Recent work has shown that front-end code generated by Large Language Models (LLMs) can embed deceptive designs. To assess the magnitude of this problem, identify the factors that influence deceptive design production, and test strategies for reducing deceptive designs, we carried out two studies which generated and analyzed 1,296 LLM-generated web components, along with a design rationale for each. The first study tested four LLMs for 15 common ecommerce components. Overall 55.8% of components contained at least one deceptive design, and 30.6% contained two or more. Occurence varied significantly across models, with DeepSeek-V3 producing the fewest. Interface interference emerged as the dominant strategy, using color psychology to influence actions and hiding essential information. The first study found that prompts emphasizing business interests (e.g., increasing sales) significantly increased deceptive designs, so a second study tested a variety of prompting strategies to reduce their frequency, finding a values-centered approach the most effective. Our findings highlight risks in using LLMs for coding and offer recommendations for LLM developers and providers.

In-network aggregation accelerates distributed training by offloading gradient aggregation to the programmable switches. However, in multi-tenant learning environments, contention for limited switch memory can cause memory overflows that significantly degrade aggregation throughput. To mitigate memory overflow, prior work has proposed selective in-network aggregation, which allocates switch memory to a subset of jobs based on memory availability. This approach classifies jobs into INA jobs (which perform in-network aggregation) and PS jobs (which perform in-server aggregation). Despite these efforts, network congestion occurs and slows INA jobs, resulting in inefficient memory utilization and reduced aggregation throughput. In this paper, we present FlexINA, which rethinks selective in-network aggregation to deliver high aggregation throughput. The first challenge is how to avoid excessive memory under-utilization by INA jobs during network congestion. FlexINA introduces INA-aware congestion control, prioritizing reducing the sending window of PS jobs during network congestion. The second challenge is how to allow PS jobs to utilize under-utilized aggregators without affecting the aggregation of INA jobs. FlexINA implements an adaptive head-tail aggregation, optimizing memory usage by combining static head mapping for INA jobs (to use allocated head memory) and dynamic tail mapping for PS jobs (to use under-utilized tail memory). We implement a FlexINA prototype and evaluate it on both a small-scale testbed and in large-scale simulation experiments. Our evaluation shows that FlexINA improves aggregation throughput by up to$1.9\times $and$1.4\times $compared to selective in-network aggregation NetPack (ATP) and NetPack (A2TP), respectively.

The live chats of developers often contain meaningful information in the form of issue–solution pairs. The issue–solution pairs can offer helpful references to others who seek solutions for the similar issues, which can improve software development efficiency by facilitating issue solving. However, previous approaches such as ISPY still struggle with unsatisfactory extraction accuracy, due to the entanglement and complexity of issue-solution pairs' feature information. To address these challenges, we propose an approach namedIS-Hunterfor mining issue-solution pairs from real-time chat data. Specifically,IS-Hunterconsists of four main components: the data preprocessing component disentangles and denoises raw chat logs, the utterance embedding component embeds utterances into vectors that subsequent components can easily process, the feature extraction component obtains textual, heuristic, and contextual feature that determines whether an utterance is topic-relevant, and the issue–solution pair prediction component predicts the utterance whether is an issue or a solution. The experimental results show that the performance of IS-Hunter outperforms the baseline methods in issue-detection and solution-extraction in terms of Precision, Recall, and F1-score. Compared with baseline methods, in issue-detection, IS-Hunter, respectively, achieves an average precision, recall, and F1-score of 0.74, 0.74, and 0.74, and it marks an obvious 4.23% improvement over the state-of-the-art approaches. Simultaneously, in solution-extraction, IS-Hunter achieves an average precision, recall, and F1-score of 0.83, 0.90, and 0.86 which is 4.88% higher than the best baseline methods.

Memory disaggregation, a promising technique allowing applications to use remote memory, is increasingly appealing in datacenters due to its high resource utilization. Operationally, the application’s host server constantly evicts unused data to remote to make room for memory allocation of new pages. Inefficient evictions allow memory usage to hit its limit, resulting in application blocking, which brings severe throughput degradation. However, most existing works neglect the importance of eviction. They offload the eviction to a background thread and set a fixed trigger timing, rendering a belated eviction. Worse still, they overlook the impact of network congestion on eviction efficiency, making their strategy flawed in large-scale scenarios. In this paper, we present Glitter, an adaptive, multi-level awareness eviction solution that accelerates applications by minimizing the overhead of application blocking from host and network aspects. For host, Glitter presents an adaptive eviction threshold adjustment to optimize the eviction timing, reducing the occurrence of application blocking. For network, Glitter adopts an eviction flow scheduling to address the hazards posed by flow contention at switches, decreasing the duration of each application blocking. Through comprehensive experiments, Glitter gives an average 1.4 throughput boost to Fastswap, a state-of-the-art disaggregated×memory system.