This paper specifies the Inference Battery Token (IBT), an ERC-20 on Base that mints when the inference orchestrator verifies a failed AI response, compensating the subscriber and capturing a structured DPO preference pair in one operation. Minting occurs through two pathways: automatic spot-checks that re-execute sampled requests on trusted nodes, and subscriber-initiated flags confirmed through the same re-execution pipeline. Each verified failure mints tokens proportional to the compute cost wasted, ranging from 1 IBT for embeddings to 20 IBT for extended reasoning. Subscribers redeem IBT for battery credits, permanently burning tokens. A genesis allocation of 10,000,000 IBT bootstraps exchange liquidity, network operations, community grants, and early investor capital with 12-month vesting. Because minting depends on AI failure rates and redemption increases with adoption, circulating supply contracts as models improve. Contributors who serve compute earn platform credits and elect payouts in USD or IBT, with a 15% bonus for token election. Hardware contributors may also earn IBT by generating zero knowledge proofs for the verification system during idle GPU windows. Revenue from selling accumulated failure records to frontier labs through the data access tiers described in the companion paper provides fundamental backing for token value.
Zhang Dayong, Nur Haliza Abdul Wahab, Juniardi Fadila, Arafat Al-Dhaqm · 8 authors
Practical Byzantine Fault Tolerance (PBFT) serves as a cornerstone consensus protocol for distributed systems. However, its inherent limitations, including quadratic communication complexity, scalability bottlenecks, and insufficient privacy protection, hinder its applicability in large-scale and privacy-sensitive environments. This study presents a systematic and comprehensive review of cryptographic advancements aimed at addressing these challenges. By analyzing peer-reviewed literature from 2015 to 2025, we demonstrate that the integration of Verifiable Random Function (VRF) and Boneh–Lynn–Shacham (BLS) aggregate signatures effectively reduces PBFT's communication complexity from O(N²) to O(N) or even O(logN), significantly enhancing scalability and reducing consensus latency. Moreover, advanced cryptographic schemes such as zero-knowledge proofs, homomorphic encryption, group signatures, ring signatures, hash ring, threshold signatures, attribute-based Encryption and lattice-based cryptography are shown to substantially strengthen consensus efficiency, privacy preservation and node security. Despite these improvements, trade-offs arise in terms of computational overhead and system complexity. The findings provide critical insights into the synergetic application of cryptography within PBFT-based systems and offer future directions for constructing scalable, secure, and privacy-preserving distributed architectures, particularly in Internet of Things and other resource-constrained scenarios.
Modern decentralized computing relies on two core architectural pillars: peer-to-peer (P2P) network topographies and cryptographic distributed ledgers. While early logical overlays prioritized file distribution without structural validation, contemporary blockchain deployments demand a stateful, highly adversarial communication layer. This paper provides an exhaustive analysis of the structural intersection between P2P routing mechanisms and consensus verification. We dissect the operational evolution from stateless distributed file indexes to stateful, trustless ledgers. Furthermore, we model the mathematical dynamics of epidemic data propagation, isolate systemic network-layer threat vectors such as boundary routing manipulation and node isolation attacks, and critique structural solutions implemented to scale data dissemination without inducing centralization.
This thesis addresses two research areas: scalable distributed ledgers for micro-transactions, and the automation of assembly planning in manufacturing industries. Established blockchain solutions are robust and reliable. Being distributed and decentralized, they avoid a single point of failure, and fault-tolerant consensus mechanisms ensure that the system works as intended even when some participants are faulty or malicious. However, their main weakness is scalability. The two most popular and well-known blockchain solutions, Bitcoin and Ethereum, require all nodes to store all transactions, and their transaction throughput is far too low to compete with traditional, centralized transaction processing systems. To improve scalability, systems have been developed that split the network nodes into groups that can process transactions in parallel, a technique known as sharding. We propose a sharded system called ScaleGraph that uses a novel architecture with one transaction per block and one shard per account, designed to maximize parallelism. The design is inspired by concepts from distributed hash tables, particularly to define shards based on a logical distance metric for node IDs and account IDs. Nodes store and process only transactions involving those accounts that are close to the node according to the distance metric. This greatly reduces the storage burden on each node and allows any number of transactions involving distinct accounts to be validated in parallel. We also design a new cross-shard transaction commit protocol for this architecture. The protocol offers global serializability and inevitable atomic commit, without the need for an abort path. This is achieved using only shard-local consensus and certificate exchange, rather than global or joint cross-shard consensus. Manufacturing is a highly complex process in many industries and involves many different planning problems where increasing automation has the potential to make manufacturing more efficient. This thesis presents a proof-of-concept solution to the kitting layout problem, where a list of parts has to be placed on a kitting wagon for delivery to an assembly line station. However, some problems have proven difficult to automate in practice, despite decades of research. One such problem, assembly line balancing, is analyzed in depth. We identify fundamental challenges that make the goal of complete automation implausible in some industries, such as automotive manufacturing. Human intervention is thus unavoidable, suggesting that bridging the gap between theory and practice requires decision support systems for assisted, iterative, and interactive planning. The thesis also includes preliminary work on the product sequencing problem, limited to framing the use case, assumptions, and requirements. Subsequent ongoing work suggests strong parallels to assembly line balancing, indicating that the identified challenges and possibilities for addressing them reflect a broader pattern in industrial planning automation.
The current internet architecture was fundamentally designed for deterministic data packet transport and applicationlevel request-response interactions, not for the semantic exchange, verification, governance, and replay of autonomous machine reasoning. As autonomous AI agents scale globally to orchestrate critical infrastructure, medical networks, corporate supply chains, and legal workflows, traditional integration patterns create structural bottlenecks. These limitations introduce severe risks of cognitive fragmentation, black-box opacity, and cascade errors across organizational boundaries. This paper proposes the Cognitive Internet Layer (CIL), a protocol-oriented overlay architecture positioned above conventional network transport and below autonomous AI applications. CIL introduces the Reasoning Exchange Protocol (REP) to route structured decision envelopes containing reasoning metadata rather than raw payloads. To resolve real-world deployment trade-offs, the framework integrates Zero-Knowledge Proofs (ZKPs) for privacy-preserving verification and a Tiered Execution Architecture to isolate highthroughput edge transactions from deep asynchronous multi-agent consensus validation.
Enterprises are rapidly shifting from human-interpreted dashboards to Autonomous Analytical Entities (AAE) that execute decisions directly on production systems. This transition introduces a new failure mode—Agentic Divergence—where decentralized agents act on misaligned, drifted, or out-of-scope data products and metadata, leading to high-impact errors at scale. This paper proposes the Autonomous Analytical Coherence (AAC) framework, centered on an Analytical Control Plane (ACP) that inserts a mandatory, machine-enforced governance layer between AAEs and decentralized data products. The ACP mandates Agentic Data Contracts (ADC) as runtime dependencies and enforces Kullback–Leibler (KL) divergence-based drift checks within Trusted Execution Environments (TEE) to safeguard both analytical coherence and data sovereignty. Simulation-based experiments across finance and logistics workloads indicate that AAC reduces erroneous autonomous transactions by 77% compared with uncoordinated agent deployments, with only a 25 ms median increase in latency. These results demonstrate that treating governance as a runtime dependency is a practical path toward safe, high-stakes autonomous analytics in enterprise data meshes.
Card-based zero-knowledge proof (ZKP) protocols allow a prover to convince a verifier that it knows a witness of a given statement, without revealing any information, using a physical deck of playing cards. Previous studies have focused on puzzles with a specific connected component, such as a simple cycle and a polyomino. In this study, we propose a unified approach to handle a family of connected components, including a tree, path, cycle, and polyomino. This approach achieves this verification in O(mn) steps relative to a given grid size m × n. Using this approach, we construct a card-based ZKP protocol for Nurimeizu, where the goal is to find the shortest path on a given grid.
Ioannis Tzannetos, Danai Balla, Aris Pagourtzis, Vassilios Vescoukis
Non-fungible tokens (NFTs) have created vibrant digital marketplaces where unique assets are exchanged across domains such as art, gaming, and music. While current infrastructures are optimized for pairwise, currency-backed trades, they provide limited support for multi-party swaps of indivisible assets based on user preferences. In practice, liquidity is not always desirable—participants may wish to exchange directly for assets they deem equally valuable, bypassing auctions or currency markets. In this paper, we propose BarterSwap, a protocol to address this gap by leveraging the Top Trading Cycles (TTC) algorithm to enable efficient multi-party NFT exchanges on Ethereum. Our protocol identifies preference-based dependencies among users and executes swaps without requiring external liquidity. We implement and deploy our solution on the Ethereum blockchain, demonstrating that it remains practical for a reasonably large number of participants. Finally, we release our implementation publicly and provide a detailed cost analysis, offering a concrete path toward fair and efficient preference-based NFT exchanges.
This technical report presents the reference implementation of Ternary Moral Logic (TML) within the Ethereum Virtual Machine (EVM) ecosystem. It addresses the limitations of traditional "Code is Law" architectures by introducing a finite state machine that enforces a mandatory third state—the "Sacred Zero" or Epistemic Hold—allowing smart contracts to pause execution when pre-defined ethical conditions are unmet. The report moves beyond theoretical ethics to specify the Solidity design patterns, storage layouts, and cryptographic verification methods required to make TML enforcement non-bypassable and auditable. Key Technical Contributions: Finite State Machine (FSM): Implements a mandatory "Sacred Zero" state (State 0) that acts as an "Epistemic Hold," distinguishing between valid (1), invalid (-1), and uncertain (0) transaction states. Dual-Lane Latency Architecture: Defines a "Fast Lane" for synchronous, clear-cut transactions and a "Slow Lane" for ambiguous cases requiring governance or oracle resolution, preventing head-of-line blocking. Cryptographic Provenance: Utilizes EIP-712 typed data signing to bind off-chain AI/Oracle verdicts to on-chain execution, preventing replay attacks and ensuring distinct domain separation. Privacy Preservation: Integrates Zero-Knowledge Proofs (ZK-SNARKS) to verify the execution of moral logic models without revealing sensitive input data or proprietary model weights ("Glass Box" architecture). Immutable Core Pattern: Rejects standard upgradeable proxy patterns in favor of an "Immutable Core" architecture to eliminate administrative "God Mode" and ensure constitutional constraints cannot be bypassed by key holders. Formal Verification: Demonstrates safety and liveness properties (e.g., "No Silent Pause," "Eventual Resolution") using TLA+ (Temporal Logic of Actions) to mathematically prove the system's robustness.
This thesis investigates how GossipSub configuration choices influence data dissemination for a Data-Availability Sampling (DAS) workload under strict consensus time bounds. Using a configurable PeerSim-based simulator, we model a FullDAS-like setting in which a block producer erasure-codes a blob into a 2D extended matrix of share segments, parti- tions row/column segments into topics (custody-style sharding), and executes a two-phase workflow: seeding, where share segments are disseminated over topic meshes, and sampling, where validators must retrieve uniformly random share segments within a T_DAS = 4 s deadline. We systematically vary topic granularity (TOPICS), segmentation (segment amount, SA), replication (K-copies), bandwidth caps, and omission fault rate α, and measure phase success rates, completion-time distributions (with emphasis on tail latency), bandwidth consumption, and duplication overhead. The results show that segmentation and replication dominate performance and overhead: increasing SA from coarse to moderate values reduces duplication with diminishing returns beyond SA ≈ 1-16, while larger K increases redundancy and overhead and mainly provide a robustness margin under adverse conditions. Seeding completes quickly and remains resilient for SA≥ 4 even at high omission, whereas sampling is tail-latency dominated and degrades more sharply as α increases, leading to widespread deadline misses near α = 0.5. Based on these findings, we adopt TOPICS= 256, SA= 8, K = 4, and a conservative per-node bandwidth cap of 60 Mbit/s for faulted multi-slot experiments to isolate GossipSub dynamics from bandwidth saturation.
Robiah Arifin, Wan Azelee Wan Abu Bakar, Mustafa Man, Mohamad Afendee Mohamed · 5 authors
The issue of fake certificates has been widely identified, and their prevalence has increased significantly in recent years. This growing trend has become a global concern due to its adverse impact on educational standards. A key factor contributing to the problem is the continued reliance on manual processes for issuing and verifying certificates. To address these challenges, this study proposes the use of an authority round (AuRa) consensus algorithm for managing certificate data on the Ethereum blockchain. AuRa, a member of the proof of authority (PoA) family, facilitates consensus among nodes distributed across multiple servers and networks. This mechanism plays a vital role in preserving the integrity and decentralization of the blockchain while ensuring the security of transactional data. Furthermore, the study investigates how AuRa enables efficient certificate data transactions within a private Ethereum environment. It also evaluates the algorithm's performance in terms of transaction speed per second (TPS) and throughput per second (TGS), demonstrating its effectiveness for managing certificate transactions on a blockchain network. Then the TPS and TGS results substantiate the suitability of AuRa for digital certificate generation, evidenced by its stable and efficient performance within a controlled private server environment.
Bitcoin's limited programmability and transaction throughput have historically prevented native Bitcoin from participating in decentralized finance (DeFi) applications. Existing solutions depend on honest-majority thresholds, or centralized custodial entities that introduce significant trust requirements. This paper introduces Bitcoin Smart Accounts (BSA), a novel protocol that enables native Bitcoin to access DeFi through trust-minimized infrastructure while maintaining self-custody of funds. BSA achieves this through a combination of emulated Bitcoin covenants using Partially Signed Bitcoin Transactions (PSBTs) and Taproot scripts, a Trusted Execution Environment (TEE)-based arbitration system, and destination chain smart contracts that enable DeFi platforms to accept self-custodial Bitcoin as collateral without necessitating protocol-level modifications. The setup leverages liquidity secured by the Lombard Security Consortium which provides a twofold advantage: for a DeFi protocol, liquidators rely on fungible assets with deep liquidity to quickly exit positions, while for a depositor, the general trust assumptions of honest majority (m-of-n) are reduced to existential honesty (1-of-k). We present the complete protocol design, including the Bitcoin architecture, the TEE-based arbitration mechanism, and the Smart Account Registry for protocol management. We provide a security analysis that demonstrates the correctness, safety, and availability properties under our trust model. Our design enables native Bitcoin to serve as collateral in lending markets and other DeFi protocols without requiring users to relinquish custody of funds.
Blockchain technology has transformed distributed systems by enabling mutually untrusted nodes to reach agreement without a central authority. Such trustless decentralized paradigm relies on the robustness of system design mainly from two components: the consensus layer governing block production and the data layer governing data consumption. However, these pillars of robustness could be undermined in a Byzantine environment, where adversarial participants may strategically misbehave, leading to biased data production and compromised data access. This thesis systematically addresses robustness vulnerabilities across both layers, ensuring that blockchain systems remain fair, predictable, and verifiable throughout the entire data lifecycle. At the consensus layer, we first address selfish mining in Proof-of-Work (PoW), which allows adversarial miners to gain disproportionate revenue. We introduce an unfairness metric based on the divergence between computing power and mining revenue, and propose Tit-for-Tat (TFT), a block-promotion strategy that detects suspicious forking behavior and selectively delays block propagation. To optimize this defense, we formulate the Delay Vector problem and develops efficient approximation algorithms. Second, we study block withholding in Byzantine Fault Tolerant Proof-of-Stake (BFT-PoS), where proposers may delay blocks to extract additional Maximal Extractable Value (MEV). To restore predictable block generation, we propose InTime, an incentive mechanism that rewards timely proposals according to transaction arrival rates observed across the network. InTime combines an Arrival Rate Incentive, a Committee Time Witness workflow, and a Shift-Mean Estimation algorithm to collect and verify timing information under adversarial conditions. At the data layer, we propose the Merkle Bloom Filter Tree (MBFT), a framework for authenticated aggregate queries with keyword and range predicates. MBFT supports efficient verification for complex on-chain queries, including time-window queries, while controlling storage overhead. We design a novel Merge Bloom Filter (MBF) for space-efficient handling of dynamic sets during query authentication.
Despite its serious consequences, network partitioning remains one of the most overlooked threats in blockchain systems. When a blockchain network splits into disconnected segments-due to routing failures, deliberate attacks, or infrastructure outages-nodes in each segment continue building their own version of the ledger independently. When connectivity is restored, one chain must be discarded, and any transactions recorded only on the discarded chain are lost. If an attacker deliberately timed payments to both sides of the partition simultaneously, those payments may succeed on both chains-a double-spend. The severity of this threat varies by consensus design. Public proof-of-work chains like Bitcoin face double-spend risk. Proof-of-stake networks like Ethereum risk finality delays and validator penalties. Permissioned systems like Hyperledger Fabric halt block production entirely. Despite these distinct consequences, the research literature treats these platforms in isolation. This survey unifies the analysis. We review over twenty peer-reviewed works (2002-2024), covering distributed systems theory, deliberate attack vectors, consensus protocol behavior, and detection and mitigation strategies. We present a comparative taxonomy, identify four open research problems, and conclude that existing mitigations are largely reactive and platform-specific-a cross-platform approach to partition resilience is still needed.
In this paper, a scalable interoperable hybrid blockchain systems based on a novel seven-layer architecture is proposed. The model thereby solves the three problems that have restricted the development of traditional blockchains, i.e., low transaction throughput, inability of cross-chain communication, architectural rigidity, by clearly dividing responsibilities into separate layers dedicated to core infrastructure, operation systems, and application ecosystems. It applies a hybrid consensus approach where Proof of Stake (PoS) is adopted for global finality and Practical Byzantine Fault Tolerance (PBFT) is employed for shard-level consensus, offering energy efficiency as well as fault tolerance. With rollups, sharding, and interoperability protocols e.g. Polkadot, and IBC, it boasts high performance, modular extensibility and app-ability for real-world use-cases such as finance, IoT and healthcare. The proposed architecture would act as a basis for development in AI-driven smart contracts, privacy-preserving computation and quantum-secured consensus.
Proof-of-Stake (PoS) consensus protocols commonly employ epochs as temporal abstractions to simplify validator accounting, reward distribution, and slashing enforcement. These designs assume clean and synchronized state transitions across epoch boundaries. In practice, distributed systems exhibit asynchronous execution, delayed finality, and implementation divergence.This paper introduces and analyzes <b><i>Validator Epoch Reset Collisions</i></b>, a class of temporal desynchronization vulnerabilities in which validator state resets, reward counters, slashing windows, or participation flags become inconsistently applied across epoch boundaries. We demonstrate how such collisions create exploitable enforcement gaps that can be leveraged to evade penalties, duplicate rewards, or bypass participation requirements—without violating protocol rules. We argue that epoch-based accounting introduces structural risks to economic security unless continuity-enforcing safeguards are applied.
Blockchain as a promising technology is gaining its popularity ever since proof-of-work based Bitcoin came to the world. Nevertheless, Bitcoin achieves consensus at an expensive cost of energy. Proof-of-stake is one of the solutions for such a problem. Participants of PoS protocols achieve dynamic-availability in permissionless settings. Parties can join and leave the protocol at their will without notifying others. However, such protocol relies heavily on a central clock, providing the function of synchrony by collecting the finish status of every honest participant. In our protocol, the global function maintains the round information for each participant no longer needed. We analyze and modify the round into real-time based round model. Message delivery delay is also taken into consideration of the round length. However, participant need the connection of a real-world time global clock which is crucial to calculate the current round. And round length also is adjusted due to the changing network situation at the start of every new epoch.
GLYPH is a transparent verification layer for Ethereum for trustless on-chain verification of heterogeneous proof systems. It unifies upstream SNARK and STARK settlement through a single packed arity-8 sumcheck verifier over p = 2^128 - 159, while preserving upstream assumptions. The design centers on a universal adapter surface, UCIR compilation, and a chain-bound artifact interface for stateless verification. Benchmark evidence in the whitepaper reports 29.45k total transaction gas in recorded testnet receipts. This record includes the whitepaper and the formal proof appendix.
<b><i>Zombie Validator Resurrection</i></b> is a core consensus-layer exploit in Proof-of-Stake (PoS) and alternative Layer-1 networks where inactive, slashed, or economically abandoned validators regain influence without restoring proportional economic security. Through protocol gaps, state resets, or weak liveness enforcement, validators that should be neutralized re?-enter consensus, undermining safety assumptions and enabling stealth attacks. This paper formalizes the structural conditions enabling zombie validators, analyzes common resurrection mechanisms, and examines systemic risks to consensus integrity. We propose mitigation strategies to enforce validator lifecycle accountability and safeguard decentralized networks against stealth reactivation attacks.
Distributed ledger technologies rely heavily on consensus mechanisms to maintain a synchronized, tamper-resistant, and decentralized state across a network of mutually untrusted nodes. Conventionally, analyses of these mechanisms concentrate on cryptographic security, equilibrium in game theory, and network latency but often consider system dynamics to be linear predictable or stationary. This paper applies chaos theory to provide an integrated complex systems framework for the nonlinear, dynamic behaviors of three classical blockchain consensus paradigms: Proof of Work (PoW), Proof-of-Stake (PoS), and Byzantine Fault Tolerance (BFT). Through nonlinear feedback loops modeling transaction flows, validator behaviors, and fork-generation processes under the right boundary conditions local computational or stake centralization, sudden network propagation delays, and targeted malicious adversarial perturbations- we prove that deterministic chaos is self-generating. Employing state-space reconstructions, sensitivity analyses to initial conditions, and qualitative descriptions of phase trajectories, this work charts the transition between stable decentralized consensus phases as echoed through chaotic divergence or quasi-permanent chain splits. Results identify major flaws in classical protocols and provide principles to design the next-generation robust chaos-tolerant distributed architectures.
The finite 1-bullet silent duel is considered, involving two duelists who shoot with exponentially-convex accuracy through a uniformly quantized time. The duel is a symmetric matrix game whose optimal value is 0, and each of the duelists has the same optimal behavior, whether it is in pure or mixed strategies. The actual beginning is never optimal in the duel. Apart from the very end of the duel, the conditions for the optimal time moment existence are found. Numerical experiments confirm that the optimality can be manipulated by changing the accuracy factor that scales the payoffs. The results are applicable in systems under limited or censored communication with uncertainty, latency, and lucrative delayed actions. Some examples of such set-ups are time-sensitive information release (privacy and censorship), queueing and load balancing (information science and telecommunication systems), and block proposal timing for decentralized consensus protocols (in Proof-of-Work and Proof-of-Stake).