General-purpose Vision-Language Models (VLMs) are increasingly integral to modern AI systems for document understanding, yet their ability to perform fine-grained layout analysis remains severely underdeveloped. Overcoming this limitation requires large-scale, high-fidelity training datasets. However, current annotation methods that rely on parsing rendered PDFs are costly, error-prone, and difficult to scale. We propose a different paradigm: extracting ground-truth layout directly from the LaTeX compilation process rather than the final PDF. We present LaTeX2Layout, a generalizable procedural pipeline that recovers pixel-accurate bounding boxes and reading order from compiler traces. This enables the generation of a 140K-page dataset, including 120K programmatically generated synthetic variants that more than double the layout diversity of real-world data. Using this dataset, we fine-tune an efficient 3B-parameter VLM with an easy-to-hard curriculum that accelerates convergence. Our model achieves Kendall's tau=0.95 for reading order and mAP@50=0.91 for element grounding, delivering nearly 200% relative improvement over strong zero-shot baselines such as GPT-4o and Claude-3.7.

The Energy Internet (EI) aims to build a sustainable energy ecosystem by connecting diverse energy sources and prosumers. Mobile Crowd Sensing (MCS) enables efficient data collection for monitoring and aggregation from distributed devices. Given the complex behavior of workers driven by self-interest, recruiting trustworthy, high-quality, and inexpensive workers remains a significant challenge in research and practice. Previous studies often assume that worker characteristics are known or can be obtained after data collection. However, evaluating worker qualities is quite challenging in the face of multi-source data and complex workers. To address this, we propose a Completion Rate Learning based Multi-Armed Bandit reverse Auction (CRL-MABA) scheme for identifying and selecting high-quality workers in MCS. Our CRL-MABA scheme first proposes a Spatial-Temporal Upper Confidence Bound (STUCB) method to recruit workers, considering both the quality of workers for exploitation and the spatiotemporal features for exploration. In addition, the Dual-Stage Data Estimation Mechanism (DSDEM) and Long-Term and Short-Term Memory Learning (LTSTML) are designed to identify workers accurately and efficiently. Importantly, our proposed scheme avoids the impractical assumptions in previous works while satisfying important criteria such as truthfulness, individual rationality, and computational efficiency. The effectiveness of our scheme is demonstrated through extensive experimental results, which show its superiority over existing strategies.

Mobile crowdsourcing (MCS) is an emerging paradigm that harnesses the collective power of the crowd to tackle large-scale tasks. To ensure the high-quality worker selection, various combinatorial multiarmed bandit (CMAB)-based schemes have been proposed. However, previous schemes often overlook critical issues. First, the post-unknown worker recruitment (PUWR) problem emerges when the quality of a worker remains unknown despite reported worker data. Second, the presence of Sybil Requesters is often neglected, who manipulate ratings to deceive workers for malicious purposes. To tackle these challenges, we present an innovative scheme called bi-directional trust verification-based CMAB (BTV-CMAB). First, we propose a truth quality discovery approach that effectively addresses the PUWR problem by estimating worker quality. Additionally, we employ a BTV mechanism to assess the Degree of Trust (DoT) of requesters and the reputation of workers. To select top-notch workers for MCS, we combine the worker quality and reputation into an upper confidence bound (UCB) index. The effectiveness of the BTV-CMAB scheme is supported by theoretical proof, which demonstrates its ability to ensure truthfulness and individual rationality. Furthermore, experimental results reveal promising improvements achieved by our scheme, including a 17.44%, increase in the platform’s revenue and a significant decrease in regret of up to 88.26%. To the best of our knowledge, this study is the first to propose utilizing a BTV mechanism to effectively address the PUWR problem and counter the threat of Sybil attacks in the CMAB-based worker recruitment process.