Permissionless consensus protocols require a scarce resource to regulate leader election and provide Sybil resistance. Existing paradigms such as Proof of Work and Proof of Stake instantiate this scarcity through parallelizable resources like computation or capital. Once acquired, these resources can be subdivided across many identities at negligible marginal cost, making linear Sybil cost fundamentally unattainable. We introduce Proof of Commitment (PoCmt), a consensus primitive grounded in a non-parallelizable resource: real-time human engagement. Validators maintain a commitment state capturing cumulative human effort, protocol participation, and online availability. Engagement is enforced through a Human Challenge Oracle that issues identity-bound, time-sensitive challenges, limiting the number of challenges solvable within each human window. Under this model, sustaining multiple active identities requires proportional human-time effort. We establish a cost-theoretic separation showing that protocols based on parallelizable resources admit zero marginal Sybil cost, whereas PoCmt enforces a strictly linear cost profile. Using a weighted-backbone analysis, we show that PoCmt achieves safety, liveness, and commitment-proportional fairness under partial synchrony. Simulations complement the analysis by isolating human-time capacity as the sole adversarial bottleneck and validating the predicted commitment drift and fairness properties. These results position PoCmt as a new point in the consensus design space, grounding permissionless security in sustained human effort rather than computation or capital.
Daniel Qian, Xiyu Hao, Jinkun Geng, Yuncheng Yao · 7 authors
As Byzantine Fault Tolerant (BFT) protocols are increasingly adopted for user-facing applications such as payments and smart contracts, it is crucial that they provide low latency. To reduce latency, some BFT consensus protocols use a leaderless, speculative, fast path where clients broadcast requests directly to replicas, enabling end-to-end commit latency of two message delays ($2Δ$). However, such a fast path is extremely fragile: concurrent requests can cause replicas to diverge when they receive requests in different orders, triggering costly recovery procedures. This paper presents Aspen, a leaderless speculative BFT protocol that handles concurrent requests while achieving near-optimal latency of $2Δ+ ε$. The $ε$ term is a short waiting delay introduced by Aspen's best effort ordering layer, which uses loosely synchronized clocks and network delay estimates to provide a tentative order. To make its fast path even more robust to intermittent divergence, Aspen adds extra replicas ($n = 3f + 2p + 1$) as well as novel recovery mechanisms that allow the system to tolerate divergence while preserving safety and performance. In experiments with geo-distributed replicas, Aspen reduces the median latency of requests by $1.1\times$--$3.8\times$ compared to state-of-the-art BFT protocols, while sustaining up to $0.75\times$ the peak throughput of throughput-optimized designs.
Trust between entities in any scenario without a trusted third party is very difficult, and trust is exactly what blockchain aims to bring into the digital world with its basic features. Many applications are moving to blockchain adoption, enabling users to work in a trustworthy manner. The early generations of blockchain have a problem; they cannot share information with other blockchains. As more and more entities move their applications to the blockchain, they generate large volumes of data, and as applications have become more complex, sharing information between different blockchains has become a necessity. This has led to the research and development of interoperable solutions allowing blockchains to connect together. This paper discusses a few blockchain platforms that provide interoperable solutions, emphasising their ability to connect heterogeneous blockchains. It also discusses a case study scenario to illustrate the importance and benefits of using interoperable solutions. We also present a few topics that need to be solved in the realm of interoperability.
We introduce Auditable Proof-of-Work (APoW), a novel proof-of-work (PoW) construction inspired by Hashcash-style nonce searching, which enables the auditing of other miners' work through accountable re-scanning of the nonce space. The proposed scheme allows a miner to probabilistically attest to having searched specified regions of the nonce space in earlier mining rounds, while concurrently earning rewards for performing productive work for a new block or pool share. This capability enables miners belonging to a mining pools to audit another miner's claimed effort retroactively, thereby allowing the probabilistic detection of block withholding attacks (BWAs) without requiring trusted hardware or trusted third parties. As a consequence, the construction supports the design of decentralized mining pools in which work attribution is verifiable and withholding incentives are substantially reduced. The scheme preserves the fundamental properties of conventional PoW, including public verifiability and difficulty adjustment, while adding an orthogonal auditability layer tailored to pool-based mining. Finally, while a full deployment of APoW in Bitcoin would require a consensus rule change and minor modifications to mining ASICs, the construction remains practically useful even without consensus changes, for instance, as a pool-level auditing mechanism that enables verifiable pay-for-auditing using existing pool reserves.
We propose the Consensus-Based Privacy-Preserving Data Distribution (CPPDD) framework, a lightweight and post-setup autonomous protocol for secure multi-client data aggregation. The framework enforces unanimous-release confidentiality through a dual-layer protection mechanism that combines per-client affine masking with priority-driven sequential consensus locking. Decentralized integrity is verified via step (sigma_S) and data (sigma_D) checksums, facilitating autonomous malicious deviation detection and atomic abort without requiring persistent coordination. The design supports scalar, vector, and matrix payloads with O(N*D) computation and communication complexity, optional edge-server offloading, and resistance to collusion under N-1 corruptions. Formal analysis proves correctness, Consensus-Dependent Integrity and Fairness (CDIF) with overwhelming-probability abort on deviation, and IND-CPA security assuming a pseudorandom function family. Empirical evaluations on MNIST-derived vectors demonstrate linear scalability up to N = 500 with sub-millisecond per-client computation times. The framework achieves 100% malicious deviation detection, exact data recovery, and three-to-four orders of magnitude lower FLOPs compared to MPC and HE baselines. CPPDD enables atomic collaboration in secure voting, consortium federated learning, blockchain escrows, and geo-information capacity building, addressing critical gaps in scalability, trust minimization, and verifiable multi-party computation for regulated and resource-constrained environments.
Bitcoin's block reward is scheduled to decline to zero, raising concerns about whether the network can remain secure once miners rely solely on transaction fees. This paper seeks to identify the conditions under which large-scale and persistent deviation from honest mining can arise. We analyze and compare the payoffs of honest and deviating miners in a sequential decision model, and identify a deviation threshold $G_t$ at which honest mining ceases to be privately optimal. Around the 2024 Bitcoin halving, we show that current mining behavior does not exhibit large-scale or structural deviation. However, when the block reward is removed, the $G_t$ criterion implies that deviation can arise even with a very small fraction of transaction fees. Finally, we evaluate three protocol-level mechanisms: Base Fee, Fee Floor, and an adaptive maximum block size rule, and show that their combination raises the deviation threshold and mitigates incentive breakdown in a fee-only regime. These results provide a practical benchmark for assessing Bitcoin's security as block rewards disappear.
Federated learning (FL) enables collaborative model training while preserving data privacy, yet both centralized and decentralized approaches face challenges in scalability, security, and update validation. We propose ZK-HybridFL, a secure decentralized FL framework that integrates a directed acyclic graph (DAG) ledger with dedicated sidechains and zero-knowledge proofs (ZKPs) for privacy-preserving model validation. The framework uses event-driven smart contracts (EDSCs) and an oracle-assisted sidechain to verify local model updates without exposing sensitive data. A built-in challenge mechanism efficiently detects adversarial behavior. In experiments on image classification and language modeling tasks, ZK-HybridFL achieves faster convergence, higher accuracy, lower perplexity, and reduced latency compared to Blade-FL and ChainFL. It remains robust against substantial fractions of adversarial and idle nodes, supports sub-second on-chain verification with efficient gas usage, and prevents invalid updates and orphanage-style attacks. This makes ZK-HybridFL a scalable and secure solution for decentralized FL across diverse environments.
The real estate sector remains highly dependent on manual document handling and verification, making processes inefficient and prone to fraud. This work presents a system that integrates optical character recognition (OCR), natural language processing (NLP), and verifiable credentials (VCs) to automate document extraction, verification, and management. The approach standardizes heterogeneous document formats into VCs and applies automated data matching to detect inconsistencies, while the blockchain provides a decentralized trust layer that reinforces transparency and integrity. A prototype was developed that comprises (i) an OCR-NLP extraction pipeline trained on synthetic datasets, (ii) a backend for credential issuance and management, and (iii) a frontend supporting issuer, holder, and verifier interactions. Experimental results show that the models achieve competitive accuracy across multiple document types and that the end-to-end pipeline reduces verification time while preserving reliability. The proposed framework demonstrates the potential to streamline real estate transactions, strengthen stakeholder trust, and enable scalable, secure digital processes.
This paper introduces Bitcoin-IPC, a protocol that scales Bitcoin through a network of permissionless, interconnected, programmable Proof-of-Stake (PoS) Layer-2 chains, called subnets, whose stake is denominated in L1 BTC. These subnets rely on Bitcoin L1 for the communication of critical information, settlement, and security. Subnets can communicate with each other and with Bitcoin: users deposit BTC from Bitcoin to a subnet and withdraw it back, and transfer wBTC directly between subnets. We provide formal definitions of these bridge protocols, incorporating a firewall property that limits the impact of malicious subnets on the security of the broader network. Our design, inspired by SWIFT messaging and embedded within Bitcoin's SegWit mechanism, enables seamless value transfer across L2 subnets. Uniquely, this mechanism reduces the virtual-byte cost per transaction (vB/tx) by up to 23x, compared to transacting natively on Bitcoin L1, effectively increasing monetary-transaction throughput from 7 tps to over 160 tps, without requiring any modifications to Bitcoin L1.
Healthcare AI needs large, diverse datasets, yet strict privacy and governance constraints prevent raw data sharing across institutions. Federated learning (FL) mitigates this by training where data reside and exchanging only model updates, but practical deployments still face two core risks: (1) privacy leakage via gradients or updates (membership inference, gradient inversion) and (2) trust in the aggregator, a single point of failure that can drop, alter, or inject contributions undetected. We present zkFL-Health, an architecture that combines FL with zero-knowledge proofs (ZKPs) and Trusted Execution Environments (TEEs) to deliver privacy-preserving, verifiably correct collaborative training for medical AI. Clients locally train and commit their updates; the aggregator operates within a TEE to compute the global update and produces a succinct ZK proof (via Halo2/Nova) that it used exactly the committed inputs and the correct aggregation rule, without revealing any client update to the host. Verifier nodes validate the proof and record cryptographic commitments on-chain, providing an immutable audit trail and removing the need to trust any single party. We outline system and threat models tailored to healthcare, the zkFL-Health protocol, security/privacy guarantees, and a performance evaluation plan spanning accuracy, privacy risk, latency, and cost. This framework enables multi-institutional medical AI with strong confidentiality, integrity, and auditability, key properties for clinical adoption and regulatory compliance.
While Ethereum has successfully achieved dynamic availability together with safety, a fundamental delay remains between transaction execution and immutable finality. In Ethereum's current Gasper protocol, this latency is on the order of 15 minutes, exposing the network to ex ante reorganization attacks, enabling MEV extraction, and limiting the efficiency of economic settlement. These limitations have motivated a growing body of work on Speedy Secure Finality (SSF), which aims to minimize confirmation latency without weakening formal security guarantees. This paper surveys the state of the art in fast finality protocol design. We introduce the core theoretical primitives underlying this space, including reorganization resilience and the generalized sleepy model, and trace their development from Goldfish to RLMD-GHOST. We then analyze the communication and aggregation bottlenecks faced by single-slot finality protocols in large validator settings. Finally, we survey the 3-slot finality (3SF) protocol as a practical synthesis that balances fast finality with the engineering constraints of the Ethereum network.
Alejandro Ranchal-Pedrosa, Benjamin Marsh, Lefteris Kokoris-Kogias, Alberto Sonnino
Modern blockchains increasingly adopt multi-proposer (MCP) consensus to remove single-leader bottlenecks and improve censorship resistance. However, MCP alone does not resolve how users should disseminate transactions to proposers. Today, users either naively replicate full transactions to many proposers, sacrificing goodput and exposing payloads to MEV, or target few proposers and accept weak censorship and latency guarantees. This yields a practical trilemma among censorship resistance, low latency, and reasonable cost (in fees or system goodput). We present Sedna, a user-facing protocol that replaces naive transaction replication with verifiable, rateless coding. Users privately deliver addressed symbol bundles to subsets of proposers; execution follows a deterministic order once enough symbols are finalized to decode. We prove Sedna guarantees liveness and \emph{until-decode privacy}, significantly reducing MEV exposure. Analytically, the protocol approaches the information-theoretic lower bound for bandwidth overhead, yielding a 2-3x efficiency improvement over naive replication. Sedna requires no consensus modifications, enabling incremental deployment.
We describe the Lockchain Protocol, a lightweight Bitcoin meta-protocol that enables highly efficient transaction discovery at zero marginal block space cost, and data verification without introducing any new on-chain storage mechanism. The protocol repurposes the mandatory 4-byte nLockTime field of every Bitcoin transaction as a compact metadata header. By constraining values to an unused range of past Unix timestamps greater than or equal to 500,000,000, the field can encode a protocol signal, type, variant, and sequence identifier while remaining fully valid under Bitcoin consensus and policy rules. The primary contribution of the protocol is an efficient discovery layer. Indexers can filter candidate transactions by examining a fixed-size header field, independent of transaction payload size, and only then selectively inspect heavier data such as OP RETURN outputs or witness fields. The Lockchain Protocol applies established protocol design patterns to an under-optimised problem domain, namely transaction discovery at scale, and does not claim new cryptographic primitives or storage methods.
The security and decentralization of Proof-of-Work (PoW) have been well-tested in existing blockchain systems. However, its tremendous energy waste has raised concerns about sustainability. Proof-of-Useful-Work (PoUW) aims to redirect the meaningless computation to meaningful tasks such as solving machine learning (ML) problems, giving rise to the branch of Proof-of-Learning (PoL). While previous studies have proposed various PoLs, they all, to some degree, suffer from security, decentralization, or efficiency issues. In this paper, we propose a PoL framework that trains ML models efficiently while maintaining blockchain security in a fully distributed manner. We name the framework SEDULity, which stands for a Secure, Efficient, Distributed, and Useful Learning-based blockchain system. Specifically, we encode the template block into the training process and design a useful function that is difficult to solve but relatively easy to verify, as a substitute for the PoW puzzle. We show that our framework is distributed, secure, and efficiently trains ML models. We further demonstrate that the proposed PoL framework can be extended to other types of useful work and design an incentive mechanism to incentivize task verification. We show theoretically that a rational miner is incentivized to train fully honestly with well-designed system parameters. Finally, we present simulation results to demonstrate the performance of our framework and validate our analysis.
With the rise of cryptocurrencies, many new applications built on decentralized blockchains have emerged. Blockchains are full-stack distributed systems where multiple sub-systems interact. While many deployed blockchains and decentralized applications need better scalability and performance, security is also critical. Due to their complexity, assessing blockchain and DAPP security requires a more holistic view than for traditional distributed or centralized systems. In this thesis, we summarize our contributions to blockchain and decentralized application security. We propose a security reference architecture to support standardized vulnerability and threat analysis. We study consensus security in single-chain Proof-of-Work blockchains, including resistance to selfish mining, undercutting, and greedy transaction selection, as well as related issues in DAG-based systems. We contribute to wallet security with a new classification of authentication schemes and a two-factor method based on One-Time Passwords. We advance e-voting with a practical boardroom voting protocol, extend it to a scalable version for millions of participants while preserving security and privacy, and introduce a repetitive voting framework that enables vote changes between elections while avoiding peak-end effects. Finally, we improve secure logging using blockchains and trusted computing through a centralized ledger that guarantees non-equivocation, integrity, and censorship evidence, then build on it to propose an interoperability protocol for central bank digital currencies that ensures atomic transfers.
Decentralized federated learning (DFL) enables collaborative model training without centralized trust, but it remains vulnerable to Byzantine clients that poison gradients under heterogeneous (Non-IID) data. Existing defenses face a scalability trilemma: distance-based filtering (e.g., Krum) can reject legitimate Non-IID updates, geometric-median methods incur prohibitive $O(n^2 d)$ cost, and many certified defenses are evaluated only on models below 100M parameters. We propose Spectral Sentinel, a Byzantine detection and aggregation framework that leverages a random-matrix-theoretic signature: honest Non-IID gradients produce covariance eigenspectra whose bulk follows the Marchenko-Pastur law, while Byzantine perturbations induce detectable tail anomalies. Our algorithm combines Frequent Directions sketching with data-dependent MP tracking, enabling detection on models up to 1.5B parameters using $O(k^2)$ memory with $k \ll d$. Under a $(σ,f)$ threat model with coordinate-wise honest variance bounded by $σ^2$ and $f < 1/2$ adversaries, we prove $(ε,δ)$-Byzantine resilience with convergence rate $O(σf / \sqrt{T} + f^2 / T)$, and we provide a matching information-theoretic lower bound $Ω(σf / \sqrt{T})$, establishing minimax optimality. We implement the full system with blockchain integration on Polygon networks and validate it across 144 attack-aggregator configurations, achieving 78.4 percent average accuracy versus 48-63 percent for baseline methods.
Public blockchains inherently offer low throughput and high latency, motivating off-chain scalability solutions such as Payment Channel Networks (PCNs). However, existing PCNs suffer from liquidity fragmentation-funds locked in one channel cannot be reused elsewhere-and channel depletion, both of which limit routing efficiency and reduce transaction success rates. Multi-party channel (MPC) constructions mitigate these issues, but they typically rely on leaders or coordinators, creating single points of failure and providing only limited flexibility for inter-channel payments. We introduce Hypergraph-based Multi-Party Payment Channels (COALESCE), a new off-chain construction that replaces bilateral channels with collectively funded hyperedges. These hyperedges enable fully concurrent, leaderless intra- and inter-hyperedge payments through verifiable, proposer-ordered DAG updates, offering significantly greater flexibility and concurrency than prior designs. Hence our, design eliminates routing dependencies, avoids directional liquidity lock-up, and does not require central monitoring services such as watchtowers. Our implementation on a 150-node intra-hyperedge achieves a transaction success rate of approximately 94% under heavy load (larger payment sizes), while full hyperedge evaluation over a 15,000-node network sustains success rates in the range of 85% to 95%, without HTLC expiry or routing failures, highlighting the robustness of COALESCE.
Damilare Peter Oyinloye, Mohd Sameen Chishti, Jingyue Li
Single-bridge blockchain solutions enable cross-chain communication. However, they are associated with centralization and single-point-of-failure risks. This paper proposes Proof of Success and Reward Distribution (PSCRD), a novel multi-bridge response coordination and incentive distribution protocol designed to address the challenges. PSCRD introduces a fair reward distribution system that equitably distributes the transfer fee among participating bridges, incentivizing honest behavior and sustained commitment. The purpose is to encourage bridge participation for higher decentralization and lower single-point-of-failure risks. The mathematical analysis and simulation results validate the effectiveness of PSCRD using two key metrics: the Gini index, which demonstrates a progressive improvement in the fairness of the reward distribution as new bridge groups joined the network; and the Nakamoto coefficient, which shows a significant improvement in decentralization over time. These findings highlight that PSCRD provides a more resilient and secure cross-chain bridge system without substantially increasing user costs.
N Mangala, Murtaza Rangwala, S Aishwarya, B Eswara Reddy · 8 authors
Healthcare has become exceptionally sophisticated, as wearables and connected medical devices revolutionize remote patient monitoring, emergency response, medication management, diagnosis, and predictive and prescriptive analytics. Internet of Things and Cloud computing integrated systems (IoT-Cloud) facilitate sensing, automation, and processing for these healthcare applications. While real-time response is crucial for alleviating patient emergencies, protecting patient privacy is paramount in data-driven healthcare. In this paper, we propose a multi-layer IoT, Edge, and Cloud architecture to enhance emergency healthcare response times by distributing tasks based on response criticality and data permanence requirements. We ensure patient privacy through a Differential Privacy framework applied across several machine learning models: K-means, Logistic Regression, Random Forest, and Naive Bayes. We establish a comprehensive threat model identifying three adversary classes and evaluate Laplace, Gaussian, and hybrid noise mechanisms across varying privacy budgets, with supervised algorithms achieving up to 83.6% accuracy. The proposed hybrid Laplace-Gaussian noise mechanism with adaptive budget allocation provides a balanced approach, offering moderate tails and better privacy-utility trade-offs for both low and high-dimension datasets. At the practical threshold of $\varepsilon$=5.0, supervised algorithms achieve 80-81% accuracy while reducing attribute inference attacks by up to 18% and data reconstruction correlation by 70%. We further enhance security through Blockchain integration, which ensures trusted communication through time-stamping, traceability, and immutability for analytics applications. Edge computing demonstrates 8$\times$ latency reduction for emergency scenarios, validating the hierarchical architecture for time-critical operations.
The rising demand for collaborative machine learning and data analytics calls for secure and decentralized data sharing frameworks that balance privacy, trust, and incentives. Existing approaches, including federated learning (FL) and blockchain-based data markets, fall short: FL often depends on trusted aggregators and lacks Byzantine robustness, while blockchain frameworks struggle with computation-intensive training and incentive integration. We present \prot, a decentralized data marketplace that unifies federated learning, blockchain arbitration, and economic incentives into a single framework for privacy-preserving data sharing. \prot\ enables data buyers to submit bid-based requests via blockchain smart contracts, which manage auctions, escrow, and dispute resolution. Computationally intensive training is delegated to \cone\ (\uline{Co}mpute \uline{N}etwork for \uline{E}xecution), an off-chain distributed execution layer. To safeguard against adversarial behavior, \prot\ integrates a modified YODA protocol with exponentially growing execution sets for resilient consensus, and introduces Corrected OSMD to mitigate malicious or low-quality contributions from sellers. All protocols are incentive-compatible, and our game-theoretic analysis establishes honesty as the dominant strategy. We implement \prot\ on Ethereum and evaluate it over benchmark datasets -- MNIST, Fashion-MNIST, and CIFAR-10 -- under varying adversarial settings. \prot\ achieves up to 99\% accuracy on MNIST and 90\% on Fashion-MNIST, with less than 3\% degradation up to 30\% Byzantine nodes, and 56\% accuracy on CIFAR-10 despite its complexity. Our results show that \prot\ ensures privacy, maintains robustness under adversarial conditions, and scales efficiently with the number of participants, making it a practical foundation for real-world decentralized data sharing.
Zero-knowledge proofs (ZKPs) are central to secure and privacy-preserving computation, with zk-SNARKs and zk-STARKs emerging as leading frameworks offering distinct trade-offs in efficiency, scalability, and trust assumptions. While their theoretical foundations are well studied, practical performance under real-world conditions remains less understood. In this work, we present a systematic, implementation-level comparison of zk-SNARKs (Groth16) and zk-STARKs using publicly available reference implementations on a consumer-grade ARM platform. Our empirical evaluation covers proof generation time, verification latency, proof size, and CPU profiling. Results show that zk-SNARKs generate proofs 68x faster with 123x smaller proof size, but verify slower and require trusted setup, whereas zk-STARKs, despite larger proofs and slower generation, verify faster and remain transparent and post-quantum secure. Profiling further identifies distinct computational bottlenecks across the two systems, underscoring how execution models and implementation details significantly affect real-world performance. These findings provide actionable insights for developers, protocol designers, and researchers in selecting and optimizing proof systems for applications such as privacy-preserving transactions, verifiable computation, and scalable rollups.
Dinh C. Nguyen, Md Bokhtiar Al Zami, Ratun Rahman, Shaba Shaon · 6 authors
Quantum federated learning (QFL) is emerging as a key enabler for intelligent, secure, and privacy-preserving model training in next-generation 6G networks. By leveraging the computational advantages of quantum devices, QFL offers significant improvements in learning efficiency and resilience against quantum-era threats. However, future 6G environments are expected to be highly dynamic, decentralized, and data-intensive, which necessitates moving beyond traditional centralized federated learning frameworks. To meet this demand, blockchain technology provides a decentralized, tamper-resistant infrastructure capable of enabling trustless collaboration among distributed quantum edge devices. This paper presents QFLchain, a novel framework that integrates QFL with blockchain to support scalable and secure 6G intelligence. In this work, we investigate four key pillars of \textit{QFLchain} in the 6G context: (i) communication and consensus overhead, (ii) scalability and storage overhead, (iii) energy inefficiency, and (iv) security vulnerability. A case study is also presented, demonstrating potential advantages of QFLchain, based on simulation, over state-of-the-art approaches in terms of training performance.
Z. Liang, Bin Chen, Litao Ye, Chen Sun · 6 authors
The ERC4907 standard enables rentable Non-Fungible Tokens (NFTs) but is limited to single-user, single-time-slot authorization, which severely limits its applicability and efficiency in decentralized multi-slot scheduling scenarios. To address this limitation, this paper proposes Multi-slot ERC4907 (M-ERC4907) extension method. The M-ERC4907 method introduces novel functionalities to support the batch configuration of multiple time slots and simultaneous authorization of multiple users, thereby effectively eliminating the rigid sequential authorization constraint of ERC4907. The experiment was conducted on the Remix development platform. Experimental results show that the M-ERC4907 method significantly reduces on-chain transactions and overall Gas consumption, leading to enhanced scalability and resource allocation efficiency.
We present Areon, a family of latency-friendly, stake-weighted, multi-proposer proof-of-stake consensus protocols. By allowing multiple proposers per slot and organizing blocks into a directed acyclic graph (DAG), Areon achieves robustness under partial synchrony. Blocks reference each other within a sliding window, forming maximal antichains that represent parallel ``votes'' on history. Conflicting subDAGs are resolved by a closest common ancestor (CCA)-local, window-filtered fork choice that compares the weight of each subDAG -- the number of recent short references -- and prefers the heavier one. Combined with a structural invariant we call Tip-Boundedness (TB), this yields a bounded-width frontier and allows honest work to aggregate quickly. We formalize an idealized protocol (Areon-Ideal) that abstracts away network delay and reference bounds, and a practical protocol (Areon-Base) that adds VRF-based eligibility, bounded short and long references, and application-level validity and conflict checks at the block level. On top of DAG analogues of the classical common-prefix, chain-growth, and chain-quality properties, we prove a backbone-style $(k,\varepsilon)$-finality theorem that calibrates confirmation depth as a function of the window length and target tail probability. We focus on consensus at the level of blocks; extending the framework to richer transaction selection, sampling, and redundancy policies is left to future work. Finally, we build a discrete-event simulator and compare Areon-Base against a chain-based baseline (Ouroboros Praos) under matched block-arrival rates. Across a wide range of adversarial stakes and network delays, Areon-Base achieves bounded-latency finality with consistently lower reorganization frequency and depth.