This paper presents the philosophical foundations of NeuroGraph, a distributed ledger protocol that replaces classical Byzantine Fault Tolerant voting with emergent consensus via a Neural Directed Acyclic Graph. Rather than treating consensus as something that must be explicitly negotiated through voting rounds, NeuroGraph treats consensus as an emergent property of the network’s structure. Inspired by biological neural systems, the protocol enables global agreement to arise from many simple local interactions, eliminating the need for leaders, committees, or formal voting. This document explores the philosophical shift that underpins the NeuroGraph approach and its implications for decentralized computing.
Current mobile System-on-Chip (SoC) architectures suffer from a fundamental ”Reactive Polling Tax,” where high-level software must frequently interrupt low-power hardware states to query rawsensor telemetry. This paper introduces the Contextual Neural Bus (CNB), a dedicated, asynchronous hardware-level interconnect designed to shift context awareness from volatile software cycles to deterministic silicon logic. By utilizing a decentralized multi-modal fusion layer, the CNB generates Universal Intent Tokens (UITs)—64-bit cryptographic primitives that represent verified user states at the physical layer. Unlike legacy co-processors that merely buffer data, the CNB integrates a Hardware-Resident Zero-Knowledge Proof (ZKP) Generator to provide mathematical certainty of user intentwhile physically isolating raw biometric and environmental telemetry within a secure silicon enclave. Preliminary simulations using a digital-twin SoC model indicate a 90.8% reduction in interrupt driven power consumption, effectively achieving ”Energy-Neutral Privacy” by utilizing the resulting power surplus to offset cryptographic overhead. Furthermore, the architecture introduces Predictive L3 Cache Pre-warming, which anticipates user interactions to virtually eliminate ”cold-start” application latencies. By anchoring proactive computing in the deterministic reliability of silicon, this work establishes a scalable, privacy-first path toward zero-latency, energy-autonomous mobile ecosystems.
This paper proposes a thermodynamic architecture for distributed ledger computing based on the concepts of Logical Grounding and phase-coherent synchronization. Conventional computing dissipates computational entropy as waste heat and relies on amplitude-based control and clock synchronization, which limits energy efficiency and scalability. The proposed framework treats unused computational resources as logical entropy sinks and redirects entropy flow through potential gradients into these regions. The absorbed signals are transformed into deep resonance signals that maintain global synchronization via phase coherence rather than amplitude control. By combining logical grounding, negative-pressure information circulation, and phase-coherent synchronization engines, the architecture suggests a new entropy-aware computing paradigm that may improve energy efficiency, distributed scalability, and system resilience in large-scale computing environments.
Recently, there has been a significant discourse in the AI community regarding "Hierarchical Reasoning LLMs," which attempt to categorize and optimize probabilistic generation tasks to reduce computational overhead. While such hierarchical inference structures optimize generation speed and coherence, they fundamentally fail to resolve the core structural crises of modern Generative AI: inevitable hallucination and extreme structural energy consumption (GPU lock-in). This paper introduces the "Hierarchical Stateless Key Generation" (HSKG) and the Mersenne Stateless Architecture, challenging the premise of neural network 'reasoning.' Instead of storing data within 820GB of neural weights and using probabilistic matrix multiplication, HSKG mathematically maps 'Absolute Truth' data into a 4096-dimensional Mersenne Prime Lattice. During query resolution, the system simply retrieves a 4KB Phase Coordinate and instantaneously materializes the data in RAM, only to vaporize it when the session terminates. By abandoning the "search and compute" paradigm for "coordinate retrieval," HSKG enforces a mathematical 0% hallucination rate, 0-byte persistent storage, and sub-0.01% GPU utilization, establishing a definitive paradigm for enterprise Zero-Trust knowledge systems. This paper explicitly defines the term "Hierarchical Stateless" to contrast with the probabilistic "Hierarchical Reasoning" of contemporary LLMs, establishing a rigorous mathematical protocol for deterministic, zero-hallucination data materialization without persistent models or physical data transfer. * Version 2.0 Update: Added section 7.A (Empirical Validation via DevTools: The 0-Byte Payload Proof). [Version 4.0 Update (Mar 2, 2026)] Formally established the "Four-Pillar Verification Metrics" table to empirically prove the 0-Byte Payload and Minimum Kolmogorov Descriptive Length. Inserted Section VIII: Disrupting Existing Paradigms (Architectural Supremacy Matrix), demonstrating the superiority over FIDO2/WebAuthn and Zero-Knowledge Proofs (ZKP). Included Supplementary Material: Independent 3rd-Party Forensic Audit Report by Claude 4.6 verifying 100% Stateless Zero-Payload execution.
The traditional paradigm of centralized artificial intelligence systems often faces significant challenges when confronted with complex, dynamic, and uncertain real-world environments. These challenges include issues of scalability, resilience to partial failures, and adaptability to unforeseen circumstances. This paper introduces the concept of "Emergent Ensembles," a transformative approach rooted in self-organizing collective intelligence, designed to address these limitations for adaptive AI systems. Drawing inspiration from natural collective behaviors such as ant colonies and bird flocks, Emergent Ensembles propose a decentralized architecture where numerous autonomous agents collaborate, adapt, and self-organize through local interactions to achieve complex global objectives. The framework emphasizes key attributes such as task generalization, collective resilience, scalability, and self-assembly, enabling systems to dynamically reconfigure their structure, behavior, and scale during inference. We explore the underlying principles of self-organization, decentralized decision-making, adaptive learning, and context-rich communication protocols, such as gossip mechanisms, that facilitate the emergence of intelligent global behavior from simple local rules. By integrating AI-driven adaptive nodes capable of autonomous power adjustment and leveraging multi-layer perceptron models for local decision-making, these ensembles demonstrate enhanced connectivity, robustness, and energy efficiency. This work outlines a conceptual framework for designing, analyzing, and engineering resilient, scalable, and adaptive AI systems, paving the way for innovative applications in fields ranging from robotics and optimization to environmental monitoring and smart cities. The ultimate goal is to foster AI systems that can exhibit robust performance and self-sustainment in highly dynamic and unpredictable real-world scenarios, addressing computational bottlenecks and ethical considerations inherent in decentralized AI.
M.Z. Haider, M.U. Ghouri, Tayyaba Noreen, M. Salman
Blockchain systems face persistent challenges of scalability, latency, and energy inefficiency. Existing consensus protocols such as Proof-of-Work (PoW) and Proof-of-Stake (PoS) either consume excessive resources or risk centralization. This paper proposes \textit{Proof-of-Spiking-Neurons (PoSN)}, a neuromorphic consensus protocol inspired by spiking neural networks. PoSN encodes transactions as spike trains, elects leaders through competitive firing dynamics, and finalizes blocks via neural synchronization, enabling parallel and event-driven consensus with minimal energy overhead. A hybrid system architecture is implemented on neuromorphic platforms, supported by simulation frameworks such as Nengo and PyNN. Experimental results show significant gains in energy efficiency, throughput, and convergence compared to PoB and PoR. PoSN establishes a foundation for sustainable, adaptive blockchains suitable for IoT, edge, and large-scale distributed systems.
This project presents QoreChain, a novel Layer 1 blockchain architecture that addresses two critical challenges facing distributed ledger technology: vulnerability to quantum computing attacks and inefficient network resource allocation. As cryptographically relevant quantum computers are projected to emerge within 5-10 years, current blockchain infrastructures relying on elliptic curve cryptography face existential security threats. Simultaneously, existing networks struggle with scalability, cross-chain interoperability, and intelligent resource optimization. QoreChain introduces a quantum-native security architecture implementing ML-KEM (Kyber-1024) for post-quantum key exchange with migration pathways to Dilithium and Falcon signatures. Our hybrid classical-PQC bridge protocol enables seamless cryptographic migration without network disruption while maintaining backward compatibility—a capability absent in current blockchain platforms. Beyond quantum resistance, QoreChain integrates artificial intelligence at the protocol level through an Adaptive Intelligence Layer that performs dynamic transaction routing, predictive resource allocation, and cognitive consensus optimization, achieving demonstrable performance improvements: 5,914+ transactions per second with sub-second finality, 40% reduction in finality times during peak loads, and 60% reduction in cross-chain swap slippage through AI-driven liquidity positioning. The architecture comprises three synergistic innovations: (1) a multi-layer scalability framework with AI-driven chain selection routing transactions across main chain, sidechains, and paychains based on value and computational requirements; (2) the QoreChain Consensus Algorithm (QCA) extending Combined Proof of Stake with reputation-weighted validator selection and temporal consensus layering enabling parallel consensus sessions across different time horizons; and (3) comprehensive developer tooling including natural language smart contract generation with cross-chain compilation, automated vulnerability detection using predictive AI, and voice-first accessibility features. We demonstrate QoreChain's practical applicability through integration specifications for enterprise environments (financial services, defense, healthcare), IoT deployments with hardware-optimized lightweight cryptography for resource-constrained devices, and universal cross-chain connectivity supporting Ethereum, Solana, TON, BSC, Avalanche, and Cosmos ecosystems via IBC, LayerZero, and proprietary protocols. Performance benchmarks, security proofs, and economic sustainability models validate QoreChain's viability as future-proof blockchain infrastructure for the post-quantum era.Abstract content goes here
Brain–Computer Interfaces (BCIs) represent a transformative paradigm in human–machine interaction, enabling direct communication between neural signals and external devices. They hold immense promise in domains such as medical neuroprosthetics, defense communication, and immersive gaming. However, the neural data they process is highly sensitive, and current BCI frameworks that rely on centralized servers and traditional encryption are vulnerable to data breaches, manipulation, and the emerging threats of quantum decryption. These limitations highlight the urgent need for secure, privacy-preserving, and resilient architectures for BCI communication. To address these challenges, this paper introduces NeuroGuard, a blockchain-based framework enhanced with Post-Quantum Cryptography (PQC) algorithms—CRYSTALS-Kyber for secure key exchange and Dilithium for digital signatures—combined with Zero-Knowledge Proofs (ZKPs) for lightweight device authentication. Neural data packets are logged in a decentralized ledger, ensuring immutability, transparency, and tamper-proof communication. A prototype system was implemented using an EEG-based BCI headset with edge preprocessing and blockchain-secured communication. Experimental results demonstrate a 31% improvement in attack resistance, 22% reduction in latency, and complete removal of central points of failure compared to traditional BCI security models. The novelty of NeuroGuard lies in integrating PQC, blockchain, ZKPs, and edge intelligence into a unified BCI security architecture, paving the way for future quantum-resilient neural communication systems.
Blockchain sharding is a promising approach to improving system scalability. However, traditional designs rely on lock-based cross-shard commit protocols, which introduce significant performance bottlenecks due to repeated on-chain communication and consensus. The emergence of complex cross-shard contracts further exacerbates these issues. Although recent off-chain execution models reduce on-chain overhead by decoupling contract execution from consensus, they still incur high communication costs and struggle to maintain state consistency. To address these challenges, this paper presents a sharding framework that seamlessly integrates on-chain and off-chain processing. By leveraging Trusted Execution Environments (TEEs), the framework enables secure and efficient off-chain execution of cross-shard smart contracts. It incorporates an off-chain execution hub for verifiable contract execution and a state-aware cross-shard commit protocol to guarantee correctness. Furthermore, a genetic algorithm-based contract-migration strategy dynamically reduces cross-shard interactions. Prototype evaluations show that the proposed framework significantly outperforms mainstream sharding solutions, achieving at least 2.1× higher throughput and reducing cross-shard transaction latency by over 52.6%.
Cross-chain decentralized finance ( DeFi) applications enable the seamless transfer of assets and data across diverse blockchain networks, thereby enhancing liquidity and user flexibility. However, the distributed nature and inter-operability of these networks introduce significant security challenges, ranging from double-spending and smart contract vulnerabilities to mismatches in consensus mechanisms and privacy risks. In this survey, we introduce a novel review about the recent literature that explores these multifaceted challenges. We categorize the research based on attack vectors and the security requirements of cross-chain systems, discuss state-of-the-art solutions including advanced cryptographic techniques and rigorous auditing practices, and outline open problems that warrant further explorations. Our work provides a comprehensive analysis of the current security landscape in cross-chain DeFi and emphasizes future research directions.
Physical Unclonable Functions (PUFs) and Hardware Security
Non-fungible tokens (NFT) have recently become a popular method of tokenizing \& commercializing personal artifacts. Designing NFTs requires selecting different blockchain-based consensus models, encryption techniques, and distribution mechanisms. Existing NFT design techniques use computationally complex encryption models like Elliptic Curve Cryptography (ECC), Advanced Encryption Standard (AES), etc., which restricts their general-purpose usability, limiting their scalability for real-time use cases. To overcome this drawback, while maintaining high security, this text proposes a design of a lightweight, restrictive non-fungible token based on Practically Unclonable Functions (PuFs) via image signature patterns. The proposed model initially collects context-specific information sets about the entity that needs tokenization and uses this information to generate restrictive hash sets. These hash sets are passed through a customized PuF model, which generates image-like hash signatures. The generated hash signatures are iteratively embedded into unique images, which are fused via a dual visual encryption-decryption process. The encryption process generates 2 image sets, for distribution among the buyer \& seller, while the decryption process aggregates these image sets to form a single file token. These tokens are passed through another encryption-decryption-based validation process while reselling operations. Due to use of PuFs and restrictive hash sets, the proposed model is capable of deployment for low-power IoT applications and can be scaled for general-purpose scenarios. The proposed model was tested on different NFT use cases, and showcased 10.4% lower processing delay, 8.3% lower energy consumption during selling, and 4.9% lower energy consumption during reselling processes. The tokens generated via this model were also tested under different attack types, and similar efficiency levels were observed under real-time scenarios.
Open access
Physical Unclonable Functions (PUFs) and Hardware Security
The rising energy demands of large data centers call for energy-efficient AI training methods. Federated Learning (FL), a decentralized paradigm, offers a solution by enabling model training across distributed devices without centralizing sensitive data. This review explores FL's integration with distributed data centers to achieve energy efficiency, analyzing methods like federated averaging and energy-aware protocols to minimize resource use. It highlights techniques such as model compression, quantization, and adaptive FL to reduce on-device computation while maintaining performance. Practical implementation is discussed through tools like TensorFlow Federated and PySyft, with case studies from healthcare, finance, and IoT showcasing cost reductions and sustainability. Future research directions include combining FL with edge computing and low-power AI hardware, emphasizing FL's potential for scalable, sustainable AI.
Zacharoula Sereti, Emmanouil Mavrikos, Christos Cholevas, George E. Tsekouras
Non-fungible tokens (NFTs) are unique digital assets powered by blockchain technology, enabling secure and decentralized ownership and monetization across diverse industries. However, their energy consumption is primarily linked to energy-intensive consensus mechanisms and has raised significant environmental concerns. This survey provides a comprehensive analysis of the evolution and use of token standards and blockchain workflows in developing green NFT technologies, emphasizing the transition to energy-efficient consensus mechanisms. By seamlessly blending technological acumen with a discerning gaze, the current analysis suggests that, apart from the blockchain consensus mechanisms, the environmental impact of NFTs should also be investigated and linked to certain blockchain factors such as interoperability, scalability, sustainability, and Layer-2 scaling solutions. As such, the current endeavor offers a perspective on the symbiotic relationship between blockchain and NFTs by identifying pathways to balance innovation with environmental stewardship. Finally, this paper offers valuable insights into the role of green NFTs in fostering sustainable digital economies by exploring under-represented applications in various economic and industrial domains.
Xander Pottier, Thomas De Ruijter, Jonas Bertels, Wouter Legiest · 6 authors
The Multi-Scalar Multiplication (MSM) is the main barrier to accelerating Zero-Knowledge applications. In recent years, hardware acceleration of this algorithm on both FPGA and GPU has become a popular research topic and the subject of a multi-million dollar prize competition (ZPrize). This work presents OPTIMSM: Optimized Processing Through Iterative Multi-Scalar Multiplication. This novel accelerator focuses on the acceleration of the MSM algorithm for any Elliptic Curve (EC) by improving upon the Pippenger algorithm. A new iteration technique is introduced to decouple the required buckets from the window size, resulting in fewer EC computations for the same on-chip memory resources. Furthermore, we combine known optimizations from the literature for the first time to achieve additional latency improvements. Our enhanced MSM implementation significantly reduces computation time, achieving a speedup of up to x12.77 compared to recent FPGA implementations. Specifically, for the BLS12-381 curve, we reduce the computation time for an MSM of size 224 to 914 ms using a single compute unit on the U55C FPGA or to 231 ms using four U55C devices. These results indicate a substantial improvement in efficiency, paving the way for more scalable and efficient Zero-Knowledge proof systems.
Imran Hussain, Hafiz Ashiq Hussain, Nasim Ullah, Stanislav Mišák
An evolving energy system with a dispersed infrastructure may not be compatible with traditional centralized optimization and management techniques. Blockchain, a peer-to-peer immutable distributed ledger technology, has the potential to significantly contribute to the management of emerging trends of decentralized power networks. However, complex optimization problems associated with the decentralized power grid are poorly integrated into the existing blockchain applications. Here, we suggest Proof of Inherent Intelligence (PoII), a novel prosumer-centric consensus mechanism designed to assist multi-interest party optimization challenges of the distributed power grid. We demonstrate PoII’s operation and performance with comprehensive mathematical modeling of energy pool-market trading and scheduling optimization problems. The efficiency of the proposed framework is evaluated against the existing blockchain applications for peer-to-peer energy transactions in terms of latency, throughput, tolerance against adversaries, vulnerability, and optimization capabilities. A thorough case study of the power grid that includes thermal, wind, and intermittent generation sources is presented to assess the effectiveness of the proposed consensus mechanism. Power demand, reserves, trading, and scheduling scenarios in both the day-ahead and balancing markets are among the peer-to-peer energy transactional elements that are assessed to support the efficacy of the suggested consensus approach.
With the advances of quantum computing the security of existing cryptographic frameworks is increasingly at risk. Accordingly, in the present study, we investigate the integration of post-quantum cryptographic algorithms into Hyperledger Fabric, a blockchain framework, to safeguard it against emerging quantum threats. To this end, a modified Cryptogen tool was developed to generate X.509 certificates with both classical and post-quantum cryptographic keys. Furthermore, using tools like Hyperledger Caliper and Prometheus for empirical analysis, we demonstrate that this hybrid approach effectively strengthens security without affecting system performance. These results not only improve the security of Hyperledger Fabric, but also offer a practical guide for adding post-quantum cryptography to blockchain technologies.
With the rapid development of Decentralized Finance (DeFi) and Real-World Assets (RWA), the importance of blockchain oracles in real-time data acquisition has become increasingly prominent. Using cryptographic techniques, threshold signature oracles can achieve consensus on data from multiple nodes and provide corresponding proofs to ensure the credibility and security of the information. However, in real-time data acquisition, threshold signature methods face challenges such as data inconsistency and low success rates in heterogeneous environments, which limit their practical application potential. To address these issues, this paper proposes an innovative dual-strategy approach to enhance the success rate of data consensus in blockchain threshold signature oracles. Firstly, we introduce a Representative Enhanced Aggregation Strategy (REP-AG) that improves the representativeness of data submitted by nodes, ensuring consistency with data from other nodes, and thereby enhancing the usability of threshold signatures. Additionally, we present a Timing Optimization Strategy (TIM-OPT) that dynamically adjusts the timing of nodes' access to data sources to maximize consensus success rates. Experimental results indicate that REP-AG improves the aggregation success rate by approximately 56.6\% compared to the optimal baseline, while the implementation of TIM-OPT leads to an average increase of approximately 32.9\% in consensus success rates across all scenarios.
In the era of Web3, decentralized technologies have emerged as the cornerstone of a new digital paradigm. Backed by a decentralized blockchain architecture, the Web3 space aims to democratize all aspects of the web. From data-sharing to learning models, outsourcing computation is an established, prevalent practice. Verifiable computation makes this practice trustworthy as clients/users can now efficiently validate the integrity of a computation. As verifiable computation gets considered for applications in the Web3 space, decentralization is crucial for system reliability, ensuring that no single entity can suppress clients. At the same time, however, decentralization needs to be balanced with efficiency: clients want their computations done as quickly as possible. Motivated by these issues, we study the trade-off between decentralization and efficiency when outsourcing computational tasks to strategic, rational solution providers. Specifically, we examine this trade-off when the client employs (1) revelation mechanisms, i.e. auctions, where solution providers bid their desired reward for completing the task by a specific deadline and then the client selects which of them will do the task and how much they will be rewarded, and (2) simple, non-revelation mechanisms, where the client commits to the set of rules she will use to map solutions at specific times to rewards and then solution providers decide whether they want to do the task or not. We completely characterize the power and limitations of revelation and non-revelation mechanisms in our model.