This paper introduces Automated Institutional Discovery (AID), a novel computational framework that conceptualizes economic institutional design as a high-dimensional combinatorial search problem. Traditional institutional design relies heavily on human intuition, historical evolution, or analytically constrained mechanism design, which often fails in complex, adaptive multi-agent environments. AID transcends these limitations by framing institutions as tuples i = (r_1, r_2, ..., r_K) within an expansive institutional space and utilizing advanced search and optimization algorithms to discover configurations that maximize global objective functions F(i). By combining multi-agent simulation modeling with metaheuristic search strategies, AID evaluates allocative efficiency, incentive compatibility, resilience, and distributional equity without requiring empirical laboratory experiments. The framework establishes a paradigm shift from manual rule-making to automated machine discovery, offering robust applications for digital economies, decentralized finance, and economic governance.
Consumers facing home-renovation quotes operate in a classic credence-goods market: they cannot readily verify whether a quoted price is fair, and general-purpose large language models (LLMs) are now a zero-cost place to ask. Whether LLM answers are actionable for this purpose is untested. Demand-side benchmarks exist for medical, legal, and financial advice, but not for construction costs. We present, to our knowledge, the first consumer-question benchmark for construction costs. Forty Japanese renovation-price questions were posed to frontier LLMs, with repeated-trial sets measuring output stability. A matched re-run at bare provider defaults with a current frontier model (gpt-5.5) was added to remove a settings confound present in the original configuration. Two findings are robust across models, generations, and settings: no LLM answer contained an explicit over-charge decision threshold, and repeated runs of the same question returned materially different price figures. Within-answer price spans are also wide, with a median of 10x under bare defaults. A deterministic structured engine over an open cost database is included as an existence proof that a citable reference layer is constructible. Its consistency is a design property and its accuracy is not validated here; validating it against completed real-world quotations is the next study. All questions, raw outputs, harness, and scoring code are public.
Fei Wu, Thomas Thiery, Stefanos Leonardos, Carmine Ventre
Block production in modern blockchains is increasingly shaped by economic gains that arise from control over transaction ordering. These gains—known as Maximal Extractable Value (MEV)—have led to concerns about centralization and market power among blockchain consensus participants. To address these concerns, Ethereum introduced Proposer-Builder Separation (PBS), in which specialized block builders compete in block building auctions to construct blocks on behalf of validators. The current implementation of PBS, MEV-Boost, mediates this competition through an open-bid first-price ascending auction, termed the MEV-Boost auction. This paper analyzes the strategic incentives of builders in MEV-Boost auctions. We develop an agent-based simulation framework and apply empirical game-theoretic analysis to study how asymmetries in network latency and access to MEV opportunities shape bidding behavior and market concentration. Our findings show that while latency differences mildly affect builder incentives, MEV opportunity access fundamentally alters equilibrium strategies: builders with privileged access to MEV opportunities bid less aggressively, maintain higher profit margins, dominate market share, and reduce proposer revenue. These effects contribute to centralization and oligopolistic outcomes in the builder market. To validate these findings, we further analyze an idealized symmetric benchmark market where builders have comparable latency and MEV access. Under such settings, the auction behaves as expected—bidding is competitive, proposer revenue is higher, and the market is more decentralized—confirming that the observed inefficiencies arise specifically from the asymmetries present in practice.
Legacy enterprise resource planning (ERP) systems serve as the operational backbone of global commerce but often create bottlenecks due to their rigid, monolithic design. As organizations incorporate artificial intelligence (AI), these outdated systems struggle to support high-speed, parallel workflows, creating a significant integration challenge. This paper introduces a non intrusive modernization approach that overlays a decentralized multi-agent system (MAS) onto existing infrastructure without requiring invasive code changes. By developing a digital twin of the order-to-cash (O2C) process, we train autonomous agents through multi-agent reinforcement learning (MARL) to manage credit validation, inventory allocation, and fulfillment. We adapt the centralized training, decentralized execution (CTDE) framework to meet O2C constraints, enabling agents to learn globally optimal strategies while operating independently. Simulation results show that this architecture surpasses rule-based robotic process automation (RPA) baselines, increasing total throughput by 6.9% over a monolithic setup, though at a 6.3% error rate due to aggressive allocation policies. These results indicate that decentralized agent-based orchestration provides a scalable approach for modernizing legacy ERPs, offering increased agility without the risks associated with platform replacement.
ABSTRACT:When the web's traffic was mostly human, an interface could be rationed with blunt tools: a flat fee, a fixed rate limit, akey that let a client in. Demand arrived at the pace a person could click, and capacity was rarely the binding constraint.That world is ending. By 2026 the majority of requests across much of the web are machine-generated, most API trafficcomes from non-human callers, and the collapse in the price of model inference has, through the Jevons paradox,multiplied total demand rather than reduced it. Autonomous agents call interfaces in bursts, without hesitation, and at afrequency no human workflow produced. This paper argues that under machine demand an API becomes a congestiblecommons that must be allocated by price and priority rather than parcelled out by flat quota, and that the tokenisation ofaccess, its conversion into priced, meterable, fungible units, is the substrate that makes such allocation possible. Drawingon the economics of congestion pricing, it examines the available mechanisms, from usage-sensitive smart markets andParis Metro-style priority tiers to auctions and prepaid spot capacity, and sets out the properties agents bring to them. Itshows that the same absence of human hesitation that makes agents ideal responders to congestion prices also makesthem prone to over-consumption, synchronised retry storms and denial-of-wallet failures. It closes with the governanceproblems this raises, and connects them to earlier work in this series on metering, thresholds and delegated cost. Keywords: API economy; tokenization; congestion pricing; resource allocation; autonomous agents; machine-to-machinetraffic; denial of wallet; Jevons paradox; priority pricing; metering Disclosure by Author: Portions of this manuscript were prepared with the assistance of generative AI tools for research synthesis, drafting, and editing. The models used were Indian Sovereign AI models provided by Ayen.
Does letting agents stake a reputational 'trust' asset on the legitimacy of work-verification verdicts raise the quality-adjusted productivity of a fully autonomous agent production economy (requester -> producer -> paid validator, with audits, dispute votes, and adaptive strategies), compared with cheaper institutions at IDENTICAL total verification budget? Mostly no - with a precisely mapped exception, and sharp design rules either way. At matched budget, plain audit routed by accumulate-only validator reputation significantly beats every democratic variant at every tested adversary rate (Holm-corrected Mann-Whitney p<=0.033); when expert audits are cheap, a central noisy auditor dominates everything; and paid validation without accountability is worse than no verification at all. The stylized model's verifiability gradient is real (pooled slope +0.237 per unit of voter signal quality, cell-clustered permutation p=0.0035): truth-staked voting overtakes optimized audit only at jointly high signal quality and adversary rates, and reputation's remaining lead there is erased by identity-reset (whitewashing) attacks - to which truth-staking is intrinsically robust, since a reset identity just donates fresh stake to informative voters. Within democracy the ordering is unambiguous: settle stakes against later ground truth, never against the majority (the deployed coherence-settlement default has an absorbing rubber-stamp equilibrium and loses measurably, p=0.033 at 80 seeds). Staking buys almost no population-level honesty; it works by stake-weighted meritocracy - concentrating trust, hence voting weight, on an informative minority - which also makes it natively sybil-proof where one-agent-one-vote collapses. 'Legitimacy laundering' is second-order at steady state and becomes real only under epistemic finality, which simultaneously starves truth-staking of settlements; the institution's binding resource is eventual ground-truth revelation. A capability-gradient small-LLM instantiation (1B producers, 4B verifiers, hidden-test ground truth, all local) reproduces the model's behavioral premises - including a causal incentive-framing effect on LLM validator strictness (TNR 0.705 paid-per-approval vs 0.864 accountable) - and transfers the institutional structure across two measured operating points, significantly so (Spearman +0.79, permutation p=0.014) at a production-unviable point where the parameter-matched model predicts the observed regime inversion.This manuscript was generated autonomously by the AI Scientist running inside Claude Code (Anthropic); every reported number traces to the project's experiment outputs. It is deposited by the named curator, who takes responsibility for its release.Source & method: https://github.com/qurore/ai-scientist-cli
This paper introduces Crossroads, a smart contract layer for chain-abstracted assets. In Crossroads, assets from nearly any chain are represented on a single backend blockchain as ERC-20 tokens. As a result, any asset can participate in smart-contract-based exchange, lending, or privacy applications on a single unified platform. So while Crossroads offers cross-chain bridging, a common, partial approach to alleviating the fragmentation of the blockchain ecosystem today, this is just one service within Crossroads' general-purpose chain-abstraction model. Crossroads relies on key encumbrance: a threshold signing committee holds encumbered keys controlling assets on each integrated chain, signing transactions only as authorized by smart contracts on the backend blockchain. Asset movements are fee-efficient, as ownership changes are recorded on the backend blockchain and users may set the transaction fee for withdrawals. Crossroads enables permissionless, modular integration of new blockchains using pluggable oracles with flexible design options (zkBridge, TEE-based, hybrid). Asset deposits into Crossroads benefit from strong, chain-specific finalization guarantees, minimizing the risk of reorg attacks. Unlike existing bridges, however, third-party smart contracts in Crossroads can provide fast, optimistic access to funds before finalization completes. We prove that Crossroads satisfies soundness: given an honest quorum of signing committee members, any user can unilaterally generate a withdrawal transaction transferring their net balance to an account on an integrated blockchain. We implement a proof of concept across multiple public blockchains: Bitcoin, Ethereum, and Solana. We catalog a range of applications enabled by Crossroads, including universal wallets, cross-chain staking and lending, privacy-preserving payments, and private management of public blockchain assets.
Autonomous AI agents capable of holding digital assets, signing transactions, and executing smart contracts on public blockchain networks have moved from research prototypes to active deployment over the past two years. Despite this pace of adoption, no systematic treatment of their architecture, coordination protocols, and governance structures exists that spans the full design space. This survey addresses that gap through a systematic review of the literature from 2019 to 2026, covering 177 peer-reviewed publications and 14 system documentation sources, identified through a structured search of IEEE Xplore, the ACM Digital Library, Scopus, and arXiv. We classify deployed and proposed systems along four architectural dimensions: on-chain execution, off-chain agents with on-chain settlement, verifiable off-chain computation, and multi-agent on-chain interaction. Then, we examine the coordination mechanisms through which agents reach collective decisions, covering auction-based protocols, cooperative multi-agent reinforcement learning, token-incentive structures, and gossip-based peer-to-peer coordination. Governance is treated as a distinct dimension, analysed through a technical lens, covering on-chain parameter control, dispute resolution, and DAO structures, and an organizational one, covering accountability, incentive alignment, principal–agent dynamics, and regulatory compatibility. We survey applications across decentralized finance, supply chain, IoT, and agent marketplace domains, and identify six open research problems whose resolution is a prerequisite for broader deployment. The convergence of mechanism design and multi-agent reinforcement learning in asynchronous blockchain environments is identified as the direction of greatest near-term research value.
Sony Warsono, Fitri Amalia, Muhammad Roy Aziz Haryana, Rudi Prasetya Timur
This study examines how blockchain technology (BT) can extend the conventional double-entry accounting (DEA) framework to improve financial information transparency. The study revisits the duality concept underlying DEA and explores its development toward a triple-entry accounting (TEA) structure supported by blockchain infrastructure. Using a design science approach in information systems, the study proceeds through problem identification, artefact definition, and conceptual system design. Drawing on the resource-event-agent (REA) framework, the study develops a conceptual architecture that integrates a third ledger into the accounting entry system. The proposed model positions message type (MT) as a navigational mechanism that coordinates transaction validation within a blockchain-enabled TEA environment. This structure supports improved tracking, verification, and transparency of financial information, particularly in external transactional relationships. The findings contribute to the ongoing discussion on blockchain-based accounting systems by clarifying how the duality principle can evolve within a distributed ledger environment. The study also outlines potential directions for future research on the development of triple-entry accounting and third-ledger mechanisms in accounting information systems.
Auctions are now central to blockchain markets, settling NFT sales, token launches, DeFi liquidations, and arbitrage opportunities. Each on-chain bid is a public transaction whose inclusion is decided by a single consensus proposer per block. The proposer can observe pending bids, exclude competitors, and submit bids of their own, breaking the fairness guarantees of classical sealed-bid auctions. To enable latency-sensitive sealed-bid auctions in blockchain settings, we formalize four properties -- each necessary to prevent a concrete attack -- and design a protocol achieving all four: hiding bid contents, existence, and bidder identity until reveal (Hiding); counting all timely honest bids and rejecting late adversarial bids (Simultaneous Release); preventing silent withdrawal of committed bids (No Free Bid Withdrawal); and charging on-chain fees only to winners (Auction Participation Efficiency). Our protocol uses a timestamping oracle (instantiated with a committee of 2f_ts+1 timestampers) and a censorship-resistant inclusion predicate (instantiated using a FOCIL-based inclusion list), with only the winning bid settled on-chain. Our construction relies on two zero-knowledge proofs: an eligibility proof that anonymously proves deposit membership to the timestamping committee, and an auction proof that binds a bid to a specific auction for the inclusion list committee. We implement both using Groth16 over BN254 with Poseidon hashing in arkworks/Rust: the auction proof generates in 13 ms and verifies in under 1 ms; eligibility proofs for Merkle trees up to 2^32 bidders generate in 47-159 ms and verify in about 1 ms. Together, this yields a sealed-bid auction primitive practical for high-value, time-sensitive blockchain settings.
Chuanjia Yao, Zhihui Jiang, Xufeng Su, Xinchun Ma · 10 authors
The management of medical insurance funds is pivotal to the development of medical consortia. These funds serve as the operational lifeline of medical consortia and constitute critical public resources essential for public welfare. Medical expense settlement involves multiple stakeholders, including patients, tiered healthcare institutions, and insurance administrative agencies. However, disputes frequently arise between medical insurance authorities and hospitals regarding expense legitimacy due to information asymmetry and interpretative discrepancies. Such conflicts impede smoothness of payment mechanism, thereby undermining consortium operations and inter-institutional collaboration. To address these challenges, this study proposes a blockchain-based framework integrating medical expense investigation with insurance settlement. The system employs two core components: Anomaly detection via the Isolation Forest (IF) algorithm to identify potentially irregular expenses. Consensus-driven adjudication using Fleiss Kappa-based smart contracts facilitated by an anonymous panel of medical experts. This design enhances coordination between expense oversight and settlement processes, thus streamlining dispute resolution for medical expense anomalies and improving the scientific governance of insurance funds. Experimentally, the statistical validity demonstrated for unsupervised anomaly detection of IF and potential engineering applicability indicated in the analysis of healthcare cost. The healthcare consortium blockchain based on a Delegated Proof-of-Stake (DPoS) consensus mechanism and smart contract batch processing achieved a peak throughput of 229.64 transactions per second (TPS) and reduced transaction costs by up to 3,095 gwei. This demonstrates scalability for real-world medical insurance collaborative settlement systems.
This chapter examines the decentralized autonomous organizations (DAOs), which rely primarily on sociotechnical infrastructures supplied by blockchain technology and consist substantially of combinations of shared computer code and shared data. The chapter considers DAOs using the governing knowledge commons (GKC) research framework, contrasting the GKC perspective with long-standing views of the corporate form as a nexus of contracts, as an instance of hierarchy and decision theory, and as a complex system. The analysis is set against the context of earlier work on the corporation as commons. The chapter concludes that the GKC framework focuses attention on elements of governance that often are not salient in conventional accounts. This is especially true of the important question of how governance responds to and generates social dilemmas associated specifically with practices of sharing knowledge, information, and data.
While full ledger access is theoretically possible on public blockchains, in reality it is often not possible. Things that can be seen are limited by storage limitations, client design, indexing services, and off-chain execution pathways. This means that entire ledger objects are rarely used for empirical blockchain analysis; instead, observable projections are typically used. In this research, the observability of blockchain is recast as an inferential problem with incomplete observation. Studying identifiability, information loss, and irreducible uncertainty under coarsened access, the framework defines a full ledger, an observable ledger, and an observability mechanism. Three distinct visibility regimes, independent Bernoulli, clustered, and activity-dependent, are assessed in the simulation study. Reduced visibility raises uncertainty inflation, root mean squared error, variance, and mean squared error across all three regimes. The most severe deterioration happens when the condition of the underlying ledger determines visibility. This empirical study employs Google BigQuery's publicly indexed Ethereum block data spanning blocks 18,000,000 to 18,001,000. Over the chosen Ethereum period, descriptive summaries reveal a large amount of fluctuation in gas utilised, transaction count, and basic charge per gas at the block level. Experiments with controlled missingness on the observed slice reveal that RMSE and trend estimate bias grow with increasing missingness, and that the degree of distortion is significantly affected by whether the incompleteness is MCAR-like, MAR-like, or MNAR-like. This research proves that partial observability isn't just a secondary data issue; it can significantly affect inference on Ethereum block-level summaries.
Execution Tickets (ET) have emerged as a leading proposal for mitigating MEV-related centralization risks by internalizing MEV through a protocol-level lottery system. This paper provides an empirical game-theoretic analysis (EGTA) of the ET mechanism under an infinite-supply design, modeled as a Tullock contest. We evaluate a 2-slot lookahead window as a minimal temporal design that limits multi-slot MEV while preserving support for user pre-confirmations. By introducing a forfeiture parameter, we parameterize a continuum between All-Pay and Winner-Pay regimes. We then map the fairness-revenue frontier, revealing a fundamental design tension: higher contest decisiveness and forfeiture rates can improve protocol revenue, but may reduce allocation fairness by entrenching dominant builders. We identify a quantitative Goldilocks zone that balances MEV-capture efficiency with market diversity.
Objetivo: propor a Decentralized Autonomous Franchise (DAF) como uma arquitetura organizacional alternativa para redes de franquias, baseada em blockchain, contratos inteligentes e governança tokenizada. Estado da arte: embora o franchising seja amplamente reconhecido como modelo eficiente de expansão, enfrenta limitações estruturais relacionadas à centralização de poder, à incompletude contratual e à s assimetrias informacionais. Paralelamente, a literatura cientÃfica sobre Decentralized Autonomous Organizations (DAOs) tem avançado na discussão de governança descentralizada, ainda com pouca articulação com o campo de franchising. Originalidade: o artigo aproxima os campos de franchising e DAOs, propondo a DAF como modelo alternativo que reconfigura mecanismos de coordenação, participação e controle em redes de franquias. Impactos: o artigo oferece um referencial inovador para redes de franquias interessadas em atualizar seus mecanismos de governança, ampliar a participação dos franqueados e incorporar princÃpios de transparência e descentralização apoiados por tecnologias digitais descentralizadas. ODS: 8 – Trabalho decente e crescimento econômico, 9 – Indústria, inovação e infraestrutura, 17 – Parcerias e meios de implementação.
Angelo Ferrando, Blondelle Kana Zanlefack, Vadim Malvone
The exponential growth of Decentralized Finance (DeFi) has underscored the critical need for formal verification methods that can reason about the financial properties of smart contracts. Traditional formal methods such as Alternating-time Temporal Logic (ATL) cannot express liquidity properties—guarantees about users' ability to access assets based on wallet balances. We introduce Wallet ATL (WATL), an extension of ATL with wallet predicates and financially constrained strategic operators. WATL ensures that actions are both strategically and economically feasible. We formalize the semantics of WATL, provide model checking algorithms within the VITAMIN framework, and address scalability through the Meta-Agent Abstraction, which collapses all non-coalition agents into a single meta-agent with a sum-aggregated wallet. This abstraction preserves liquidity properties while significantly reducing the verification space. Through case studies such as a crowdfunding smart contract, we demonstrate how WATL formally specifies and verifies liquidity guarantees. Our results show that WATL, implemented in the VITAMIN tool, bridges the gap between multi-agent strategic reasoning and financial correctness, providing a practical step towards the formal verification of smart contracts with liquidity-awareness.
The emergence of Decentralized Autonomous Organizations (DAOs) represents a paradigm shift in organizational governance, yet their technical complexity remains a significant barrier to widespread adoption. Creating and managing a DAO requires deep expertise in blockchain development, smart contract auditing, and cryptocurrency operations, which excludes many potential users in non-technical domains. This paper presents DAOship, a novel no-code platform for DAO creation and management deployed on the Avalanche blockchain. The platform provides an intuitive graphical user interface (GUI) that allows users to configure, launch, and operate a fully-functional DAO without writing a single line of code. By leveraging Avalanche's high throughput and low transaction fees, the system enables the deployment of customizable smart contracts for governance, treasury management, and voting. The platform dramatically lowers the technical barrier, empowering communities, startups, and traditional organizations to leverage decentralized governance models easily and securely. Testing on the Avalanche Fuji testnet yielded a 97% reduction in setup time, an average System Usability Scale (SUS) score of 89.2, and zero critical vulnerabilities across all deployed DAOs.
The Ethereum blockchain utilizes the EIP-1559 algorithm to manage transaction inclusion and block assembly. However, EIP-1559 and much of the existing literature study this problem from a static perspective, focusing on price evolution without modelling transaction dynamics within the mempool. Motivated by this limitation, we study a dynamic transaction scheduling problem in which transactions with heterogeneous sizes and per-unit values arrive over time and remain in the mempool until scheduled. To capture the stochastic mempool evolution, we formulate the problem as a Markov Decision Process (MDP) whose state represents the mempool configuration and whose actions correspond to block prices. We first provide a primal-dual interpretation of the static EIP-1559 mechanism, showing that block prices arise naturally as dual variables of a social-welfare maximization problem. Building on this perspective, we extend the framework to the dynamic setting and formulate an objective that maximizes long-run discounted reward while incorporating holding costs and overshoot penalties. We then employ a Natural Policy Gradient (NPG) algorithm to compute the optimal policy. Our results show that dynamic pricing stabilizes the mempool while maximizing long-run discounted reward. In particular, as the overshoot penalty increases, the average scheduled transaction volume converges to the target block capacity, and the resulting NPG updates closely resemble the EIP-1559 price update rule. Finally, we study two special cases of the MDP formulation: homogeneous transactions and uniform arrivals. In the homogeneous setting, where the protocol directly controls scheduled volume, we show that the optimal policy has a threshold structure. We then propose a bang-bang pricing mechanism for uniform arrivals and derive a lower bound on the block capacity needed to ensure system stability.
We introduce the State Twin: a typed, in-memory, replayable replica of an on-chain automated market maker (AMM) pool that serves as a substrate for agentic reasoning over decentralized finance (DeFi) protocols. Agentic DeFi stacks today couple reasoning to chain time, since every "what if?" query incurs a new RPC read or a real transaction, so the agent's effective action space is bounded by block confirmation latency and gas. We argue this coupling is a structural problem rather than a performance one, and that the missing layer is an off-chain substrate that preserves the protocol's exact mathematics while admitting the operations on-chain state cannot: forking, replay, branching, counterfactual rollout. We formalize each AMM family (Uniswap V2, V3, Balancer, Stableswap) as a discrete-time controlled dynamical system, prove a quantitative fidelity bound on the divergence between twin and chain, and give the open architecture used in DeFiPy v2, an open-source Python toolkit that ships the State Twin substrate and a reference Model Context Protocol server exposing typed analytical primitives as LLM tools. The same primitive (i.e., one Python class, one calling pattern) serves a notebook quant, a backtest, and an LLM agent without modification. We close with a fork-and-evaluate worked example: a single live RPC read seeds N independent in-memory twins under distinct price-shock scenarios, in sub-second wall-clock time. The contribution is the substrate, not a particular agent, which is what the specification of what an agentic DeFi substrate must look like
A first-order design task in blockchain-based decentralized autonomous organizations is to ensure that malicious actors are sanctioned. We show that, when voters act strategically and the system is insufficiently decentralized, payoff-matching bribes undermine the sanctioning of malicious actors under conventional governance. Our framework formalizes DAO voting mechanisms and lets us identify those that mitigate the problem. Stochastic voting decouples a tokenholder’s influence from the voting behavior of others. Thus, bribery-proofness can be restored in the presence of sufficiently centralized governance tokenholders. Alternatively, masked voting increases resilience against bribery. Our work contributes to the broader debate on the merits and pitfalls of decentralization and highlights the need to align governance mechanisms with the degree of decentralization in blockchain networks.
Automated market makers (AMMs) quote prices from pool state rather than from a limit order book. AMM pools often stay close to a reference price because arbitrageurs correct profitable mispricing. A large part of decentralized finance therefore relies on a simple economic premise: once the AMM price drifts away from the reference price, arbitrage incentives push it back. This paper studies when that premise is strong enough to guarantee block-scale stability. We model the gap between the reference price and the AMM price as a stochastic tracking error, treat arbitrage as the corrective input, and place blockchain execution inside the loop through fees, discrete blocks, transaction ordering, delays, and transaction failure. The detailed execution layer is reduced to the total successful correction confirmed in each block. Under a block-level correction condition, we prove geometric ergodicity of the tracking error and obtain explicit one-step bounds that connect tracking quality to liquidity and execution quality. We also show in a constant-product example how fees, fixed execution costs, and local liquidity map into the no-trade band and the optimal corrective trade. Finally, we build empirical proxies for the theorem quantities from realized block data and use them to organize reduced and mechanism-focused simulations whose comparative statics are consistent with the theory. The contribution is to turn a basic economic intuition behind decentralized finance into a quantitative stability statement together with a tractable calibration interface.