Recent progress in video generative models has enabled the creation of high-quality videos from multimodal prompts that combine text and images. While these systems offer greater controllability, they also introduce new safety risks, as harmful content can arise from individual modalities or their interaction. Existing safety methods are often limited to text-only inputs, require the user to manually specify the risk category in advance, or only audit content after generation, making them unable to proactively address compositional multimodal risks. We present ConceptGuard, a safeguard framework that proactively detects and mitigates unsafe semantics in multimodal video generation before harmful content is produced. ConceptGuard operates in two stages. First, a contrastive detection module fuses image and text inputs and computes their similarity against the risk concept set , automatically retrieving the top-k concepts most relevant to the input without requiring any prior assumption about the risk category. Second, a semantic suppression mechanism uses these identified concepts to remove unsafe semantics from both the text and the visual input during early generation steps. To support training and evaluation, we introduce ConceptRisk, a large-scale dataset for multimodal risk detection, and T2VSafetyBench-TI2V, the first benchmark for evaluating safety in Text-and-Image-to-Video generation. Experiments on both benchmarks show that ConceptGuard outperforms existing baselines in both risk detection and safe video generation.

Multimodal Automated Program Repair (MAPR) extends traditional program repair by requiring models to jointly reason over source code, textual issue descriptions, and visual artifacts such as GUI screenshots. While recent LLM-based repair systems have shown promising results, existing approaches face several limitations: rigid workflow pipelines restrict exploration during debugging, visual reasoning is often performed over full-page screenshots without localized grounding, and failed repair attempts are rarely transformed into reusable knowledge. To address these challenges, we propose FailureMem, a multimodal repair framework that integrates three key mechanisms: a hybrid workflow–agent architecture that balances structured localization with flexible reasoning, active perception tools that enable region-level visual grounding, and a Failure Memory Bank that converts past repair attempts into reusable guidance. Experiments on SWE-bench Multimodal demonstrate FailureMem improves the resolved rate over GUIRepair by 3.7%.

Legal Case Retrieval (LCR) aims to identify cases that are legally similar to a given query case. Existing research has largely recast LCR as the task of modeling the textual similarity between the query case and a historical case. While this approach can handle single-defendant cases fairly easily, such surface-level matching proves insufficient for multi-defendant cases, which typically requires modeling and aligning defendants’ roles and judgment outcomes in different cases. To address the challenges associated with Multi-defendant LCR (Multi-LCR), we (1) present Multi-ELAM and Multi-LeCaRDv2, two multi-defendant datasets we create by enriching widely used benchmarks with defendant-level annotations, and (2) introduce a novel role- and judgment-aware framework for Multi-LCR. Experimental results show that our approach outperforms state-of-the-art baselines, highlighting its effectiveness for Multi-LCR.