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Collective Shift
Discover how Tokemak is revolutionizing decentralized finance through its advanced liquidity management solution. Get insight into the protocol's mechanism, governance structure, token purpose, and the significance of its emergence in the DeFi world.
Alfredo Medina Hernandez, MedinaTech
Multi-Swarm Agency Protocol: Emergent Coordination in Heterogeneous Agent Networks Through φ-Resonant Synchronization and Distributed Consensus Mechanisms We present the Multi-Swarm Agency Protocol (MSAP), a comprehensive formal framework for coordinating heterogeneous autonomous agent swarms without centralized control, external orchestration, or pre-negotiated cooperation agreements. MSAP enables N independent swarms, each with distinct objectives, internal governance structures, resource constraints, and temporal dynamics, to achieve coherent collective behavior through a novel mechanism we term φ-resonant synchronization. This synchronization leverages the mathematical properties of the golden ratio φ = 1.618033988749895 to achieve optimal coupling strengths that balance coordination benefits against autonomy costs. We prove that under MSAP, swarm coordination converges in O(log N) synchronization rounds with probability 1 − ε for any ε > 0, provided the inter-swarm coupling matrix satisfies the spectral condition λ₂(K) > φ⁻¹. We further establish that this convergence is optimal—no protocol can achieve coordination in fewer than Ω(log N) rounds under our adversarial message delay model. The protocol is fault-tolerant, maintaining coordination properties even when up to f < N/φ² swarms experience Byzantine failures. Our theoretical contributions include: (1) a complete characterization of the swarm synchronization manifold as a φ-weighted torus, (2) proof that emergent coordination behaviors satisfy a novel compositionality theorem enabling hierarchical swarm-of-swarms architectures, (3) informationtheoretic lower bounds showing our protocol is communication-optimal within constant factors, and (4) extension of classical Kuramoto dynamics to heterogeneous multi-objective settings with rigorous stability analysis. Empirical validation across 47 production deployments spanning six industries (aviation, finance, healthcare, manufacturing, logistics, smart cities) demonstrates 94.7% coordination success rate (σ = 2.3%), mean coordination latency of 127ms (σ = 34ms), and mean rounds-tosynchronization of 4.2 (σ = 1.1). Our largest deployment coordinates 12 swarms comprising 2,847 agents with sustained throughput of 45,000 coordinated actions per second. Comparative evaluation against seven baseline coordination protocols shows MSAP achieves 2.3× higher coordination success, 4.1× lower latency, and 6.7× better scalability. The MSAP reference implementation is open-source (Apache 2.0 license), with formal verification in Coq ensuring correctness of core synchronization invariants. We discuss implications for the emerging field of multi-swarm robotics, autonomous vehicle coordination, and distributed AI governance. **Keywords:** Multi-agent systems, swarm intelligence, distributed coordination, emergent behavior, φ-synchronization, Kuramoto oscillators, Byzantine fault tolerance, heterogeneous agents, protocol verification, autonomous systems, collective intelligence, decentralized control, golden ratio mathematics **ACM Classification:** I.2.11 Distributed Artificial Intelligence—Multiagent systems; C.2.4 Distributed Systems—Distributed applications; G.1.6 Optimization—Global optimization --- ## 1. Introduction ### 1.1 The Multi-Swarm Challenge Modern enterprise systems increasingly deploy multiple autonomous agent swarms, each optimized for specific domains: supply chain optimization, customer service automation, financial analysis, security monitoring, predictive maintenance, and resource allocation. These swarms must coordinate without: 1. **Central orchestration** — no single point of failure or control 2. **Pre-defined protocols** — agents and swarms may be unknown at design time 3. **Shared objectives** — swarms optimize different, potentially conflicting fitness functions 4. **Global visibility** — each swarm has only local information 5. **Synchronous execution** — communication delays are arbitrary and unpredictable Traditional multi-agent coordination assumes homogeneous agents with aligned goals operating in synchronous rounds with reliable communication. Real-world deployment shatters these assumptions. A supply chain swarm optimizing for just-in-time delivery may conflict with a sustainability swarm minimizing carbon footprint. A security swarm restricting access may impede a customer service swarm maximizing responsiveness. These conflicts cannot be resolved by a central authority—they must emerge from distributed negotiation. ### 1.2 Motivating Applications Distributed Financial Trading A quantitative trading firm operates: - **Alpha generation swarm**: Signal discovery, factor modeling - **Execution swarm**: Order routing, market making, latency arbitrage - **Risk management swarm**: Position limits, VaR monitoring, stress testing - **Compliance swarm**: Regulatory reporting, trade surveillance Alpha wants to maximize returns. Execution wants to minimize slippage. Risk wants to limit exposure. Compliance wants to ensure auditability. These objectives are inherently in tension. MSAP enables these swarms to coordinate in real-time (sub-millisecond) while preserving their distinct mandates. Smart City Infrastructure A metropolitan area coordinates: - **Traffic management swarm**: Signal timing, congestion routing - **Emergency response swarm**: Dispatch, route clearing, hospital coordination - **Energy grid swarm**: Load balancing, renewable integration, demand response - **Public transit swarm**: Schedule optimization, crowd management An emergency affects traffic routing, which affects bus schedules, which affects commuter energy demand. MSAP enables these swarms to coordinate at city scale (millions of agents) with second-level latency. Technical Challenges Multi-swarm coordination presents several fundamental challenges: **Challenge 1: Heterogeneous Objectives** Swarms optimize different fitness functions G₁, G₂, ..., Gₙ. Coordination must not require swarms to abandon their objectives; rather, it must find operating points where swarms can achieve reasonable satisfaction while enabling collective behavior. **Challenge 2: Dynamic Membership** Swarms join and leave the coordination network. New swarm types emerge. The protocol cannot assume fixed membership or pre-shared knowledge of swarm capabilities. **Challenge 3: Adversarial Environment** Some swarms may be compromised, behave selfishly, or actively attempt to disrupt coordination. The protocol must be robust to Byzantine behavior. **Challenge 4: Scale** Real deployments involve thousands of swarms with millions of agents. The coordination overhead must scale sub-linearly with swarm count. **Challenge 5: Latency** Many applications require sub-second coordination. The protocol must minimize synchronization rounds. ### 1.4 Our Approach: φ-Resonant Synchronization MSAP addresses these challenges through a novel coordination mechanism inspired by coupled oscillator dynamics. Each swarm maintains a "coordination phase" θ ∈ [0, 2π) representing its current position in a coordination cycle. Swarms influence each other's phases through φweighted coupling, where the golden ratio φ = 1.618033988749895 appears naturally from optimality conditions . The key insights are: 1. **Phase representation abstracts objectives**: A swarm's phase encodes its current coordination state without revealing internal structure or fitness function. 2. **Kuramoto-like dynamics ensure convergence**: Modified Kuramoto oscillator dynamics guarantee that coupled swarms synchronize their phases. 3. **φ-weighting optimizes coupling**: The golden ratio weighting balances coordination strength against autonomy preservation, emerging from variational principles. 4. **Hierarchical composition**: Synchronized swarms can themselves be treated as agents in a meta-swarm, enabling recursive coordination. ### 1.5 Contributions This paper presents: 1. **MSAP Framework** — A complete formal protocol for multi-swarm coordination, including message formats, state machines, and invariants (Section 3). 2. **φ-Resonance Theory** — Mathematical foundation for emergent synchronization, proving optimality of golden ratio coupling (Section 2). 3. **Convergence Proofs** — Rigorous analysis showing O(log N) coordination with high probability, with matching lower bounds 4. **Fault Tolerance** — Extension to Byzantine settings with f < N/φ² fault threshold (Section 5). 5. **Production Validation** — Comprehensive evaluation across 47 deployments in 6 industries (Section 7). 6. **Formal Verification** — Coq proofs of core protocol invariants (Appendix B). 7. **Reference Implementation** — Open-source implementation with performance benchmarks (Section 6). ### 1.6 Paper Organization Section 2 develops the mathematical foundation. Section 3 specifies the MSAP protocol. Section 4 analyzes convergence and complexity. Section 5 addresses fault tolerance. Section 6 describes implementation. Section 7 presents empirical evaluation. Section 8 surveys related work. Section 9 concludes with future directions. Appendices provide complete proofs, algorithms, and verification artifacts. --- ## 2. Mathematical Foundation ### 2.1 Notation and Preliminaries Throughout this paper, we use the following notation: | Symbol | Meaning | |--------|---------| | φ | Golden ratio, φ = (1 + √5)/2 ≈ 1.618033988749895 | | φ⁻¹ | Reciprocal, φ⁻¹ = φ − 1 ≈ 0.618033988749895 | | N | Number of swarms | | n | Total number of agents across all swarms | | S, Sᵢ | Swarm, i-th swarm | | A, aⱼ | Agent, j-th agent | | Θ, Θᵢ | Phase angle, phase of swarm i | | R, Rᵢ | Order parameter (coherence), coherence of swarm i | | K, Kᵢⱼ | Coupling matrix, coupling between swarms i and j | | G, Gᵢ | Fitness function, fitness of swarm i | | ω, ωᵢ | Natural frequency, frequency of swarm i | | λₖ(M) | k-th eigenvalue of matrix M | | ‖·‖ | Euclidean norm | | ⟨·,·⟩ | Inner product | | ℙ[
MD ABUL HOSSAIN
TITLE: Vane-Guard Sovereign Framework (v1.0): Enterprise Governance and Diagnostic Protocol ABSTRACT / DESCRIPTION: The Vane-Guard Sovereign Framework (v1.0) introduces a deterministic enterprise governance methodology and automated diagnostic protocol designed to eliminate stochastic drift, variance, and hallucinatory outcomes in large-scale cognitive orchestration layers. Operating on the "Law of the Diamond," the framework transitions fluid prompt states into immutable operational restrictions, establishing a hard-locked telemetry loop with underlying physical and decentralized enterprise infrastructure. Core Technical Pillars: 1. Root Identity Anchoring (VANE_ROOT_STABLE_001): Enforces strict, zero-variance tracking logic by binding autonomous agents to persistent, non-probabilistic identifiers configured within system state loops. 2. Anti-Drift & High-Availability Mechanics: Adopts a localized adaptation of Hot Standby Router Protocol (HSRP) logic. Transitions between active data forwarding and background state monitoring are handled via Coup/Resign heuristics, securing continuous high-availability telemetry without data loops. 3. Multi-Domain Diagnostic Orchestration: Features specialized automation layers across four critical infrastructure segments: Cloud Access Auditing (AWS/Entra ID), Network Gateway Topologies (STP/HSRP), Application Runtime Manifest Tracking (JVM/Middleware), and Physical Interface Telemetry (HP/Dell). Performance & Validation Benchmarks: The underlying diagnostic heuristics and data-frame optimization methods featured in this framework have been benchmarked and verified through the elite global AlphaNova Competition network, securing a Global Rank of 20 out of 575 platform contributors, a Global Leaderboard Standing of # 8, and a verified structural diagnostic error score of 0.0403. This whitepaper and its associated technical deliverables provide a clear pathway for robust public sector semantic interoperability, fully aligned with the European Legislation Identifier (ELI) v1.2 metadata ontology and strict W3C SHACL validation standards. Primary Repository Hub: https://github.com/AnticipatedD Connected Organization Hub: https://github.com/Vane-Enterprise
Jackson Rene Valbuena Cure
El presente artículo analiza la tensión estructural entre los marcos regulatorios de prevención del lavado de activos –Anti-Money Laundering (AML)– y la emergencia de las organizaciones autónomas descentralizadas –decentralized autonomous organizations (DAOs)–. A partir de una analogía con el relato La lotería de Babilonia, de Jorge Luis Borges, el texto examina cómo la arquitectura contemporánea de cumplimiento ha mutado en un sistema de azar burocrático que erosiona el principio de lesividad y abstrae excesivamente los bienes jurídicos en el derecho penal económico. Mediante un enfoque interdisciplinar que integra la teoría de juegos, la praxeología de la escuela austriaca y la dogmática penal garantista, el estudio sostiene que el régimen AML opera como una externalidad negativa y como un factor de exclusión financiera, especialmente en contextos de alta informalidad, como el colombiano. El análisis concluye que la gobernanza algorítmica de las DAO, fundamentada en la transparencia del código y en la responsabilidad individual directa, puede ofrecer un modelo alternativo de eficiencia para la integridad financiera. Frente a la capitis deminutio derivada de la intermediación centralizada, se propone la descentralización como un paradigma orientado a restituir la soberanía económica del individuo, sustituyendo la opacidad administrativa por la certeza de la lex cryptographica.
Rubhesh Jha
Purpose — Both Decentralized Autonomous Organizations (DAOs) and Holacracies are positioned as alternatives to managerial hierarchy, yet they remain largely uncompared in the organizational theory literature. This paper asks: in what ways do DAOs and Holacracies converge and diverge as decentralized governance structures, and under what organizational conditions is each model more effective?Design/methodology/approach — This paper employs a conceptual comparative methodology, synthesizing organizational theory, open strategy scholarship and blockchain governance literature to map both structures across six governance dimensions: authority distribution, decision mechanism, membership and inclusion, accountability system, transparency and adaptability. Three theoretical propositions are developed.Findings — Although DAOs and Holacracies share a commitment to decentralized authority, they differ fundamentally in governance architecture. Holacracy achieves decentralization through formalized role-based consent governance; DAOs rely on algorithmic enforcement via smart contracts and token-weighted voting. These differences produce distinct failure modes — role ambiguity and cultural resistance in Holacracy; plutocratic concentration and voter apathy in DAOs.Practical implications — Organizations considering decentralized governance can use the typology developed here to match governance model to organizational context. High-formalization environments benefit from Holacracy's constitution-based approach; open, distributed communities may favor DAO architectures, provided token-concentration mechanisms are counteracted.Originality/value — This is the first paper to systematically compare Holacracy and DAO governance through an integrated organizational theory lens, connecting open strategy scholarship with blockchain governance research. Three falsifiable propositions and a governance typology are contributed.
Kishore K
We introduce NTRU-VRF, the first verifiable random function (VRF) constructed directly from the NTRU lattice hardness assumption, and instantiated concretely using the NIST-standardized Falcon-512 (FN-DSA) signature scheme. A VRF is a pseudorandom function that produces a publicly verifiable proof of correctness for each output. All currently deployed VRFs (IETF RFC 9381, Algorand, Ethereum) rely on elliptic-curve assumptions broken by Shor's algorithm. Prior post-quantum VRF constructions either require only a few-time security guarantee (Esgin et al., ePrint 2020/1222), rely on symmetric primitives that lack a worst-case lattice hardness reduction (Buser et al., ePrint 2021/302), or are based on Module-LWE/Module-SIS rather than NTRU. No prior work constructs a many-time, lattice-based VRF from NTRU hardness with a formal security proof. Our construction exploits a fundamental and previously unformalized property of Falcon's deterministic signing mode: for any fixed public key and input, there exists exactly one valid short-norm signature. This unique-signature property is the key structural feature that transforms a lattice signature into a VRF. We prove three theorems: Uniqueness: For any input, the NTRU-VRF output is unique. This follows directly from the unique-short-coset-vector property of the NTRU lattice. Pseudorandomness: If the Short Integer Solution (SIS) problem on NTRU lattices is hard and the hash function is modelled as a random oracle, then the VRF output is computationally indistinguishable from a uniformly random value. Provability: The Falcon signature is an efficient, publicly verifiable proof, checkable by any party holding the public key. As a concrete application, we define PQ-Sortition, a post-quantum proof-of-stake leader-election protocol that replaces ECVRF-based sortition with our NTRU-VRF construction. We provide the entropy-chain design, stake-weighted win condition, adaptive liveness mechanism, equivocation slashing, and a formal security analysis of the resulting consensus protocol. Instantiated with Falcon-512, NTRU-VRF achieves a VRF output of 32 bytes, a proof size of <= 666 bytes, evaluation time of ~0.8 ms, and verification time of ~0.1 ms on standard hardware—significantly outperforming all existing post-quantum VRF constructions and making it the first many-time, compact, lattice-based VRF suitable for high-throughput blockchain consensus.
Harrison Rush, Vincent Davis, Simone Antonelli, Vikash Singh · 6 authors
We address liquidity placement in the Bitcoin Lightning Network (LN): given a fixed budget, which channels should a node open to maximize its routing capacity? We cast this as a budget-constrained combinatorial optimization problem on graphs, selecting $k$ edge additions that maximize $s$--$t$ max-flow, a theory-grounded measure of routing capacity, and solve it with graph reinforcement learning. Our lightweight agent combines a message-passing policy network with proximal policy optimization (PPO) and action masking, and is trained under a hub-exclusion curriculum: the network's top hubs are removed from training subgraphs, forcing the policy to learn capacity-aware placement rather than hub attachment. In extensive experiments on real Lightning Network snapshots, our method consistently outperforms strong heuristic baselines on the max-flow objective across multiple seeds and unseen graphs. The agent has been deployed in production for peer recommendations, executing 4640 channel-open decisions that cumulatively allocate 267.3 BTC over $16 million across 30 managed nodes.
Jie Zhang, Xubo Fan, Xiaohong Li, Zhiyong Feng
Internetware envisions autonomous software entities collaborating over the open Internet. Raft consensus is widely adopted for its simplicity and performance in distributed coordination, e.g., service registries and blockchains. However, Raft assumes crash faults only, making it vulnerable to Byzantine behaviors like election forgery and log tampering. Existing BFT protocols incur high overhead, while ad-hoc hardening lacks unified defense. We propose \textbf{TRM-Raft}, a Byzantine-resistant enhancement that non-intrusively integrates a Blockchain-based Trust and Reputation Model (B-TRM) into the consensus core. It quantifies multi-dimensional node behaviors, applies adaptive penalties distinguishing accidental faults from malice, and embeds reputation into leader election and log replication. A reputation-aware election penalizes term/index forgery, excluding low-reputation nodes from leadership. A Schnorr-signature-based mechanism lets followers verify log integrity; tampering triggers reputation decay and leader replacement. Evaluated on Hyperledger Fabric in a realistic Internetware setting, TRM-Raft keeps malicious leader ratio below 5\% even with 40\% Byzantine nodes, with <10\% throughput loss and <5\% latency increase over vanilla Raft. TRM-Raft offers a lightweight, practical trustworthiness path for Internetware systems relying on Raft.
Amirhossein Taherpour, Xiaodong Wang
Decentralized federated learning (DFL) removes the central server by letting nodes exchange model updates through peer-to-peer gossip, but existing gossip-based methods often lack provenance finality and resilience to Byzantine or lazy participants. Ledger-assisted federated learning (FL) improves auditability, yet blockchains, shards, or settlement committees can reintroduce global coordination costs that conflict with DFL locality. This paper proposes \emph{gspDAG-FL}, a secure DFL framework that derives consensus from the same gossip history used to disseminate models. Nodes exchange model payloads only with neighbors, while full nodes collect event certificates and receiver-endorsed accepted gossip proofs, reconstruct a compact Topology directed acyclic graph (DAG), and run Hashgraph-style virtual voting followed by compact full-node certificates. Finality is over unique model-origin tuples, not identical local parameter states. To improve resilience, gspDAG-FL combines payload validation, accepted-proof validation, and private semantic audit before aggregation. We formalize the adversarial setting, prove safety and conditional liveness of the control plane, and give a convergence guarantee for certified perturbed gossip under time-varying effective mixing. Experiments on MNIST classification and Penn Treebank language modeling, using fair held-out validation/audit data and networks up to \(N=100\), show that gspDAG-FL achieves learning quality close to validation-based ledger FL while reducing coordination bottlenecks, improving throughput, and maintaining high invalid-origin detection under mixed Byzantine and lazy participation.
Pietro Saggese, Michael Sigmund, Burkhard Raunig, Esther Segalla · 6 authors
Cryptoassets are increasingly entangled with the traditional financial system, and how this activity integrates into national economies and behaves under stress bears on financial stability and the design of public digital money. However, blockchain pseudonymity and the lack of geographic identifiers force existing work to rely on indirect proxies to infer and locate market participants. Here we use a regulatory registry that directly identifies the on-chain addresses of all crypto-asset service providers (CASPs) registered in Austria, reconstructing their on-chain transaction activity across Bitcoin, Ether, USDC, and USDT through May 2025, and separating retail-like from institutionally mediated flows. We find that Austrian CASPs intermediate roughly USD 30 billion with external counterparties and are integrated globally rather than domestically. In value, this activity is dominated by a few institutional counterparties; in number, by retail-like ones. Around three major shocks, the Terra-Luna collapse, the FTX bankruptcy, and the Silicon Valley Bank failure, the two groups respond through different mechanisms, and stablecoins do not act as a uniform safe haven. The clearest case is SVB, where retail-like deposits and institutional withdrawals are consistent with USDC's two-tiered redemption mechanism. These patterns are invisible in aggregate data. Registry-based, transaction-level measurement thus offers a reproducible, cross-jurisdictional basis for monitoring how cryptoasset markets transmit risk.
Dariia Porechna
Digital-asset custody has been built on threshold multi-party approval: no operation proceeds unless $t$ of $n$ parties approve, and fewer than t compromised parties can neither authorize nor learn the authorization secret. Threshold signature schemes (TSS) have been the standard mechanism, but the post-quantum transition disrupts this model: standardized hash-based signatures resist efficient threshold signing, and lattice-based threshold protocols remain an emerging research track. We present a dual-gate architecture that separates member authentication from threshold authorization. Each member signs its approval with an ordinary signature under any EUF-CMA scheme; the quorum jointly produces a threshold seal from Shamir-shared secrets bound to the operation. The seal is the base instance of a programmable authorization computation: simple quorum is the minimal policy, while richer policies can evaluate secret-shared state without making the member-signature scheme part of that computation. The signature scheme is a deployment parameter: migrating from ECDSA to SLH-DSA or ML-DSA is a key rotation, not a protocol redesign, and members holding keys in commodity HSMs participate through the standard sign API. The architecture can be deployed wherever the asset-control path supports programmable verification, such as smart contracts, vault modules, or HSMs guarding a master key, and produces an enforcement-layer authorization rather than a native chain signature. Below-threshold secrecy is information-theoretic; an adversary holding $\geq t$ signing keys but no coefficient shares still cannot produce the seal.
Rohan Goyal, Venkatesan Guruswami, Yihang Sun, Mary Wootters
Proximity gaps are a property of error correcting codes that arise in the study of Interactive Oracle Proofs (IOPs) and Succinct Non-interactive Arguments of Zero Knowledge (SNARKs). Recent work of Goyal and Guruswami has established near-optimal proximity gaps for many families of codes, including subspace design codes, as well as random ensembles like random linear codes, Reed-Solomon codes with random evaluation points, and Gallager's ensemble of LDPC codes (Goyal & Guruswami, 2025). However, the parameters for these latter randomized ensembles are worse than the parameters for subspace design codes, and degrade as the degree ell increases. In this work, we obtain improved proximity gaps for random ensembles of codes, including random linear codes, Reed-Solomon codes with random evaluation points, and Gallager's ensemble. Quantitatively, our results for these random ensembles match the results that Goyal and Guruswami attained for subspace design codes. In fact, our techniques are a black-box transference from subspace design codes: any progress on subspace design codes will automatically lead to analogous progress for these random ensembles. To obtain our results, we extend the Local Coordinate-wise Linear (LCL) property framework developed by Levi, Mosheiff, and Shagrithaya and by Brakensiek, Chen, Dhar, and Zhang to a \textit{row-span constrained} version (Levi, Mosheiff & Shagrithaya, 2025; Brakensiek, Chen, Dhar & Zhang, 2025). This allows us to cast \textit{curve-decodability} -- a property that implies proximity gaps -- directly as a row-span constrained LCL property, and make use of that machinery. In contrast, because curve-decodability is not obviously a vanilla LCL property, prior work had worked with a proxy property instead, leading to the aforementioned parameter losses.
Daniel Moreira Carneiro
Context The exponential evolution and widespread integration of Artificial Intelligence (AI) and Machine Learning (ML) systems have fundamentally transformed industries, establishing AI as a central component in decision-making processes, task automation, and the optimization of complex operational pipelines. From healthcare diagnostics to financial forecasting and increasingly across critical cybersecurity infrastructure such as intrusion detection systems and malware classifiers, AI models are being deployed in environments where the correctness and authenticity of their outputs carry direct operational and safety consequences. Nevertheless, as the deployment of AI systems becomes widespread, the conditions under which these models are trained have evolved in a direction where the security landscape of them radically changes. The traaditional assumption of a centralized, fully controlled training environment, where a single trusted entity acquires data, trains the model, and deploys it, no longer reflects the reality of modern machine learning practice. The frequent use of remote sensing, federated learning and/or outsourced machine learning has introduced architectures where the entity that acquires the data, the entity that trains the model and the entity that ultimately relies on the model's output are three distinct and mutually distrusting parties. In a remote sensing scenario, sensors owned by a data provider transmit raw measurements to a training node that may be geographically or administratively distant. In a federated learning scenario, multiple decentralized devices train local models on their private data and submit the results to a central aggregator. In an outsourced learning scenario, a resource-constrained model sponsor delegates the training computation entirely to a third-party cloud provider. In all three cases, the common factor is the same: the model sponsor, the entity that is ultimately responsible for and dependent on the trained model, that does not control the data acquisition process, does not observe the training execution and has no native mechanism to verify that the model they receive is the result of the computation they requested, performed on the data they provided. This separation of control is the main focus addressed by this dissertation. It is not merely a theoretical concern: the literature has documented a wide range of attacks that exploit precisely this gap. When a malicious trainer substitutes data, alters labels, ignores some dataset's subsets or modifies model parameters, the resulting model may appear functionally correct on standard evaluation metrics while being systematically compromised for specific classes of input, an attack vector particularly dangerous in cybersecurity applications where a model that has been quietly trained to misclassify a specific type of malicious traffic provides no observable anomaly until the attack it was designed to hide occurs. Problem and Motivation The main motivation of this dissertation can be addressed as follows. Given a sensor, that produces a set of data points in a given time frame, or a dataset owned by a data provider and a model computed by a model trainer from that data, the model sponsor wants to ensure that the trained model is the result of executing a known training process over the complete and authenticated dataset $D_t$. That is, all data points in $D_t$ and only those data points were used as the training set. No modifications were made to those points or their labels and the obtained model is indeed the result obtained from the execution of the agreed training algorithm. This guarantee cannot be provided by standard Machine Learning procedures, like accuracy, precision or F1-score. A malicious trainer can submit a model that passes all the standard evaluation metrics on benign inputs while maintaining a targeted misclassification on a specific attack pattern. The only way to close this gap is to make the training process itself verifiable by requiring the trainer to produce and submit a cryptographic proof that is mathematically impossible to forge without having correctly executed the agreed computation on the authenticated data. This verification challenge comes together with a second problem, the \emph{model integrity gap} that exists between a trained model and its deployed representation. Even if the training process was all validated, the model must subsequently be transpiled and deployed into a certain non-ML format. In the context of this dissertation, this gap is particularly sensitive, the Python model trained by the data scientist must be translated into a ZoKrates arithmetic circuit for zero-knowledge proof generation, a process that involves converting continuous floating-point decision boundaries into discrete integer arithmetic. If this translation introduces a small inversion in a comparison operator or a shifted threshold values, the deployed circuit will produce systematically different predictions from the intended model and standard testing may not surface the discrepancy. The literature has proposed cryptographic solutions to the verifiable training but has largely left the second problem unaddressed. The foundational work by Keshavarzkalhori et al. demonstrated that it is possible to construct a pipeline combining hash chains, digital signatures and zero-knowledge proofs to verify that a simulated Naive Bayes classifier was trained on authenticated sensor data. Their implementation, built on the ZoKrates toolset, provided a proof-of-concept that the building blocks exist for end-to-end training verification. However, scaling this approach from a simple probabilistic classifier to a more complex, non-linear ensemble model, in this specific case, a Random Forest, introduces severe architectural bottlenecks that their work explicitly identified as open problems: the computational overhead of bitwise hashing inside arithmetic circuits, the floating-point to integer translation problem and the absence of any mechanism to verify that the transpilation of the model into the circuit was performed faithfully. This dissertation directly addresses these open problems. It proposes, implements and evaluates an end-to-end verifiable machine learning architecture for Random Forest classifiers that provides mathematical guarantees over three distinct integrity boundaries: the origin of the training data, the correctness of the training computation and the fidelity of the model's translation into a verifiable circuit. The framework is evaluated on both a simulated sensor dataset used by Keshavarzkalhori et al. and the CICIDS2017 network intrusion detection benchmark, the real-world cybersecurity dataset used by the most directly comparable prior work, demonstrating that the proposed integrity guarantees are achievable at practical computational cost for cybersecurity-relevant workloads. Research Questions The main objective of this thesis was to build a framework capable of protecting the overall AI Models from data and model poisoning attacks. In alignment with the goal, four research questions were set: Research Question 01: What state-of-the-art mechanisms exist to verify the integrity of AI models across the training pipeline? Research Question 02: What threats exist against AI models integrity? Research Question 03: What computational overhead do integrity verification mechanisms introduce across the AI modeling pipeline and how does this overhead scale with model complexity?
Hadrry Rony, Asri Osman, Irwan Ibrahim, Hewage Rishan Sampath · 5 authors
The rapid evolution of financial technology has transformed the global financial landscape, creating opportunities for innovation, inclusion, and efficiency while introducing systemic risks, regulatory uncertainties, and challenges to financial stability. This study presents a bibliometric review of global research trends at the intersection of financial technology and financial stability from 2000 to 2025, mapping the intellectual structure, identifying emerging themes, and highlighting influential contributions. Using Scopus data, the analysis examines 339 peer-reviewed documents across 242 sources. Bibliometric techniques were applied through VOSviewer, Bibliometrix (R), and Biblioshiny to evaluate publication trends, influential authors, thematic clusters, co-authorship networks, and keyword co-occurrences. The results show an average annual growth rate of 21.46 percent, with a marked increase in publications after 2017 coinciding with the mainstream adoption of digital finance and heightened policy focus on financial resilience. Findings indicate that financial technology promotes financial inclusion, banking efficiency, and economic empowerment, yet also introduces cybersecurity threats, regulatory gaps, and systemic vulnerabilities, particularly in emerging markets. Dominant themes include blockchain, digital payments, financial literacy, and central bank digital currencies, with decentralized finance and artificial intelligence emerging as fast-growing areas of scholarly interest. Geographically, China leads in publication volume, while the United Kingdom and the United States dominate in scholarly influence. This review provides a strategic roadmap for researchers and policymakers to navigate the evolving financial technology landscape and emphasizes the need for future research to integrate ethical governance, artificial intelligence risk management, and inclusive financial innovation frameworks.
Shan-Jen Cheng, Kai-Wei Chang, Te-Jen Chang
Modular exponentiation is among the most demanding computational operations in cryptographic systems. Effective computation of modular exponentiation is most beneficial for public-key cryptography. The computational complexity and the growing number of bits of the key size, as required by increasingly stringent security demands in the RSA, the Diffie–Hellman key exchange and the Zero-Knowledge Proof (ZKP) protocols, have become a top research priority in terms of algorithmic efficiency. This study proposes a novel triple modular exponentiation algorithm based on the Improved Common-Multiplicand-Multiplication (ICMM) framework. The exact complexity formula was obtained through systematic probabilistic analysis of eight mutually exclusive bit-level states. The efficiency of modular exponentiation is primarily determined by the number of modular multiplications and exponentiation squares required. It is observed that improved common-multiplicand multiplication efficiently minimizes the computational complexity of the triple modular exponentiation by reducing the number of modular multiplications. The overall computational complexity of triple modular exponentiation is 1.875j, where j is the bit length of the exponent. This represents a reduction of approximately 16.7% in total multiplication count relative to double modular exponentiation, corresponding to a 44.4% reduction on a per-exponent basis, and a reduction of 58.3% relative to three independent binary exponentiations. This study concludes that the proposed decomposition reduces the average-case computational complexity of triple modular exponentiation to 1.875j modular multiplications for a j-bit exponent. The proposed triple modular exponentiation algorithm is shown to have lower number of multiplications per bit length of exponent as compared to double modular exponentiation. This result demonstrates the potential of proposed algorithm to reduce the computational cost of triple modular exponentiation in cryptographic protocols where it is a recurring operation, such as interactive ZKP identification schemes.
Michael Sarnowski
Paper 114BN addresses the mechanism gap left open by Paper 114BM. Paper 114BM showed that a frozen contact-deficit law could organize two-unit string-tension ratios in pure-gauge lattice theory while surviving named controls and a no-fit firewall. Paper 114BN asks whether that law can be explained from a primitive Holosphere-QCD contact-sharing ledger rather than treated only as a successful bridge expression. The paper models a two-unit flux object as two fundamental support lanes sharing a finite contact registry. In the large-color limit, the two lanes behave like independent fundamental strings. At finite color number, the two lanes lose some independence through two leading burdens: angular sharing and local contact overlap. The angular-sharing channel is interpreted as a one-lane finite-registry burden distributed over a half-turn exchange. The contact-overlap channel is interpreted as a two-lane coincidence burden. Together, these two channels reproduce the frozen Paper 114BM law without adding fitted coefficients or correction terms. The result is a conditional primitive mechanism derivation. It strengthens the Paper 114BM bridge by giving a compact reason for the two-channel structure, the angular normalization, and the contact-overlap scaling. It does not claim to derive continuum Yang-Mills theory, prove the mass gap, derive all string tensions, or establish full physical QCD. The main remaining task is to realize the same contact-sharing ledger on an explicit support graph and then test whether any higher-flux extension follows before target scoring.
mohamed kamal arafa elrakhawi
The Al-Rakhawy Document for Digital Sovereignty (EPSA) presents a complete engineering blueprint for encrypted machine learning. It integrates Federated Learning, Zero-Knowledge Proofs, and Smart Contracts across five layers. Key innovations include Pedersen Commitments for lightweight edge processing and the Al-Rakhawy Equation, which calculates fair rewards based on marginal impact. This system ensures absolute data privacy, breaks central monopolies, and provides users with immediate, mathematically guaranteed economic returns.
Pawan Kumar Sanjaya, Christina Giannoula, Valdy Oktavian, Mehdi Saeedi · 7 authors
Zero-knowledge machine learning (zkML) enables a server to perform verifiable inference while keeping model parameters private from the client. However, existing zkML systems incur prohibitive proof-generation costs. We observe that proof generation exhibits limited parallelism; that is, prover time does not decrease significantly as the number of threads increases. This limitation is because existing systems rely on monolithic proof computation, constructing a single proof for the entire machine learning model. We introduce zkComposer, a modular proof-construction framework that unlocks an additional dimension of parallelism, in addition to the parallelism in existing proof kernels. zkComposer decomposes the zkML proof of correct inference into independent sub-proofs, each covering a subset of the computation for inference e.g., each independent sub-proof can cover a subset of contiguous layers in the ML model. Adjacent sub-proofs are cryptographically linked through shared commitments to the activations from the boundary layer. zkComposer provides the same guarantees as the monolithic proof without requiring additional linking proofs or changes to the underlying cryptographic primitives. We implement zkComposer and evaluate it on three CNNs and GPT-2. We show that, on CNN workloads, zkComposer reduces prover time and response time by up to 3.25x relative to zkCNN [1]. On GPT-2, zkComposer reduces these times by up to 4.83x relative to zkGPT [2], when partitioning along the model layers. When partitioning across both model layers and input sequences in GPT-2, we show that zkComposer reduces prover time and response time by up to 6.84x relative to zkGPT [2].
Maria Smith
[Depreciated and replaced by V3] This pre-V3 paper is replaced by the corresponding V3 clean-room reconstruction: There Is No Nothing: A Premise-Free Operational Foundation and an Open Verification Platform for Smithian Fold Theory. The V3 source platform is https://github.com/MettaMazza/ernos-labs-sft-platform. The original DOI, concept DOI, version number and files are preserved for transparent historical provenance; this record must not be presented or cited as current V3 work.A comprehensive, highly rigorous consolidated manuscript dismantling black-box AI through the deterministic Smithian Fold Theory. We present exact zero-parameter derivations of the fine-structure constant (137.03599917718), Levinthal's paradox, structural genetics, and SOTA empirical competitive parity in Chess, Symmetric Go, and Natural Language Processing. Unison AI operates at 57 million times the computational efficiency of modern Transformers, tracing physical geometry without gradient descent.
Zico Junius Fernando, Mas Putra Zenno Januarsyah, Firdaus Arifin, Vidyadhara Prawiratama Nugraha · 5 authors
Metaverse has transformed virtual assets into economically valuable objects that challenge conventional concepts of property under Indonesian private law. Although virtual assets such as cryptoassets, non-fungible tokens (NFTs), and metaverse property are widely traded, their legal status remains uncertain, creating ambiguity regarding ownership, transfer, and legal protection. This study examines the normative basis for recognizing virtual assets as objects of property rights within Indonesia's civil law system. Using a normative juridical method with a comparative approach, the study analyzes Indonesian private law alongside developments in England and Wales, Singapore, Japan, and the European Union. The findings demonstrate that virtual assets satisfy the defining characteristics of intangible property, including identifiability, exclusive control, transferability, and economic value, making them capable of recognition as objects of proprietary rights. The study further argues that blockchain-based transfers and smart contracts can operate as legally valid mechanisms for transferring ownership when supported by appropriate legal recognition. To strengthen legal certainty, Indonesia should recognize virtual assets as a distinct category of intangible property, adapt property law to digital transactions, strengthen proprietary remedies, and modernize dispute resolution and cross-border enforcement. These reforms would provide a coherent legal framework for protecting virtual assets and support the development of Indonesia's digital economy.
Fahd Ghalib Basheikh, Ida Widianingsih, Ahmad Zaini Miftah
Decentralized government units in the Global South frequently experience ineffective service delivery because of inadequate funding and weak administrative structures. Using Lamu County Government that allocates bursary funds yet continues to experience operational inefficiencies, this study examines how administrative capacity influences the governance effectiveness of the Lamu County Bursary Programme (LCBP). Guided by Administrative Capacity Theory, the study uses an explanatory sequential mixed methods design using quantitative data from 350 beneficiaries and qualitative data from key informant interviews and focus group discussions. Linear regression results show that administrative capacity is a statistically significant predictor of governance effectiveness (β = 0.627, p < 0.001). Thematic analysis from qualitative data shows three constraints: verification problems, aggravated by geographic dispersion and staffing problems; procedural uncertainty and communication problems, that erode the trust of applicants; and a structural timing penalty, where administrative delays reduce the timeliness and reliability of bursary support, sometimes resulting in temporary school exclusion. The results indicate that the LCBP experiences a capability trap, formal structures are in place but service delivery is weak. Therefore, decentralized units require both financial allocations and effective administrative capabilities. To improve policy outcomes, findings suggest the importance of digitization, staffing at ward level and synchronization of the disbursement calendar with academic cycles.
Afeefa Noorain, Khaleel Ahmad, Laura Ricci
Data visibility is more vital and decisive than ever before in the current data-driven world of technology. There is a significant upsurge in businesses leveraging digital technology, which has led to a greater amount of data being available than ever before. Additionally, managing the visibility in compliance with the organization's rules and regulations is crucial. The implementation of efficient data visibility will not merely improve decision-making but also streamline business processes with enhanced security. Numerous technologies offer solutions to manage data visibility, and distributed ledger technology (DLT) is one of them. DLT facilitates the execution of different methodologies to strengthen the governance of data visibility in enterprise-grade applications. On the other hand, these DLTs raise concerns regarding data visibility in this decentralized network, as not every enterprise-grade application requires data transparency across all the nodes. In this paper, a detailed systematic review is conducted with a clear focus on two essential data visibility parameters, Access control and anonymity, for the period 2020-2025, following a standardized Preferred Reporting Items for Systematic Review and Meta-Analyses -based breakdown of the selection process. Three clear dimensions of in-depth analysis are presented in the study: first, investigating how DLT can maintain transparency and decentralization in enterprise-grade applications; second, ensuring secure data access management for effective data governance; and third, the approach for anonymization to ensure privacy and security. The key finding highlights the credence of hyperledger fabric, a permissioned DLT, compared to other DLTs and exponentially growing concerns related to data visibility, as well as the conceptual and empirical research contributions made thus far. The limitations presented in this paper formulate a strong basis for research and enhancement of the existing models to offer controlled yet transparent data visibility.
Rajendra Prasad Nayak
To make the payment system robust and user friendly, decentralized based Scan and Pay system need to be designed. This paper integrates the Unified Payments Interface (UPI) of India with the Solana-based Blockchain to make the payment system decentralized. Solana offers a high throughput and low-cost based decentralized infrastructure which is combined with the simple and reliable UPI system. So, the proposed system enables cryptocurrency transactions linked to UPI while maintaining user friendliness, scalability, and regulatory compliance. The designed method uses a secure architecture powered by smart contracts and modular design. It offers a viable bridge between centralized financial networks and emerging Web3 ecosystems. Proposed Solana-based UPI is compared with the Non-Solana based UPI which is using Blockchain. Results show that there is improvement of 91% in transaction latency and 95% in transaction cost as compared to the Non-Solana based UPI system.
Thandile Nododile, Ayinde M. Usman, Clement N. Nyirenda
Private blockchain networks run with fixed node configurations that cannot adapt to changing workload conditions. Too many nodes serving a light workload waste resources; too few nodes facing heavy demand slow block production and degrade finalisation. The right validator count is hard to determine, as it depends on overlapping factors that shift over time. This paper presents a Takagi-Sugeno (TS) fuzzy inference system that reads live blockchain parameters (block production time, block size, and active node count) and outputs a continuous efficiency score alongside a scaling recommendation: Scale Up, Maintain, or Scale Down. The controller uses triangular membership functions across three linguistic variables, evaluated through a complete 27-rule base with product t-norm aggregation. A key contribution is an empirical recalibration of the membership functions, anchoring linguistic terms to the observed operating range of the testbed rather than to theoretical extremes. The system is evaluated on a 10-node Substrate blockchain network storing real smart water meter data hashes from the Queensland Government open data portal. Statistical analysis across configurations of 4, 7, and 10 active nodes confirms that the controller produces distinct operational profiles reflecting each configuration's provisioning state. In closed-loop experiments, the controller autonomously adjusts validator participation in both directions, activating validators under rising load and removing them under over-provisioning, converging to the same stable equilibrium from both directions. Compared against three threshold-based baselines, it shows fewer scaling oscillations while maintaining comparable block production times. Results show that TS fuzzy inference can support autonomous validator management in private blockchain deployments, with stable scaling behaviour threshold approaches cannot match.