Blockchain Papers

Amelia Lo, Clarie Ku

No abstract is available for this record.

Hampus Linden

This thesis examines whether cryptocurrencies can function as diversification or risk-reducing assets relative to the Swedish equity market during periods of financial stress. Using daily data for Bitcoin, Ethereum and Ripple from 2018 to 2024, their dynamic relationship with the OMX30 index is analyzed. To provide a broader benchmark, gold, the German DAX index, and the U.S. S&P 500 index are included as comparison assets. Periods of financial stress are identified as episodes in which the OMX30 declines by at least 10 percent from a recent peak. Time-varying correlations are estimated using a Dynamic Conditional Correlation GARCH (DCC-GARCH) model, allowing the analysis of how interasset relationships evolve over time. In addition, hedge effectiveness measures are employed to assess the cryptocurrencies practical ability to reduce portfolio risk.The results show that Bitcoin, Ethereum and Ripple exhibit weak but positive correlations with the Swedish equity market, implying that they may serve as diversifiers but not ashedges or safe-havens. During periods of financial stress, correlations tend to increase rather than decrease, indicating limited protective properties. Hedge effectiveness estimates further suggest that the risk-reducing capacity of cryptocurrencies is unstable and generally weak. Incontrast, gold displays more consistent negative correlations and superior hedging performance. Overall, the findings suggest that cryptocurrencies offer limited diversification benefits for Swedish investors and should not be considered reliable risk-mitigating assets during market stress.

Chris Brzuska, Michael Klooß, Ivy K. Y. Woo

No abstract is available for this record.

Vicheslav Ryzhkov, Yash Madhwal, Yury Yanovich

No abstract is available for this record.

Jiandong Zhang, Han Jiang, Chenkai Zeng, Qi Feng · 8 authors

Secure multiparty computation (MPC) over Z2kis more efficient than computations over fields, and studying MPC protocols under malicious security has practical application value. Malicious security with a dishonest majority over rings remains challenging. The most popular approach is SPDZ2k, however, this is a specific protocol that does not support the transformation of any existing semi-honest MPC protocols into malicious security protocols. The zero knowledge proof (ZKP)-based compiler satisfies this requirement. Existing state-of-the-art protocols have logarithmic online communication overhead in terms of the circuit size |C|, and their direct application to rings is nontrivial as they were originally designed for finite fields. In this work, we investigate the communication overhead to develop malicious security protocols. We bridge the gap between malicious security with abort and semi-honest security, by constructing a “GMW-style” verification protocol to achieve malicious security in a dishonest majority setting. This approach incurs a constant online communication overhead by enhancing the machinery of zero-knowledge fully linear interactive oracle proof (zk-FLIOP). Additionally, we extend the zk-FLIOP to work over any ring by invoking reverse multiplication friendly embeddings (RMFEs). Our results show that the online communication complexity of the verification process depends on only the security parameter, the number of parties, and the ring size. Furthermore, for small-scale circuits over Z2, we designed a distributed lookup table argument where both the total communication complexity and the computational cost are independent of the circuit size but of the input wires.

Syed sajjad Shah

No abstract is available for this record.

Salvatore Furnari

No abstract is available for this record.

Ciprian Pater

No abstract is available for this record.

Sai Srikanth Madugula, jose Luis de la Rosa Esteva, Daya Shankar

This paper presents an integrated framework for decentralized invoice-backed loan underwriting combining interpretable machine learning, dynamic pricing algorithms, and on-chain trust infrastructure. We develop and validate SHAP-explainable ML models for real-time default probability assessment, design a Reverse Kelly AMM smart contract for optimal risk-adjusted loan pricing, integrate ERC-725 identity and on-chain reputation scoring with an automated insurance reserve, and deploy the system on Ethereum testnet with end-to-end functional and security testing. Stress testing across simulated default and fraud scenarios demonstrates the model achieves AUC-ROC of 0.89 on validation data, maintains LP yields of 12–18% under normal conditions while containing non-performing loan ratios below 3% under adverse scenarios, and sustains reserve solvency across 95th percentile stress events. The framework addresses critical gaps in DeFi lending by bridging regulatory interpretability requirements with decentralized credit assessment, demonstrating both technical feasibility and economic viability for permissionless SME financing at scale.

Rajesh Daruvuri, Kiran Kumar Patibandla, Pravallika Mannem, Mounica Yenugula

No abstract is available for this record.

Jie Luo, Wei-Che Tsai, Kuang‐Chieh Yen

No abstract is available for this record.

11/11 AI Research Division

RFC-EG-0500 defines the normative federation and distributed enforcement model of the 11/11 execution governance architecture. The specification formalizes governance federation across multiple independent authorities, enabling distributed execution governance across cloud providers, jurisdictions, organizations, and runtime domains while preserving fail-closed enforcement and cryptographic verification guarantees. The document defines:• Federation topologies• Trust Bundle construction and lifecycle• Cross-issuer artifact recognition• Federated revocation propagation• Distributed audit ledger reconciliation• Cross-authority operation lineage• Federation bridge events• Multi-authority DAG semantics• Federated freshness and revocation guarantees• Conformance requirements for issuing and recognizing authorities RFC-EG-0500 operates as the distributed governance and federation layer of the execution governance ecosystem and complements:• RFC-EG-0010 — Execution Lineage Specification• RFC-EG-0100 — Authorization Artifact Specification• RFC-EG-0200 — Runtime Admission Specification• RFC-EG-0300 — 512 Cypher Specification• RFC-EG-0400 — 11/11 Lang Syntax and Semantics• DOC-EG-001 — Execution Governance Architecture• DOC-EG-002 — Deterministic Execution Authority Architecture• DOC-EG-003 — Execution Governance for Agentic AI Systems Public Doctrine Series / RFC-EG Serieshttps://www.11aiblockchain.com Execution Governance™Patent Pending

Abuzar Khan, Ahmad Junaid, Abid Iqbal, Ghassan Husnain · 6 authors

No abstract is available for this record.

Awinash Kumar

Enterprise analytics powered by large language models hosted on the cloud are suffering from crippling costs caused by tokens, "Lost in the Middle Syndrome", and privacy exposure. In this paper, we present AK Aletheo Pro, an open-source decentralized web application that can perform secure browser-edge analytics with an innovative self-healing "Neural Handshake" framework, which extracts a statistical "Data DNA Profile" directly on the client side. A proof-of-concept implementation reveals that by replacing multi-megabyte CSV database transfers with an 85 KB statistical approximation there is a significant reduction of 68.8x query latency (124s-> 1.8s), and a 1200x reduction of query costs in enterprise analytics scenarios. A Structural Healing Engine performs JSON schema validation and a Complexity Scorer optimizes visualization rendering, and a Symmetrical Multilingual Engine maintains bidirectional language translation. The application was tested on a 10,000-row database, rendering 60 FPS consistently while maintaining an offline auto-fallback mechanism with 100% effectiveness. An excellent System Usability Scale SUS of 90.8 was recorded. This suggests that AK Aletheo Pro could serve as a secure alternative to enterprise and healthcare information analytics systems.

Nick Bettencourt, Xiaowei Ding, Kay Giesecke

As high-quality public web corpora become increasingly exhausted, clean longcontext documents have become a scarce and expensive source of training data for large language models (LLMs). Existing long-context corpora are often proprietary and costly to acquire, synthetically generated, or concentrated in narrow domains such as programming. We introduce the Stanford EDGAR Filings Dataset (SEFD), an open reconstruction of SEC filings into layout-faithful MultiMarkdown for financial language modeling and evaluation. SEFD makes audited financial statements, risk disclosures, ownership reports, accounting notes, and market-moving event filings usable as long-context pretraining data and as a basis for financial reasoning, forecasting, compliance, and document understanding. The resulting corpus is token-efficient, model-ready, and has less than 0.1% overlap with Common Crawlderived corpora. We release SEFD-v1, a 152B-token initial public snapshot, and provide corpus-level analyses of a larger 18.5M-filing archive estimated at 550B tokens. We further introduce two SEFD-derived benchmarks: EDGAR-Forecast, which evaluates filing-grounded numerical forecasting after model knowledge cutoffs, and EDGAR-OCR, which evaluates transcription of complex financial tables.

Unknown author, Unknown author

No abstract is available for this record.

Mohammed Abdel-Nasser, Sami El-Ferik, Ramy Rashad, Abdul-Wahid A. Saif

No abstract is available for this record.

Korey Taylor

In American criminal law, the Supreme Court has long required proof beyond a reasonable doubt before the state may deprive an individual of liberty for a criminal offense. Yet probation revocation proceedings-where courts frequently impose incarceration based on allegations of new misconduct-often permit imprisonment upon substantially lower burdens of proof. This Article argues that the constitutional logic of In re Winship extends to revocation proceedings that may result in confinement and that due process therefore requires proof beyond a reasonable doubt when the state seeks to incarcerate a probationer. This Article is among the first to center the burden of proof itself as a constitutional defect in probation revocation doctrine. Existing scholarship has critiqued revocation procedures broadly but has not squarely confronted the burden of proof as a component of evidentiary standards utilized in probation revocation hearings and their application in the revocation of probation sentences. This Article argues that these lax standards violate probationers' rights and proliferate punitive jail sentences in both federal and state jurisdictions. It thus calls for the required use of a higher standard even in probation revocation, at least when incarceration is imposed as a revocation sentence.

Mikio Hanaeda

A single disclosure is read by several audiences that summarise it differently: a market by its posterior mean, a court by a coherent downside floor, a prudential regulator by an expected shortfall of losses, an ESG rater by a dispersion statistic. Existing results characterise gamingproofness one reader at a time-a reader is safe exactly when its functional is convex in the posterior-and say nothing when readers disagree in curvature, which is the normal case. We show that the object governing the many-audience problem is not a direction but a cone: the set of liability calibrations under which no disclosure can shelter is the dual cone of the gaming-gain vectors, and it is polyhedral whenever the reads are piecewise linear on a common complex. Membership is a linear feasibility problem in the calibration and a supporting affine minorant, not an optimisation over disclosures. The count of audiences never enters: two convex readers aggregated by a "satisfy any one of them" rule are gameable, while five convex readers at arbitrary weights are not. Six prohibitions follow, each a corollary rather than a caution. Never aggregate verifiers by an infimum-and note that this defect survives the removal of every ingredient the ratings-shopping literature blames. Never let verifiers read separate documents; the sign here is opposite to the familiar recommendation that verifiers be sealed from the market. Never attempt to repair a gameable read with a harsher liability schedule, since a tent in the read defeats every increasing schedule. Never give a dispersion audience positive weight, since its gaming gain is exactly the mean-informativeness the deterrence audience is meant to receive. Comparative statics are available but not where one expects them: the tipping ratio is not a function of the prior's dispersion, is a monotone threshold in the prior mass on the worst state, and is not monotone in the court's own tail level-widening it helps until it overshoots that mass, and then tips the lattice.

Elif Dicle Demir, Idil Gorgulu, Mehmet Cengiz Onbaşli

No abstract is available for this record.

Luca Magni

This article applies a cognitive-architectural framework of recursive reinterpretation, developed in prior work on self, ego, and identity (Magni, in press), to the analysis of ideological propaganda and deradicalization. That prior work established that self, ego, and identity function primarily as systems for resource-efficient learning through recursive reinterpretation of finite biographical experience, with inversion (Differenza Inversa), the reversal of an existing self-structure asymmetry to generate a new vantage point, proposed as the fundamental operator. I identify five inhibitory mechanisms: saturation of inversion space, blockage of spiral temporality, substitution of inversion with reflection, colonization of the shadow, and selective punishment of identity-incompatible inversions. Together these amount to a model of propaganda as recursive depth reduction. The deradicalization implications follow directly: standard counter-narrative approaches fail not because they are badly designed but because they address belief content while the architectural damage that prevents belief revision goes unrepaired. A four-phase recursive restoration model is developed, sequencing architectural repair against established clinical and social psychological resources. A research program of four falsifiable hypotheses closes the paper, connecting the theoretical account to experimental paradigms in cognitive, political, and clinical psychology.

Goodness Kalu, Uchenna Ejike, Joseph Edet, Temitope T. Fagbuyi · 6 authors

Most research on liquidity crises in financial markets relies on expensive institutional data or coarse daily aggregates, leaving little room for reproducible, operationally deployable risk monitoring. We propose an early warning system for liquidity stress in cryptocurrency markets using freely available high-frequency trade data from major exchanges. Unlike prior work that evaluates models using static accuracy measures, we emphasise the evaluation gap: standard classification metrics ignore the temporal dimension critical for risk management, where a perfect prediction with zero lead time offers no practical value. Our contribution is fourfold. First, we introduce a regimeaware labelling method that adapts stress-event definitions to different volatility conditions using trade-based liquidity indicators, ensuring both statistical balance and economic realism. Second, we develop and benchmark a suite of machine learning models—from interpretable baselines to advanced sequence architectures—using a purged expanding walk-forward validation framework on four years of five-minute resolution data across Bitcoin, Ethereum, and Solana. Third, we provide rigorous explainability analysis using SHAP feature attribution and Integrated Gradients, with surrogate fidelity validation confirming attribution validity at a mean agreement rate of 91.8%. Fourth, we address the methodological concern that HMM stress labels are self-referential through a three-tier external validation framework and a controlled experiment comparing global versus locally-fitted HMMs across all four walk-forward folds, using genuinely independent validators— price drawdown, documented crisis timestamps, and crossasset simultaneous stress —none of which were HMM training features.

Sujung Cho, Kyung-Shick Choi, Amy Hyunjeong Lim

Cryptocurrency has become a dominant payment mechanism in contemporary romance scams, yet victims’ transaction experiences are not uniform and may follow distinct pathways with different risk profiles and laundering consequences. Using 106 U.S.-based cryptocurrency-facilitated romance scam complaints drawn from Chainabuse and validated through open-source blockchain forensics, this study applies latent class analysis to identify subgroups of victims based on transaction modalities and to examine how risk indicators relate to monetary loss and downstream money-laundering techniques. Results support a two-class solution: (1) fake investment service websites, where offenders route deposits through platform-like dashboards with fabricated balances, and (2) direct deposit crypto wallets, where victims transfer cryptocurrency directly to offender-controlled addresses. Within-class heterogeneity was evident: cryptocurrency type (Bitcoin/Ethereum vs. Tron), deception type (investment vs. emergency), and blockchain risk flags showed differential associations with losses across transaction contexts. Distal outcome analyses indicated that laundering strategies varied by class: direct deposit cases exhibited heavier reliance on anonymization (mixers/tumblers), whereas fake investment website cases more frequently involved self-funding and swaps. Framed by Cyber Routine Activities Theory, findings highlight how routine digital behaviors and weak digital guardianship shape victim pathways and offender concealment strategies, informing targeted prevention, compliance screening, and investigative prioritization in cryptocurrency-enabled romance fraud.

Zhiyi Wang, Huaxi Zhang

Generative AI has become part of the financial information infrastructure, yet its role in shaping the functioning of capital markets remains poorly understood. We examine 252 service outages identified from the status pages of OpenAI and Anthropic and align their onset times with high-frequency Binance data for Bitcoin and Ethereum.Using a stacked difference-in-differences design, we compare market conditions before and after each outage with those on historical control days matched by weekday and UTC time. Within 15 minutes of outage onset, Amihud illiquidity and the trading-value-scaled price range decline by 15.88\% and 10.87\%, respectively, relative to their counterfactual levels. The decomposition yields point estimates consistent with both narrower price movements and greater trading value. Spot Kyle $\lambda$ falls, while directional order flow and visible depth in perpetual-contract order books do not change significantly. The response is concentrated in ETH, remains robust to alternative specifications and finite-sample inference, and does not differ systematically across providers.