Despite the growing demand for multilingual game localization, systematic evaluation frameworks for game translation constraints remain largely unexplored. As part of the WMT26 Test Suite Track, we propose KUEST, a test suite comprising 1,796 English-to-Korean segments across four core categories, and evaluate 27 submitted systems. Experimental results demonstrate that standard metrics obscure true model capabilities (e.g., the overall 1st-place system dropping to 15th in creative translation). Furthermore, we reveal residual capability fragmentation—category-specific variance that survives the shared general-competence factor and inverts top-tier rankings—and observe a "prompt gap” where context metadata degrades performance. As such, KUEST serves not only as a test suite but also as an evaluation framework that multi-dimensionally dissects domain-specific translation blind spots hidden behind single composite scores. The dataset is available at https://huggingface.co/datasets/JudyChoi/KUEST.

Recent advances in LLMs and VLMs have enabled safety systems to reason beyond simple risk patterns toward more contextual and semantic safety concerns. However, as risk patterns continue to evolve and safety rules become more complex, existing training-based end-to-end safeguards face persistent challenges in adaptability and explainable reasoning over complex safety rules. To address these challenges, we propose GuardEn (Guarding by Safety Rule Entailment), an executable safeguard framework that decomposes safety policies into atomic propositions through Safety-Rule Compilation, modeling their composition as executable code. At test time, Scene-Grounded Execution instantiates these atomic propositions with contextual visual information derived from scene graphs, enabling rule-grounded and interpretable safety reasoning. Experiments on SafetyVisionBench demonstrate the effectiveness of programmable safeguard for complex visual safety assessment, achieving an average improvement of 9.8 F1 points over the strongest baseline across four domains.

With the advent of large language models, the complexity of multi-document summarization task has been substantially reduced. The summarization process must effectively handle noisy documents that are irrelevant to the main topic while preserving essential information. Recently, Chain-of-Density (COD) and Chain-of-Event (CoE) have proposed prompts to effectively handle the noisy documents by using entity-centric approaches for the summarization. However, CoD and CoE are prone to information loss during entity extraction due to their tendency to overly filter out entities perceived as less critical but that could still be important. In this paper, we propose a novel instruction prompt termed as Chain of Knowledge Graph (CoKG) for multi-document summarization. Our prompt extracts entities and constructs relationships between entities to form a Knowledge Graph (KG). Next, the prompt enriches these relationships to recognize potentially important entities and assess the strength of each relation. If the acquired KG meets a predefined quality level, the KG is used to summarize the given documents. This process helps alleviate the information loss in multi-document summarization. Experimental results demonstrate that our prompt effectively preserves key entities and is robust to noisy documents.