The evaluation of Large Language Models (LLMs) for code generation relies heavily on the quality and robustness of test cases.However, existing benchmarks often lack coverage for subtle corner cases, allowing incorrect solutions to pass.To bridge this gap, we propose CodeHacker, an automated agent framework dedicated to generating targeted adversarial test cases that expose latent vulnerabilities in program submissions.Mimicking the hack mechanism in competitive programming, CodeHacker employs a multistrategy approach, including stress testing, anti-hash attacks, and logic-specific targeting to break specific code submissions.To ensure the validity and reliability of these attacks, we introduce a Calibration Phase, where the agent iteratively refines its own Validator and Checker via self-generated adversarial probes before evaluating contestant code.Experiments demonstrate that CodeHacker significantly improves the True Negative Rate (TNR) of existing datasets, effectively filtering out incorrect solutions that were previously accepted.Furthermore, generated adversarial cases prove to be superior training data, boosting the performance of RL-trained models on benchmarks like LiveCodeBench.Our code is available at https://github.com/shi0712/ CodeHacker.

Recommender systems are increasingly spreading to different areas like e-commerce or video streaming to alleviate information overload. One of the most fundamental methods for recommendation is Collaborative Filtering (CF), which leverages historical user-item interactions to infer user preferences. In recent years, Graph Neural Networks (GNNs) have been extensively studied to capture graph structures in CF tasks. Despite this remarkable progress, local structure modeling and embedding distortion still remain two notable limitations in the majority of GNN-based CF methods. Therefore, in this paper, we propose a novel Hyperbolic Graph Transformer architecture, to tackle the long-tail problems in CF tasks. Specifically, the proposed framework is comprised of two essential modules: 1) Local Hyperbolic Graph Convolutional Network (LHGCN), which performs graph convolution entirely in the hyperbolic manifold and captures the local structure of each node; 2) Hyperbolic Transformer, which is comprised of hyperbolic cross-attention mechanisms to capture global information. Furthermore, to enable its feasibility on large-scale data, we introduce an unbiased approximation of the cross-attention for linear computational complexity, with a theoretical guarantee in approximation errors. Empirical experiments demonstrate that our proposed model outperforms the leading collaborative filtering methods and significantly mitigates the long-tail issue in CF tasks. Our implementations are available in https://github.com/EnkiXin/Hgformer.

Battery energy storage systems (BESS) are crucial for maintaining grid frequency stability, particularly with the increasing integration of intermittent renewable energy sources. However, energy delivered from BESS drifts over time due to asymmetric charging and discharging inefficiencies, posing significant challenges for effective frequency support. In this work, simulations revealed that energy drift accelerated early crossing of the state of charge (SOC) boundaries. A sensitivity analysis of asymmetric inefficiencies showed that improving charging and discharging consistency reduced energy drift and decreased frequency of energy transactions. C-rate constraints highlighted a trade-off between BESS safety and the rapidity of frequency support responses. Simplified dynamic droop control was shown to stabilize early-stage grid operations but required degradation-informed BESS control to mitigate the energy drift. The sizing of BESS showcased a trade-off between energy drift and cost. These findings offer valuable insights into designing safe and robust SOC management strategies for grid-connected BESS. Broadly, this work enhances fundamental understanding of energy drift arising from inherent battery degradation and its impact on the reliable energy storage systems for the stable and sustainable power grid.