This study presents a comprehensive empirical comparison between quantum machine learning (QML) and classical machine learning (CML) approaches in Automated Market Makers (AMM) and Decentralized Finance (DeFi) trading strategies through extensive backtesting on 10 models across multiple cryptocurrency assets. Our analysis encompasses classical ML models (Random Forest, Gradient Boosting, Logistic Regression), pure quantum models (VQE Classifier, QNN, QSVM), hybrid quantum-classical models (QASA Hybrid, QASA Sequence, QuantumRWKV), and transformer models. The results demonstrate that hybrid quantum models achieve superior overall performance with 11.2\% average return and 1.42 average Sharpe ratio, while classical ML models show 9.8\% average return and 1.47 average Sharpe ratio. The QASA Sequence hybrid model achieves the highest individual return of 13.99\% with the best Sharpe ratio of 1.76, demonstrating the potential of quantum-classical hybrid approaches in AMM and DeFi trading strategies.

As decentralized finance (DeFi) evolves, distinguishing between user behaviors - liquidity provision versus active trading - has become vital for risk modeling and on-chain reputation. We propose a behavioral scoring framework for Uniswap that assigns two complementary scores: a Liquidity Provision Score that assesses strategic liquidity contributions, and a Swap Behavior Score that reflects trading intent, volatility exposure, and discipline. The scores are constructed using rule-based blueprints that decompose behavior into volume, frequency, holding time, and withdrawal patterns. To handle edge cases and learn feature interactions, we introduce a deep residual neural network with densely connected skip blocks inspired by the U-Net architecture. We also incorporate pool-level context such as total value locked (TVL), fee tiers, and pool size, allowing the system to differentiate similar user behaviors across pools with varying characteristics. Our framework enables context-aware and scalable DeFi user scoring, supporting improved risk assessment and incentive design. Experiments on Uniswap v3 data show its usefulness for user segmentation and protocol-aligned reputation systems. Although we refer to our metric as zScore, it is independently developed and methodologically different from the cross-protocol system proposed by Udupi et al. Our focus is on role-specific behavioral modeling within Uniswap using blueprint logic and supervised learning.

Rug pulls in Solana have caused significant damage to users interacting with Decentralized Finance (DeFi). A rug pull occurs when developers exploit users' trust and drain liquidity from token pools on Decentralized Exchanges (DEXs), leaving users with worthless tokens. Although rug pulls in Ethereum and Binance Smart Chain (BSC) have gained attention recently, analysis of rug pulls in Solana remains largely under-explored. In this paper, we introduce SolRPDS (Solana Rug Pull Dataset), the first public rug pull dataset derived from Solana's transactions. We examine approximately four years of DeFi data (2021-2024) that covers suspected and confirmed tokens exhibiting rug pull patterns. The dataset, derived from 3.69 billion transactions, consists of 62,895 suspicious liquidity pools. The data is annotated for inactivity states, which is a key indicator, and includes several detailed liquidity activities such as additions, removals, and last interaction as well as other attributes such as inactivity periods and withdrawn token amounts, to help identify suspicious behavior. Our preliminary analysis reveals clear distinctions between legitimate and fraudulent liquidity pools and we found that 22,195 tokens in the dataset exhibit rug pull patterns during the examined period. SolRPDS can support a wide range of future research on rug pulls including the development of data-driven and heuristic-based solutions for real-time rug pull detection and mitigation.

In the digital era, blockchain technology, cryptocurrencies, and non-fungible tokens (NFTs) have transformed financial and decentralized systems. However, existing research often neglects the spatiotemporal variations in public sentiment toward these technologies, limiting macro-level insights into their global impact. This study leverages Twitter data to explore public attention and sentiment across 150 countries, analyzing over 150 million geotagged tweets from 2012 to 2022. Sentiment scores were derived using a BERT-based multilingual sentiment model trained on 7.4 billion tweets. The analysis integrates global cryptocurrency regulations and economic indicators from the World Development Indicators database. Results reveal significant global sentiment variations influenced by economic factors, with more developed nations engaging more in discussions, while less developed countries show higher sentiment levels. Geographically weighted regression indicates that GDP-tweet engagement correlation intensifies following Bitcoin price surges. Topic modeling shows that countries within similar economic clusters share discussion trends, while different clusters focus on distinct topics. This study highlights global disparities in sentiment toward decentralized finance, shaped by economic and regional factors, with implications for poverty alleviation, cryptocurrency crime, and sustainable development. The dataset and code are publicly available on GitHub.

We explore the adoption of graph representation learning (GRL) algorithms to investigate similarities across services offered by Decentralized Finance (DeFi) protocols. Following existing literature, we use Ethereum transaction data to identify the DeFi building blocks. These are sets of protocol-specific smart contracts that are utilized in combination within single transactions and encapsulate the logic to conduct specific financial services such as swapping or lending cryptoassets. We propose a method to categorize these blocks into clusters based on their smart contract attributes and the graph structure of their smart contract calls. We employ GRL to create embedding vectors from building blocks and agglomerative models for clustering them. To evaluate whether they are effectively grouped in clusters of similar functionalities, we associate them with eight financial functionality categories and use this information as the target label. We find that in the best-case scenario purity reaches .888. We use additional information to associate the building blocks with protocol-specific target labels, obtaining comparable purity (.864) but higher V-Measure (.571); we discuss plausible explanations for this difference. In summary, this method helps categorize existing financial products offered by DeFi protocols, and can effectively automatize the detection of similar DeFi services, especially within protocols.

Decentralized Autonomous Organization (DAO) provides a decentralized governance solution through blockchain, where decision-making process relies on on-chain voting and follows majority rule. This paper focuses on MakerDAO, and we find three voter coalitions after applying clustering algorithm to voting history. The emergence of a dominant voter coalition is a signal of governance centralization in DAO, and voter coalitions have complicated influence on Maker protocol, which is governed by MakerDAO. This paper presents empirical evidence of multicoalition democracy in DAO and further contributes to the contemporary debate on whether decentralized governance is possible.

The world of blockchain and cryptocurrency has witnessed remarkable advancements in the past decade. Satoshi AI ( Satoshi AI is backed by Satoshi Foundation), a revolutionary platform that combines the power of artificial intelligence (AI) and decentralized finance (DeFi), is one such innovation that promises to change the way we mine cryptocurrencies and engage in decentralized financial activities. Satoshi AI is an AI-powered cryptocurrency mining platform that aims to streamline the process of mining digital currencies. By harnessing the capabilities of machine learning algorithms, the platform is able to optimize the mining process and maximize profits for its users. The system analyzes market trends, evaluates mining difficulty, and adjusts the mining process in real-time to ensure optimal returns.

Ai-Da will open her solo exhibition LEAPING INTO THE METAVERSE at the Venice Biennale this year curated by Aidan Meller. Philosophers amongst you will be familiar with the work of Rene Descartes – a mathematician, epistemologist, and rationalist – much of his work laid the ground for modern philosophy and in particular the strand that has grown out of Hobbes and Locke that informs a lot of the 17th century and the formation of states and societies thereafter. There is one eerie and unsettling aspect of his life that is gaining greater attention. Descartes had a relationship with a servant (Helen van der Strom), and their relationship produced a young daughter Francine, to whom Descartes was very attached. Tragically, Francine died of scarlet fever, aged five, and so distraught was Descartes that he had a robot or automata (clockwork, lifelike doll) built in her likeness.

Singapore, Jan. 08, 2023 (GLOBE NEWSWIRE) -- A platform launched by DefiLabs in 2021 is now ready to harness the power of Artificial Intelligence (AI) to revolutionize the world of decentralized finance (DeFi). DefiLabs is a fully intelligent and decentralized asset management crypto staking platform which is built on Binance Smart Chain. DefiLabs uses artificial intelligence based on quantum algorithms to dynamically manage portfolios, perform efficient asset allocation, and predictively model asset management strategies. Thus, make profits for users in different fields such as lending, leverage mining, and cross-chain mining to improve their portfolio in the fast-paced world of DeFi. DefiLabs deploy an ecosystem of Artificial Intelligence to support trading decisions on DefiLabs which will help increase the value and returns of the users by providing liquidity to DeFi markets.

If one has followed developments in the crypto verse for the past few years, they would have recognized the power of buzzwords. The simple act of adding "blockchain" to the description of a product in 2017 led to a spike in the company's valuation. Announcing an "ICO" about the same time, enabled pre-seed startups to raise sums previously accessible only to established companies with a track record of continuous delivery. Some projects went even further by combining buzzwords for maximum impact over an audience affected by a severe case of the FOMO (fear of missing out -- ed.n) virus. The irrational exuberance of the ICO craze saw projects combining blockchain with artificial intelligence, machine learning, IoT, Web 3, VR, and AR, fully-capitalizing on the appeal of the Fourth Industrial Revolution narrative.