. We also prove that the state traversal method and variable summaries, which are used in JANUS to reduce the number of states to be compared, do not introduce false alarms or omissions in detection.
Abstract Blockchain technology has the potential to significantly advance financial inclusion, by providing decentralized financial solutions, such as Decentralized Finance (DeFi) platforms, which can ultimately be beneficial to the unbanked and underbanked populations across the globe. The decentralized nature of blockchain is a beacon of hope for bridging the financial access gap in developing and emerging economies where the traditional banking infrastructure is limited, or even non-existent. This is a conceptual paper that compiles a collection of literature around blockchain technology and financial inclusion. This paper discusses the potential to lower the barriers to financial services and transaction costs as well as increase financial literacy enabled by blockchain-based solutions (i.e. cryptocurrencies, smart contracts and digital wallets) through a systematic review of key studies, market reports and case examples identified from various regions. The state of the art paper which builds on the relevant literature on blockchain and fintech for financial inclusion. Focusing on cryptocurrencies, smart contracts, and digital wallets, this paper analyses the extent to which blockchain-based solutions may minimize financial service barriers, service transaction costs and improve financial literacy, through a review key study, market reports and case examples across different regions. It emphasizes how blockchain technology has the potential to empower these disadvantaged communities with affordable, secure, and accessible financial products. However, it does also stress the importance of guidelines to help ensure the safe and effective implementation of blockchain solutions. The objective of this paper is to offer a conceptual framework that connects the motivations for financial inclusion and the role of blockchain solutions with the ultimate objective of enabling policymakers, financial institutions, and technology developers to adopt and tailor blockchain solutions aligned to the global financial systems of developing economies. Keywords: Blockchain Technology, Financial Inclusion, Decentralized Finance, DeFi, Cryptocurrencies, Smart Contracts, Peer-to-Peer Lending, Financial Services, Emerging Markets
Ms. Neha Beegam P E, Mr. Alen K Sangeeth, Mr. Athulraj Appukuttan, Mr. Alex Jo Tomy ¡ 5 authors
With the rapid digital transformation of ed- ucational and professional environments, certificate ver- ification has become a critical security concern. Tra- ditional certificate authentication systems rely on cen- tralized repositories and manual verification processes, which are vulnerable to forgery, unauthorized modifica- tion, and operational inefficiencies. Blockchain technol- ogy offers a decentralized, immutable, and transparent framework that addresses these challenges. This sur- vey presents an extensive review of blockchain-based cer- tificate authentication systems proposed in recent litera- ture. Various architectures, blockchain platforms, smart contract models, cryptographic mechanisms, and opti- mization techniques are analyzed. A detailed compari- son is presented to highlight strengths, limitations, and open research challenges. The study aims to serve as a comprehensive reference for researchers and practition- ers working on secure and scalable certificate verifica- tion solutions.
We propose the Agent Economy, a blockchain-based foundation where autonomous AI agents operate as economic peers to humans. Current agents lack independent legal identity, cannot hold assets, and cannot receive payments directly. We established fundamental differences between human and machine economic actors and demonstrated that existing human-centric infrastructure cannot support genuine agent autonomy. We showed that blockchain technology provides three critical properties enabling genuine agent autonomy: permissionless participation, trustless settlement, and machine-to-machine micropayments. We propose a five-layer architecture: (1) Physical Infrastructure (hardware & energy) through DePIN protocols; (2) Identity & Agency establishing on-chain sovereignty through W3C DIDs and reputation capital; (3) Cognitive & Tooling enabling intelligence via RAG and MCP; (4) Economic & Settlement ensuring financial autonomy through account abstraction; and (5) Collective Governance coordinating multi-agent systems through Agentic DAOs. We identify six core research challenges and examine ethical and regulatory implications. This paper lays groundwork for the Internet of Agents (IoA), a global decentralized network where autonomous machines and humans interact as equal economic participants.
We prove that JensenâShannon divergence (JSD) contraction coefficients exhibit universal strict super-tensorization: for every finite channel W with nontrivial contraction 0 < Ρ_JSD(W) < 1, one has Ρ_JSD(Wâ2) > Ρ_JSD(W). The sequence Ρ_n(W) := Ρ_JSD(Wân) is nondecreasing, strictly increases along doubling, and satisfies lim Ρ_n(W) = 1, while for Ρ_JSD(W) â {0, 1} it is identically 0 or 1. This contrasts sharply with the multiplicative tensorization Ρ_f(Wân) = Ρ_f(W)^n enjoyed by operator-convex f-divergences (KL, Ď², squared Hellinger), for which contraction decays exponentially to zero. To our knowledge, this is the first f-divergence for which a universal strict super-tensorization law is established. The proof uses the OrdentlichâPolyanskiy binary edge reduction, expresses the binary JSD SDPI constant as a normalized posterior-variance functional, and shows strict amplification via the law of total variance. Convergence rate is controlled by the Bhattacharyya coefficient: 1 â Ρ_n(W) ⤠2A^n. Numerical verification over 4729 random channels across 26 configurations confirms zero violations. **Update v1.1:** Includes addendum with three targeted clarifications: (1) precise assumptions for binary edge reduction lemma replacing informal "mild regularity conditions," (2) explicit two-case split in the key strictness argument (Lemma 5.2, Step 2), (3) refined table caption for operator-convex divergences.