Large language models (LLMs) are increasingly used as rerankers in information retrieval, yet their ranking behavior can be steered by small, natural-sounding prompts.To expose this vulnerability, we present Rank Anything First (RAF), a two-stage token optimization method that crafts concise textual perturbations to consistently promote a target item in LLMgenerated rankings while remaining hard to detect.Stage 1 uses Greedy Coordinate Gradient to shortlist candidate tokens at the current position by combining the gradient of the rank-target with a readability score; Stage 2 evaluates those candidates under exact ranking and readability losses using an entropy-based dynamic weighting scheme, and selects a token via temperature-controlled sampling.RAF generates ranking-promoting prompts tokenby-token, guided by dual objectives: maximizing ranking effectiveness and preserving linguistic naturalness.Experiments across multiple LLMs show that RAF significantly boosts the rank of target items using naturalistic language, with greater robustness than existing methods in both promoting target items and maintaining naturalness.These findings underscore a critical security implication: LLMbased reranking is inherently susceptible to adversarial manipulation, raising new challenges for the trustworthiness and robustness of modern retrieval systems.

Dataset Distillation (DD) aims to generate a compact synthetic dataset that enables models to achieve performance comparable to training on the full large dataset, significantly reducing computational costs. Drawing from optimal transport theory, we introduce WMDD (Wasserstein Metric-based Dataset Distillation), a straightforward yet powerful method that employs the Wasserstein metric to enhance distribution matching. We compute the Wasserstein barycenter of features from a pretrained classifier to capture essential characteristics of the original data distribution. By optimizing synthetic data to align with this barycenter in feature space and leveraging per-class BatchNorm statistics to preserve intra-class variations, WMDD maintains the efficiency of distribution matching approaches while achieving state-of-the-art results across various high-resolution datasets. Our extensive experiments demonstrate WMDD's effectiveness and adaptability, highlighting its potential for advancing machine learning applications at scale.

Transactional isolation is critical to the functional correctness of database management systems (DBMSs). Much effort has recently been devoted to finding isolation bugs and validating isolation fulfilment in production DBMSs. However, there are still challenges that existing isolation checkers have not yet fully addressed. For instance, they may overlook bugs, incur high checking overhead, and return hard-to-understand counterexamples. We present IsoVista, the first black-box isolation checking system that encompasses all the following features. It builds on faithful characterizations of a range of isolation levels, ensuring the absence of both false positives and missed bugs in collected DBMS execution histories. IsoVista exhibits superior checking efficiency, compared to the state-of-the-art, and visualizes violation scenarios, facilitating the understanding of bugs found. It also supports profiling and benchmarking the performance of isolation checkers under various workloads, assisting developers of both DBMSs and checkers. We showcase all these features through user-friendly interfaces.