Di Wu

The paper demonstrates that on public blockchains, the ability to dictate transaction order (ordering power) is the true source of sanctioning power, as block producers can extract value (SE-MEV) by prioritizing transactions that evade sanctions.

The paper empirically investigates whether AI-generated reviews can improve the drafting process of academic papers, finding that AI reviews cover many human-identified issues but also introduce novel, potentially misleading suggestions.

The paper empirically investigates whether AI-generated reviews can improve the drafting process of academic papers, finding that AI reviews cover many human-identified issues but also introduce novel, potentially misleading suggestions.

This study successfully demonstrates that federated learning can achieve prediction accuracy comparable to centralized modeling for multi-center sepsis prediction while fundamentally preserving patient data privacy.

This paper proposes PTFEA, a curriculum-learning-inspired framework that translates fine-tuning strategies into interpretable context engineering for Multimodal Entity Alignment (MMEA), demonstrating consistent outperformance on five public datasets.

This paper proposes RL-MACRO, a cybernetic closed-loop intelligence framework for autonomous robotic craniotomy, which includes a CNN-LSTM observer for temperature reconstruction, an offline Implicit Q-Learning policy, and a novel dual-head Actor for coordinating cutting parameters.

The authors introduce KEEP-Bench, a diagnostic benchmark to assess compression faithfulness and evaluate the performance of context compression models.