This paper studies a dynamic assortment problem on a two-sided service platform with incomplete information and heterogeneous customers, and develops a data-driven algorithm to learn parameters and op…

This paper compares traditional machine learning models (Random Forests, XGBoost, SVM) against a complex Unified Multi-Task Time Series Model for churn prediction, concluding that conventional methods…

The paper empirically investigates the lead marketing ecosystem, revealing a highly non-compliant system that aggressively collects, shares, and monetizes sensitive personal data through deceptive bro…

This paper models Ethereum's mempool as a dynamic scheduling problem using an MDP, showing that dynamic pricing stabilizes the system and maximizes long-run rewards, and that the optimal policy conver…

The paper proposes a novel Large Neighborhood Search (LNS) method, incorporating hybrid destroy operators and an exact repair solver, to effectively solve the Capacitated Facility Location Problem wit…

This paper models transaction fee dynamics on blockchains by treating the transaction queue as a priority queue, providing analytical insights into how user delay costs influence fees.

The paper models latent worker preferences in gig labor markets using the Preisach hysteresis model, demonstrating that predicting acceptance rates can simultaneously reduce labor costs and increase s…

The paper introduces Hyperparam, a set of lightweight JavaScript libraries designed to enable direct, model-aware querying of unstructured data (like agent traces) within client-side AI applications.

FedIDM introduces a novel federated learning framework that uses iterative distribution matching to achieve fast and stable convergence and maintain high model utility even when facing a large proport…

The paper introduces a secure Federated RAG system that enables confidential retrieval and LLM inference across distributed, private data silos.

The paper proposes a scalable, market-analysis-driven methodology to assess national charging station cybersecurity by extrapolating field test results from a manageable subset of stations to estimate…

The paper proposes a proactive client selection framework that optimizes the selection of client subsets to ensure high data utility and fairness before federated learning begins, leading to faster an…

The paper proposes a robust, multi-stage pipeline combining rule-based classification and machine learning to map noisy retail product names to standardized consumption categories, finding that simple…

The paper analyzes Ethereum builder transactions to show that builder centralization is an emergent property of the Proposer-Builder Separation (PBS) architecture, driven by specific order flow and ME…

The paper proposes and proves the security of a generic, full end-to-end credential revocation system for European Digital Identity Wallets, relying on a single server and secure channels.

This paper addresses the operational challenge of adopting Post-Quantum Cryptography (PQC) in complex financial TLS environments by presenting a methodology to automatically profile and normalize cryp…

The study demonstrates that conditioning AI brand recommendations on a user's persona significantly alters the recommended product set, particularly for mid-market brands, and this effect is largest o…

This study analyzed I2P's routing topology and found no significant evidence that peer selection is influenced by geographic location, suggesting highly random global mixing.

The paper empirically analyzes the impact of cross-chain interoperability on DeFi lending protocols, finding that bridge volume significantly affects performance but that increased integrations can si…

The paper proposes n-VM, a novel Layer-1 architecture that unifies multiple heterogeneous virtual machines (VMs) onto a shared consensus and state layer, solving cross-chain fragmentation issues.