3D Semantic Scene Completion (SSC) aims to infer voxel-level occupancy and semantics from partial 2D observations. However, existing methods often rely on global attention or uniform voxel modeling, which may cause semantic interference across unrelated regions and degrade performance under occlusion. To address this, we propose HGroupScene, a unified framework that integrates spatial priors and region-constrained reasoning for robust SSC. HGroupScene introduces: 1) a Hierarchical Grouping Module that partitions the voxel space into subregions and performs semantic aggregation via differentiable Gumbel-Softmax attention; 2) a dual-branch architecture composed of an Explicit Constraint Branch for extracting region-level structural features and an Implicit Diffusion Branch for fine-grained semantic reasoning; and 3) a Region-Constrained Feature Diffusion Mechanism that enables controllable feature propagation under the guidance of structural region priors. Extensive experiments on SemanticKITTI and SSCBench-KITTI360 under both single-frame and multi-frame settings demonstrate that HGroupScene achieves competitive or superior performance compared to state-of-the-art methods, validating the effectiveness of spatially structured semantic modeling.

Despite the significant impact of visual events on human cognition, understanding events in videos remains a challenging task for AI due to their complex structures, semantic hierarchies, and dynamic evolution. To address this, we propose the task of video event understanding that extracts event scripts and makes predictions with these scripts from videos. To support this task, we introduce VidEvent, a large-scale dataset containing over 23,000 well-labeled events, featuring detailed event structures, broad hierarchies, and logical relations extracted from movie recap videos. The dataset was created through a meticulous annotation process, ensuring high-quality and reliable event data. We also provide comprehensive baseline models offering detailed descriptions of their architecture and performance metrics. These models serve as benchmarks for future research, facilitating comparisons and improvements. Our analysis of VidEvent and the baseline models highlights the dataset's potential to advance video event understanding and encourages the exploration of innovative algorithms and models.

Script event induction, which aims to predict the subsequent event based on the context, is a challenging task in NLP, achieving remarkable success in practical applications. However, human events are mostly recorded and presented in the form of videos rather than scripts, yet there is a lack of related research in the realm of vision. To address this problem, we introduce AVEP (Action-centric Video Event Prediction), a task that distinguishes itself from existing video prediction tasks through its incorporation of more complex logic and richer semantic information. We present a large structured dataset, which consists of about 35K annotated videos and more than 178K video clips of event, built upon existing video event datasets to support this task. The dataset offers more fine-grained annotations, where the atomic unit is represented as a multimodal event argument node, providing better structured representations of video events. Due to the complexity of event structures, traditional visual models that take patches or frames as input are not well-suited for AVEP. We propose EventFormer, a node-graph hierarchical attention based video event prediction model, which can capture both the relationships between events and their arguments and the coreferencial relationships between arguments. We conducted experiments using several SOTA video prediction models as well as LVLMs on AVEP, demonstrating both the complexity of the task and the value of the dataset. Our approach outperforms all these video prediction models. We will release the dataset and code for replicating the experiments and annotations.

Large Language models (LLMs) usually rely on extensive training datasets. In the financial domain, creating numerical reasoning datasets that include a mix of tables and long text often involves substantial manual annotation expenses. To address the limited data resources and reduce the annotation cost, we introduce FinLLMs, a method for generating financial question-answering (QA) data based on common financial formulas using LLMs. First, we compile a list of common financial formulas and construct a graph based on the variables these formulas employ. We then augment the formula set by combining those that share identical variables as new elements. Specifically, we explore formulas obtained by manual annotation and merge those formulas with shared variables by traversing the constructed graph. Finally, utilizing LLMs, we generate financial QA data that encompasses both tabular information and long textual content, building on the collected formula set. Our experiments demonstrate that the synthetic data generated by FinLLMs effectively enhances the performance of various numerical reasoning models in the financial domain, including both pre-trained language models (PLMs) and fine-tuned LLMs. This performance surpasses that of two established benchmark financial QA datasets.

Large language models (LLMs) require vast and high-quality training datasets, which are often difficult to annotate, particularly in domains such as finance. In finance, the datasets not only encompass large volumes of textual data but also involve intricate numerical reasoning, frequently presented in the form of tables. One significant challenge in this domain is the processing of exceptionally long financial statements and complex tables, which can hinder the reasoning capacity of LLMs and negatively impact their accuracy. To mitigate the adverse effects of lengthy text passages and complex tables, this study focuses on identifying and extracting key information (gold facts) that are essential for solving financial problems, with the aim of enhancing both reasoning quality and data efficiency. We investigate the disparities between various financial datasets and examine different model extraction techniques. Our experimental findings demonstrate that the precise extraction of these key facts from both textual and tabular data substantially improves the accuracy of LLMs-based question answering, while simultaneously reducing the amount of data necessary for effective model performance.