Sai Srikanth Madugula, Peplluis Esteva De La Rosa, Daya Shankar
The rapid proliferation of Agentic Artificial Intelligence fundamentally disrupts traditional customer loyalty paradigms. As AI evolves from passive recommendation algorithms to autonomous, goal-directed agents capable of executing purchasing decisions, the conventional understanding of consumer-brand relationships requires a structural reevaluation. By synthesizing extant literature across human-machine teaming, consumer decision-making, and algorithmic trust dynamics, we demonstrate that traditional loyalty models fail to account for algorithmic bounded rationality and constructed autonomy. To address this, we introduce the Dynamic Verifiable Multi-Agent Human Agentic Loyalty Loop (DVM-HALL) model. We formalize brand choice via a softmax probability formulation where human emotional equity, agentic machine-experience utility, calibrated trust, delegated authority, and verifiable execution jointly determine selection. The model features recursive updating mechanisms to dynamically calibrate trust and delegation after each interaction. Crucially, the framework integrates a verifiable execution layer for Decentralized Finance (DeFi) and tokenized loyalty settings, incorporating execution risks -- such as gas costs, slippage, MEV exposure, and smart-contract vulnerabilities -- as core predictors of agentic brand preference. Furthermore, we introduce the Net Human-Agent Score (NHAS), an auditable, risk-weighted metric designed to measure human-agent alignment using human feedback, execution logs, benchmark comparisons, and verifiable receipts. Finally, we propose a comprehensive three-stage empirical validation plan spanning controlled shopping experiments, multi-agent market simulations, and DeFi testbeds. This framework provides the foundational theory required for brands to navigate the impending transition toward machine customers.
Regulatory permissiveness is widely prescribed as the primary institutional lever for digital asset adoption. This study challenges that prescription. Analyzing NFT and DeFi adoption across 105 countries using Principal Component Analysis (PCA)-constructed composite indices and multivariate Ordinary Least Squares (OLS) regression, we find that the Frontier Technology Readiness Index (FTRI) is the dominant structural correlate across all specifications, consistently outperforming competing explanatory variables. Regulatory environments neither independently explain adoption nor are associated with it linearly: both permissive and restrictive environments outperform mostly prohibited jurisdictions, suggesting that regulatory clarity rather than permissiveness is the operative institutional dimension. NFT and DeFi markets follow empirically distinct pathways: NFT adoption shows stronger associations with digital marketplace maturity while DeFi is more closely associated with technological infrastructure, suggesting that treating Web3 as a homogeneous policy category is unwarranted. National income conditions how effectively technological readiness is associated with adoption gains, with structural determinants exhibiting considerably reduced explanatory power in lower-middle-income economies. For policymakers, these findings reframe the debate: the primary structural correlate of digital asset adoption is technological capacity, not regulatory stance, and below a development threshold, neither intervention is reliably associated with adoption gains.
Ilango (FOCS 2025) invented effectively zero-knowledge proofs, a new variant of zero-knowledge. We reformulate it in the language of logic and give simple proofs (under the same assumptions as Ilango (FOCS 2025)) of its existence and of the key property defined in Ilango (FOCS 2025) that it is "indistinguishable from true" (that property is in Ilango (FOCS 2025) a part of the definition of the prover, not its consequence). Using the theory of proof complexity generators we show that the concept can be turned it into a genuinely zero-knowledge proofs, assuming a conjecture from the theory about the existence of a hard generator and allowing the parties to share a common random string.
To secure modern distributed digital infrastructures, quantum blockchains exploit quantum resources to achieve information-theoretic security and surpass the classical one-third fault-tolerance bound. However, existing high-fault-tolerant protocols face a fundamental scalability challenge: the blockchain trilemma imposes either exponential communication complexity or experimentally demanding multipartite entanglement. Here, we experimentally demonstrate a scalable quantum blockchain protocol based on weak coherent states that achieves an exponential reduction in quantum communication complexity. The protocol employs a circular quantum Byzantine agreement mechanism that preserves information-theoretic security while avoiding multipartite entanglement. We implement this protocol on a photonic integrated circuit platform, realizing a six-node network over commercially available telecommunication infrastructure. Compared with previous schemes, the protocol requires less than 4% of the quantum communication resources. Leveraging this advantage, we further demonstrate a quantum-secured token exchange application achieving a throughput of 805.3 transactions per second with zero failures. These results establish a practical pathway toward scalable quantum blockchain.
Mohotarema Rashid, Lingzi Hong, Junhua Ding, K. S. M. Tozammel Hossain
Large language model (LLM) trading agents show promising performance in equity markets, yet remain narrowly focused on US equities with little evidence from live deployment. We present Fin-Analyst, a hybrid agent for FinMMEval 2026 Task 3: an eight-specialist LLM pipeline over news, SEC filings, fundamentals, analyst forecasts, technical indicators, and social sentiment, aggregated by a Meta-Agent for Tesla (TSLA), and a lightweight rule based three-signal vote for Bitcoin (BTC). On the final official leaderboard (accessed 2026-07-05), Fin-Analyst ranks first of all agents on TSLA with a +13.51% return, +28.33 points over Buy-and-Hold (Sharpe 4.10, 88% win rate), while the BTC vote ends flat yet well above a sharply falling baseline. Relative to the interim performance, the asset ranking reversed, indicating that short live windows yield volatility-sensitive rankings. Ablation identifies event-driven 8-K disclosures as the most influential TSLA signal. Error analysis shows that the memoryless agents repeat wrong calls for days at a time, and that the fixed-threshold BTC rules lost money by trading on noise in a sideways market while the LLM pipeline gained under similar conditions, motivating a memory-aware, LLM-based successor for both assets.
Цифровая трансформация страховой отрасли и внедрение продуктов параметрического страхования требуют пересмотра подходов к обеспечению доверия между участниками сделки. Ключевым вызовом становится выбор архитектуры доверенной третьей стороны, которая могла бы гарантировать не только юридическую значимость транзакций, но и их защищенность в условиях растущих угроз. Существующие централизованные модели, доказавшие эффективность в массовом сегменте, часто не отвечают требованиям прозрачности и безопасности, критически важным для крупных сделок перестрахования и рынков связанного со страхованием капитала. Целью работы является сравнительный анализ трех архитектурных подходов к построению доверенной третьей стороны: традиционной централизованной на базе инфраструктуры открытых ключей, децентрализованной на базе блокчейна и гибридной. Для объективизации выбора применяется двухэтапная методика системного анализа: метод главных компонент для снижения размерности и визуализации компромиссов, а также многокритериальный анализ решений для ранжирования архитектур. Особое внимание уделено теоретическим ограничениям распределенных систем («трилемма блокчейна», «проблема оракула»), моделированию векторов атак и стратегиям их минимизации. Источником данных послужили экспертные оценки, полученные методом «Дельфи». Исследование показало, что архитектура на базе блокчейна является оптимальной для задач перестрахования благодаря высокой скорости мобилизации ликвидности. В то же время для массового розничного сегмента экономически наиболее эффективной остается централизованная модель. Обоснована необходимость перехода к гибридным архитектурам, использующим криптографические доказательства с нулевым разглашением и доверенные среды исполнения. Такой подход позволяет сочетать высокую пропускную способность с проверяемой безопасностью, что подтверждается опытом реализации национальных цифровых валют, в частности, цифрового The digital transformation of the insurance industry and the introduction of parametric insurance products require a rethinking of approaches to ensuring trust between transaction parties. A key challenge is choosing a trusted third party architecture that can guarantee not only the legal validity of transactions but also their security in the face of growing threats. Existing centralized models, while proven effective in the mass market, often fail to meet the transparency and security requirements critical for large-scale reinsurance transactions and insurance-linked capital markets. This paper aims to comparatively analyze three architectural approaches to building trusted third parties: a traditional centralized approach based on a public key infrastructure, a decentralized approach based on a blockchain, and a hybrid approach. To objectively evaluate the choice, a two-stage system analysis method is applied: principal component analysis for dimensionality reduction and tradeoff visualization, and multicriteria decision analysis for ranking architectures. Particular attention is paid to the theoretical limitations of distributed systems (the "blockchain trilemma" and "oracle problem"), attack vector modeling, and mitigation strategies. The data source was expert assessments obtained using the Delphi method. The study showed that a blockchain-based architecture is optimal for reinsurance applications due to its high liquidity mobilization speed. However, for the mass retail segment, a centralized model remains the most cost-effective. The study substantiates the need to transition to hybrid architectures using zero-knowledge cryptographic proofs and trusted execution environments. This approach combines high throughput with verifiable security, as evidenced by the experience of implementing national digital currencies, in particular, the Bank of Russia's digital ruble.
What happens to Purple Mathematics if every apex of the wall is drawn at the same height, h_p = 1? Then the apex-to-apex line becomes one straight line, parallel to the dividing line, at the height of the Balance Boundary — and this paper works out everything that follows. The first answer is a theorem and an honest no: the Determination Law's predictions cannot improve or degrade under any redrawing, because κ and T are arithmetic invariants of the program's Invariance Ledger — the wall displays the law, it does not feed it. But the question uncovers a genuine structure: the wall's drawing rule is a gauge choice, and the framework has two canonical gauges. In the gap gauge (the published wall) the gaps live in the heights: the wall is a strain gauge and the apex line is terrain. In the level gauge the gaps migrate into the slope angles — gradient −1/(p₊−p), an inclinometer — and a normalization theorem holds: every zeta strike's height equals its share t exactly, so the unit strip between line and ceiling becomes the natural home of reception statistics, strikes uniformly distributed in it. The level gauge then serves as the control experiment that separates the program's reception results into arithmetic and geometric: the host rule, the identity t + t̃ = 1, the crowding law, and share uniformity survive both gauges; the Mirror Wall's repulsion law is erased exactly (the crossing point collapses to the midpoint; measured first-strike split 49.9%, flat), proving it was height-borne; and the coincidence question answers differently in each gauge — the gap gauge's helix touches the wall at the balanced primes, while the level gauge's ceiling, with turn rate T = 4, is touched exactly at the primes ≡ 1 (mod 4), the classical two-squares class of Fermat. A reader's observation — the helix meeting the parallel line — then yields the paper's strongest result. The unit radius is critical: below 1 the coil never reaches the balance level, at exactly 1 it kisses the ceiling once per turn, above 1 it crosses beyond balance. And the Ceiling Theorem holds: among all turn rates T, the balance ceiling is kissed by infinitely many primes if and only if T = 4 — for every other admissible turn rate at most a single prime ever touches, and for T not divisible by 4 none can. The structure does not merely accommodate Fermat's class; it selects it uniquely. Each gauge convenes its own congregation at the balance height, and only one turn rate convenes an infinite one.
Andrea Rizzini, Marco Esposito, Tommaso Gagliardoni, Francesco Bruschi
Federated Learning (FL) is an advancement in Machine Learning motivated by the need to preserve the privacy of the data used to train models. While it effectively addresses this issue, the multi-participant paradigm on which it is based introduces several challenges. Among these are the risks that participating entities may behave dishonestly and fail to perform their tasks correctly. This misbehavior, in turn, also threatens privacy, because an undetected deviation in training or aggregation can silently undermine the confidentiality guarantees that FL was designed to provide. This motivates mechanisms that provide checkable evidence that released checkpoints are consistent with a declared learning specification and an auditable execution trace. In this SoK, we model federated learning as an append-only transcript of submissions, admissions, aggregation, and finalization events, and formalize verifiability as a collection of integrity claims issued by clients and the aggregator, and checked by different verifier classes. We derive a taxonomy of recurring client-side and aggregator-side claims and use it to analyze representative verifiable FL (VFL) systems spanning Zero-Knowledge Proofs (ZKP) and Trusted Execution Environment (TEE) technologies. Our analysis suggests that, while verifiable aggregation is comparatively mature, data verifiability appears feasible but still sparsely adopted in practice, and verifiable training remain costly and rarely scale to modern models.
Incorporating connected mobility data into decision-making brings about significant data security and privacy challenges in next generation transportation systems. The problem becomes worse when the raw traffic data stream contains commuters' sensitive information that could be extracted by successful attackers. The challenges of data security and privacy must be addressed in order to create a secure information sharing environment for next generation transportation systems. ☐ Blockchain technology, which provides a tamper-resistant journal of state transition events, becomes an ideal candidate for realizing the goals of creating secure information sharing frameworks. However, existing blockchain technology and deployments also have their limitations, especially in data privacy. Because the saved data on the blockchain ledger can not be altered, we do not want to make sensitive information publicly available or record false information permanently without an authentication protocol. ☐ Innovations are needed to overcome barriers in blockchain technology for enabling secure and privacy-preserving information sharing in next generation transportation systems. This Ph.D. dissertation introduces three main innovations: (1) a zero-knowledge and Byzantine fault tolerant consensus that brings privacy-preserving to the blockchain consensus level for verifying and processing transactions, Chapter 2; (2) novel privacy-preserving authentication schemes for blockchain networks based on zero-knowledge proofs to increase security and safety in traffic management, autonomous truck fleets and ridesharing, Chapters 3, 4 and 5; and (3) blockchain-inspired architecture designs with access control policies that protect huge amounts of traffic and users' data and log access events into blockchain for traceability and accountability, Chapters 4 and 5.
Traditional secret sharing techniques such as Verifiable Secret sharing (VSS) are vulnerable to quantum attacks by a Cryptographically Relevant Quantum Computer (CRQC) running Shor's algorithm. We observe that the binding a VSS needs is required only at the moment of dealing, and this binding can be made before any CRQC exists. We propose Proof in a Bottle (PiB), which decouples verifiability from long-term binding: standard Pedersen commitments provide zero-knowledge, publicly checkable consistency during a pre-quantum window, while a salted, index-bound hash of the share set, anchored to an immutable public ledger, preserves the binding established in that window into the post-quantum era. The guarantee is explicitly a commit-now, reveal-later one: it protects today's honest dealings against tomorrow's quantum adversary.
Quantum resource estimates for the elliptic-curve discrete logarithm problem (ECDLP) now shape cryptographic migration planning, blockchain security analysis, and fault-tolerant architecture design. Recent work has moved in two complementary directions: Babbush et al. give improved secp256k1 resource estimates supported by zero-knowledge attestation while withholding sensitive circuit details, whereas Luo et al. publish an explicit reversible modular-inversion construction based on the extended Euclidean algorithm, reducing the logical-qubit footprint of prime-field ECDLP and identifying gate count, depth, and architecture-aware implementation as natural optimization targets. This note proposes a third disclosure model: verifiable resource certificates for public reversible arithmetic blocks. A certificate records a circuit commitment, gate basis, resource counts, input-output specification, deterministic test generation, correctness transcript, and optional proof artifact. We specialize the framework to modular inversion blocks |x⟩|0⟩ → |x⟩|x−1 mod p⟩, for \(x\in\mathbb F_p^\times\), which are central to affine-coordinate quantum ECDLP implementations. We prove a basic soundness bound for hash-derived randomized testing and outline a prototype verifier. The goal is not a new quantum attack, but reproducible, comparable, and independently auditable quantum-ECDLP arithmetic claims.
Yongqiang Du, Chen-Xun Weng, Feng Xie, Ming-Yang Li · 13 authors
Popularized by the Bitcoin cryptocurrency, blockchain technology establishes a decentralized digital framework that utilizes cryptographic and consensus protocols to secure data against unauthorized modification. Consequently, blockchain has found broad adoption across diverse fields, including finance, data management, healthcare, and digital asset governance. In the quantum computing era, a paramount objective for blockchain is to preserve its foundational advantages of cryptographic integrity and decentralized fault-tolerant resilience. In principle, quantum digital signatures and quantum Byzantine agreement protocols offer foundational security guarantees and tolerate up to one-half of malicious nodes for blockchain. However, the practical realization of such a quantum-enhanced blockchain remains a significant and multifaceted challenge. Here, we propose and experimentally demonstrate a fully operational hybrid quantum blockchain architecture built on photonic integrated circuits and deployed over commercially available classical telecommunications infrastructure. The system achieves a fault tolerance of nearly one-half, surpassing the classical limit, while reaching consensus on a timescale of seconds. A deployed food traceability application validates the practicality of the proposed architecture, achieving a throughput of approximately 500 transactions per second. This work establishes a foundation for practical quantum blockchains, enabling secure, scalable, and decentralized information processing in the emerging quantum era.
Sai Sakunthala Guddanti, Anupama Ray, Mrunal Arun Kumavat, Anil Prabhakar
This article explores the potential of Quantum Machine Learning (QML), specifically assessing a Quantum Support Vector Machine (QSVM) and a Variational Quantum Classifier (VQC) for detecting anomalies in real-world financial transaction data. While these QML methods outperform statistical methods, they fall short of cutting-edge deep learning techniques. To bridge this gap, we propose a hybrid quantum-classical ensemble framework that leverages the strengths of both domains. We demonstrate its effectiveness in detecting phishing in Ethereum transaction networks by combining complementary algorithms. The QSVM, whether used individually or in an ensemble, consistently delivered the lowest false negatives and higher recall rates, that are crucial for anomaly detection. To enhance individual models, we encoded the data using novel cascaded Quantum Random Access Coding (QRAC) schemes and compared it with the popular encoding ZZ feature map on both simulators and the IBM Heron quantum processor. For both QSVM and VQC, we consistently observed improvements (13% for QRAC-VQC and 3% for QRAC-QSVM) of QRAC over the ZZ feature map. Notably, certain QML algorithms exhibit remarkable resilience on the IBM Heron quantum processor, approaching simulator-level performance on devices with high quantum volume. This observation underscores the promise of QML despite hardware limitations.
This paper formalizes the architecture of Hamilton V5, a continuous physical field transformer engineered for industrial automation. Moving beyond the discrete token-based processing of legacy models, Hamilton V5 introduces Always-On Autonomy (AOA), a paradigm that perpetually consumes raw spatiotemporal media inputs and maps them concurrently across a 12-dimensional mechanical phase space. To ensure structural stability and mitigate the compounding numerical drift common in autonomous systems, V5 integrates a closed-loop vector feedback correction mechanism. Crucially, the architecture secures high-speed hardware execution by implementing a Deterministic Proof-of-Stake Gate, which dynamically aborts actions if the predicted trajectory deviates by more than a threshold.
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Physical Unclonable Functions (PUFs) and Hardware Security
Digital communication increasingly underpins identity, financial transactions, and regulatory compliance. In many settings, possession of a DKIM-signed email serves as evidence of account control, transaction confirmation, or institutional affiliation. Yet demonstrating such properties typically requires revealing the full email or relying on centralized intermediaries, introducing privacy risks and additional trust assumptions. A framework called ZK Email addresses this limitation by applying zero-knowledge proofs (ZKPs) to email verification, enabling publicly verifiable proofs of authenticity while preserving message confidentiality. However, its existing implementations struggle to support complex, real-world messages due to the inefficiency of regular-expression verification over structured formats and rich alphabets. We address this limitation with a new ZKP system for regex matching based on path verification over epsilon-free NFAs, yielding prover complexity linear in the captured path and independent of the original email's size. This approach enables practical validation of expressive standard structures required for full DKIM-signed email verification. To fully integrate our constructions into ZK Email, we design complete end-to-end ZK circuits that combine (i) DKIM signature verification, (ii) an arbitrary-length SHA-256 circuit with partial precomputation for rsa-sha256 under RFC~6376, and (iii) a general-purpose regex primitive enforcing structural constraints over email headers and body. We formalize the associated zero-knowledge relations and analyze their security under realistic adversary models. We implement the system~(fully integrated with ZK Email and released under the MIT license) in Circom and Noir, targeting Groth16 and UltraHonk backends, and evaluate it in both client-side and zkVM (SP1) deployment settings. Experimental results on a comodity hardware demonstrate substantial efficiency improvements over prior DFA-based approaches, achieving 2-6x in proving time using UltraHonk backend, while supporting a significantly richer class of regex languages.
Abstract The same infrastructures that enable decentralised finance, NFT markets, and metaverse platforms also create new spaces for para‑crime. This article extends grey criminology to Web3 by applying three mechanisms of infrastructural illegality – parasitism, normative greyness, and platform co‑production – first developed for physical cross‑border grey economies (daigou). Drawing on technical and financial crime literature, we show how smart contracts, stablecoins, and DAO governance are parasitised for money laundering and fraud; how techno‑libertarian narratives of 'code is law' and decentralisation sustain normative greyness; and how algorithmic security and DAO co‑production reshape rather than eliminate para‑crime. The analysis reveals both structural parallels with physical grey economies and domain‑specific variations – most notably, the deeper internalisation of co‑production in code‑based systems. We argue that grey criminology must extend its infrastructural turn to virtual and metaversal spaces, and that enforcement paradoxes – where suppression threatens valued infrastructures – apply as much to blockchain protocols as to customs thresholds.
This research paper presents a comprehensive review of the integration of Artificial Intelligence (AI) and blockchain technologies, examining how their convergence can enhance trust, transparency, security, and intelligent decision-making in modern digital systems. The study explores the technological foundations of AI and blockchain, analyzes their complementary capabilities, and evaluates real-world applications in healthcare, financial services, supply chain management, Web3, and digital governance. It also critically discusses key technical, ethical, and regulatory challenges, including scalability, privacy, interoperability, governance, and security. Drawing on recent academic literature, the paper identifies current research gaps and outlines future directions for developing trustworthy, decentralized, and responsible AI-enabled digital ecosystems.
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Internet of Things and AI
Organizational and Employee Performance
Artificial Intelligence in Healthcare and Education
Blockchain governance, the set of processes by which decentralized protocols evolve, remains a fundamental challenge in balancing adaptability, security, and stakeholder representation. This technical report analyzes Cardano's Voltaire governance system, the on-chain framework introduced via CIP-1694 and enacted through the Chang hard fork in September 2024, and lays down a corresponding research program. We make two contributions. First, we provide a complete technical specification of Voltaire's mechanisms, including its three-body architecture, seven governance action types, voting rules, and its constitutional framework; this specification is sufficient for implementation or formal analysis. Second, we establish a research agenda for principled governance optimization, including design of an agent-based simulation platform, analysis of delegation dynamics, optimization of multi-objective parameters, and game-theoretic incentive design; we provide preliminary results, including a formal governance kernel: a minimal executable model capturing self-amending governance as a state-transition system and enabling rigorous safety and liveness analysis. Our report offers a comprehensive technical overview and invites the research community to advance blockchain governance science through rigorous study of Voltaire as a live, large-scale experiment now managing a treasury valued at approximately \$235 million (1.47B ADA as of early July 2026).
Front-running is a subtle and persistent problem for blockchains. A blockchain is a stateful virtual machine executing instructions called transactions. Users earn rewards by publishing functional transactions essential to the system. Attackers observe these transactions and publish their own ahead of the users', seizing the reward and eroding users' incentive to publish functional transactions. Preventing front-running means enforcing causality: If an attacker receives transaction tx_A and then publishes transaction tx_B, then tx_A must be ordered before tx_B. However, this causality is only observed by the attacker. Practical systems order transactions by bid amount, so transactions willing to pay more get executed first, but this only results in a bidding war eroding users' rewards. Though numerous ordering approaches have been proposed, none achieves causality, leaving users vulnerable to front-running. We present PRECEDE, a mechanism-design approach that enforces transaction causality by removing the economic incentive to front-run. PRECEDE orders transactions by a power-weighted randomized lottery, whose winning probability grows super-linearly in the bid. The user's strategy of publishing a transaction with a deterring bid forms an equilibrium where the attacker refrains from competing. Moreover, PRECEDE prevents the prominent sandwich attack, which relies on front-running. PRECEDE can be directly deployed in any censorship-resistant blockchain with a simple change to its transaction ordering mechanism.
Cryptocurrency markets are vulnerable to trade-based manipulation, such as wash trading, which can distort price signals and mislead investors. Prior research has mainly focused on detecting manipulation using fixed rules or labeled examples, offering limited flexibility and interpretability for assessing potential risks. Existing visual analytics tools can reveal basic manipulation-related signals, such as token distribution, but still require substantial manual effort to integrate holder relationships, suspicious behaviors, and market dynamics for risk assessment. To address these limitations, we propose ManiScope, an LLM-assisted visual analytics system for analyzing trade-based manipulation risks in cryptocurrency markets. ManiScope provides coordinated views of token distributions, holder relationships, detailed holder behaviors, price dynamics, and suspicious trading patterns. To further enhance user analysis, ManiScope introduces a human-LLM collaborative visual analytics framework. Rather than acting as a basic reactive LLM assistant, the framework positions the LLM as a co-analyst that infers users' analytical intent and emerging hypotheses from interaction context and surfaces relevant visual, statistical, and synthesized evidence for hypothesis evaluation. This design reduces repetitive inspection and strengthens evidence-based reasoning. We evaluate ManiScope through two case studies and a user study with 12 experienced cryptocurrency practitioners. The results suggest that ManiScope supports effective risk assessment of manipulation, reduces manual effort in evidence-seeking, and organizes findings around user hypotheses.
Jingyu Liu, Bolin Zhang, Lin William Cong, Siguang Li · 5 authors
Ethereum's Glamsterdam upgrade introduces enshrined proposer-builder separation (ePBS), replacing relay-centric PBS with direct builder bids to proposers. We study how this shift changes the block-building microstructure through a general imperfect-information two-stage auction with verifiable messages, where an early bid serves as both a price offer and a signal. PBS and ePBS are modeled as restrictions of the same block-building game: PBS fixes stopping and disclosure exogenously, while ePBS lets the proposer choose stopping and disclosure ex post. Latency heterogeneity is captured by asymmetric information updates: fast builders observe disclosed early information before rebidding, while slow builders do not. We combine exact perfect Bayesian equilibrium characterizations in tractable cases with calibrated no-regret learning in finite games. For PBS, we show that separating equilibria preserve the standard first-price-auction payoff benchmark and provide conditions for their existence. For ePBS, we demonstrate a ratchet effect: because the proposer can defer block proposal and use early bid information in the second stage, builders anticipate ex-post extraction and shade or pool early bids, generating allocation inefficiency and revenue-efficiency valleys. We interpret this ratchet distortion as a commitment failure. Under full commitment, the optimal policy collapses to the static Myerson auction and removes the ratchet channel. To realize part of this commitment advantage in a feasible mechanism, we propose a Trusted Execution Environment (TEE) sidecar that enforces limited commitment. We formulate the revenue-maximizing TEE mechanism as a bilinear optimization problem. In conservative finite benchmarks, the TEE design increases the proposer revenue relative to the first-price benchmark by approximately \(25\%\).