Natural gas pipeline leakage threatens energy supply reliability and operational safety, making accurate forecasting-based detection essential for real-time monitoring. Although supervisory control and data acquisition systems enable large-scale time-series modeling with Transformers, their deployment in pipeline monitoring still faces three challenges: (1) imbalanced modeling of high-and low-frequency components, (2) high inference latency when preserving computational accuracy, limiting real-time monitoring, and (3) the absence of a robust, adaptive threshold for discriminating normal from leakage states. To address these issues, we propose NaFreBformer, an encoder-only Transformer for pipeline monitoring. Specifically, the key components of our approach include: (1) patching the full-spectrum into multiple sub-bands, each band independently normalized and forecasted to mitigate bias, (2) approximating self-attention by the Nystrom method to improve computational efficiency, and (3) introducing a dynamic nonparametric boundary based on the Raita principle, which utilizes historical normal data to establish a boundary that effectively discriminates leakage from normal data. Experiments on real pipelines show that NaFreBformer improves Accuracy by 1.96% and F1-score by 4.23%, while reducing inference latency by 45.16%, thereby reducing missed and false alarms, shortening safety response time, and improving the operational reliability of pipeline monitoring systems.
Agent Skills are structured packages of procedural knowledge that augment large language model (LLM) agents at inference time. Despite rapid adoption, there is no standard way to measure whether they actually help. We present SkillsBench, a benchmark whose current inventory contains 87 tasks across 8 domains paired with curated Skills and deterministic verifiers. Our latest aggregate evaluation runs the 87-task benchmark under matched no-Skills and curated-Skills conditions for 18 model-harness configurations. Curated Skills raise the average pass rate from 33.9
Adaptive gradient methods are workhorses in deep learning. However, the convergence guarantees of adaptive gradient methods for nonconvex optimization have not been thoroughly studied. In this paper, we provide a fine-grained convergence analysis for a general class of adaptive gradient methods including AMSGrad, RMSProp and AdaGrad. For smooth nonconvex functions, we prove that adaptive gradient methods in expectation converge to a first-order stationary point. Our convergence rate is better than existing results for adaptive gradient methods in terms of dimension. In addition, we also prove high probability bounds on the convergence rates of AMSGrad, RMSProp as well as AdaGrad, which have not been established before. Our analyses shed light on better understanding the mechanism behind adaptive gradient methods in optimizing nonconvex objectives.
Retrieval-Augmented Generation (RAG) has demonstrated substantial advancements in various natural language processing tasks by integrating the strengths of large language models (LLMs) and external knowledge databases. However, the retrieval step introduces long sequence generation and extra data dependency, resulting in long end-to-end latency. Our analysis benchmarks current RAG systems and reveals that, while the retrieval step poses performance challenges, it also offers optimization opportunities through its retrieval pattern and streaming search behavior. We propose RAGCache, a latency-optimized serving system tailored for RAG. RAGCache leverages the retrieval pattern to organize and cache the intermediate states of retrieved knowledge in a knowledge tree across the GPU and host memory hierarchy, reducing LLM generation time. RAGCache employs dynamic speculative pipelining to exploit the streaming search behavior, overlapping retrieval with LLM generation to minimize end-to-end latency. We implement RAGCache based on vLLM and Faiss, and evaluate it on both open-source and production datasets. Experimental results demonstrate that RAGCache reduces the time to first token (TTFT) by up to 4 & times; and improves the throughput by up to 2.1 & times; compared to vLLM integrated with Faiss.
This work presents Sa2VA, the first comprehensive, unified model for dense grounded understanding of both images and videos. Unlike existing multi-modal large language models, which are often limited to specific modalities and tasks, Sa2VA supports a wide range of image and video tasks, including referring segmentation and conversation, with minimal single-stage instruction tuning. Sa2VA combines SAM-2, a foundation video segmentation model, with MLLM, advanced vision-language models, and unifies text, image, and video into a shared LLM token space. Using the LLM, Sa2VA generates instruction tokens that guide SAM-2 in producing precise masks, enabling a grounded, multi-modal understanding of both static and dynamic visual content. Additionally, we introduce Ref-SAV, an auto-labeled dataset containing over 72k object expressions in complex video scenes, designed to boost model performance. We also manually validate 2k video objects in the Ref-SAV dataset to benchmark referring video object segmentation in complex environments. Experiments show that Sa2VA achieves strong performance across multiple tasks, particularly in referring video object segmentation, highlighting its potential for complex real-world applications. In addition, Sa2VA can be easily extended to various MLLMs, including Qwen-VL and Intern-VL, which can be updated with a rapid process in current open-source MLLMs. Code and models have been provided to the community.
