Canonical reference map for the PRE-GHR publication series. Records every record in the series with its concept DOI, version history, and relational links; declares numbering conventions and known gaps; establishes citation and versioning standards. This map is itself a PRE-GHR series record. v33 (2026-08-28). Two changes. 1. PRE-GHR XXXIX v5.0 registered (version DOI 10.5281/zenodo.22145426; concept DOI 10.5281/zenodo.21889278 unchanged). v5.0 is the release version closing all six objections of an adversarial pre-submission review, one revision ticket each: Theorem 4 unilateralized with the converse demoted to an observation under an explicit complete-erasure assumption (R01); ledger counts restricted to lower witnesses, the ordering claim made conditional on a fixed normalization and full retention (R02); an explicit two-sided finite-sample bound replacing an expectation-only argument (R03); four empirical mappings corrected — schema-field disjointness separated from retained-trace intersection, join error reported two-sided with the earlier “directionally safe, never over-counting” claim withdrawn, overlap-error direction governed by an error budget, retention ratio restated in matched units (R04); measure-relative notation throughout (R05); subject classification reassessed and Related Work rebuilt (R06). This is the first subject-classification reversal recorded in this map: cs.MA is withdrawn as unsupported by the technical content — the formalism contains no agent population, strategic interaction, or equilibrium claim — and replaced by cs.CR primary with a cs.DB cross-list; Related Work now separates the lineage the paper inherits from (linked timestamping and distributed witnesses, split-view detection and the undefined gossip layer, existence-not-authenticity timestamping, provenance and lineage, record linkage, trace semantics, measure and order) from adjacent recent lines cited for comparison only, assigning priority to the sources where the paper's constructions proved to be rediscoveries. Two gaps are declared inherited rather than closed: the hash-chain anchor has no consistency-proof comparison mechanism, and the anchor-propagation layer is undefined in the source standard as well. 2. The AI-collaboration attribution note (drafted 2026-08-20, previously unpublished as a local v32.1 revision) is merged into this version. It records that papers in the series are drafted with AI assistance, that the author block is platform-plus-model double-written from XL v1.3 onward, and how the platform-only author line of earlier versions is to be read. On merge, the coverage clause of the writing-model statement was narrowed under red-pen review (2026-08-28): the claim's width is aligned to the strength of its evidence. The complement of the recorded provider-fallback events establishes that no fallback leg entered a paper-writing session; it does not establish per-paper model attribution for the entire series. The statement is therefore scoped to the drafting sessions of the pre-v1.3 papers named in the per-paper note, and the narrowing itself is recorded in the revision history so that the difference between the unpublished local note and this published version is auditable. Delivery-fingerprint discipline updated this day. A PDF's md5 is a build-instance fingerprint, not a content fingerprint: pdflatex writes /CreationDate and /ID on every build, so the same source compiled twice differs in md5 while the typeset content is identical (measured: 68 differing bytes, all inside that region). Deliverables in this series now carry file md5, a content fingerprint with the extractor and version named, page count and byte count, produced under a reproducible build with the embedded date pinned. Record count unchanged: 39 records (27 series-internal).
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Mathematics, Computing, and Information Processing
Jan 1, 2025·Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences
This mini-track focuses on how smart contracts and artificial intelligence (AI) in blockchains are used to automate decision analytics to provide new services.Smart contracts can track changes and automate decision-making in supply chain systems, ledgers recording ownership transfers, and decentralized autonomous organizations (DAOs) governance.The rapid development of blockchain technology and smart contracts in the last ten years has fueled a dramatic increase in commerce in the crypto space.Decentralized financial services (DeFi), supply chain systems, healthcare delivery, manufacturing systems, and agriculture are all being impacted by the emergence of distributed ledger technologies (DLTs) enabled by smart contracts.Smart contracts are executable codes that run on blockchains like Ethereum to facilitate, monitor, and execute transactions and agreements between parties without using traditional trusted third parties.These smart contracts automate the decision analytics required for commerce between two or more parties.Artificial intelligence is essential in detecting fraud and misuse, allowing these systems and organizations to run without human intervention.If blockchain commerce is to become widespread, it is vital to understand the characteristics and best practices needed for effective and efficient smart contracts and how AI enables these systems to operate autonomously.This mini-track encourages authors to share new and interesting theoretical and methodological perspectives on topics relevant to academic researchers and practitioners.It welcomes work-in-progress that examines existing and extended theories using smart contracts and AI in blockchain autonomous systems and organizations.It gives special consideration to research submissions when the author(s) commit to including an industry partner in their presentation.It welcomes research that reflects a range of current research methods, including case studies, analytical models, conceptual studies, econometrics, and frameworks.A range of research areas were suggested, including:
Decentralized Exchanges (DEXs) are pivotal applications in the Decentralized Finance (DeFi) landscape, aiming to facilitate trustless cryptocurrency trading by relying on smart contracts and blockchain networks. The developments in the DEXs sector began with the implementation of an Automated Market Maker (AMM) system using a simple math formula by Uniswap in 2018. Absorbing significant funding and the attention of web3 enthusiasts, DEXs have seen numerous advancements in their evolution. A notable recent advancement is the introduction of hooks in Uniswap v4, which allows users to take advantage of a wide range of plugin-like features with liquidity pools. This paper provides a comprehensive classification and comparative analyses of prominent DEX protocols, namely Uniswap, Curve, and Balancer, in addition to investigating other protocols' noteworthy aspects. The evaluation framework encompasses mechanisms, components, mathematical formulations, and the performance of liquidity pools. The goals are to elucidate the strengths and limitations of different AMM models, highlight emerging concepts in DEX development, outline current challenges, and differentiate optimal models for specific applications. The results and comparative insights can be a reference for web3 developers, blockchain researchers, traders, and regulatory parties.
This chapter provides a general overview of the Ethereum Execution Layer (EL). We begin by discussing the concept of Ethereum&s;s world state. We discover that the world state is a database mapping addresses to user accounts and application accounts. We take a look at the various transaction types that exist in the Ethereum protocol. We discuss smart contracts and how they are processed by the Ethereum Virtual Machine (EVM). Finally, we introduce the concept of blockchains and how execution layer data is stored.
This chapter provides a general overview of the Ethereum Consensus Layer (CL). It first describes the blockchain data structure and then introduces Ethereum&s;s consensus blockchain, called the Beacon Chain. We then turn to an explanation of Ethereum&s;s consensus mechanism, called Proof of Stake (PoS) or staking. It shows how staking involves validators who are incentivised to update the Beacon Chain honestly and disincentivised to engage in malicious behaviour. We conclude with advanced consensus topics such as Maximum Extractable Value (MEV) and blockchain forks.
Dongze Li, Kejia Zhang, Shiwei Li, Gang Du · 5 authors
As decentralized finance (DeFi) grows and decentralized exchanges (DEXs) expand, the security of Ethereum smart contracts and blockchain transactions is receiving increasing scholarly attention. The ERC20 token standard has facilitated the emergence of numerous honeypot contracts, which deceive traders by allowing token purchases but blocking withdrawals. This study proposes a lightweight honeypot contract detection system integrated into the go-Ethereum client (Geth). Unlike previous work, our detector does not rely on contract interaction records or source code provided by contract creators. Instead, our approach performs static data flow analysis on contract bytecode to identify honeypot mechanisms. By focusing exclusively on the control flow of the ERC20 Transfer method, our system achieves faster detection than full-contract analysis methods, with an average processing time of 9.74 milliseconds per contract. Experiments on both known honeypot contracts and real-world token contracts demonstrate the effectiveness of our approach in detecting malicious ERC20 contracts.
A consensus protocol may be defined as the mechanism through which a Blockchain network reaches consensus. Blockchains are built as distributed systems and, since they do not rely on a central authority, the distributed nodes need to agree on the validity of transactions. Proof of Authority is known to bear many similarities to Proof of Stake (PoS) and DPoS, where only a group of pre-selected authorities secure the Blockchain and are able to produce new blocks. New blocks on the Blockchain are created only when a supermajority is reached by the validators. The identities of all validators are public and verifiable by any third party, resulting in the validator’s public identity performing the role of proof of stake. Proof of importance is a consensus algorithm similar to PoS.
Delays in finance impose real costs, yet they are difficult to measure in traditional settings. Leveraging Ethereum's on-chain data and fee market, I estimate a structural queuing model that maps fee bids to time preferences and quantify delay costs in decentralized finance. The median transaction bears a 7.1 bps implicit cost, with costs highly skewed and totaling $9.88 million per day. Counterfactuals show that a flexible block gas limit as in EIP-1559 cuts the loss from delay by 32.9%, and doubling block frequency with half-size blocks cuts it by 13.6%. Combined, the two policies reduce losses by a striking 63.9%.
In the Uniswap Protocol, a price oracle is a tool used to view price information about a given asset over time and enables developers to build highly decentralized protocols with quantifiable manipulation costs using price feeds. With the adoption of Proof of Stake (PoS), oracles are theoretically less secure because a malicious validator knows whether they control the next block created, allowing them to avoid value lost to back-running an attempted oracle manipulation. However, manipulation on most Uniswap v3 TWAP oracles is not currently possible, because bad actors both need to source vast amounts of capital and then earn enough to make up for value lost to fees. This paper studies the difficulty, potential cost, and likelihood of oracle manipulations on Uniswap v3 under Ethereum PoS. We also discuss potential future innovations to create the next generation of PoS manipulation resistant oracles.
In previous chapters, you learned about the Ethereum architecture, Solidity programming, and how to debug and deploy using different local and test setups. So far so good; but is that all enough to make you a master in the Ethereum blockchain? The main challenge that the blockchain market faces today is a lack of knowledge about where this technology can be best applied. Business leaders across verticals are struggling to find use cases that would be a good fit for blockchain implementation, and loads of arguments are taking place in this area. As a blockchain expert, it’s your job to dream, innovate, come up with ideas, and advise businesses about where this technology can bring maximum benefits.
In certain mathematical statistical circles, there appear to linger doubts about the soundness of latent variables, in particular common factors in factor models. In what follows we will show, to the best of our knowledge for the first time, that a common latent factor in a factor model can be conceived of as a genuine scientific construct obeying the usual formal criteria for such constructs. Following the discussion in Simon (1977, ch. 6 .6 ) it will be shown that a common factor in a factor model can be transformed into a network of regression relationships between the manifest (observed) variables. Because this transformation is invertible, we end up with two strictly equivalent models; the factor model and a model without the factor. This shows that the latent factor in a factor model serves as a placeholder for a system of relationships between the manifest variables and thus obeys the most stringent criterion for valid scientific constructs (cf. Simon, 1977).In what follows, we present a complete constructive proof that the latent factor in a 1 -factor model can be transformed away, yielding a model composed of a network of regression relationships between the observed variables. Preliminary work in this direction can be found in a recent book by the first author that can be freely downloaded from internet (Molenaar, 2003). Also the paper by Rovine and Molenaar (2005) contains further elaborations. But in this chapter, we for 189 the first time present all steps in the proof in full detail and for the simplest possible situation, a 1 -factor model. Even though we take great care to explain the steps in the proof as clearly as possible, a lot is asked of the reader. We suppose that they have a working knowledge of structural equation modeling, are acquainted with elementary matrix algebra, and are willing to work through some unfamiliar notions taken from time series analysis. These efforts only can be asked if the returns are irresistible. And in our opinion the results presented in this chapter are quite revolutionary. For the first time a general technique, an invertible transformation, is presented with which one can remove latent variables from a latent variable model without any loss of information. This makes explicit something basic about the scientific nature of latent variables: They are real and have solid groundings in the data. It turns out to be a matter of taste whether one wants to entertain a latent variable model, or instead, its equivalent representation without latent variables. Of course, latent variable models may be better interpretable (in our opinion, they almost always are) and we certainly do not want to advertise the unconditional use of our transformation technique. But a proof that latent variables, in particular common factors, can be reduced to functional relationships between manifest variables shows that these latent variables share all their formal and semantic qualities with other respectable scientific constructs like electromagnetic potential, entropy, and so forth. This is not only important from a philosophy of science point of view, but also for discussions about the status of well-known psychometrical constructs like reliability and validity (cf. Borsboom, Mellenbergh, & Heerden, 2003). Another implication of the proof to be given shortly is that all structural equation models can be shown to be nested. This particular implication is not elaborated in this chapter, but details can be found in Rovine and Molenaar (2005) and in Molenaar (2003). To give one particular example, it can be shown that the latent growth curve model is nested under the latent simplex model (contra Rogosa & Willett, 1985; see also Mandys, Dolan, &; Molenaar, 1994). But the harvest is much bigger. If, as will be shown, it is possible to remove the factor from a 1 -factor model, then apparently it is possible to reduce the common latent dimension from one to zero. Because after the removal of the latent common factor (which defines the common one dimension in the initial 1 -factor model), there are no longer any common latent factors in the obtained equivalent model (hence the latent dimension in the latter model is zero). It is shown in Molenaar (2003) that this result holds in general. To illustrate, starting with a standard 2-factor model (where the two factors span up a two-dimensional common latent space), it is possible to transform this model to an equivalent model in which the common latent space is one-dimensional. Finally, the model obtained in the previous step can itself again be transformed into an equivalent model in which the common latent space is absent (zero-dimensional). This, of course, raises urgent questions about the proper definition of the dimension of a common latent space (like in statements such as “this psychological test is indicative of two underlying dimensions”). There are additional important implications, although of a much more theoretical nature. We mention only the direct relationship of the kind of result that we are about to prove with similar results obtained in mathematical system theory and statistical field theory (both of which are elaborated somewhat further in Molenaar, 2003). We hope and expect that sufficient reasons have been provided to carry on reading this chapter.