The Model Context Protocol (MCP) has been proposed as a unifying standard for connecting large language models (LLMs) with external tools and resources, promising the same role for AI integration that HTTP and USB played for the Web and peripherals. Yet, despite rapid adoption and hype, its trajectory remains uncertain. Are MCP marketplaces truly growing, or merely inflated by placeholders and abandoned prototypes? Are servers secure and privacy-preserving, or do they expose users to systemic risks? And do clients converge on standardized protocols, or remain fragmented across competing designs? In this paper, we present the first large-scale empirical study of the MCP ecosystem. We design and implement MCPCrawler, a systematic measurement framework that collects and normalizes data from six major markets. Over a 14-day campaign, MCPCrawler aggregated 17,630 raw entries, of which 8,401 valid projects (8,060 servers and 341 clients) were analyzed. Our results reveal that more than half of listed projects are invalid or low-value, that servers face structural risks including dependency monocultures and uneven maintenance, and that clients exhibit a transitional phase in protocol and connection patterns. Together, these findings provide the first evidence-based view of the MCP ecosystem, its risks, and its future trajectory.

In keeping with ViP, the first stage of Sequencers involves patch embedding with a 7x7 kernel. The second stage of Sequencers performs patch embedding with a 2x2 kernel, but the following two stages have no downsampling.

In this paper, we study the problem of 3D object segmentation from raw point clouds.

We provide the architecture details in Table 1. A.3 Implementation Details of Baselines Following prior works, we used ffmpeg [2] to produce the evaluation metrics for H.264 and HEVC. Then we use the following commands to compress videos with H.264 or HEVC codec under medium We also explore NeRV for video temporal interpolation task. We provide denoising results on'ig buck bunny' video in Figure 3. NeRV can reconstruct the original video with high fidelity. We test a smaller model on "Bosphorus" video, and it also has As the most popular media format nowadays, videos are generally viewed as frames of sequences.

Distinct from traditional heuristic solvers, this paper on one hand proposes an RL-based model for mixed-size macro placement, which differs from existing learning-based placers that often consider the macro by coarse grid-based mask. While the standard cells are placed via gradient-based GPU acceleration. On the other hand, a one-shot conditional generative routing model, which is composed of a special-designed input-size-adapting generator and a bi-discriminator, is devised to perform one-shot routing to the pins within each net, and the order of nets to route is adaptively learned.

We provide the architecture details in Table 1. A.3 Implementation Details of Baselines Following prior works, we used ffmpeg [2] to produce the evaluation metrics for H.264 and HEVC. Then we use the following commands to compress videos with H.264 or HEVC codec under medium We also explore NeRV for video temporal interpolation task. We provide denoising results on'ig buck bunny' video in Figure 3. NeRV can reconstruct the original video with high fidelity. We test a smaller model on "Bosphorus" video, and it also has As the most popular media format nowadays, videos are generally viewed as frames of sequences.