In briefUsing quantum annealing to detect and locate gamma-ray sources in AGILE data
SummaryThis paper reformulates maximum-likelihood gamma-ray source detection and localization as a Quadratic Unconstrained Binary Optimization (QUBO) problem and solves it with simulated quantum annealing. The method is applied to simulated observations from AGILE's Gamma-Ray Imaging Detector in the 30 MeV to 50 GeV band, generated with AGILEpy. The flux and position estimates agree with classical results within statistical uncertainty, and the correct grid node is identified in 97% of the maps.
Alessandro Rizzo, Nicol\`o Parmiggiani, Andrea Bulgarelli et al.
In briefUniPCH: A Unified Framework for Cross-Chain Payments via Payment Channel Hubs
SummaryThis paper presents UniPCH, a two-layer framework for private cross-chain payments across heterogeneous blockchains. The lower UniPC layer defines common channel state transitions and security requirements, with realizations for UTXO and smart-contract account ledgers. The upper layer introduces selective re-signing blind conditional signatures (S-RBCS) to coordinate conditional authorization across hubs, enabling a four-phase payment protocol. The authors report security properties and a Go prototype evaluation of running time and bandwidth.
In briefUndisclosed Fees in Hook-Based Decentralized Exchanges
SummaryThis paper presents a large-scale empirical study of Hook contracts on Uniswap v4 and PancakeSwap Infinity across Ethereum and BNB Smart Chain. The authors analyze 7,873 Hook deployments and 50,011 pools using bytecode analysis to identify settlement charges not itemized in standard fees. They find that 24.30% of Hooks can impose such deductions, which occur in 59.23% of relevant swaps and generate substantial revenue. The results suggest a gap between displayed fees and actual trading costs, motivating explicit disclosure.
In briefTight limits on fair AMM mechanism design under weak efficiency
SummaryThis paper studies miner extractable value (MEV) in automated market makers (AMMs), where block builders can profit by controlling transaction order. It proves that no deterministic, anonymous batch AMM can be both incentive compatible and weakly locally efficient under plain consensus without fair sequencing, even with censorship resilience and unlimited block space. The impossibility relies on Sybil identities, over-reporting budgets, and misreporting values. Removing any one of these strategic capabilities makes both properties achievable under censorship resilience.
WhyProves impossibility of IC and wLE for deterministic anonymous batch AMMs under plain consensus, with tight characterization of strategy restrictions.
In briefSybil-proof adaptive clinching auctions and their use in blockchain market design
SummaryThis paper examines whether the adaptive clinching auction for budget-constrained bidders stays robust when a bidder splits its budget across fake identities. The authors propose a generalized adaptive clinching framework and prove that bidders gain nothing by splitting into any number of identities, as long as their total reported budget does not exceed their enforceable true budget. They extend the mechanism to a two-sided automated market maker for decentralized finance and show, through impossibility results, that these guarantees are essentially the best possible.
WhyProves Sybil-robust incentive compatibility for adaptive clinching and designs a two-sided AMM, backed by impossibility results and proofs with broad DeFi relevance.
In briefFIDES-Live: Checking Whether Trading AI Agents Behave Consistently
SummaryThis paper presents FIDES-Live, an extension of the FIDES framework for auditing sequences of trading agent actions, orders, execution receipts, and reports. It assesses decision validity, adherence to numerical commitments, reconciliation with execution and ledger records, and report accuracy grounded in receipts. The authors retrospectively audited ten inference routes over 7,299 cryptocurrency account-rounds and a 310-round equity pilot. The paper states that its findings do not establish long-run trading skill, faithfulness of explanations, or causal benefits of audit feedback.
In briefPhysical structure as an organizing principle for universal Hamiltonian learning
SummaryThis paper introduces UniTB, a universal E(3) equivariant framework that combines density-functional tight-binding physics with deep learning to predict electronic Hamiltonians across many chemistries. Trained on 29,242 crystals, it reaches a 0.48 meV matrix-element error on nonmagnetic test crystals spanning 68 elements. A faster variant, UniTB-Flash, is also presented, and both models are used to screen 16,195 crystals.
Mingkang Liu, Zhanghao Zhouyin, Guanghui Wang et al.
In briefFuji: How Demand Bursts Take Shape, What They Consist Of, and How They Spread on Robinhood Chain
SummaryThis paper measures sudden surges in demand on a blockchain marketplace, using Robinhood Chain, a layer-2 network, over its first three active months and more than 50 million Uniswap swaps. During bursts, traders touch about as many pools as in quiet periods, mostly different ones, and trade more intensively, with median peaks 2.5 times normal load. The rise and fall of demand are described by two exponential functions, and the authors compare two explanations for bursts, finding externally triggered cascades fit the data better than self-excitation.
In briefWeekend Crypto Volatility and Monday Risk: Whether weekend crypto price swings predict Monday variance in traditional markets.
SummaryThis paper studies whether volatility in cryptocurrencies such as Bitcoin, Ether and XRP, which trade through the weekend, predicts Monday variance in the S&P 500, gold and a US Treasury ETF. The authors add a common weekend crypto factor to heterogeneous autoregressive models using daily data from 2017 to 2026. Predictive content is significant after 2024 and reduces out-of-sample losses, though statistical evidence varies across markets.
