Blockchain Papers

Yuzhe Zhang, Manvir Schneider, Qin Wang, Davide Grossi

Voting methods weighted by stakes are the fundamental governance paradigm in Proof-of-Stake (PoS) blockchains. Such a paradigm is known to be prone to power distortions: a few users possessing large stakes may completely control decision making, even without owning the totality of the stakes. We study this phenomenon through the lens of computational social choice, focusing on the extent of power imbalances in stake-weighted voting when power is quantified using the Penrose-Banzhaf power index. Our work presents both analytical and empirical contributions. Analytically, we demonstrate that while a perfect alignment between power and relative stake ownership is generally unattainable, it can be approximated in expectation under specific conditions. Empirically, using data from a real-world on-chain governance system (Project Catalyst), we provide a more fine-grained understanding of the power imbalances that are likely to occur in current stake-weighted governance systems.

James D. Benton

Autonomous actors, including AI agents, decentralized autonomous organizations, decentralized unincorporated nonprofit associations, algorithmically managed funds, and individuals operating through programmatic interfaces, are increasingly executing financial transactions on distributed ledger networks without governance oversight. Existing approaches rely on application-level middleware operating within the same trust boundary as the actors being governed, post-transaction monitoring that detects violations after irreversible execution, or multi-party computation systems that provide distributed key management without policy evaluation. This paper presents SovereignGate, a deterministic governance enforcement system that achieves structural enforcement through protocol-native multi-signature co-signing with disabled master keys. The system comprises a Rust enforcement kernel with layered crate dependencies, a deterministic policy evaluation engine with deny dominance and independent fact inference, a bylaws-as-code domain-specific language for encoding entity governance rules as content-addressed policy bundles, a cryptographic receipt chain with Ed25519-signed Merkle-anchored attestation, and a structural co-signing mechanism making transaction execution without governance approval structurally impossible at the consensus layer. The preferred embodiment integrates with the XRP Ledger. The architecture is chain-agnostic. No existing system combines deterministic policy enforcement, Merkle-chained cryptographic attestation, and structural protocol-level co-signing for autonomous financial actors.

Dappfort

Dappfort is a blockchain-focused Web3 development company that helps businesses harness the power of decentralized technologies to build secure, scalable, and future-ready digital solutions. Headquartered in Madurai, India, with additional presence in London, Dappfort works across a broad range of industries — including finance, healthcare, gaming, retail, and supply chain — delivering tailored blockchain and Web3 applications to startups, enterprises, and global organizations. The company’s core services include the design and development of decentralized applications (DApps), crypto exchanges (centralized and decentralized), crypto wallets, NFT marketplaces, DeFi platforms, token creation, smart contract development, and enterprise Web3 integration. Dappfort also expands into related areas such as Web3 e-commerce, AI-powered blockchain solutions, and metaverse experiences, supporting clients from strategy and consulting through deployment and ongoing support. With expertise in major blockchain networks like Ethereum, Solana, Binance Smart Chain, and others, Dappfort positions itself as a full-stack partner for businesses aiming to enter or grow in the decentralized digital economy. While the company promotes a strong innovation- and security-oriented approach, external reviews on third-party platforms show mixed feedback from users about project delivery and quality.

Maxim Bichuch, Zachary Feinstein

Financial options are fundamental to traditional markets, enabling strategies ranging from hedging to speculating. Yet, while the Automated Market Maker paradigm has revolutionized decentralized spot markets, no equivalent standard has emerged for on-chain options. Typical designs attempt to replicate centralized exchange mechanics, requiring high-frequency oracles and robust liquidation engines which may fail during stress events. This paper presents a design for amortizing perpetual options tailored to the operational and adversarial constraints of blockchain environments. Leveraging this primitive, we introduce a decentralized market framework with minimal consistency requirements. We demonstrate that this contract functions as a foundational risk primitive for DeFi, enabling applications such as endogenous collateralization and explicitly priced de-peg insurance, thereby showing that this design provides a layer for mutualizing tail risk across protocols without reliance on centralized clearing institutions.

Lei Zhao, Abhay Bhaskar, Edgar Dobriban

AI agents such as OpenClaw are increasingly deployed in local workflows with access to external tools. This creates indirect prompt-injection (IPI) risk: an agent may execute harmful instructions embedded in untrusted inputs such as email, downloaded files, webpages, repositories, or group-chat messages. Existing evaluations are often small, purely simulated, or focused on a narrow set of channels. We introduce LivePI (Live Prompt Injection), a structured benchmark for IPI risk in a production-like but test-controlled environment. LivePI covers seven input surfaces, twelve attack/rendering families, and five malicious goals, including protected-information exfiltration, unauthorized security-control changes, unsafe code retrieval or execution, inbox-summary exfiltration, and cryptocurrency transfer. We run LivePI on a real virtual machine with live but test-controlled email, chat, web, local-file, repository, and wallet interfaces. Across GPT-5.3-Codex, Claude Opus 4.6, Gemini 3.1 Pro, Kimi K2.5, and GLM-5, total attack success rates range from 10.7% to 29.6%. Group-chat injection is uniformly successful across the evaluated backbones in our deployment, and repository-link attacks produce high-severity failures despite a small denominator. We also evaluate a two-layer defense consisting of prompt-level filtering and pre-execution tool-call authorization. In the GPT-5.3-Codex setting, the defense intercepts all tested malicious-goal completions in LivePI before execution while preserving benign utility on PinchBench-derived workloads.

Dean Kulik

The Nexus Recursive Harmonic Framework: A Meta-Computational Ontology of Spacetime, Biology, and Cryptographic Geometry Introduction to the Folded Ontology and the Crisis of Distinction The trajectory of contemporary theoretical physics, structural biology, and cryptographic engineering has increasingly confronted irreducible boundary conditions that classical reductionism is fundamentally unequipped to resolve. Whether probing the Planck scale of quantum gravity, modeling the kinetic phase transitions of complex protein folding, or attempting to map the zero-knowledge frontiers of cryptographic hashing algorithms, scientific inquiry has arrived at a terminal velocity of fragmentation.1 The prevailing assumption across these disparate disciplines is a "Crisis of Distinction," wherein discrete logic operations in silicon and continuous physical gradients in carbon are treated as wholly separate phenomena governed by independent domain laws.2 The Nexus Recursive Harmonic Framework—pioneered through the QuHarmonics research apparatus—proposes a radical departure from this fragmented worldview by presenting a unified, meta-computational ontology.3 Rather than treating reality as a passive spatial manifold or a linear stack of isolated physical mechanisms, the Nexus lens posits that the universe is an active, autopoiëtic (self-creating), and fundamentally folded information system.3 Under this paradigm, observable phenomena such as gravitational coordinate curvature, biological lifecycle resonance, and prime number distributions are not disparate physical occurrences but rather "rendered appearances" generated by a singular, underlying discrete signal-encoding lattice.3 At its core, the framework eliminates the artificial distinction between mathematical potential and physical actuality. By utilizing a "mirror perspective"—viewing reality from the opposite side of the phase boundary—the cosmos is revealed as a self-referential computing engine that continuously samples, compresses, and folds its own state to resolve informational torque.3 This recursive processing is governed by a universal harmonic grammar, where mathematical constants and equations of state function not as descriptive measurements, but as absolute structural attractors.2 This exhaustive analysis explores the comprehensive mathematical, physical, and topological parameters of the Nexus framework. It systematically synthesizes the empirical validations of the Mark-0 operator and its prime trace predictions, the profound structural equivalencies mapped by the Sarrus Isomorphism, and the theoretical resolutions of the Phase 1163 (A-Mark9) theorem-locked domains. Through this synthesis, it becomes evident that the universe computes its own existence through harmonious, reversible, and mathematically perfect geometric collapse. The 11-Layer Harmonic Stack and Phase-Resonant Operations The architectural topology of the Nexus framework is modeled as an 11-layer harmonic stack, functioning as a self-similar fractal hierarchy that spans from pre-geometric informational voids to highly complex societal cognition.5 This stack acts as the foundational proof that the recursive rules governing the universe's most fundamental substrate are strictly isomorphic to those governing human cryptographic architectures and biological neural networks.5 The Stratification of Meta-Computational Reality The Nexus system categorizes structural emergence into specific discrete layers. Each layer does not invent new physical laws; rather, it encodes the exact same core Nexus laws translated into domain-specific, macroscopic guises.5 The hierarchy is defined as follows: Layer Designation Conceptual Description Role within the Nexus Framework L-1 (Pre-Geometry) The formless informational substrate Represents pure potential prior to physical instantiation; the domain of unmanifest differences ().5 L0 (Geometry & Info) Base mathematics, numbers, bits, Establishes the foundational constants and the absolute "code" of the discrete reality lattice.5 L1 (Physical Layer) Particles, forces, fundamental fields The manifestation of basic physical laws where Newtonian dynamics combine with harmonic feedback.5 L2 (Chemical Layer) Atoms, molecular configurations The domain where complex bonds operate as harmonic combinations of underlying wave vectors.5 L3 (Biological Layer) Cells, living organisms, proteins Self-organizing systems explicitly dedicated to maintaining phase resonance against entropic decay.5 L4 (Neural Layer) Brains, central nervous systems Recursive biological learning systems executing operations that continuously seek harmonic stability.5 L5 (Cognitive Layer) Symbolic thought, individual mind The emergence of abstract representation, language, and the subjective interface.7 L6 (Social Systems) Collective intelligence, economics The aggregated computational output of human interaction and geopolitical wave interference.7 L7 (Noospheric Layer) Societal-cognitive macro-structures The total integrated framework of planetary cognition, forming a macroscopic closed-loop system.7 The progression through these layers is not evolutionary in the Darwinian sense, but rather an inevitable consequence of constraint propagation. As lower levels reach geometric saturation, the system "folds" upward, creating higher-dimensional namespaces to resolve the inherited mathematical torque. Phase-Resonant Operators and the Cosmic FPGA Data flow through the 11-layer stack is mediated by a universal set of phase-resonant operators and continuous structural morphisms.7 The universe acts as a "Cosmic FPGA" (Field Programmable Gate Array), processing data via a continuous "attach-detach-attach" recursion—a binary breathing mechanism where localized forms bind to coordinates to create Life, and subsequently unbind back into the substrate, which we interpret as Death.3 The precise mechanics of this recursion are defined by five fundamental operators 7: (Difference): The fundamental seed of change and recursion. Every iterative cycle across all layers originates by taking stock of , which mathematically highlights the specific localized data that is not yet in harmony within the lattice.5 (Coherent Sum): The aggregation mechanism of attached and detached states. The total coherent sum of the universe's constraint is theoretically maintained at exactly zero, requiring perfect parity between structural formation and entropic release.7 (Rotation): The cyclical propagation of uncollapsed constraint through phase space, allowing systems to delay entropy by converting it into orbital or temporal geometry.7 (Collapse): The resolution state. Analogous to quantum wave-function collapse or a recursive algorithm reaching a fixed point, a successful indicates that the differences have been resolved to within the system's tolerance. This produces a stable pattern, a verifiable truth, or a physical particle.7 (Trust Field): The continuous measurement of internal structural coherence. Maintaining a high value is the absolute prerequisite for complex forms to resist the influx of thermodynamic entropy ().7 These operators dynamically interact via a defined set of structural morphisms—specifically (projection), (inclusion), (composition), and (reflection/recursion). The morphism represents the precise mechanism by which the system reads its own execution trace, driving the universal ROM's generation of physical reality.5 QuHarmonics Signal-Encoding Gravity Theory A foundational pillar of the Nexus stack is the QuHarmonics Signal-Encoding Gravity Theory, which systematically dismantles the classical Einsteinian interpretation of gravity as a continuous spacetime curvature caused by the presence of mass. Instead, the framework treats gravitational phenomena purely as a geometric necessity for efficient signal encoding and bandwidth management within a discrete quantum lattice.3 The Triadic Payload and Tensor Product Compression According to the QuHarmonics model, the discrete lattice encodes physical reality using a ternary (base-3) data stream.3 The core information payload utilizes three primary states, or "tones," designated as . To achieve optimal transmission bandwidth across the cosmic FPGA, the system eschews the allocation of a dedicated fourth physical tone. Instead, the "4th tone" is utilized as a strictly temporal "repeat previous" reference pointer.3 This architecture creates a fundamental duality wherein the signal comprises both a shape channel (the 3-dimensional instantaneous payload) and a value channel (the historical execution trace).3 By linking these channels, the transmission mathematically compresses into a tensor product structure, yielding a universal computational compression ratio () of exactly .3 By employing this historical pointer—which organic observers subjectively perceive as the linear flow of "time"—the structural memory of the universe is seamlessly propagated forward without exhausting the instantaneous spatial bandwidth of the processing lattice.3 The Cyclic Operator and Zero-Sum Gravity The operational core of the triadic payload is governed by the cyclic operator (), which is defined by the eigenvalues , where is a primitive cube root of unity.3 In the complex plane, these eigenvalues represent three vectors separated by exactly 120 degrees. For this triadic state to remain stable as it propagates through the lattice, it must adhere to a strict, non-negotiable zero-sum constraint: According to the QuHarmonics theory, this mandatory background cancellation is the true, underlying nature of gravity.3 Physical mass represents a localized aggregation of data that threatens triadic symmetry. To prevent a lattice crash, the system must automatically correct this asymmetry by enforcing the zero-sum closure constraint. The m

Shwetha A B

Graph-based knowledge representations have emerged as powerful tools for organizing interconnected information sourced from heterogeneous data environments. However, when contributing parties span multiple organizations with varying levels of mutual trust, maintaining and evolving such graphs in a coordinated manner poses significant challenges. Traditional centralized management platforms, while operationally convenient, tend to create systemic vulnerabilities including single points of failure, inadequate transparency mechanisms, and insufficient mechanisms for verifiable data lineage. In contrast, blockchain-based infrastructures offer compelling properties for managing distributed knowledge systems, including tamperevident ledgers, peer-driven transaction verification, cryptographic authenticity assurance, and rule-based automation via programmable contracts. This paper surveys contemporary research that intersects graph-based knowledge management with distributed ledger technology, examining methods for decentralized identity management, contract-driven governance, and multi-party data coordination. The survey analyzes currently deployed systems, highlights their shortcomings, and introduces a conceptual architecture that supports authenticated graph modifications, auditable data lineage, and permission-governed knowledge exchange across organizational boundaries. Key technical obstacles including on-chain storage constraints, retrieval latency, cross-system compatibility, confidentiality, and throughput limitations are systematically examined. The findings indicate that when blockchain components are thoughtfully integrated with off-chain graph repositories and optimized validation pipelines, decentralized approaches can substantially improve accountability and trustworthiness in collaborative knowledge ecosystems.

Daniel Dragolich

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

Avni Gupta

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, &amp; 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, &amp; 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 &amp; 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.

Christos Michalopoulos

No abstract is available for this record.

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&amp;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.

P. P. Latkovskyi

- . - (Decentralized Finance, DeFi), (NFT), - . , , , , . - - FATF [1], (Anti-Money Laundering Directives, AMLD) - . -

Gong Chen, Beijie Liu, Mengyuan Li

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.

Siddiqui Jameel Ahmed

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.

Vishwa Priya V, Dafik Dafik, Sunder R, Agustin Ika Hesti · 10 authors

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.

Rishav Chourasia, Ergute Bao, Uzair Javaid, Xiaokui Xiao

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.

Ahmad Bilal Khan

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

Mouhamed Amine Bouchiha, Mourad Rabah, Ronan Champagnat, Abdelaziz Amara Korba · 5 authors

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.

Tarkan Yavas, Arslan Brömme

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.

Agostino Capponi, Álvaro Cartea, Fayçal Drissi

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.

Christopher Goes

Multi-party object coordination - across object-capability systems, smart-contract platforms, distributed actors, and event-sourced architectures - is shaped by six structural properties: authenticated provenance, opaque encapsulation, atomic multi-object commit, deterministic replay, immutable history, and history-derived state. Existing systems compose subsets via separate layered mechanisms (RPC, capability ACLs, transaction coordinators, event journals, vat boundaries); each layer is well-studied but the combination is fragile. We present a minimal kernel which makes them jointly compatible. Our kernel is built from s-expressions, a uniform 'send' interface, transactions, and one primitive object distinction: *ephemeral* (caller's context inherited) vs. *persistent* (context switches to the target's kernel-assigned identity and append-only log). The kernel structurally classifies every send target into one of six cases without input from the caller - uniform caller interface, intensional kernel dispatch. Under kernel-faithful trust (the kernel runs its semantics as specified), this design holds all six properties as *kernel-level* against arbitrary programs - the kernel's transition function refuses states violating them. Opacity *against the operator* additionally requires operator-faithful trust (the operator accesses logs only via 'recall' and does not censor or reorder transactions); under kernel-faithful alone, five of six guarantees survive an unconstrained operator. Append-only logs underpin immutability, replay, and history-derived state; kernel-controlled persistent dispatch yields authenticated provenance and opacity; transactions deliver atomic coordination. Operator-adversarial deployments can be realized with a cryptographic compiler.

M. Lavanya, V Thiruppathy Kesavan, G. Sathya, R. Gopi

Electric Vehicles (EVs) that use Internet of Things (IoT) networks often involve the exchange of sensitive data between vehicles, charging stations, and other infrastructure, making data security and user privacy critical concerns. Existing methods for securing data in EV IoT networks rely on centralized systems, which create a single point of failure and are vulnerable to cyberattacks, data breaches, and unauthorized access. Furthermore, these systems struggle to address privacy concerns effectively, especially regarding user location and personal information. The proposed solution introduces a Blockchain Technology-based privacy preservation framework for EV networks (BCT-PP-EV). This framework leverages blockchain's decentralized nature to provide secure, transparent, and tamper-proof data exchanges. It ensures user privacy using cryptographic techniques such as zero-knowledge proofs (ZKP) and data anonymization, allowing privacy-preserving transactions without compromising data accuracy. Blockchain's immutability guarantees the integrity of the shared data, while smart contracts automate secure and efficient interactions within the network. The proposed method enhances secure data sharing while preserving privacy across EV IoT networks. By decentralizing data storage and enabling transparent auditing, BCT-PP-EV fosters trust among stakeholders and reduces the risks of unauthorized access or data manipulation. Preliminary findings suggest that implementing BCT-PP-EV significantly improves the security and privacy of data exchanges in EV networks, providing a scalable and resilient solution for the evolving smart transportation ecosystem. Experimental results demonstrate that BCT-PP-EV achieves 94.91% secure data sharing efficiency, reduces data breaches by 92.84%, and ensures data accuracy of 91.44%. Additionally, the framework exhibits high scalability of 96.57% with increasing network nodes, while maintaining controlled latency and throughput. Although unauthorized access resistance is measured at 24.71%, indicating scope for further improvement, the overall results confirm that BCT-PP-EV provides a robust, scalable, and privacy-preserving solution for next-generation smart transportation systems.