Scaling multimodal large language models (MLLMs) to long videos is constrained by limited context windows. While retrieval-augmented generation (RAG) is a promising remedy by organizing query-relevant visual evidence into a compact context, most existing methods (i) flatten videos into independent segments, breaking their inherent spatio-temporal structure, and (ii) depend on explicit semantic matching, which can miss cues that are implicitly relevant to the query’s intent. To overcome these limitations, we propose VideoStir, a structured and intent-aware long-video RAG framework. It firstly structures a video as a spatio-temporal graph at clip level, and then performs multi-hop retrieval to aggregate evidence across distant yet contextually related events. Furthermore, it introduces an MLLM-backed intent-relevance scorer that retrieves frames based on their alignment with the query’s reasoning intent. To support this capability, we curate IR-600K, a large-scale dataset tailored for learning frame–query intent alignment. Experiments show that VideoStir is competitive with state-of-the-art baselines without relying on auxiliary information, highlighting the promise of shifting long-video RAG from flattened semantic matching to structured, intent-aware reasoning. Codes and checkpoints are available at https://github.com/RomGai/VideoStir.

Large language models (LLMs) have shown significant promise in embodied decision-making tasks within virtual open-world environments. Nonetheless, their performance is hindered by the absence of domain-specific knowledge. Methods that finetune on large-scale domain-specific data entail prohibitive development costs. This paper introduces VistaWise, a cost-effective agent framework that integrates cross-modal domain knowledge and finetunes a dedicated object detection model for visual analysis. It reduces the requirement for domain-specific training data from millions of samples to a few hundred. VistaWise integrates visual information and textual dependencies into a cross-modal knowledge graph (KG), enabling a comprehensive and accurate understanding of multimodal environments. We also equip the agent with a retrieval-based pooling strategy to extract task-related information from the KG, and a desktop-level skill library to support direct operation of the Minecraft desktop client via mouse and keyboard inputs. Experimental results demonstrate that VistaWise achieves state-of-the-art performance across various open-world tasks, highlighting its effectiveness in reducing development costs while enhancing agent performance.

The primary objective of sign language translation (SLT) is to transform sign language videos into natural sentences.A crucial challenge in this field is developing signer-independent SLT systems which requires models to generalize effectively to signers not encountered during training.This challenge is exacerbated by the limited diversity of signers in existing SLT datasets, which often results in suboptimal generalization capabilities of current models.Achieving robustness to unseen signers is essential for signer-independent SLT.However, most existing method relies on signer identity labels, which is often impractical and costly in real-world applications.To address this issue, we propose the Signer Diversity-driven Data Augmentation (SDDA) method that can achieve good generalization without relying on signer identity labels. SDDA comprises two data augmentation schemes. The first is data augmentation based on adversarial training, which aims to utilize the gradients of the model to generate adversarial examples. The second is data augmentation based on diffusion model, which focuses on using the advanced diffusion-based text guided image editing method to modify the appearances of the signer in images. The combination of the two strategies significantly enriches the diversity of signers in the training process.Moreover, we introduce a consistency loss and a discrimination loss to enhance the learning of signer-independent features.Our experimental results demonstrate our model significantly enhances the performance of SLT in the signer-independent setting, achieving state-of-the-art results without relying on signer identity labels.