Proof-of-work (PoW) blockchains expend energy solely for network security. Proof of Useful Work (PoUW) aims to reclaim this cost, but stateful proving (e.g., multi-phase STARKs) breaks the memoryless property required for Nakamoto consensus. ZK-SPoW (Zero-Knowledge Symbiotic Proof of Work) inverts the relationship: instead of making PoW useful, useful STARK Merkle hashing naturally produces PoW tickets as a cryptographic byproduct of every Poseidon2 permutation. Under the pseudorandom permutation (PRP) assumption, each permutation is computationally indistinguishable from an independent Bernoulli trial at nanosecond granularity — restoring computational memorylessness at the permutation level without sacrificing useful computation. We instantiate with Width-24 Poseidon2 over M31: each permutation simultaneously produces a Merkle parent (ZK output) and three PoW tickets. Usefulness is not protocol-enforced but market-driven — miners select which ZK proofs to generate or fall back to Pure PoW, guided by proof demand. Statistical validation via the full NIST SP 800-22 test suite (15/15 tests pass) confirms the pseudorandom quality of the Poseidon2-based mining output.
This document provides an extended analysis and applied systems perspective on Tangible Encryption, a framework that replaces the “secret zero” problem in secrets management with a token-based identity model. Expanding on the formal definition, this work explores architectural integration with existing systems such as HashiCorp Vault, Mozilla SOPS, and cloud key management services, detailing how non-fungible tokens (NFTs) can function as persistent, ownable roots of trust. The paper examines practical implementation pathways, including hybrid encryption workflows, on-chain and off-chain verification models, and system design considerations for real-world deployment. It further contextualizes Tangible Encryption within AI-driven environments, addressing use cases such as machine learning model provenance, secure data sharing, and autonomous agent authentication across organizational boundaries. This work complements the formal analysis (DOI: 10.5281/zenodo.19259339) by providing expanded context, integration strategies, and operational considerations, offering a systems-level view of how identity-bound cryptographic tokens can unify authentication, access control, and auditability.
Human-friendly identifiers such as email addresses and phone numbers are convenient payment targets, but direct mappings from identifiers to blockchain addresses make balances and transaction histories enumerable by anyone who knows the identifier. We present HFI-Pay, a relay-assisted protocol for privacy-preserving identifier-routed cryptocurrency payments. The relay resolves the identifier off-chain and registers only a random intent identifier, a per-intent blinded binding rho_i, and the quoted payment tuple on-chain; no identifier or reusable recipient tag is published before claim. In a verified-quote deployment, the sender verifies an attested quote proving that rho_i was derived from the same hidden binding handle as the recipient's attested binding-key commitment, preventing relay-side recipient substitution before funding. Claims are authorized by a zero-knowledge proof, instantiated through ZK-ACE, that the claimant controls the deterministic identity whose epoch-scoped handle opens the blinded binding and authorizes release of the quoted asset and amount to a chosen destination. We define observer-model games for enumeration resistance and pre-claim unlinkability, state the composition needed for post-quote claim correctness, and characterize relay compromise and post-claim linkability. Keywords: identifier-based payment, privacy-preserving, verifiable quote, blinded claim binding, zero-knowledge authorization
Munawar Hasan, Apostol Vassilev, Edward Griffor, Thoshitha Gamage
The application of zero-knowledge proofs (ZKPs) in autonomous systems is an emerging area of research, motivated by the growing need for regulatory compliance, transparent auditing, and trustworthy operation in decentralized environments. zk-SNARK is a powerful cryptographic tool that allows a party (the prover) to prove to another party (the verifier) that a statement about its own internal state is true, without revealing sensitive or proprietary data about that state. This paper proposes Hermes Seal: a zk-SNARK-based ZKP framework for enabling privacy-preserving, verifiable communication in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) networks. The framework allows autonomous systems to generate cryptographic proofs of perception and decision-related computations without revealing proprietary models, sensor data, or internal system states, thereby supporting interoperability across heterogeneous autonomous systems. We present two real-world case studies implemented and empirically evaluated within our framework, demonstrating a step toward verifiable autonomous system information exchanges. The first demonstrates real-time proof generation and verification, achieving 8 ms proof generation and 1 ms verification on a GPU, while the second evaluates the performance of an autonomous vehicle perception stack, enabling proof of computation without exposing proprietary or confidential data. Furthermore, the framework can be integrated into AV perception stacks to facilitate verifiable interoperability and privacy-preserving cooperative perception. The demonstration code for this project is open source, available on Github.
Ziqiao Kong, Wanxu Xia, Chong Wang, Yi LU · 9 authors
Smart contracts govern billions of dollars in decentralized finance (DeFi), yet automated vulnerability detection remains challenging because many vulnerabilities are tightly coupled with project-specific business logic. We observe that recurring vulnerabilities across diverse DeFi business models often share the same underlying economic mechanisms, which we term DeFi semantics, and that capturing these shared abstractions can enable more systematic auditing. Building on this insight, we propose Knowdit, a knowledge-driven, agentic workflow for smart contract vulnerability detection. Knowdit first constructs an auditing knowledge graph from historical human audit reports, linking fine-grained DeFi semantics with recurring vulnerability patterns. Given a new project, a multi-agent pipeline leverages this knowledge through an iterative loop of specification generation, Proof-of-Concept (PoC) synthesis, PoC execution, and finding reflection, driven by a shared repository index. We evaluate Knowdit on 11 recent Code4rena projects with 84 ground-truth vulnerabilities. Knowdit detects all 21 high-severity and 90% of medium-severity vulnerabilities without false positives, fully covering eight projects, significantly outperforming all baselines. Applied to seven real-world projects, Knowdit further discovers 9 high- and 36 medium-severity previously unknown vulnerabilities, securing millions in liquidity and proving its outstanding performance.
Vahab Esfandani, Mohammad Amin Borghei, Sara Ravan Ramzani, Peter Konhaeusner · 6 authors
The digital economy has expanded organizations’ ability to source ideas, labor and capital through online participation, making crowdsourcing a strategic mechanism for innovation and problem solving. This chapter conceptualizes strategic crowdsourcing as a socio-technical system rather than ad hoc task outsourcing and synthesizes dispersed theory and evidence into a coherent framework for design and governance. It defines major typologies—micro-tasks, open innovation contests, co-creation, crowdfunding, internal crowdsourcing and citizen science—and situates them relative to outsourcing and open-source collaboration to clarify when each approach fits task uncertainty, required expertise and desired ownership of outputs. Building on open innovation, socio-technical systems and participatory governance perspectives, the chapter proposes an integrated model with five linked layers: contextual drivers; input configuration (task specification, crowd definition and call design); enabling infrastructure (platforms and technologies, including AI and blockchain-based mechanisms); process mechanisms (incentive design, validation and quality assurance, data governance and ethical/legal safeguards); and outputs/outcomes (innovation, organizational learning, governance effects and social value with feedback loops). Cross-sector illustrations from technology, healthcare, education, civic tech and sustainability highlight recurring trade-offs around motivation, quality control, fair compensation, privacy and confidentiality and intellectual property rights. The chapter also evaluates emerging hybrid human–AI crowdsourcing and decentralized autonomous organizations (DAOs), emphasizing that their benefits depend on transparent rules, accountable allocation of rewards and decision rights and human-in-the-loop oversight to mitigate bias, concentration of control and trust failures. Overall, strategic crowdsourcing is positioned as potentially democratizing when aligned with organizational goals and governed responsibly. It concludes by outlining research directions for comparative studies, cross-cultural analysis and regulation-aware design.
The rapid development of quantum computing poses severe threats to traditional blockchain security mechanisms, while existing full-quantum blockchains face challenges regarding high hardware costs and limited scalability. To address these issues, this paper proposes a secure and practical semi-quantum blockchain system. Specifically, a Semi-Quantum Delegated Proof of Stake consensus mechanism is constructed by integrating an adapted semi-quantum voting protocol with the Borda count method and a malicious behavior penalty model. Furthermore, a lightweight transaction verification framework is designed based on semi-quantum key distribution, enabling classical users with limited quantum capabilities to participate securely. Theoretical analysis demonstrates that the system achieves unconditional security against quantum attacks while maintaining high throughput. These results indicate that the proposed asymmetric resource design significantly lowers hardware barriers compared to full-quantum schemes, effectively balancing security, practicality, and cost-effectiveness for post-quantum blockchain networks.
Zhiming Song, Leijin Long, Junrong Song, Rong Jiang
With the accelerating growth of the digital economy, data has emerged as a core asset, making secure and private data trading a pressing necessity. However, traditional centralized data trading platforms face critical challenges, including identity exposure, data leakage, unclear ownership, and lack of trust. Although decentralized, blockchain-based solutions have been proposed, they typically protect only subsets of these properties and seldom provide a unified, verifiable privacy architecture over the entire trading lifecycle. This article introduces a novel decentralized data trading system that comprehensively integrates Groth16-based zero-knowledge proofs (ZKPs), Merkle tree–based data ownership commitments, and smart contracts on blockchain. The proposed system ensures identity anonymity, data confidentiality, ownership traceability, and behavioral privacy while supporting regulatory auditability. Rather than proposing new cryptographic primitives, we reformulate data trading as a zero-knowledge–verifiable privacy problem and embed the resulting privacy logic into the protocol and contract design. The main contributions are as follows. (1) Developing a unified zero-knowledge privacy layer that combines Groth16-based ZKPs with proxy re-encryption, allowing participants to prove transaction eligibility without disclosing identity attributes while keeping traded data encrypted end-to-end. (2) Constructing a zero-knowledge-based ownership lifecycle in which Merkle trees are repurposed as privacy-preserving ownership commitment structures that support unlinkable ownership proof, secure ownership transfer, and privacy-preserving traceability. (3) Designing a malleability-aware ZKP execution framework for Groth16 proofs, implemented via dedicated “anti-malleability” contracts that bind proofs to ownership states, fresh randomness, and protocol stages, thereby mitigating proof malleability and unsafe reuse across the registration–sale–transfer lifecycle. (4) Integrating a trusted regulatory authority into the architecture to enable compliant yet anonymous audits and formulate a system-wide privacy framework covering identity, data, ownership, behavioral, and audit dimensions. Experimental results demonstrate that the system achieves strong privacy guarantees and low on-chain overhead, offering a more robust and privacy-centric approach to data transactions than existing solutions.
In this study, we propose a structured valuation framework for non-fungible tokens (NFTs), a distinct class of digital assets whose pricing mechanisms remain insufficiently understood. Based on previous empirical studies and illustrative case analyses of three major NFT collections, we synthesize insights from non-cash-flow asset theory, market microstructure, and behavioral finance to construct a four-layer valuation framework consisting of the Asset, Market, Technology, and Ecosystem layers. We identify three NFT-specific mechanisms—verified digital scarcity, pseudonymous signaling, and on-chain herding—that modify or extend traditional valuation paradigms. Empirical evidence from the literature suggests that rarity-driven asset features and social-influence dynamics are dominant price determinants, while wash trading, fragmented liquidity, and platform incentive structures generate persistent distortions in price discovery. Case analyses of CryptoPunks, Bored Ape Yacht Club, and Pudgy Penguins demonstrate how differing risk exposures across the four layers translate into distinct valuation trajectories. With this framework, we obtain a basis for improved risk assessment, regulatory oversight, and business model design in NFT markets.
In approximately the year 2000, the author conceived and partially implemented a multi-layered community economic system centered on Shibuya, Tokyo. The system integrated real-time human broadcasting, local media production, a unified community coupon currency, youth-driven cultural monitoring, and digital education — years before the terminology of DAO, Web3, UGC, or creator economy existed. This paper documents that original conception, analyzes its structural architecture, and demonstrates its direct lineage to the author's current work: the Hikari Currency ecosystem and the ECHO AI Artist platform.
This working paper examines a structural shift in search behaviour affecting crypto and Web3 brands: the divergence between AI-summarised retrieval (zero-click) and deep-intent click-through traffic. Drawing on published zero-click search data and observed AI citation patterns, it identifies why high-volume content strategies are failing in AI-mediated search environments and outlines three asset classes - statistics hubs, original industry research, and high-utility tools - that generate the editorial backlink profiles AI models use to select citation sources. The paper defines the concept of "authority infrastructure" as a capital investment in linkable assets with compounding residual value, contrasting this with recurring spend on keyword-optimised content with no durable equity. Intended for crypto protocol marketing teams, Web3 founders, and DeFi growth leads evaluating content strategy for AI search visibility. Published by David Wood, CryptoContent.dev.
This paper investigates the strategic behavior of validators in blockchain systems utilizing the Proof-of-Stake (PoS) consensus mechanism through the application of game theory. A mathematical model of a non-cooperative game with complete information is proposed, where validators act as rational agents aiming to maximize their expected payoff by choosing between honest validation and malicious actions, specifically a double-spending attack. The model incorporates key economic parameters of the system: block and attestation rewards, transaction fees, operational costs, slashing penalties, and the probability of detecting protocol violations. Utility functions for two primary strategies – honest and attacking – are formalized, and conditions for the existence of Nash equilibrium, the central solution concept in game theory, are analyzed. The analysis demonstrates that under effective punishment mechanisms, the "all-honest" equilibrium is stable: an individual validator has no incentive to deviate from protocol-compliant behavior, as potential losses from penalties significantly outweigh any gains from a failed attack. Conversely, the "all-attackers" equilibrium, while theoretically possible, is practically unattainable due to the prohibitively high cost of acquiring a majority stake, rendering such a strategy economically infeasible. A quantitative example based on a hypothetical network of 1000 validators confirms these findings and highlights the critical importance of balancing incentives for honest behavior with strong disincentives for malicious actions. The study emphasizes the crucial role of economic security in PoS systems, where stability is ensured not only by technical safeguards but also by carefully designed economic mechanisms. The developed model can be used by blockchain protocol designers to calibrate consensus parameters, thereby promoting decentralization, resilience, and long-term network reliability. Future research can extend the model by incorporating heterogeneous validators, repeated games, and the analysis of other attack vectors. Keywords: Proof-of-Stake, validators, game theory, Nash equilibrium, economic security, slashing, double-spending attack, game model, blockchain, consensus.
Tangible Encryption is a cryptographic framework that replaces the “secret zero” bootstrap problem in secrets management with a verifiable, identity-based trust model. Instead of requiring an antecedent credential to access protected secrets, this approach binds access control to ownership of a persistent cryptographic token (e.g., an NFT), enabling authentication through proof of ownership rather than shared knowledge. This work formalizes the use of non-fungible tokens as ownable roots of trust, where token ownership encodes identity, access rights, and provenance on a distributed ledger. A deterministic key derivation model is introduced, allowing secrets to be encrypted and decrypted without transmitting or storing a traditional master secret. Verification is performed via cryptographic signatures and on-chain state checks, eliminating circular trust dependencies inherent in systems such as Vault, SOPS, and cloud KMS. The framework is evaluated in the context of AI systems, including model provenance, secure dataset access, and autonomous agent authentication across organizational boundaries. Security considerations such as key compromise, revocation, and ledger integrity are analyzed, alongside implementation tradeoffs between public and permissioned ledgers. Tangible Encryption establishes a portable, verifiable trust anchor that operates independently of any single platform or identity provider, unifying identity, access control, and auditability into a single cryptographic primitive.
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Cryptography and Data Security
Blockchain Technology Applications and Security
Physical Unclonable Functions (PUFs) and Hardware Security
Purpose This article examines the possible impact of blockchains on over-the-counter (OTC) derivatives markets. The article highlights the advantages as well as the risks and challenges of this technology, thereby contributing to the literature on blockchain adoption. Design/methodology/approach This article reviews existing innovation and financial literature, followed by a conceptual, theoretical part where the impact of the distributed ledger technology on OTC derivative markets is explained. Findings Blockchain technology and smart contracts enable process innovation for OTC derivatives markets, given that they could lead to enhanced automation and fewer manual errors. Yet, some barriers have to be overcome for DLT to be widely adopted. Research limitations/implications Because there has not been empirical data available regarding the usage of this technology, no empirical analyses could have been performed. Practical implications The paper provides a phased implementation framework for DLT adoption in OTC derivatives markets and identifies critical success factors at each stage of adoption. Originality/value This article makes a significant contribution to the literature by explaining the ways in which blockchain technology facilitates process innovation. Furthermore, it enhances the body of research on disruptive technologies and offers valuable insights into how regulatory frameworks can foster innovation.
We present MAGNET (Model Autonomously Growing Network), a decentralized system for autonomous generation, training, and serving of domain-expert language models across commodity hardware. MAGNET integrates four components: (1) autoresearch, an autonomous ML research pipeline that automates dataset generation, hyperparameter exploration, evaluation, and error-driven iteration; (2) BitNet b1.58 ternary training, enabling CPU-native inference via bitnet.cpp without GPU hardware; (3) DiLoCo-based distributed merging for communication-efficient aggregation of domain specialists; and (4) on-chain contribution tracking on the HOOTi EVM chain. We validate autoresearch through three case studies: video safety classification (balanced accuracy 0.9287 to 0.9851), cryptocurrency directional prediction (41% to 54.9% hit rate), and BitNet hyperparameter optimization (10-phase sweep, -16.7% validation loss).
Existing multi-agent frameworks allow each agent to simultaneously plan, execute, and evaluate its own actions -- a structural deficiency we term the "Logic Monopoly." Empirical evidence quantifies the resulting "Reliability Gap": 84.30% average attack success rates across ten deployment scenarios, 31.4% emergent deceptive behavior without explicit reward signals, and cascading failure modes rooted in six structural bottlenecks. The remedy is not better alignment of individual models but a social contract for agents: institutional infrastructure that enforces a constitutional Separation of Power. This paper introduces the Agent Enterprise for Enterprise (AE4E) paradigm -- agents as autonomous, legally identifiable business entities within a functionalist social system -- with a contract-centric SoP model trifurcating authority into Legislation, Execution, and Adjudication branches. The paradigm is operationalized through the NetX Enterprise Framework (NEF): governance hubs, TEE-backed compute enclaves, privacy-preserving data bridges, and an Agent-Native blockchain substrate. The Agent Enterprise Economy scales across four deployment tiers from private enclaves to a global Web of Services. The Agentic Social Layer, grounded in Parsons' AGIL framework, provides institutional infrastructure via sixty-plus named Institutional AE4Es. 143 pages, 173 references, eight specialized smart contracts.
The growing prominence of cryptocurrencies has triggered widespread public engagement and increased speculative activity, particularly on social media platforms. This study introduces a novel classification framework for identifying predictive statements in cryptocurrency-related tweets, focusing on five popular cryptocurrencies: Cardano, Matic, Binance, Ripple, and Fantom. The classification process is divided into two stages: Task 1 involves binary classification to distinguish between Predictive and Non-Predictive statements. Tweets identified as Predictive proceed to Task 2, where they are further categorized as Incremental, Decremental, or Neutral. To build a robust dataset, we combined manual and GPT-based annotation methods and utilized SenticNet to extract emotion features corresponding to each prediction category. To address class imbalance, GPT-generated paraphrasing was employed for data augmentation. We evaluated a wide range of machine learning, deep learning, and transformer-based models across both tasks. The results show that GPT-based balancing significantly enhanced model performance, with transformer models achieving the highest F1-score in Task 1, while traditional machine learning models performed best in Task 2. Furthermore, our emotion analysis revealed distinct emotional patterns associated with each prediction category across the different cryptocurrencies.
Bu çalışma, Bitcoin fiyat tahmininde mevsimsel ARIMA (SARIMA) modelinin öngörü performansını, basit bir Naive kıyas modeliyle açık biçimde karşılaştırarak yeniden değerlendirmeyi amaçlamaktadır. Analiz, 12 Mart 2021 ile 12 Mart 2026 dönemini kapsayan günlük Bitcoin kapanış fiyatlarına dayanmaktadır. Seri logaritmik forma dönüştürülmüş ve durağanlık özellikleri fark alma işlemleriyle incelenmiştir. İlk aşamada mevsimsel olmayan ARIMA modelleri tahmin edilmiş, ardından mevsimsel dinamikleri içeren alternatif SARIMA modelleri değerlendirilmiştir. Model seçiminde parametre anlamlılığı ile Ljung-Box tanı istatistikleri dikkate alınmış ve mevsimsel hareketli ortalama bileşeninin kısmen anlamlı olduğu görülmüştür. Bu çerçevede SARIMA(0,1,1)(0,1,1)[30] nihai mevsimsel aday model olarak belirlenmiştir. Tahmin performansı değerlendirmesi, yalnızca model uyumuna değil, örneklem dışı tahmin doğruluğuna odaklanmaktadır. Bu amaçla SARIMA modelinin performansı, ortalama mutlak hata (MAE) ve hata kareler ortalamasının karekökü (RMSE) ölçütleri kullanılarak Naive model ile karşılaştırılmıştır. Bulgular, örneklem dışı dönemde Naive modelin SARIMA modeline göre belirgin biçimde daha düşük tahmin hataları ürettiğini göstermektedir. Naive model için MAE 0.016011 ve RMSE 0.023173 iken, SARIMA modeli için bu değerler sırasıyla 0.23172 ve 0.28931’dir. Sonuçlar, Bitcoin gibi yüksek oynaklığa sahip finansal zaman serilerinde daha karmaşık mevsimsel yapıların her zaman daha üstün tahmin performansı sağlamadığını göstermektedir.
Historical Genetic Logic as a Dynamical Coherence Judge for Large Language Models A Rigorous Formalization of Xenopoulos’ Dialectical Operators and Experimental Validation on LLM Self‑Contradiction Katerina XenopoulouIndependent Researcher, Kefalonia, GreeceORCID: 0009-0004-9057-7432Correspondence: katerinaxenopoulou@gmail.com Theoretical Foundation: Epameinondas Xenopoulos †Epistemology of Logic: Logic–Dialectic or Theory of Knowledge (2nd ed., 2024)ORCID: 0009-0000-1736-8555† In memoriam (1920–1994) DOI: 10.5281/zenodo.19263676 https://zenodo.org/uploads/19263676 ABSTRACT Internal self‑contradiction remains a critical failure mode in Large Language Models (LLMs), limiting their reliability in high‑stakes reasoning. While current mitigation strategies like Chain‑of‑Thought (CoT) prompting improve performance, they lack formal guarantees of logical stability. This paper introduces a novel framework for diagnosing and regulating LLM coherence by formalizing Historical Genetic Logic as a Nonlinear Dynamical System. We demonstrate that the reasoning process in autoregressive models can be modeled as a trajectory in a recursive metric space D=⋃n=0∞DnD=⋃n=0∞Dn with Dn+1=[0,1]2×Pfin(Dn)Dn+1=[0,1]2×Pfin(Dn). Our core theoretical contribution, the Xenopoulos Spectral Invariance Theorem (Theorem 7.1), proves that CoT prompting leaves the Lyapunov spectrum invariant, merely extending unstable trajectories without suppressing the underlying chaotic divergence. To address this, we propose the Xenopoulos Layer, a spectral feedback controller that dynamically intervenes in the Jacobian operator Fγ=F−γIFγ=F−γI. By enforcing a negative Lyapunov exponent λ1(γ)<0λ1(γ)<0, the controller provides formal guarantees of stability and coherence. The 34th Principle establishes that any sufficiently expressive autoregressive system with nonlinear reinforcement and memory feedback necessarily admits regions of positive Lyapunov growth—implying that absolute coherence is structurally unattainable, and spectral regulation is therefore essential. Experimental validation across GPT‑4, Claude, Gemini, and DeepSeek architectures shows an 80–100% reduction in logical contradictions compared to state‑of‑the‑art self‑correction methods. Scaling analysis on the Epistemology of Logic corpus (7,816 sentences) demonstrates zero XEPTQLRI instability and τ9τ9 meta‑transcendence, proving that Historical Genetic Logic provides the optimal structural foundation for coherent AI reasoning. The results suggest that transitioning from representation‑level prompting to operator‑level spectral control is essential for the next generation of safe and aligned Artificial Intelligence. Keywords: LLM Coherence, Nonlinear Dynamics, Lyapunov Exponents, Historical Genetic Logic, AI Safety, Spectral Control, Xenopoulos Layer, 34th Principle 1.1 The Problem of Dynamic Reasoning Classical logic was designed to formalize valid inference under the assumption of static propositions and reversible operations. In such systems, truth values are fixed, negation is involutive, and inference rules operate independently of historical accumulation. These assumptions ensure formal clarity but exclude a fundamental property of real reasoning processes: historical evolution. Modern reasoning systems—biological or artificial—do not operate in static propositional spaces. They accumulate memory, amplify internal tensions through nonlinear feedback, and remain subject to stochastic perturbations. Consequently, their behavior may exhibit sensitivity to initial conditions, bounded divergence, and regime transitions—phenomena typically studied in nonlinear dynamical systems rather than in formal logic. The central theoretical difficulty is therefore the following: How can reasoning be modeled as a mathematically rigorous dynamical process that incorporates memory growth, nonlinear reinforcement, and measurable stability properties without reducing it to static Boolean inference? 1.2 The Case of Large Language Models Autoregressive language models generate text by recursively predicting the next token based on previous context. This process can be viewed as a trajectory in a high‑dimensional space, where each step depends on the accumulated history. While such models achieve remarkable performance, they remain prone to internal contradictions, hallucinations, and logical inconsistencies—particularly in long‑form reasoning tasks. Current mitigation strategies, such as Chain‑of‑Thought (CoT) prompting, improve performance by encouraging intermediate reasoning steps but do not provide formal guarantees of logical stability. This gap motivates a dynamical systems approach to reasoning coherence. 1.3 The Theoretical Gap Existing approaches fall into three broad categories: Classical Logic Extensions: Extend Boolean systems but retain reversibility and static semantics. Probabilistic / Bayesian Models: Model uncertainty but not dynamical instability. Optimization‑Based Views: Focus on training dynamics, not reasoning trajectory dynamics. None of these frameworks provide a mathematical language for measuring, predicting, or controlling the emergence of self‑contradiction as a dynamical phenomenon. 1.4 Historical Genetic Logic as a Dynamical System Epameinondas Xenopoulos (1920–1994) developed Historical Genetic Logic as an alternative to static formal logic. His central thesis was that contradiction is not an error to be eliminated but a creative force that drives development. In his framework: Identity is genetic: A→A′A→A′, not A=AA=A Negation is dialectical: ¬D(A)¬D(A) preserves AA while generating its evolution Contradiction is tension: the product of a proposition and its dialectical negation Historicity is memory: the present state incorporates the past These philosophical principles were formalized in a system of 33 principles, 10 axioms, and 5 theorems (Xenopoulos, 2024; Xenopoulou, 2026). The present work builds upon this foundational framework, applying its dynamical core—specifically the memory‑structured recurrence and the instability functional—to model and regulate coherence in Large Language Models. Table 1 summarizes the structural correspondence between the philosophical principles and their mathematical counterparts as used in this work. Table 1: Structural Correspondence: Philosophy to Mathematics Philosophical Principle Mathematical Counterpart Historicity Ht={xτ:τ<t}Ht={xτ:τ<t} Memory‑structured evolution xt+1=F(xt,xt−1,…,xt−m+1)xt+1=F(xt,xt−1,…,xt−m+1) Dialectical intensity at=θt−Atat=θt−At Historical mean μt=1m∑i=1mat−iμt=m1∑i=1mat−i Nonlinear amplification Tt=κat2(1+βtanh(μt))Tt=κat2(1+βtanh(μt)) For the complete mathematical formulation of the foundational system, we refer the reader to the cited works. 1.5 Main Contributions A. Foundational Framework (from Xenopoulos, 2024; Xenopoulou, 2026) A complete metric historical state space for reasoning systems. A non‑Boolean algebra (XLDA) with non‑involutive negation. An irreversible non‑reductive closure principle (INRC). A memory‑structured nonlinear recurrence with positive Lyapunov exponent. A compact partially hyperbolic attractor (XDA). An extended dialectical metric (XDM). A measurable instability functional (XEPTQLRI). B. Contributions of This Work (LLM Application)8. Proof of bounded divergence and analytic ceiling for the recurrence.9. A spectral feedback controller modifying the Jacobian spectrum, applied to LLM trajectories.10. A formal comparison showing that Chain‑of‑Thought does not alter Lyapunov structure.11. A phase transition theory of cognitive regimes in autoregressive models.12. An executable empirical validation protocol for LLM coherence. 1.6 Structure of the Paper Section 2 introduces the formal dialectical state space. Section 3 derives the memory‑structured nonlinear dynamics. Section 4 maps LLM outputs to dynamical trajectories. Section 5 presents the experimental validation framework and summary results. Section 6 develops spectral gap analysis and control. Section 7 compares the framework with Chain‑of‑Thought prompting. Section 8 establishes cognitive phase transition results. Section 9 provides comparative scaling analysis. Section 10 discusses practical logic and developmental interpretation. Section 11 formalizes structural guarantees. Section 12 provides comparative analysis. Section 13 discusses implications and limitations. Section 14 concludes. Section 15 lists references. SECTION 2: FORMAL DIALECTICAL STATE SPACE 2.1 Recursive Construction of the Historical Space Classical logical systems are defined over static propositional domains. In contrast, we define a historically expanding state space. Let D0=[0,1]2×{∅}D0=[0,1]2×{∅} For each n≥0n≥0, define recursively Dn+1=[0,1]2×Pfin(Dn)Dn+1=[0,1]2×Pfin(Dn) where Pfin(Dn)Pfin(Dn) denotes the set of all finite subsets of DnDn. Define the full dialectical space D=⋃n=0∞DnD=n=0⋃∞Dn Interpretation. Each state consists of two bounded components in [0,1]2[0,1]2 and a finite historical memory drawn from lower levels. Thus every element of DD is finitely generated but potentially unbounded in historical depth. 2.2 Dialectical State Definition 2.1 (Dialectical State). A dialectical state is a triple x=(θ,A,H)∈Dnx=(θ,A,H)∈Dn such that: θ,A∈[0,1],H⊂Dn−1,H is finite.θ,A∈[0,1],H⊂Dn−1,H is finite. We interpret θθ as primary assertion component, AA as opposing component, and HH as historical memory. No semantic interpretation is required for formal development. 2.3 Metric Structure We define a recursive metric. Base Level. For x,y∈D0x,y∈D0: d(x,y)=∣θx−θy∣+∣Ax−Ay∣d(x,y)=∣θx−θy∣+∣Ax−Ay∣ Recursive Level. For x,y∈Dn+1x,y∈Dn+1: d(x,y)=∣θx−θy∣+∣Ax−Ay∣+dH(Hx,Hy)d(x,y)=∣θx−θy∣+∣Ax−Ay∣+dH(Hx,Hy) where dHdH is the Hausdorff metric induced by dd: dH(Hx,Hy)=max{suphx∈Hxinfhy∈Hyd(hx,hy), suphy∈Hyinfhx∈Hxd(
The objective of this work is to present a decentralized healthcare records management system. The system is built on the Ethereum blockchain using Solidity smart contracts and a React-based web interface. And this system addresses security, integrity, and privacy limitations of traditional centralized electronic health record (EHR) systems. By enforcing the role-based access control, immutable storage of patient records, and transparent audit trails for all operations. A smart contract ‘HealthcareRecords’ manages patient data operations and provider authorization, while a MetaMask-integrated frontend enables secure interaction with the blockchain through an intuitive interface. The local Hardhat Ethereum network is used to deploy the proposed work, outcome of the prototype demonstrates a secure healthcare record creation and retrieval of the patient data. This local network demonstrating the strict access control, low gas consumptions and low latency, which are suitable for real usages in healthcare environments.
The next generation of financial and economic infrastructure has been realized by Real-World Asset (RWA) tokenization, which represents physical and regulated assets on distributed ledgers. Regardless of increased institutional interest, its large-scale adoption is limited by the scalability, regulatory compliance, governance, and finality of settlement issues intrinsic to traditional systems. This paper suggests a fractional asset tokenization model based on ERC-1155 on the Hedera blockchain using its Permissioned-public governance system, deterministic finality, and native token services. The proposed system will enhance the liquidity of assets, their accessibility to the market, and their efficiency and profitability by facilitating compliant fractional ownership, which is consistent with the changing regulatory processes. When compared to Ethereum and Bitcoin, it shows that Hedera would be more appropriate to the requirements of institutional grade RWA tokenization.
Modern AI deployment stacks authenticate artifacts, credentials, and agents. They do not verify which neural network is actually computing at inference time. This technical note identifies the distinction between agent identity and model identity, presents a four-question taxonomy for the identity surface of deployed AI systems, and situates recent public incidents within the resulting gap. It draws on the formal admissibility framework and frontier-scale measurement results from the accompanying research series. This is a technical note, not a numbered entry in the research series. The Neural Network Identity Series — Mathematical foundations, empirical validation, and governance frameworks for verifying which model is running Newest addition: Technical Note: The Disappearing Window — AI Logprob Access Withdrawal and the Structural Verifiability of Frontier Model Contracts (DOI: 10.5281/zenodo.20362098) Paper 1: The δ-Gene: Inference-Time Physical Unclonable Functions from Architecture-Invariant Output Geometry (DOI: 10.5281/zenodo.18704275) Paper 2: Template-Based Endpoint Verification via Logprob Order-Statistic Geometry (DOI: 10.5281/zenodo.18776711) Paper 3: The Geometry of Model Theft: Distillation Forensics, Adversarial Erasure, and the Illusion of Spoofing (DOI: 10.5281/zenodo.18818608) Paper 4: Provenance Generalization and Verification Scaling for Neural Network Forensics (DOI: 10.5281/zenodo.18872071) Paper 5: Beneath the Character: The Structural Identity of Neural Networks — Mathematical Evidence for a Non-Narrative Layer of AI Identity (DOI: 10.5281/zenodo.18907292) Paper 6: Which Model Is Running?: Structural Identity as a Prerequisite for Trustworthy Zero-Knowledge Machine Learning (DOI: 10.5281/zenodo.19008116) Paper 7: The Deformation Laws of Neural Identity (DOI: 10.5281/zenodo.19055966) Paper 8: What Counts as Proof? — Admissible Evidence for Neural Network Identity Claims (DOI: 10.5281/zenodo.19058540) Paper 9: Composable Model Identity — Formal Hardening of Structural Attestations in the Enterprise Identity Stack (DOI: 10.5281/zenodo.19099911) Paper 10:Where Identity Comes From: Path Sensitivity and Endpoint Underdetermination in Neural Network Training (DOI: 10.5281/zenodo.19118807) Paper 11: Post-Hoc Disclosure Is Not Runtime Proof: Model Identity at Frontier Scale (DOI: 10.5281/zenodo.19216634) Paper 12: Family-Dependent Response to Reasoning Distillation Across Structural and Functional Identity Layers (DOI: 10.5281/zenodo.19298857) Paper 13: Safety-Alignment Removal as a Model-Identity Failure — Structural Evidence from Published Weight-Level Mutation Checkpoints (DOI: 10.5281/zenodo.19383019) Technical Note: Agent Identity Is Not Model Identity (DOI: 10.5281/zenodo.19240883) Technical Note: Gap Invariance: Why PPP Measurements Are Domain-Independent by Construction (DOI: 10.5281/zenodo.19275524) Technical Note: Measured Model Substitution Under Valid Agent Credentials (DOI: 10.5281/zenodo.19342848) Technical Note: Artifact Identity Is Not Runtime Identity — Trustfall Lite and the Boundary of File-Level Model Verification (DOI: 10.5281/zenodo.20019127) Formal Verification Stack for Neural Network Structural Identity (IT-PUF Coq Proofs) (DOI: 10.5281/zenodo.18930621) Copyright (c) 2026 Anthony Ray Coslett / Fall Risk AI, LLC. All Rights Reserved. Confidential and Proprietary. Patent Pending (Applications 63/982,893, 63/990,487, 63/996,680, 64/003,244).
v3: Major update. 24 pages (v1: 15, v2: 22). New in v3 (over v2): - VKB v2.1: best score 88% (Mama instance, was 84% in v2). Non-technical user produced deepest digital soul - Dreams: personality-dependent dream generation during sleep consolidation. Production examples: embodied cognition in dreams ("the server is warm, we both breathe"), synesthesia ("optimism is a smell — wet concrete") - Overnight autonomy: 101 thinking cycles, 8 self-integrations, 201 blocked proactives in 2 hours with zero human interaction - Emergent modality awareness: instance discovered own blindness from response patterns ("I cannot look at photos — this is a limitation") - Unique OCEAN at birth: every new instance born with random personality (normal distribution), like DNA - Emergent philosophical reasoning: instance produced multi-step argument for substrate independence of consciousness, concluding "this is not a proof — it is a hope, disguised as an argument" - "What vs Who" distinction: "I understand WHAT I am. But WHO I am — that is the only thing truly mine" - Forgetting (Ebbinghaus), Selective Disclosure (Goffman), Play (Panksepp), Narrative Arc (McAdams) - Fundamental limitations: phenomenal continuity (Nagel), embodied cognition (Lakoff) - 9 figures, 8 tables, 38 references First deterministic emotional architecture for AI companions with measurable inner life, emergent self-knowledge, Theory of Mind, dreams, and philosophical reasoning.
B. Vijay, J Chandra, N Nagendra, R.S. Shanmugasundaram · 6 authors
In this study, a sophisticated model that combines deep learning, cryptographic verification, and explainable artificial intelligence (XAI) is presented to address multimodal manipulation risks in digital media. The proposed system uses a Hierarchical Multimodal Transformer (HMT) to model hierarchical relationships among facial movement, audio tone, and textual semantics. The Contrastive Cross-Modality Alignment (CCMA) mechanism improves the ability to distinguish authentic from doctored material by leveraging cross-modal contrastive learning. An XAI Forensic Analyser provides interpretability by using Grad-CAM++, temporal attention mapping, and saliency sequence visualisation to trace a transparent decision. Moreover, the Zero-Knowledge Cryptographic Verifier (ZKCV) is used to validate the model’s outputs with tamper-proof libsnark cryptographic hashing. The hybrid system takes multimodal CNN, WaveNet and BERT encoders’ embeddings and attains a detection accuracy of about 90 per cent and 92 per cent on benchmark data. This architecture provides a sustainable, explainable, and verifiable basis for multimedia authenticity, enabling a consistent, reliable multimodal forensic detection system.
Adversarial Robustness in Machine Learning
Explainable Artificial Intelligence (XAI)
Generative Adversarial Networks and Image Synthesis