Network Diagram Comprehension (NDC) is a vital task for networking professionals, offering essential insights into network topology and configurations. However, NDC remains a labor-intensive process heavily reliant on human expertise, with existing tools falling short in addressing this challenge. It is critical to develop an Automatic NDC (ANDC) system that ensures high faithfulness and completeness in information extraction while supporting practical, end-to-end NDC applications. Moreover, a comprehensive dataset and benchmark are necessary to systematically evaluate and drive the progress of ANDC.In this work, we introduce Layered Extractor of Network Diagrams (LEND), the first ANDC system designed to comprehensively and faithfully extract and utilize information from network diagrams. LEND employs a three-stage pipeline: (1) a layer extractor to decompose diagrams and identify key elements with a denoising cascade, (2) an inter-layer combiner to reconstruct entity relations with positional and domain knowledge, and (3) a task-specific interpreter for networking applications.To support this effort, we develop two extensive NDC datasets comprising over 4,000 network diagrams and icons from diverse sources, along with the first benchmark to evaluate ANDC systems across three distinct metrics. Empirical experiments demonstrate that LEND outperforms existing methods by achieving at 1.21– 5.10× better faithfulness and completeness, and improves its capability as a NetOps engineer by 30.5% on the Cisco Certified Network Associate (CCNA) exam.

Large Language Model (LLM) agents are capable of task execution across various domains by autonomously interacting with environments and refining LLM responses based on feedback. However, existing model serving systems are not optimized for the unique demands of serving agents. Compared to classic model serving, agent serving has different characteristics: predictable request pattern, increasing quality requirement, and unique prompt formatting. We identify a key problem for agent serving: LLM serving systems lack session-awareness. They neither perform effective KV cache management nor precisely select the cheapest yet competent model in each round. This leads to a cost-quality tradeoff, and we identify an opportunity to surpass it in an agent serving system. To this end, we introduce AgServe for AGile AGent SERVing. AgServe features a session-aware server that boosts KV cache reuse via Estimated-Time-of-Arrival-based eviction and in-place positional embedding calibration, a quality-aware client that performs session-aware model cascading through real-time quality assessment, and a dynamic resource scheduler that maximizes GPU utilization. With AgServe, we allow agents to select and upgrade models during the session lifetime, and to achieve similar quality at much lower costs, effectively transcending the tradeoff. Extensive experiments on real testbeds demonstrate that AgServe (1) achieves comparable response quality to GPT-4o at a 16.5\% cost. (2) delivers 1.8$\times$ improvement in quality relative to the tradeoff curve.

The increasing deployment of large language models (LLMs) in the cybersecurity domain underscores the need for effective model selection and evaluation. However, traditional evaluation methods often overlook specific cybersecurity knowledge gaps that contribute to performance limitations. To address this, we develop CSEBenchmark, a fine-grained cyber-security evaluation framework based on 345 knowledge points expected of cybersecurity experts. Drawing from cognitive science, these points are categorized into factual, conceptual, and procedural types, enabling the design of 11,050 tailored multiple-choice questions. We evaluate 12 popular LLMs on CSEBenchmark and find that even the best-performing model achieves only 85.42% overall accuracy, with particular knowledge gaps in the use of specialized tools and uncommon commands. Different LLMs have unique knowledge gaps. Even large models from the same family may perform poorly on knowledge points where smaller models excel. By identifying and addressing specific knowledge gaps in each LLM, we achieve up to an 84% improvement in correcting previously incorrect predictions across three existing benchmarks for two cybersecurity tasks. Furthermore, our assessment of each LLM's knowledge alignment with specific cybersecurity roles reveals that different models align better with different roles, such as GPT-4o for the Google Senior Intelligence Analyst and Deepseek-V3 for the Amazon Privacy Engineer. These findings underscore the importance of aligning LLM selection with the specific knowledge requirements of different cybersecurity roles for optimal performance.

With the increasing of ubiquitous devices with cameras, video content is widely produced in the industry. Automation video summarization allows content consumers effectively retrieve the moments that capture their primary attention. Existing supervised methods mainly focus on frame-level information. As a natural phenomenon, video fragments in different shots are richer in semantics than frames. We leverage this as a free latent supervision signal and introduce a novel model named multi-level spatiotemporal network (MLSN). Our approach contains Multi-Level Feature Representations (MLFR) and Local Relative Loss (LRL). MLFR module consists of frame-level features, fragment-level features, and shot-level features with relative position encoding. For videos of different shot durations, it can flexibly capture and accommodate semantic information of different spatiotemporal granularities; LRL utilizes the partial ordering relations among frames of each fragment to capture highly discriminative features to improve the sensitivity of the model. Our method substantially improves the best existing published method by 7% on our industrial products dataset LSVD. Meanwhile, experimental results on two widely used benchmark datasets SumMe and TVSum demonstrate that our method outperforms most state-of-the-art ones.