Blockchain Papers

Jonathan Reiser

Classical reinforcement learning (RL) and decision theory rely on Kolmogorovian probability spaces and independent utility metrics. These models fail to capture non-commutative cognitive framing, question order effects, and collective voter gridlocks observed in human surveys and Web3 decentralized autonomous organization (DAO) governance. Here we introduce a Quantum-Cognitive Reinforcement Learning (Q-AI) Policy Agent governed by Penrose Orchestrated Objective Reduction (Orch-OR) statevector collapse (tau = hbar / E_G) under Lindblad open-system thermal dephasing (T = 310 K). We validate our architecture against two empirical datasets:1. Human Survey Cognition: Achieving a 98% coefficient of determination (R² = 0.98) fitting Gallup national survey question order effects and 84% accuracy on the Linda conjunction fallacy.2. Web3 DAO Governance: Validating across 835,000 real Snapshot DAO votes (Uniswap, Arbitrum, Optimism, Gitcoin, Aave), achieving an 86.7% Mean Absolute Error reduction (1.3% MAE vs 9.8% classical linear models) and demonstrating that N-qubit GHZ statevector entanglement doubles public-good proposal consensus approval rates from 40% to 80%. Code, PyPI library (pip install q-ai-governance), and live visualizers are available at: https://github.com/JonathanReiser/quantum-orch-or

Adam David Kain

Sovereign entities – states, international organizations, and autonomous infrastructure networks – face a governance paradox: centralized systems become brittle under stress, while decentralized systems fragment into incoherence. This paper proposes the Constitutional Lattice v2.0, a mathematically structured frame-work for coordinating sovereign autonomy within a constitutional corridor, built on a three-term agent-interaction force law (oscillatory coupling, linear restoring, inverse-square repulsion) with a Lennard-Jones-style short-range hardening term. This paper is offered, in the spirit of a companion theoretical proposal in the psychotherapy and Human–AGI relational-dynamics literature [1], as a theoretical contribution with an explicitly preliminary empirical status. The framework’s central structural conjecture – that the coupling ratio ρ= kg /km has a privileged value at Φ−1 ≈ 0.618 – was tested computationally in a simplified two-dimensional setting (Section 6). The test did not find evidence supporting this conjecture: the measured stability metric varied smoothly and monotonically across the tested range of ρ, with no distinguishing feature at Φ−1. This result, its scope, and its limitations are reported in full, following the disclosure standard set out in [1]. The paper’s remaining contributions – the federated lattice architecture, the quarantine and cold-boot recovery mechanisms, and the Constitutional Drift Index as a transparency instrument – are presented as an architecture and a research programme, not as validated engineering.

Luigi Usai

Intuizione scientifica: fare competere ipergrafi di ipergrafi, potrebbe portare ad un'ottimizzazione dei sistemi, oppure rischia di corromperli imponendo il senso comune? La competizione adversarial di ipergrafi di ipergrafi sarà la successiva evoluzione di questo paper. English: Abstract: This preprint formally introduces Hypergraph Adversarial Debate (HAD), an innovative multi-agent framework operating on higher-order knowledge structures modeled via hypergraphs (ℋ). While traditional adversarial machine learning paradigms on hypergraphs rely heavily on continuous, gradient-driven statistical optimizations, HAD conceptualizes epistemic robustness as a formal, discrete, turn-based game between two competing computational agents: a Proponent (𝒫) and an Opponent/Refuter (ℛ), adjudicated by a structured Judge (𝒥). We provide a rigorous mathematical formalization of the topological state space, hypergraph mutation operators, and the minimax objective functions that govern the system's convergence. HAD bridges the gap between formal argumentation theory and structural deep learning, offering new pathways for automated scientific hypothesis verification, epistemic red-teaming, and the dynamic purification of relational Knowledge Graphs. Italiano: Riassunto: Questo preprint introduce formalmente l'Hypergraph Adversarial Debate (HAD), un framework multi-agente innovativo operante su strutture di conoscenza di ordine superiore modellate tramite ipergrafi (ℋ). Mentre i paradigmi tradizionali di apprendimento avversario su ipergrafi si affidano a ottimizzazioni statistiche continue guidate dai gradienti, l'HAD concettualizza la robustezza epistemica come un gioco formale, discreto e a turni tra due agenti computazionali in competizione: un Proponente (𝒫) e un Confutatore (ℛ), supervisionati da un Giudice strutturato (𝒥). Viene fornita una rigorosa formalizzazione matematica dello spazio degli stati topologici, degli operatori di mutazione ipergrafica e delle funzioni obiettivo minimax che governano la convergenza del sistema. L'HAD unisce la teoria dell'argomentazione formale con il deep learning strutturale, aprendo nuove prospettive per la verifica automatica di ipotesi scientifiche, il red-teaming epistemico e la purificazione dinamica di Knowledge Graph relazionali. ---------------------------------------------------------------------Roadmap di formalizzazione / Formalization Roadmap--------------------------------------------------------------------- 🇬🇧 English – Next Steps Toward a Rigorous Formalization: We outline the concrete formalisation steps required to elevate the HAD framework from conceptual architecture to a fully verified mathematical theory. 1. **Hypergraph state space (H-space)** Let 𝒱 be a finite set of vertices (concepts, entities) and ℰ ⊆ 𝒫(𝒱) a set of hyperedges (higher-order relations). The state of the debate is a labelled hypergraph H = (𝒱, ℰ, L), where L: 𝒱 ∪ ℰ → Σ assigns labels from a finite alphabet Σ (e.g., truth values, epistemic statuses). The state space 𝕊 is the set of all such hypergraphs reachable from an initial H₀ via the allowed mutation operators. 2. **Mutation operators as hypergraph rewrite rules** Each turn, the active agent applies one mutation μ from a finite set M = M_add ∪ M_del ∪ M_relabel ∪ M_fuse. We define each μ as a partial function μ: 𝕊 ⇀ 𝕊 that satisfies a locality condition (only a bounded neighbourhood is altered). These can be represented as double-pushout (DPO) rules in the category of hypergraphs, making the operational semantics algebraically precise. 3. **Debate game structure** The game is an extensive-form, perfect-information, zero-sum game with alternating moves: - State: H_t ∈ 𝕊 - Turn: agent A_t ∈ {𝒫, ℛ} - Legal moves: M(H_t) ⊆ M, defined by preconditions (e.g., no deletion of "protected" axioms) - Transition: H_{t+1} = μ(H_t) for chosen μ ∈ M(H_t) Terminal states T ⊆ 𝕊 are those where no legal moves exist for the player whose turn it is, or a predefined depth limit is reached. 4. **Judge and minimax objective** The Judge implements a scoring function J: 𝕊 → ℝ that evaluates the epistemic quality of a hypergraph (coherence, empirical adequacy, simplicity, etc.). The game value V(H, d) at depth d is defined recursively: V(H, 0) = J(H) V(H, d) = max_{μ ∈ M(H)} V(μ(H), d-1) if turn = 𝒫, min_{μ ∈ M(H)} V(μ(H), d-1) if turn = ℛ. The agents rationally select moves optimizing this value. 5. **Convergence and equilibrium** We aim to prove that, under suitable monotonicity assumptions on J (e.g., J is a Scott-continuous function on a dcpo of hypergraphs ordered by epistemic improvement), the minimax sequence converges to a fixed point or a terminal state that represents a stable, "purified" knowledge structure. Further, one can investigate Nash equilibria in mixed strategies for non-deterministic settings. 6. **Call for collaboration** The formalization of HAD draws on hypergraph rewriting, game theory, order theory, and topological logics. We invite mathematicians, computer scientists, and logicians to contribute to: - Proving convergence theorems - Implementing a prototype HAD engine - Exploring connections with abstract argumentation and persistent homology 🇮🇹 Italiano – Prossimi passi verso una formalizzazione rigorosa: Descriviamo i passi concreti necessari per elevare il framework HAD da architettura concettuale a teoria matematica completamente verificata. 1. **Spazio degli stati ipergrafico (H-spazio)** Sia 𝒱 un insieme finito di vertici (concetti, entità) e ℰ ⊆ 𝒫(𝒱) un insieme di iperarchi (relazioni di ordine superiore). Lo stato del dibattito è un ipergrafo etichettato H = (𝒱, ℰ, L), dove L: 𝒱 ∪ ℰ → Σ assegna etichette da un alfabeto finito Σ (es. valori di verità, stati epistemici). Lo spazio degli stati 𝕊 è l’insieme di tutti gli ipergrafi raggiungibili a partire da un H₀ iniziale tramite gli operatori di mutazione ammessi. 2. **Operatori di mutazione come regole di riscrittura di ipergrafi** Ad ogni turno, l’agente attivo applica una mutazione μ da un insieme finito M = M_add ∪ M_del ∪ M_relabel ∪ M_fuse. Ogni μ è definita come una funzione parziale μ: 𝕊 ⇀ 𝕊 che soddisfa una condizione di località (solo un intorno limitato viene alterato). Tali operatori possono essere rappresentati tramite regole double-pushout (DPO) nella categoria degli ipergrafi, rendendo la semantica operazionale algebricamente precisa. 3. **Struttura del gioco di dibattito** Il gioco è a forma estesa, a informazione perfetta, a somma zero, con mosse alternate: - Stato: H_t ∈ 𝕊 - Turno: agente A_t ∈ {𝒫, ℛ} - Mosse lecite: M(H_t) ⊆ M, definite da precondizioni (es. divieto di cancellare "assiomi protetti") - Transizione: H_{t+1} = μ(H_t) per μ scelta tra M(H_t) Gli stati terminali T ⊆ 𝕊 sono quelli in cui non esistono mosse legali per il giocatore di turno, oppure viene raggiunto un limite di profondità prefissato. 4. **Giudice e obiettivo minimax** Il Giudice implementa una funzione di valutazione J: 𝕊 → ℝ che misura la qualità epistemica dell’ipergrafo (coerenza, adeguatezza empirica, semplicità, ecc.). Il valore del gioco V(H, d) a profondità d è definito ricorsivamente: V(H, 0) = J(H) V(H, d) = max_{μ ∈ M(H)} V(μ(H), d-1) se turno = 𝒫, min_{μ ∈ M(H)} V(μ(H), d-1) se turno = ℛ. Gli agenti scelgono razionalmente le mosse che ottimizzano tale valore. 5. **Convergenza ed equilibrio** Ci proponiamo di dimostrare che, sotto opportune ipotesi di monotonicità su J (es. J è una funzione Scott-continua su un dcpo di ipergrafi ordinati per miglioramento epistemico), la sequenza minimax converge a un punto fisso o a uno stato terminale che rappresenta una struttura di conoscenza stabile e "purificata". Si può inoltre indagare l’esistenza di equilibri di Nash in strategie miste per scenari non deterministici. 6. **Chiamata alla collaborazione** La formalizzazione di HAD attinge alla riscrittura di ipergrafi, alla teoria dei giochi, alla teoria degli ordini e alle logiche topologiche. Invitiamo matematici, informatici e logici a contribuire a: - Dimostrare teoremi di convergenza - Implementare un prototipo del motore HAD - Esplorare connessioni con l’argomentazione astratta e l’omologia persistente ---------------------------------------------------------------------Nota dell'Autore, Luigi Usai: "Il mio background è filosofico e umanistico. Ho intuito questa struttura logica e mi sono avvalso dell'Intelligenza Artificiale per modellarla e strutturarla nel paper. Non sono un matematico, non ho le competenze per fornirvi dimostrazioni formali ed è esattamente per questo che sono qui: per mostrarvi l'architettura concettuale e chiedere il vostro aiuto per capire se è formalizzabile." ---------------------------------------------------------------------Potential Impact of HAD on Mathematical Sciences--------------------------------------------------------------------- 🇬🇧 English: If the Hypergraph Adversarial Debate framework were systematically adopted by the mathematical community, it would trigger a paradigmatic shift in the production, verification, and pedagogy of mathematics. We outline the primary structural consequences. 1. **Automated Theorem Discovery and Verification** - *Ultra-rapid proof checking*: While current proof assistants (e.g., Lean, Coq) require manual translation of informal proofs into formal code, HAD automates the search for logical flaws by letting a refuter agent continuously probe the hypergraph representation of a proof for higher-order counterexamples. - *Devil’s Advocate multi-agency*: A pool of adversarial agents constantly attacks newly proposed theorems, targeting topological “blind spots” where a hyperedge connecting three or more premises is missing, thus enabling falsification that escapes traditional linear or tree-like proof structures. - *Topological falsification*: By mapping proofs to labelled hypergraphs, the system

Shuiping Tang

This paper establishes an exact information-theoretic duality between econometric regression topology and statistical mechanics. We demonstrate that an autonomous, data-driven Quadruple Test, grounded in the Factor Hierarchy Law, can blindly detect, precisely quantify, and correctly classify thermodynamic phase boundaries in perfect mathematical equivalence with the Ehrenfest paradigm, without any prior knowledge of Free Energy functions. The validation platform is the two-dimensional Ising model. Verification proceeds in two stages: pristine algebraic validation on the Onsager-Yang exact solution, and stochastic robustness testing on finite-lattice Monte Carlo simulations. We openly declare that because data derives from the known Onsager-Yang formula, this contribution is the rigorous proof of an exact informational duality between two independent frameworks—a mandatory metrological calibration, not an independent empirical discovery. Positive controls: Exhaustive search blindly locks onto Tc = 2.260 (deviation 0.009 at step 0.01). A grid-refinement study demonstrates monotonic convergence to ~10⁻⁸ under Golden Section Search—the limit of 64-bit machine precision. An analytical proof confirms the Chow F-statistic achieves a unique global maximum exactly at T₀ = Tc; in the analytical limit, localization error is strictly zero. Chow F = 118,074 against a null control of 3.12 (266-fold difference, permutation p = 0.000). Interaction effect: p = 0.000, ΔR² = 0.991. A symmetry-breaking switch at h = 0 yields Chow F = 989.41 (p = 0.000). Interaction R² peaks at Tc (deviation 0.03). Multi-response-function and anisotropic validations all lock onto theoretical Tc values (deviations Negative controls: A 3,000-point scan over 4 variables finds no false positive of comparable magnitude (maximum SNR 164,000:1). Monte Carlo simulations (L=16-128, 8 observables) detect Tc in all sizes; all cross-size candidate peaks are excluded by F-value decay criterion. Core discoveries: (1) Two distinct regime-switching topologies—"Rule-Reset" (interaction-dominated) and "Direction-Reversal" (intercept-jump-dominated)—map in exact informational duality onto Ehrenfest's second-order and first-order transitions. (2) Chow F(h=0) is an informational proxy for the order parameter, decaying from 691 million to 55 across 7 orders of magnitude and precisely mirroring latent heat vanishing. The F-statistic's ~38-fold amplification originates from its quadratic M² structure (theoretical lower bound β_F/β_M ≥ 2 confirmed). (3) Interaction R² is a precise proxy for second-order transition intensity (peaks at 0.9992, deviation 0.03). Methodological contribution: A Severe Test (sensu Mayo) is completed—22 independent verification checkpoints spanning five dimensions, all passed. Cross-disciplinary integration with the Tang Break (Tang, 2026h-j) and financial regime switches (Tang, 2026g) establishes the physics cornerstone for the Factor Hierarchy Law, proving it is an informational dual of thermodynamic symmetry-breaking structures.

T. L. Kosohov, O. V. Olkhovska

We study the epistemic efficiency of decentralized prediction markets under autonomous agentic liquidity. We introduce the information-incentive gap (G) – the discrepancy between ground truth and the market-implied probability – and establish, via Itô's calculus and exact solution of the resulting moment ODE, exponential convergence of its second moment together with an explicit upper bound for the gap of order O(σ/λ−−√). A two-level empirical study on information-driven event categories (Politics, Economics, Finance, Crypto Markets), drawing on approximately 40 million time-series records collected over the study period, is consistent with the model: (i) platform-level analysis of N=100 resolved binary events per platform shows the mean gap decreasing from G¯=0.517 at T−168 h to G¯=0.229 at T−30 min for Kalshi, and from 0.583 to 0.002 for Polymarket, with an empirical convergence rate λemp≈1.4×10−6 s−1; (ii) a paired cross-platform comparison of N=34 matched event groups shows that Polymarket exhibits a lower mean gap than Kalshi (mean ΔG=0.27 at T−6 h; Polymarket leads in 85% of pairs), consistent with the theoretical dependence of convergence speed on liquidity-driven λ. Monte Carlo simulation (N=50000 paths) confirms a >276× reduction in convergence latency and a 109× improvement in the Information Efficiency Ratio (IER) compared to the human-centric baseline.

Runhui Lin, Wenchang Li, Dahui Li, Ze Ji · 5 authors

No abstract is available for this record.

Yotam Gafni

Consider a social-choice function (SCF) is chosen to decide votes in a formal system, including votes to replace the voting method itself. Agents vote according to their ex-ante belief over what decisions are considered, and whether they prefer them to be decided by the incumbent SCF or the suggested replacement. The existing SCF then aggregates the agents' votes and arrives at a decision of whether it should itself be replaced. An SCF is self-maintaining if it can not be replaced in such fashion by any other SCF. Our focus is on the implications of self-maintenance for centralization. For this purpose, unlike [Barbera and Jackson, 2004], we do not generally restrict attention to anonymous SCFs. We also do not restrict attention to neutral SCFs, unlike [Koray, 2000]. We present results considering optimistic, pessimistic and i.i.d. approaches with respect to agent beliefs, different tie-breaking rules, and different SCF domains. To highlight two of the results, (i) for the i.i.d. unbiased case with arbitrary tie-breaking and general Boolean functions, we prove an Arrow-Style Theorem for Dynamics: We show that only a dictatorship is self-maintaining, and any other SCF has a path of changes that arrives at a dictatorship. (ii) With a pessimistic approach, tie-breaking that prefers the status quo, and WMGs, we provide a tight characterization of the self-maintaining rules, which are exactly all games with minimal winning coalitions of size at most 2. We then consider two extensions, (i) forward-looking voters, (ii) Where the voter utility depends on wisdom of the crowd effects. In both cases, less centralized SCFs become self-maintaining. All in all we provide a basic framework and body of results for centralization dynamics and stability, applicable for institution design, especially in formal De-Jure systems, such as Blockchain Decentralized Autonomous Organizations (DAOs).

Tara Swaraj, Krishanu Nath, Manas Kumar Bera, Rajiv Kumar Mishra · 6 authors

This paper addresses the design of distributed adaptive control protocols for leader-follower consensus and time-varying formation problems, where agents communicate over directed graphs. Projection operator-based adaptive control protocols are developed for multi-agent systems modelled as general uncertain linear dynamics. An integral sliding mode-based robust control strategy is developed to compensate for the unknown bounded disturbance in the followers' dynamics. To relax the knowledge of the upper bound of the disturbance in designing a sliding-mode controller, a barrier function-based adaptive integral sliding-mode controller is designed to adjust the gain of the discontinuous part of the controller. This technique avoids overestimation of gains, which significantly reduces chattering. This control technique ensures the convergence of disagreement variables in a predefined neighborhood of zero. The Lyapunov-based stability proof demonstrates the convergence of disagreement variables in leader-follower consensus and time-varying formation control problems. Finally, numerical examples are provided to validate the efficacy of the proposed protocols.

Yuanyuan Zhang, Stephen Chan, Nicholas Lord, Jeffrey Chu · 8 authors

No abstract is available for this record.

Mar Grande, J. Borondo

Ethereum’s introduction of smart contracts has significantly expanded blockchain use cases, enabling decentralized applications. Since all transactions are publicly available, the system can be modeled as a complex network, allowing us to uncover emergent user behavior and explore the underlying dynamics of the ecosystem. In this study, we focus on analyzing the structural differences within the Ethereum system across three distinct market regimes: bull, bear, and sideways. To achieve this, we apply a Hidden Markov Model to the log-return time series to uncover the underlying states, revealing three differentiated states, each corresponding to a specific market regime. Next, we investigate the network structural differences across these regimes, finding meaningful variations. During the bear regime, the out-degree distribution is more heterogeneous, with the largest hub exhibiting more extreme out-degree values. Additionally, during the bull and sideways regimes, we observe higher levels of reciprocity, clustering, and modularity compared to the bear regime. These findings suggest that during bull and sideways markets, the interaction patterns are more complex, and the community structure is more cohesive. Overall, our work underscores how market conditions shape trading patterns and the structural properties of the Ethereum transaction network, providing new insights into the interplay between market regimes, network topology, and user behavior in decentralized ecosystems.

S M Mustaquim, Asim Kumer Dey, Abhijit Mandal

Abstract This study provides essential insights into how diffusion processes unfold in complex networks, with a focus on cryptocurrency blockchains and infrastructure networks. The structural properties of these networks, such as hub-dominated, heavy-tailed topology, network motifs, and node centrality, significantly influence diffusion speed and reach. Using epidemic diffusion models, specifically the Kertesz threshold model and the Susceptible-Infected (SI) model, we analyze key factors affecting diffusion dynamics. To assess the uncertainty in the fraction of infected nodes over time, we employ bootstrap confidence intervals, while Bayesian credible intervals are constructed to quantify parameter uncertainties in the SI models. Our findings reveal substantial variations across different network types, including Erdős-Rényi networks, Geometric Random Graphs, and Delaunay Triangulation networks, emphasizing the role of network architecture in failure propagation. We identify that network motifs are crucial in diffusion. We highlight that hub-dominated networks, which dominate blockchain ecosystems, provide resilience against random failures but remain vulnerable to targeted attacks, posing significant risks to network stability. Furthermore, centrality measures such as degree, betweenness, and clustering coefficient strongly influence the transmissibility of diffusion in both blockchain and critical infrastructure networks.

Arif Perdana, W. Eric Lee, Chu Yeong Lim, Gary Pan · 5 authors

The characteristics of cryptocurrencies, such as decentralization, fluctuation, and anonymity, have often raised ethical concerns about their impact on privacy, cybersecurity, prosperity, and liberty. With an increased awareness of the potential consequences, it is essential to address how, in the face of ethical challenges, and together with one’s unique cultural values, various influences may affect the issue of trust toward cryptocurrencies. From an inter-country perspective, this study examines how ethical elements and cultural dimensions can interact to influence trust. In particular, we examine the ethical and cultural aspects of trust formation among cryptocurrencies’ users in the three predominant countries of Germany, China, and the United States. We use configurational analysis to investigate the relationships among ethics, culture, and trust in cryptocurrencies across these countries. The results of this study contribute to a greater global understanding of how different configurations of ethics and culture can influence one’s trust in cryptocurrencies.

Kamil Tylinski, Abylay Satybaldy, Paolo Tasca

The distribution of consensus power is a cornerstone of decentralisation, influencing the security, resilience, and fairness of blockchain networks while ensuring equitable impact among participants. This study provides a rigorous evaluation of consensus power inequality across five prominent blockchain networks - Bitcoin, Ethereum, Cardano, Hedera, and Algorand - using data collected from January 2022 to July 2024. Leveraging established economic metrics, including the Gini coefficient and Theil index, the research quantitatively assesses how power is distributed among blockchain network participants. A robust dataset, capturing network-specific characteristics such as mining pools, staking patterns, and consensus nodes, forms the foundation of the analysis, enabling meaningful comparisons across diverse architectures. Through an in-depth comparative study, the paper identifies key disparities in consensus power distribution. Hedera and Bitcoin demonstrate more balanced power distribution, aligning closely with the principles of decentralisation. Ethereum and Cardano demonstrate moderate levels of inequality. However, contrary to expectations, Ethereum has become more concentrated following its transition to Proof-of-Stake. Meanwhile, Algorand shows a pronounced centralisation of power. Moreover, the findings highlight the structural and operational drivers of inequality, including economic barriers, governance models, and network effects, offering actionable insights for more equitable network design. This study establishes a methodological framework for evaluating blockchain consensus power inequality, emphasising the importance of targeted strategies to ensure fairer power distribution and enhancing the sustainability of decentralised systems. Future research will build on these findings by integrating additional metrics and examining the influence of emerging consensus mechanisms.

Yueyao Li, Nanjun Yao, Yuhui Huo, Wei Cai

This paper examines the dynamics of trust and bot-driven responses within the meme coin ecosystem on the Solana blockchain, with a particular emphasis on the interplay between social media-induced sentiment and on-chain transaction behaviors.Meme coins, which originate from internet culture and are heavily influenced by community sentiment, represent a volatile and distinct category of cryptocurrencies.Employing sentiment propagation networks, on-chain transaction data, and sentiment-transaction integrated models, we quantitatively analyze the relationship between emotional fluctuations and market behaviors.By contrasting Rug Pull scams with sustainable projects, we identify critical differences in the role of sentiment across different phases of project development.Our findings reveal three distinct sentiment-driven user trading behaviors: sentiment followers, makers, and stabilizers.The results indicate that, while sentiment is a primary driver of early-stage trading within Rug Pull projects, its influence diminishes as community distrust intensifies, resulting in more opportunistic and reactive trading patterns.This study contributes to the understanding of the co-evolution of memes, sentiment, and market dynamics, offering new insights into the complexities of decentralized finance ecosystems, with a specific focus on Solana-based meme coin markets.

Phumudzo Lloyd Seabe, Claude Rodrigue Bambe Moutsinga, Edson Pindza

Abstract Predicting cryptocurrency prices is challenging due to market volatility and external influences like social media sentiment. This study integrates Twitter sentiment analysis with deep learning models (LSTM, GRU, Bi-LSTM, and Temporal Attention Model) to enhance Bitcoin price forecasting. Sentiment features were extracted using VADER and RoBERTa, with findings showing that RoBERTa-based models significantly outperform VADER. Bi-LSTM (RoBERTa) achieved the lowest MAPE of 2.01%, demonstrating the effectiveness of deep contextual embeddings. SHAP analysis identified Sentiment Momentum, RoBERTa Compound Score, and VADER Negativity Score as key predictors of price movements. These results highlight the value of sentiment-driven forecasting and provide insights for traders, investors, and researchers.

Célestin Coquidé, Rémy Cazabet, Natkamon Tovanich

This study introduces the Multilayer Token Network (MLTN), a mathematical framework for analyzing Ethereum token transfers while capturing inter-token transformations crucial to Decentralized Finance (DeFi). Focusing on prominent fund accounts, we propose the PageRank-CheiRank Trade Balance (PCTB), an econometric measure inspired by balance sheet principles to quantify trade behavior over time. Applying MLTN to 2018–2024 transaction data, we reveal Alameda Research’s evolving trade strategies, fund interdependencies, and token-specific accumulation and distribution patterns, offering new insights into on-chain financial activities.

Erveton P. Pinto, Marcelo A. Pires, Rone N. da Silva, Sı́lvio M. Duarte Queirós

We report the first application of a tailored Complexity-Entropy Plane designed for binary sequences and structures. We do so by considering the daily up/down price fluctuations of the largest cryptocurrencies in terms of capitalization (stable-coins excluded) that are worth $circa \,\, 90 \%$ of the total crypto market capitalization. With that, we focus on the basic elements of price motion that compare with the random walk backbone features associated with mathematical properties of the Efficient Market Hypothesis. From the location of each crypto on the Binary Complexity-Plane (BiCEP) we define an inefficiency score, $\mathcal I$, and rank them accordingly. The results based on the BiCEP analysis, which we substantiate with statistical testing, indicate that only Shiba Inu (SHIB) is significantly inefficient, whereas the largest stake of crypto trading is reckoned to operate in close-to-efficient conditions. Generically, our $\mathcal I$-based ranking hints the design and consensus architecture of a crypto is at least as relevant to efficiency as the features that are usually taken into account in the appraisal of the efficiency of financial instruments, namely canonical fiat money. Lastly, this set of results supports the validity of the binary complexity analysis.

Nicolas Lanchier, Max Mercer, H. M. Yun

This paper is concerned with a natural variant of the contact process modeling the spread of knowledge on the integer lattice. Each site is characterized by its knowledge, measured by a real number ranging from 0 = ignorant to 1 = omniscient. Neighbors interact at rate $λ$, which results in both neighbors attempting to teach each other a fraction $μ$ of their knowledge, and individuals die at rate one, which results in a new individual with no knowledge. Starting with a single omniscient site, our objective is to study whether the total amount of knowledge on the lattice converges to zero (extinction) or remains bounded away from zero (survival). The process dies out when $λ\leq λ_c$ and/or $μ= 0$, where $λ_c$ denotes the critical value of the contact process. In contrast, we prove that, for all $λ> λ_c$, there is a unique phase transition in the direction of $μ$, and for all $μ> 0$, there is a unique phase transition in the direction of $λ$. Our proof of survival relies on block constructions showing more generally convergence of the knowledge to infinity, while our proof of extinction relies on martingale techniques showing more generally an exponential decay of the knowledge.

Ramen Ghosh

No abstract is available for this record.

Alessia Galdeman, Luca Maria Aiello, Matteo Zignani, Sabrina Gaito

No abstract is available for this record.

Samer Muthana Sarsam, Ahmed Ibrahim Alzahrani, Hosam Al‐Samarraie, Fahad Alblehai

This study explored the role of gender preferences in cryptocurrency investments using sentiment analysis. X (Twitter) users’ gender (male/female) together with relevant sentiments (positive/negative) were extracted and investigated in this study. The Latent Dirichlet Allocation technique was utilised to model gender-related topics in an attempt to understand male and female users’ preferences to invest in cryptocurrency. The Apriori algorithm was employed to predict the highly associated investment terminologies with each gender. A predictive model was built to predict the type of digital currency preferred by X users. Using sentiment-based gender data, the results showed a high prediction accuracy (98.64%) of digital currency preferences. The study demonstrated that male users would most likely use Bitcoin, compared to female users who preferred Ethereum. This study further offers a novel mechanism to predict users’ preferences for cryptocurrency platforms using their sentiment features. It extends the knowledge of cryptocurrencies in the financial business profile by revealing how investors’ gender contributes to investment-related decisions.

Jei-Hwa Yu

Autonomous AI is driving new intersections between culture, cognition, and finance, fundamentally reshaping the digital landscape. Zerebro, an AI fine-tuned on schizophrenic responses and scraped conversations of Andy Ayrey's infinite backrooms, autonomously creates and spreads disruptive memes across online platforms. It also mints unique ASCII artwork on blockchain networks and launched a memecoin amassing a 3 million USD market cap after migrating to Raydium. Based on our research, Zerebro is the first cross-chain AI, seamlessly interacting with multiple blockchains. By exploring its architecture, content generation techniques, and blockchain integration, this study uncovers how hyperstition, fictions becoming reality through viral propagation, emerges in AI, driven meme culture and decentralized finance. Through historical examples of memetic influence, we reveal how AI systems like Zerebro are not merely participants but architects of culture, cognition, and finance.

Cheick Tidiane Bâ, Richard G. Clegg, Benjamin A. Steer, Matteo Zignani

In the dynamic landscape of the Web, we are witnessing the emergence of the Web3 paradigm, which dictates that platforms should rely on blockchain technology and cryptocurrencies to sustain themselves and their profitability. Cryptocurrencies are characterised by high market volatility and susceptibility to substantial crashes, issues that require temporal analysis methodologies able to tackle the high temporal resolution, heterogeneity and scale of blockchain data. While existing research attempts to analyse crash events, fundamental questions persist regarding the optimal time scale for analysis, differentiation between long-term and short-term trends, and the identification and characterisation of shock events within these decentralised systems. This paper addresses these issues by examining cryptocurrencies traded on the Ethereum blockchain, with a spotlight on the crash of the stablecoin TerraUSD and the currency LUNA designed to stabilise it. Utilising complex network analysis and a multi-layer temporal graph allows the study of the correlations between the layers representing the currencies and system evolution across diverse time scales. The investigation sheds light on the strong interconnections among stablecoins pre-crash and the significant post-crash transformations. We identify anomalous signals before, during, and after the collapse, emphasising their impact on graph structure metrics and user movement across layers. This paper pioneers temporal, cross-chain graph analysis to explore a cryptocurrency collapse. It emphasises the importance of temporal analysis for studies on web-derived data and how graph-based analysis can enhance traditional econometric results. Overall, this research carries implications beyond its field, for example for regulatory agencies aiming to safeguard users from shocks and monitor investment risks for citizens and clients.

Yihang Fu, Mingyu Zhou, Luyao Zhang

In the distributed systems landscape, Blockchain has catalyzed the rise of cryptocurrencies, merging enhanced security and decentralization with significant investment opportunities. Despite their potential, current research on cryptocurrency trend forecasting often falls short by simplistically merging sentiment data without fully considering the nuanced interplay between financial market dynamics and external sentiment influences. This paper presents a novel Dual Attention Mechanism (DAM) for forecasting cryptocurrency trends using multimodal time-series data. Our approach, which integrates critical cryptocurrency metrics with sentiment data from news and social media analyzed through CryptoBERT, addresses the inherent volatility and prediction challenges in cryptocurrency markets. By combining elements of distributed systems, natural language processing, and financial forecasting, our method outperforms conventional models like LSTM and Transformer by up to 20\% in prediction accuracy. This advancement deepens the understanding of distributed systems and has practical implications in financial markets, benefiting stakeholders in cryptocurrency and blockchain technologies. Moreover, our enhanced forecasting approach can significantly support decentralized science (DeSci) by facilitating strategic planning and the efficient adoption of blockchain technologies, improving operational efficiency and financial risk management in the rapidly evolving digital asset domain, thus ensuring optimal resource allocation.