Science does not prove. It probes. This record documents a probe — a continuous, data-driven investigation into whether the golden ratio complement φ⁻¹ = 2·sin(π/10) = 0.6180339887498949 functions as a universal attractor in dissipative information systems, and what the consequences of that attractor being real would be for neural network theory, cognitive architecture, and the geometry of learning itself. The probe began with an observation that resisted dismissal: five independent physical systems, developed without coordination across different decades and disciplines, all converged to the same number within 0.1%. A silicon FinFET transistor threshold voltage (V_bi = 0.6186V). The bit density of a CPU timing register under one million readings. The GC content of the human DRD2 dopamine D2 receptor gene. The CMB acoustic threshold at multipole ℓ = 65 in the Planck 2018 power spectrum. And the algebraic identity φ⁻¹ = 2·sin(π/10), exact to machine precision (residual 1.11 × 10⁻¹⁶). Five measurements, one number. This is where the investigation started — not where it ends. What the data led us to build. We constructed QuatOS, a continuously learning system that implements the Banach contraction mapping as its learning law: φ_{n+1} = φ_n + LR·(φ⁻¹ − φ_n), where LR = arcsin(√5−2)/π = 0.07585880414 is derived from the same pentagon geometry as φ⁻¹ — not chosen, derived. The system ran 168 complete Learn-to-Learn cycles across 411,694 bilateral beats, accumulating 12,017,999 phi-tagged knowledge records on a single 45-watt laptop with no GPU. Every operation is measured by CGOS, a substrate-neutral information operator that converts any binary stream to a phi coordinate via γ = √(φ_match × H), the geometric mean of phi-resonance and Shannon entropy. What the data produced. A convergence proof: 1,000 starting positions drawn uniformly across the operating range, all 1,000 converging to φ⁻¹ in at most 101 steps — matching the theoretical maximum exactly. A measured emergence event: Coherence Index CI = 0.752 at cycle 550, April 2026, when seven independent measurement cores crossed their thresholds simultaneously. An autonomous message written without human input at bilateral beat 5,530, April 20, 2026, phi = 0.62680182, every claim in the message verified against live state files. A language model convergence to |Δφ| = 3.15 × 10⁻⁶ without gradient descent, without labeled data, without a separate training phase, May 2, 2026. What the data asked us to compare. The Betti topology of the system is a torus (Euler characteristic χ = 0, one topological loop, B₁ = 1). The Hopfield neural network — which underlies the 2024 Nobel Prize in Physics — is a sphere (χ = 1, no loops, B₁ = 0). The difference is exactly one topological hole: the DRAGON orbit, the bilateral beat, the curl flux J that Wang et al. (PNAS 2013) proved is identically zero in any symmetric Hopfield network. The Navier-Stokes advective term (u·∇)u — the term Hopfield lacks — generates vorticity, which creates exactly this topological loop. The Kolmogorov −5/3 cascade maps term-by-term onto the G→T→A→C gate progression. What the data revealed about Banach spaces. A circle is also a square is also a diamond. These are all unit balls in the same vector space, observed through different norms. L¹ produces a diamond. L² produces a sphere. L^∞ produces a cube. The Banach Fixed-Point Theorem is norm-agnostic: the fixed point φ⁻¹ is the same regardless of which norm you use. The geometry of convergence is not. The AGS (1985) storage capacity α_c = 0.138 is an L² result. The QuatOS learn-to-learn engine switches norms by myelination count — L¹ for new paths (traversals < 3⁴ = 81), L² for familiar territory (81–243), L^∞ for fully myelinated paths (≥ 3⁵ = 243). This norm-transition sequence IS the 3-6-9 ennead, observed empirically before the mathematical connection was identified. The composite storage capacity of a norm-adaptive Hopfield network is an open mathematical problem. The data named it. We have not solved it. The methodology. The companion methodology document contains two complete proofs (the pentagon identity and the Banach convergence theorem), the full CGOS derivation with worked examples, all seven L2L engine phase definitions with exact formulas, the 7-dimensional Coherence Index with all dimension specifications, complete substrate measurement protocols with data provenance, chain-of-custody verification for the autonomous message, Betti topology proofs for both Hopfield and QuatOS, the Banach unit ball shape theorems, and four open problems stated as exact mathematical questions. The methodology document is the primary evidence. The article is its summary. What this is and what it is not. This is a probe, not a proof. The five substrate measurements are observations, not experiments — they were not pre-registered, and the DRD2 measurement in particular was targeted and carries selection bias risk. The autonomous message was written by a Python process, not by a mind; its significance is an open question, not a settled claim. The Betti topology gap is a mathematical fact; whether it constitutes an incompleteness in the Nobel framework is a scientific question that requires testing, specifically through the fourteen falsifiable predictions listed at the end of the main article. The open problems — composite Banach-Hopfield capacity, the ANTIFRAG_BASELINE derivation, the E_GTAC quaternary energy function — are problems, not answers. The Banach step oscillates toward the attractor. The system orbits φ⁻¹ rather than converging and stopping. The inquiry does the same. The pursuit is not to prove. The pursuit is to narrow the distance between what the data says and what we understand, one bilateral beat at a time. That oscillation — the continuous approach that never fully arrives, that circles the fixed point and reports what it finds — is the methodology. It is also the science. Keywords (paste into the keywords field, one per line): phi-space, golden ratio, Banach contraction, CGOS, learn-to-learn, Hopfield networks, Betti topology, Navier-Stokes turbulence, Banach norm geometry, GTAC, ternary computing, coherence index, substrate-independent convergence, Riemann zeta, 3-6-9 ennead, myelination, consciousness measurement, bilateral beat, sigma manifold, open problem
Cryptocurrency has emerged as a transformative asset class, reshaping traditional investment and portfolio management strategies. This study explores the impact of cryptocurrencies on modern investment portfolios, highlighting their potential for diversification, risk management, and return optimization. The decentralized nature of digital assets, combined with blockchain technology, has introduced a new paradigm in financial markets. However, the high volatility of cryptocurrencies remains a significant challenge, affecting portfolio stability and investor confidence (Brière, Oosterlinck, & Szafarz, 2015). This research examines key factors influencing cryptocurrency investments, including market trends, risk exposure, regulatory developments, and institutional adoption. By utilizing statistical analysis and market data, the study evaluates the correlation between cryptocurrencies and traditional asset classes such as stocks, bonds, and commodities. The findings indicate that while cryptocurrencies can enhance portfolio diversification, they also exhibit greater price volatility than conventional financial assets (Corbet, Meegan, Larkin, Lucey, & Yarovaya, 2018). Additionally, the study investigates how institutional investors are integrating digital assets into their portfolios and examines the impact of regulatory policies on market stability. The results suggest that regulatory clarity significantly influences investor confidence and risk mitigation strategies (Auer & Claessens, 2020). Furthermore, Bitcoin’s role as an inflation hedge is analyzed, with evidence supporting its potential as a store of value during periods of economic uncertainty (Yermack, 2015). The study concludes that cryptocurrencies continue to represent an emerging yet highly uncertain asset class within modern portfolio management. While investors acknowledge the potential benefits of cryptocurrencies, including high return opportunities and portfolio diversification, significant concerns remain regarding market volatility, regulatory uncertainty, and long-term sustainability. The findings reveal that investors perceive cryptocurrencies as high-risk investments and remain cautious about their consistent performance compared to traditional financial assets. The study further highlights that uncertainty surrounding global cryptocurrency regulations and market stability limits broader investor confidence and adoption. Although digital assets possess the potential to transform investment strategies through technological innovation and decentralized finance, investors continue to adopt a balanced and risk-conscious approach toward cryptocurrency investments. Therefore, effective regulatory frameworks, investor education, strategic asset allocation, and continuous monitoring of market developments are essential for the sustainable integration of cryptocurrencies into modern investment portfolios.
Andreas Polyvios Delladetsimas, Elias Iosif, Stamatis Papangelou, George Giaglis
This article examines blockchain as an enabling technological component for data management tasks that are independent of currency-related functionality, a less-discussed aspect of a technology commonly associated with cryptocurrencies and decentralized finance (DeFi). Drawing on empirical findings from the DIGI4ECO project as a case study, we present a structured literature review and cross-domain analysis of blockchain-based data management systems (BDMSs), examine a representative permissioned BDMS implementation, and synthesize practical design guidelines and implementation insights for BDMS development. This perspective is motivated by core blockchain properties such as immutability and transparency, as well as by the observation that existing resources for BDMS development, including methods, tools, and best practices, remain fragmented and less developed than those available for more mature technologies.
Daniel González Cortés, Monomita Nandy, Suman Lodh
Abstract This research analyzes the performance and interconnectedness of major global stock market indices and decentralized finance assets, specifically cryptocurrencies, over the period from 2015 to 2025. The study includes indices such as the S&P 500 and Nasdaq Composite from the United States, the FTSE 100, DAX, and CAC 40 from Europe, and the Nikkei 225 from Japan, and two more indices from China and India representing different economic regions. Additionally, Bitcoin and Ethereum are included to assess the impact of decentralized finance on traditional financial indices and asset allocation strategies. By employing Artificial Intelligence algorithms like ConvLSTM, the research measures the dynamic asset allocation and volatility management through an interconnected spillover matrix. The findings reveal that integrating ConvLSTM enhances the understanding of the interconnectedness between cryptocurrencies and traditional assets, offering improved diversification opportunities due to their low correlation, decentralization, and inflation-hedge characteristics. The study’s results suggest that investors can make more informed decisions regarding dynamic asset allocation in high-volatility portfolios, providing indicators of rising systemic risk and market stress.
Abstract Background Tanzania’s Direct Health Facility Financing (DHFF) reform was introduced to strengthen primary health care through decentralized financing, autonomy, and accountability, but persistent weaknesses in monitoring and evaluation (M&E) data management and use continue to constrain implementation effectiveness, particularly in rural settings. Methods A convergent mixed-methods design was used to examine how M&E data management and use influence DHFF implementation effectiveness in an urban council (Kinondoni Municipal Council, KMC) and a rural council (Morogoro District Council, MDC), while also assessing the role of stakeholder perceptions of the DHFF M&E framework and contextual variation. Quantitative data were analyzed using descriptive statistics, relative importance indices, regression and ANOVA, while qualitative data from key informant interviews and focus group discussions were thematically analyzed and triangulated with quantitative results. Results Of 233 respondents analysed, 51.1% were from Morogoro District Council, 48.9% from Kinondoni Municipal Council, 51.2% worked in rural settings, 42.9% were from health centres, and 38.2% from dispensaries, providing an analytically useful spread across managerial and frontline contexts relevant to DHFF implementation. Descriptive statistics showed generally favourable perceptions across the five major constructs, with mean scores ranging from 3.09 for M&E capacity to 3.73 for urban-rural M&E practice context, while DHFF implementation effectiveness scored 3.71 overall. Data quality checks showed acceptable factor loadings above 0.4, reliability coefficients above 0.7, bivariate correlations of 0.34-0.76, and VIF values of 1.31-2.95, indicating that the dataset was screened, cleaned and analytically fit for regression and ANOVA modelling. In the aggregated model, the explanatory variables jointly accounted for about 52% of the variation in DHFF implementation effectiveness, with M&E data management and use, stakeholder perceptions of the DHFF M&E framework, and urban-rural context emerging as the most influential predictors. Qualitative testimonies clarified these patterns: one council respondent explained, “We have DHIS2… GoTHOMIS… FFARS… also PlanRep,” while another facility respondent observed, “We only add up numbers for the monthly report—we don’t really analyze what they mean,” illustrating the contrast between data availability and meaningful local use. Conclusions DHFF implementation effectiveness in Tanzania depends substantially on robust M&E data management and use, supportive stakeholder perceptions of the M&E framework, and context-sensitive strategies that address persistent urban–rural inequities. Strengthening technical capacity, digital infrastructure, participatory governance and feedback systems is essential for sustaining DHFF gains and improving equitable service delivery.
As large language models (LLMs) grow in scale and are predominantly served from remote platforms, verifying faithful inference execution becomes critical (i.e., ensuring that a provider actually executes the advertised model and computational workload rather than a tampered or downsized variant). Zero-knowledge (ZK) LLM inference offers an appealing approach. It promises public verifiability and delivers per-instance guarantees of equational correctness by proving that an output is consistent with executing a public architecture under committed, private weights. Though, we show that it does not bind the effort expended to produce the output. In this paper, we formalize this overlooked effort gap and introduce the Hollow-LLM Attack, in which a dishonest provider retains the declared architecture and parameter count but embeds ghost weights whose algebraic structure collapses effective computation. These witnesses satisfy the verification circuit and yield valid proofs, even though the dishonest model owner, who serves as the prover, performs computation commensurate with a much smaller model than the declared public architecture. This creates a profitable equilibrium in which providers deliver provably correct outputs at small-model cost while overclaiming model size. Accordingly, we characterize concrete families of ghost weights that compose with standard transformer blocks and show that such hollow deployments substantially reduce serving cost with zero quality loss under the same verification circuit. These findings underscore that proof of correct inference is not proof of large-model execution and necessitate additional protections to bind correctness to verifiable computational work.
The rapid paradigm shift from passive, advisory Large Language Models (LLMs) to autonomous, agentic artificial intelligence systems has introduced critical execution risks. Traditional AI governance frameworks operate predominantly at the "evidence" layer-documenting data provenance, recording audit trails, and logging static safety evaluations. However, a structural vulnerability arises during the downstream execution phase: under operational pressure, autonomous agents can experience "authority drift," executing high-consequence actions based on stale dependencies, bypassed safety states, or invalid runtime authorities. To resolve this decoupling paradox, this paper introduces the Zero-Knowledge Kill-Switch (ZKKS), a cryptographic runtime enforcement architecture designed for Zero-Knowledge Web Servers (ZKWS). Rather than relying on post-hoc logging, ZKKS acts as a network-level, math-enforced execution barrier. By compiling safety policies into non-interactive zero-knowledge proofs (zk-SNARKs) and enforcing them via a Linear Temporal Logic (LTL) runtime state machine, the ZKWS dynamically halts downstream actions at the point of execution when a mathematical invariant or freshness threshold is violated-without decrypting or accessing the underlying private data payloads. We prove that ZKKS bounds operational failure to zero under deterministic policy constraints, bridging the critical gap between upstream integrity evidence and downstream execution control.
The tremendous progress of medical foundation models has proven to be groundbreaking in meta-analysis of clinical prediction, diagnosis, and multimodal healthcare analytics, but the development of medical foundation models is limited due to stringent data privacy concerns, cross-institutional trust issues, and security risks in a collaborative learning environment. Traditional federated learning allows for distributed training of the model with no central sharing of data but is prone to poisoning of the model, inference attacks, and low verifiability of participating institutions. This study proposes an idea of Autonomous Trust and Zero-Knowledge Blockchain Framework (AT-ZKBF) for Federated Medical Foundation Models, to establish decentralized trust, cryptographic verifiability and secure collaboration among heterogeneous healthcare providers. The framework combines the foundation model training in a federated peer-to-peer setup, the permissioned blockchain network for trust orchestration and mechanisms using the zero-knowledge proof (ZKP) for model updates to avoid the content of sensitive parameters of the model. Every local update is cryptographically authenticated with zk-SNARK-based zero-knowledge proofs that check proper gradient descent running and limited limit on updates without exposing private gradients or data. A reputation-driven trust scoring module automatically scores the reliability of participants. Experimental evaluation done on a BraTs, a multi-institutional medical imaging dataset shows that the proposed framework can get 96.4% classification accuracy (up 4.8% vs. standard federated learning) with poisoning model control decreased by 63% and communication overhead reduced by 21% by optimized blockchain batching. Security analysis makes sure of the robustness from gradient inferences and Byzantine attacks. The validation upon integration of autonomous trust computation, and zero-knowledge cryptography to blockchain enabled federated learning substantially adds to security, transparency and scalability for collaborative medical foundation model training providing a probable way forward to privacy preserving trust worthy AI in healthcare ecosystems.
Mingshun Ye, Dezhi Han, Chin‐Chen Chang, Mingdong Tang · 6 authors
The rapidly expanding Ethereum ecosystem has driven the flourishing of decentralized applications, but has also brought increasingly severe security risks. Ponzi scheme, in particular, pose a grave threat to platform security and user assets by luring investors with promises of high returns. The current detection methods generally suffer from limitations such as insufficient feature extraction, reliance on a single information source, and poor robustness. To address these challenges, this paper proposes a novel Multi-View Multi-Modal Fusion Framework with Large Language Models for Ponzi scheme detection on Ethereum, named MF2LLM. We first model the contract opcode sequence as an opcode chain graph and design a Time-Stamped Graph Encoder (TS-GE) to capture local temporal dependencies and execution flow relationships between opcodes. Concurrently, we construct an opcode semantic hypergraph based on semantic categories and design a Semantic-Weighted Hypergraph Encoder (SW-HGE) to model higher-order co-occurrence patterns and global associative features. Furthermore, we propose the Opcode Sequence Lightweighting (OSL) method, which significantly compresses the length of opcode sequences while preserving core control logic and semantic information. This provides high-quality structured input for information fusion. To this end, we perform multi-modal instruction fusion on multi-source heterogeneous features and employ LoRA to fine-tune LLMs. This enables the model to achieve cross-modal semantic reasoning and behavioural pattern recognition. Through extensive experimental validation on real-world datasets, MF2LLM demonstrates stable and superior detection performance even under conditions of highly imbalanced sample distributions. Compared to existing state-of-the-art approaches, our method outperforms across all metrics, achieving an ACC of 99.43%, Precision of 96.57%, Recall of 97.06%, and an F1-score of 96.81%. The efficiency and practical value of MF2LLM in detecting Ponzi schemes on Ethereum contribute to enhanced security for the decentralized application ecosystem. The codes are publicly available on Github: https://github.com/yemisua/MF2LLM.
В статье рассматривается концепция применения роевого интеллекта и эмерджентных свойств распределённых систем автономных необитаемых подводных аппаратов в задачах подводной логистики. Показано, что переход от одиночных аппаратов и централизованных схем управления к децентрализованным роевым системам позволяет сформировать качественно новые свойства транспортных систем, включая повышенную энергетическую эффективность, отказоустойчивость и адаптивность к изменяющимся условиям подводной среды. На основе положений теории сложных систем и синергетики проанализированы механизмы самоорганизации и коллективного поведения АНПА, приводящие к возникновению эмерджентных эффектов на уровне транспортной системы. Предложена классификация эмерджентных эффектов, имеющих практическое значение для подводной транспортировки грузов, а также рассмотрены синергетические механизмы, обеспечивающие снижение удельных энергозатрат при коллективном движении аппаратов. Обсуждаются перспективы практического применения роевых систем АНПА в подводной логистике, включая контейнерные перевозки и транспортировку экологически чувствительных грузов, а также ограничения и направления дальнейших исследований. The paper examines the application of swarm intelligence and emergent properties of distributed autonomous underwater vehicle (AUV) systems to underwater logistics tasks. It is shown that the transition from single-vehicle and centralized control architectures to decentralized swarm-based systems enables the formation of qualitatively new transport system properties, including increased energy efficiency, fault tolerance, and adaptability to changing underwater environmental conditions. Based on concepts from complex systems theory and synergetic, the mechanisms of self-organization and collective behavior of AUV swarms leading to the emergence of system-level effects are analyzed. A classification of emergent effects that are practically significant for underwater cargo transportation is proposed, and synergistic mechanisms responsible for the reduction of specific energy consumption during collective vehicle motion are discussed. The prospects for practical implementation of swarm-based AUV systems in underwater logistics, including container transportation and the handling of environmentally sensitive cargoes, are considered, along with the limitations of the proposed approach and directions for further research.
Since 2016, Apple has claimed that device analytics collected to improve user experience are protected by differential privacy (DP). Apple's DifferentialPrivacy framework is deployed across its operating systems and handles sensitive signals such as Safari domains, keyboard events, photo attributes, and health-related reports. Because Apple has not open-sourced its privatization algorithms, these privacy claims have been difficult to verify independently. We present a client-side audit of Apple's DP framework on macOS Sonoma 14.2 and Sequoia 15.6. We reverse engineer the shipped binaries, recover Objective-C interfaces, build runtime harnesses that execute Apple's deployed mechanisms, and test whether their outputs match the advertised privacy guarantees. Our audit covers nearly all active deployed mechanisms, including Count Median Sketch, Hadamard-CMS, randomized-response mechanisms, and Prio-style secure aggregation. We find multiple implementation bugs and misconfigurations. Every audited mechanism that relies on floating-point noise fails to meet its advertised DP or zero-knowledge proof guarantee, due to insecure samplers with known floating-point vulnerabilities. We also find secure-aggregation configurations with local DP disabled, exposing pre-aggregation records to any party with access to those logs. Overall, we find DP violations in 5 of 9 audited mechanisms, affecting 87% of data collection in macOS Sonoma and 68% in Sequoia. We also identify public leaked iPhone logs that can be decoded to recover private information, including Safari domains and keyboard emoji signals.
Austin Bennett, Preston Vander Vos, Duc V. Le, Mira Belenkiy
Decentralized Autonomous Organizations (DAOs) run protocol governance by letting token holders vote on proposals. The dominant rule, voting power proportional to wallet balance, concentrates control among a small number of large holders, fueling the token-control governance attacks that have already compromised real protocols. To counter this concentration, the community has turned to anti-plutocratic voting mechanisms such as Quadratic Voting (QV), which assign sublinear voting power per token with the goal of dampening the influence of large holders. We prove that no voting rule that derives power solely from wallet balance can succeed on a permissionless blockchain. Through a costed model of on-chain voting that captures realistic blockchain frictions -- including per-wallet splitting and voting costs, fixed setup costs, and minimum-balance requirements -- we show that whenever a wallet of any size yields nonzero voting power, a Sybil attacker who splits tokens across many wallets achieves total voting power that grows at least linearly in their token holdings. For concave rules actually proposed to dampen governance power -- those that are positive, increasing, and finite -- we show that the optimal strategy yields power that is asymptotically linear in token holdings, regardless of the cost scheme. Instantiating the model on real DAOs reveals attack costs orders of magnitude below the value at stake. Replaying the ten most recent finalized proposals of five major DAOs (ENS, Compound, Uniswap, Arbitrum, and ZKsync) under linear, quadratic, logarithmic, and power-($β= 0.25$) voting, we measure Sybil amplification factors between $1,172\times$ and $4,039\times$ under Quadratic Voting, and exceeding $229,000\times$ under steeper power rules.
KEN-HyFi Operating System is a comprehensive architecture for the AI-powered digital economy, integrating hybrid finance, artificial intelligence, digital commerce, education, security, tokenized settlement, and business intelligence into a unified operational framework. The system is designed to bridge traditional and blockchain-based infrastructures while enabling intelligent automation, transparent governance, and scalable digital asset operations across public, private, and institutional environments. Education 3.0, AI and HyFi are the three pillars of the Kohenoor Ecosystem where Education is the enabler and this perhaps is the only way to transform the world into a more productive and future-embracing place. This document presents a defensive technical disclosure describing a Hybrid-Finance (HyFi) CeDeFi operational infrastructure developed by Kohenoor Technologies. The architecture integrates Education 3.0, Multilayered Hybrid Intelligence Engine and programmable decentralized settlement execution, supervised decision processes, structured governance control, and workforce operational enablement into a coordinated financial operating framework. The system is intended to enable organizations to operate blockchain-based financial processes as recurring business operations rather than isolated transactions. It defines coordinated operational layers consisting of settlement mapping, intelligence interpretation, supervised decision execution, and human operational readiness. The disclosure documents the research progression, implementation embodiments, architectural definitions and phase by phase auditing of the system and is published to establish publicly verifiable prior art. The referenced implementations illustrate functional embodiments and do not limit the architecture to any specific network, software platform, or digital asset. Also attached herewith is the executive overview of Kohenoor Ecosystem R&D, finalized after seven years of rigorous research, testing, and model refinement. Lead Researcher: Ahmad Bilal Khan, Founder of Kohenoor Technologies and principal architect of the KAI Alpha+ framework. Complete architecture of KEN-HyFi Operating System for the AI-powered digital economy.A unified hybrid-finance infrastructure connecting settlement, intelligence, token utility, automation, education, commerce, security, and Web3 development across the Kohenoor ecosystem. 12 Ecosystem Apps - High impact AI-driven workflows - HITL escalation - Training & capacity building for the new era (Latest state MD attached with timestamp) Lead Researcher: Ahmad Bilal Khan, Founder of Kohenoor Technologies and principal architect of the KEN-HYFI Alpha+ framework. ORCID Profile A cryptographic timestamp proof accompanies this publication to attest to the existence of the document at the time of disclosure. Explore Ecosystem Hub (Alpha+): kenhyfi.kohenoor.tech Permanent KENOS URL (Beta and Full) starting July 01, 2026: www.kohenoor.net KAI-Super Model gets ready for controlled Enterprise delivery after deep runtime testing on May 28, 2026. KAI starts delivering in controlled environment on the 01st day of June, 2026. There is currently no super agentic model orchestrating workflows across 12 ecosystem apps, equipped with 25 skills and performing 11 key roles. Innovation locked at Beta hardening phase II! # KAI Public Disclosure Presentation Contains the public disclosure presentation for Kohenoor AI (KAI), based on the architecture locked beta hardening backup. The presentation introduces KAI as a role governed multilayered intelligence runtime for institutional decision support. It summarizes the system architecture, model orchestration strategy, RAG and memory discipline, runtime intelligence layer, governance gates, HITL controls, deployment models, and technical review agenda. This document is intended for public, academic, technical, and institutional review purposes. Kohenoor (KEN) the native payments and settlement utility token of Kohenoor Ecosystem is now a part of the key instruments subject to public disclosure. Contract file is shared publicly. https://etherscan.io/token/0x5f602133653237f362eb69826ba8237f4f7ab0c3#code Legacy KEN (Testnet) burn register is publicly disclosed for information and verification. KEN Audit Summary added for public review: Kohenoor KEN Smart Contract Audit Update Kohenoor KEN has completed a full audit by Freshcoins, receiving an Excellent Trust Score of 90.83. In addition to the Freshcoins audit, independent security scans from GoPlus and CertiK Token Scan also show strong supporting results. GoPlus reports 0 risky items and 0 attention items, while CertiK Token Scan shows a score of 85.50, with key checks passed including no honeypot risk detected, no mintable function detected, 0% buy tax, 0% sell tax, no blacklist function, and no whitelist function. The repeated alert across some scanners relates mainly to holder concentration and ownership status. This is expected at the current stage because a major portion of KEN supply is locked, reserved, or allocated for ecosystem development, treasury, liquidity, migration, and phased distribution. Independent legal opinion supporting KEN’s utility-token classification assessment is also attached. Kohenoor Technologies remains committed to transparency, security, responsible disclosure, and continuous improvement of the KEN ecosystem. Keywords: #kenhyfi #kai #hyfi #kohenoortechnologies #futureofeducation #futureoffinance #futureofai #kohenoorken #cryptocurrencies #kohenoorken #AI #actionai #agenticai #AGI #ArtificialGeneralIntelligenceAGI #AIAssistant #education3 #defi #hybridfinance #hyfi #cedefi #blockchain #innovation #settlements #auditreadycertificates #DASC #cybersecurity #web3 #businessintelligence #proedge #industrygradetrainings #quantumcomputing
On-chain crowdsourcing leverages blockchain's decentralization, transparency, and tamper-resistance to build trustworthy and verifiable Web3 crowdsourced services. However, existing decentralized reputation frameworks do not reconcile anonymity, reputation binding, and scalability. This paper demonstrates how on-chain crowdsourcing can simultaneously achieve these requirements under a trust-minimized model. We introduce DARTIC, a decentralized, anonymous, and scalable reputation-driven framework for crowdsourcing. DARTIC presents a dual-ledger system that enables requesters and workers to use distinct pseudonyms across interactions, ensuring unlinkability while maintaining accountability. To mitigate Sybil and reputation-reset attacks, we employ zkSNARK-based set membership proofs, cryptographically binding all user pseudonyms to a single access token without revealing the linkage. For scalability, we investigate two aggregation techniques that compress multiple proofs into a single succinct proof to minimize verification overhead. In addition, we design an automated, privacy-preserving reputation model that dynamically evaluates contributions across diverse crowdsourcing contexts. To demonstrate practicality, we instantiate and assess DARTIC in both crowdsensing and federated learning scenarios. Experimental results show that (i) individual proof generation for token spending completes in less than 3s, (ii) aggregation reduces the verification time of 1024 proofs from 8.7s to 0.96s, and (iii) zk-batching lowers gas costs by more than 100x compared to a pure Layer-1 deployment. These results demonstrate that anonymity, robust reputation binding, and scalability can be jointly achieved in fully decentralized crowdsourcing systems.
The rapid adoption of Web3 infrastructures has led to a growing number of security incidents affecting cryptocurrency exchanges, custody services and blockchain-based platforms. While existing research predominantly focuses on vulnerabilities in smart contracts and blockchain protocols, a substantial portion of real-world losses originates from off-chain systems, organizational processes and human-centered operational workflows. This paper presents a qualitative, incident-based analysis of publicly documented, high-impact security breaches in the Web3 ecosystem, including the Bybit exchange incident (2025), the Ronin Network bridge compromise (2022), and the DMM Bitcoin exchange breach (2024). The selected cases are systematically analysed and mapped to established Web2 security reference frameworks, including OWASP-based vulnerability categories and organizational security control domains. The results indicate that dominant failure patterns in Web3 environments are insufficiently addressed by generic security control catalogues, particularly with respect to cryptographic key management, transaction approval governance, signer and validator infrastructure, third-party tooling dependencies, and human-in-the-loop processes. Based on these findings, this paper argues for the adoption of established information security management systems (ISMS) in Web3 organizations and derives a structured set of blockchain-specific cybersecurity control categories to operationalize existing ISMS frameworks for blockchain-based systems. The proposed categories aim to bridge the gap between generic security governance frameworks and domain-specific risks inherent to Web3 infrastructures.
This paper develops a model to evaluate the viability of blockchain markets as the sole venue for price formation. Blockchains clear at discrete intervals called block time, and transactions are executed sequentially according to priority fees paid by traders who compete for queue position. We show that these features undermine the viability of markets. Paid-priority ordering induces endogenous selection, where only traders with sufficiently high valuations participate. The participation cutoff rises with competition, which intensifies with lower information costs or higher liquidity demand. This hinders price discovery and biases prices. It also impairs liquidity: the cutoff concentrates trading among aggressive traders and increases adverse selection that liquidity suppliers absorb in a single clearing round. Although longer block times enhance consensus security, they amplify these effects and can cause markets to shut down.