We examine log-optimal portfolio allocation when the long-run price of an asset follows a power-law trajectory, $P(t)=At^α$, and its instantaneous return variance decays as $σ^2(t)=σ_0^2 t^{-2γ}$. Under a zero risk-free-rate benchmark, the continuous-time Kelly fraction scales as $K^\ast(t)=(α/σ_0^2)t^{2γ-1}$. Exact temporal invariance therefore occurs when $γ=1/2$, whereas deviations from this value produce systematic age dependence in the allocation. We propose a scaling hypothesis connecting growth in network participation, effective market liquidity, and declining volatility. Under a specified set of scaling assumptions, this model predicts the benchmark exponent $γ=1/2$. Using historical daily Bitcoin prices, we estimate the power-law price exponent and examine the sensitivity of the volatility exponent to the length of the rolling window. For windows of four to nine years, the estimated volatility exponents have an arithmetic mean of 0.53 and a cross-window standard deviation of approximately 0.03. Because these estimates are obtained from overlapping observations and the same underlying price history, this spread is interpreted as a measure of model sensitivity rather than a formal confidence interval. Finally, we show that a time-dependent multiplicative contribution to return variance generally breaks exact Kelly invariance. We illustrate this result using a scenario in which Bitcoin transaction-fee variability affects the effective variance process. The results identify the conditions under which log-optimal allocation can remain stable under non-stationary power-law asset dynamics and clarify the assumptions required when applying this result to Bitcoin.

גרסה אחרונה: 18 בספט׳ 2026. קריאת המאמר והיסטוריית הגרסאות ↗

Every blockchain transaction carries a timestamp, and the chain imposes a total order. On-chain data therefore forms per-source time-series streams. However, existing systems support only basic lookups on blocks and transactions, and cannot answer time-series queries such as time-range retrieval and windowed aggregation. Offloading queries off-chain restores expressiveness, but the off-chain query layer is untrusted, so results must be verifiable. To this end, we propose VeriTS, the first verifiable time-series query framework for blockchain systems. It supports efficient range and aggregation queries. VeriTS maintains an off-chain query layer. In this layer, each stream is kept under one tree whose nodes carry authenticated aggregates, so the query index is itself the authenticated data structure. A light client thus verifies a windowed aggregate from a logarithmic number of authenticated nodes rather than from every record. VeriTS further answers error-tolerant queries from compact model representations of a stream, and extends the completeness and soundness guarantees to such approximate answers. As VeriTS never trusts the model behind a representation, a faulty or adversarial model can only widen an answer's certified interval, never falsify it. Experiments offer evidence that on windowed aggregation, VeriTS improves verification efficiency by more than two orders of magnitude over per-record proofs. On range retrieval, proofs shrink by up to 14.5x.

גרסה אחרונה: 21 בספט׳ 2026. קריאת המאמר והיסטוריית הגרסאות ↗

The rapid emergence of decentralized finance (DeFi) has introduced complex challenges for cross-chain transactions, which involve transferring assets across disparate blockchain networks. A fundamental dilemma arises between user privacy and ensuring regulatory compliance. Unlike single-chain systems, cross-chain environments must address privacy and auditability across heterogeneous architectures. Existing approaches,ranging from transparent ledgers to anonymous cryptocurrencies, fail to reconcile these two requirements, hindering regulatory adoption. This research presents an auditable cross-chain framework that integrates three core components. First, zero-knowledge proofs (ZKPs) enable compliance verification (e.g., amount non-negativity, signature validity) without revealing transaction details. Second, a light-client mechanism enables trust-minimized cross-chain verification without dependence on third-party relayers. Third, a threshold view-key mechanism based on distributed key generation (DKG) restricts that audit access to authorized entities under legal triggers such as the FATF Travel Rule and MiCA Regulation. For cross-border investigations, the framework adheres to national laws and the EU Directive on Mutual Legal Assistance. This work systematically integrates ZKPs, threshold cryptography, and light-client verification into an auditable, privacy-preserving cross-chain protocol, and evaluates a prototype on the Ethereum Sepolia testnet. The results demonstrate the practical feasibility of privacy-preserving compliance verification for cross-chain DeFi. At the same time, broader interoperability and Regulatory Technology (RegTech) impact require further validation on additional chains and with real regulatory workflows.

גרסה אחרונה: 8 באוק׳ 2026. קריאת המאמר והיסטוריית הגרסאות ↗

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.

גרסה אחרונה: 28 בספט׳ 2026. קריאת המאמר והיסטוריית הגרסאות ↗

While public blockchains provide transparent and auditable transaction histories, they inherently compromise user privacy. Existing privacy-enhancing protocols, such as those deployed on Ethereum, typically rely on succinct zero-knowledge proofs (zk-SNARKs) to obscure the transaction graph. However, implementing comparable cryptographic guarantees on high-throughput blockchains like Algorand is challenging due to strict per-call execution budgets and the state contention introduced by global Merkle accumulators. This paper presents Obscura, a decentralized, non-custodial privacy protocol tailored for constrained smart contract environments. Obscura achieves transaction anonymity using Linkable Spontaneous Anonymous Group (LSAG) signatures over the BN254 elliptic curve, verified entirely on-chain. To overcome limitations of the Algorand Virtual Machine (AVM), we introduce a novel state model that leverages Algorand's Box Storage for $O(1)$ commitment membership checks, eliminating the need for global Merkle accumulators, and a dynamic opcode-budget expansion mechanism via pooled inner application calls. Our implementation demonstrates that signer-ambiguous privacy is practical and efficient on Algorand without relying on trusted setups or succinct proofs. Obscura provides a robust privacy layer for transparent ledgers, bridging the gap between high-throughput blockchain architectures and the dual requirements of cryptographic privacy and selective auditability.

גרסה אחרונה: 4 באוק׳ 2026. קריאת המאמר והיסטוריית הגרסאות ↗

Despite holding over $1.7T in value, Bitcoin scales poorly: Layer-1 (L1) processes only a few transactions per second, and Layer-2 (L2) solutions suffer from operational and liquidity fragmentation that requires external bridges for cross-chain connectivity. We present Bitcoin-IPC, a protocol that horizontally scales Bitcoin via permissionless, interconnected, programmable Proof-of-Stake L2 subnets staked in L1 BTC. Subnets leverage Bitcoin L1 for interconnectivity, settlement, and security, including Sybil protection, equivocating validator slashing, unilateral exit for honest stake, long-range attack protection, and compromised subnet firewalling. Bitcoin-IPC cross-subnet transfers involve only source/destination subnet validators and L1, with no external bridges or per-path liquidity lock-up. Embedded and batched in Bitcoin's witness mechanism, cross-subnet settlements can approach 6 vB per transfer, 23x less than a native L1 payment. Filling Bitcoin blocks with such batches corresponds to L1 monetary throughput of about 273 transfers per second, without modifying Bitcoin.

גרסה אחרונה: 29 בספט׳ 2026. קריאת המאמר והיסטוריית הגרסאות ↗

Bitcoin operates as a macroeconomic paradox: it combines a strictly predetermined, inelastic monetary issuance schedule with a stochastic, highly elastic demand for scarce block space. This paper empirically validates the Endogenous Constraint Hypothesis, positing that protocol-level throughput limits generate a non-linear negative feedback loop between network friction and base-layer monetary velocity. Using a verified Transaction Cost Index (TCI) derived from Blockchain.com on-chain data and Hansen's (2000) threshold regression, we identify a definitive structural break at the 90th percentile of friction (TCI ~ 1.63). The analysis reveals a bifurcation in network utility: while the network exhibits robust velocity growth of +15.44% during normal regimes, this collapses to +6.06% during shock regimes, yielding a statistically significant Net Utility Contraction of -9.39% (p = 0.012). Crucially, Instrumental Variable (IV) tests utilizing Hashrate Variation as a supply-side instrument fail to detect a significant relationship in a linear specification (p=0.196), confirming that the velocity constraint is strictly a regime-switching phenomenon rather than a continuous linear function. Furthermore, we document a "Crypto Multiplier" inversion: high friction correlates with a +8.03% increase in capital concentration per entity, suggesting that congestion forces a substitution from active velocity to speculative hoarding.

גרסה אחרונה: 20 בספט׳ 2026. קריאת המאמר והיסטוריית הגרסאות ↗

Privacy-preserving blockchain systems are essential for protecting transaction data, yet they must also provide auditability that enables auditors to recover participant identities and transaction amounts when warranted. Existing designs often compromise the independence of auditing and transactions, introducing extra interactions that undermine usability and scalability. Moreover, many auditable solutions depend on auditors serving as validators or recording nodes, which introduces risks to both data security and system reliability. To overcome these challenges, we propose SilentLedger, a privacy-preserving transaction system with auditing and complete non-interactivity. To support public verification of authorization, we introduce a renewable anonymous certificate scheme with formal semantics and a rigorous security model. SilentLedger further employs traceable transaction mechanisms constructed from established cryptographic primitives, enabling users to transact without interaction while allowing auditors to audit solely from on-chain data. We formally prove security properties including authenticity, anonymity, confidentiality, and soundness, provide a concrete instantiation, and evaluate performance under a standard 2-2 transaction model. Our implementation and benchmarks demonstrate that SilentLedger achieves superior performance compared with state-of-the-art solutions.

גרסה אחרונה: 23 בספט׳ 2026. קריאת המאמר והיסטוריית הגרסאות ↗

Two MEV builders now produce nearly 80\% of Ethereum blocks. Block builders have the ability to reorder transactions on the blockchain in a way that can be harmful to participants. We estimate participants would pay in the aggregate nearly \$7.2 million per month to guarantee that they remained in the first quartile of the block. Sandwich attacks, in which a transaction is front run, are frequent, averaging more than one every two blocks. Gas fees on these transactions pay for nearly 9.6\% of the MEV payments to the validator. Reforms such as gas fee priority or private transaction pools might be helpful.

גרסה אחרונה: 21 בספט׳ 2026. קריאת המאמר והיסטוריית הגרסאות ↗

Cryptocurrencies are widely used, yet current methods for analyzing transactions often rely on opaque, black-box models. While these models may achieve high performance, their outputs are usually difficult to interpret and adapt, making it challenging to capture nuanced behavioral patterns. Large language models (LLMs) have the potential to address these gaps, but their capabilities in this area remain largely unexplored, particularly in cybercrime detection. In this paper, we test this hypothesis by applying LLMs to real-world cryptocurrency transaction graphs, with a focus on Bitcoin, one of the most studied and widely adopted blockchain networks. We introduce a three-tiered framework to assess LLM capabilities: foundational metrics, characteristic overview, and contextual interpretation. This includes a new, human-readable graph representation format, LLM4TG, and a connectivity-enhanced transaction graph sampling algorithm, CETraS. Together, they significantly reduce token requirements, transforming the analysis of multiple moderately large-scale transaction graphs with LLMs from nearly impossible to feasible under strict token limits. Experimental results demonstrate that LLMs have outstanding performance on foundational metrics and characteristic overview, where the accuracy of recognizing most basic information at the node level exceeds 98.50% and the proportion of obtaining meaningful characteristics reaches 95.00%. Regarding contextual interpretation, LLMs also demonstrate strong performance in classification tasks, even with very limited labeled data, where top-3 accuracy reaches 72.43% with explanations. While the explanations are not always fully accurate, they highlight the strong potential of LLMs in this domain. At the same time, several limitations persist, which we discuss along with directions for future research.

גרסה אחרונה: 6 באוק׳ 2026. קריאת המאמר והיסטוריית הגרסאות ↗

Decentralized finance (DeFi) can broaden access while leaving activity, network position, and infrastructure concentrated. We develop a four-dimensional framework for participation, activity distribution, structural position, and infrastructure dependence, integrating network theory, theorem-consistent agent-based simulation, and longitudinal analysis of 1,956,216 Aave V3 Pool events. We study GHO issuance on Ethereum (15 July 2023) and its first cross-chain expansion to Aave's existing Arbitrum market (2 July 2024). Excluding each activation week, mean weekly active position-holder addresses increased by 91.0% around Ethereum issuance and 1.7% around Arbitrum expansion, while activity concentration fell by 31.5% on Ethereum but rose by 58.1% on Arbitrum. On a common 2024 calendar, the Arbitrum--Gnosis DiD-style change is +1.9833 for log participation and -0.01842 for position-holder-event HHI. Rule-based simulations recover the analytical equilibrium and show why aggregate growth can coexist with lower, unchanged, or higher concentration, while chain dispersion alone cannot establish route or shared-component resilience. Role-aware analysis further shows that network-structure conclusions vary by protocol action and scale. Intellectually, the framework explains why four dimensions of decentralization can diverge. Practically, it helps researchers, protocol designers, governance communities, and policymakers assess stablecoin growth without equating adoption with decentralization.

גרסה אחרונה: 5 באוק׳ 2026. קריאת המאמר והיסטוריית הגרסאות ↗