Effective code generation requires both model capability and a problem representation that carefully structures how models reason and plan.Existing approaches augment reasoning steps or inject specific structure into how models think, but leave scattered problem conditions unchanged.Inspired by the way humans organize fragmented information into coherent explanations, we propose STORYCODER, a narrative reformulation framework that transforms code generation questions into coherent natural language narratives, providing richer contextual structure than simple rephrasings.Each narrative consists of three components: a task overview, constraints, and example test cases, guided by the selected algorithm and genre.Experiments across 11 models on HumanEval, LiveCodeBench, and CodeForces demonstrate consistent improvements, with an average gain of 18.7% in zero-shot [email protected] accuracy, our analyses reveal that narrative reformulation guides models toward correct algorithmic strategies, reduces implementation errors, and induces a more modular code structure.The analyses further show that these benefits depend on narrative coherence and genre alignment, suggesting that structured problem representation is important for code generation regardless of model scale or architecture.Our code is available here.

Anomaly detection is essential for visual inspection systems that require reliable identification of defective or abnormal samples. In real-world deployment, however, object categories and defect types can change over time, making static evaluation settings insufficient for assessing long-term adaptability and generalization. To address this limitation, we introduce a new benchmark for continual learning in anomaly detection. Our benchmark, Continual-MEGA, expands existing evaluation settings by integrating diverse public datasets with our newly proposed dataset, ContinualAD. Beyond standard continual learning settings, we additionally propose a scenario that evaluates zero-shot generalization to unseen classes not encountered during continual adaptation. This reflects recent advances in continual zero-shot research and highlights its practical significance. This setting introduces a new agenda for the anomaly detection field, and we conduct extensive evaluations of various existing anomaly detection algorithms designed for continual or zero-shot scenarios, as well as our proposed baseline methods. From our experiments, we derive three key findings: (1) existing methods exhibit significant limitations, particularly in pixel-level defect localization, (2) the proposed ContinualAD dataset is effective for the proposed benchmarking scenario, and (3) our baseline method suggests a promising direction for designing CLIP-based continual and generalizable frameworks through simple adaptation combined with feature synthesis.

Recent advancements in text-to-image (T2I) models have greatly benefited from large-scale datasets, but they also pose significant risks due to the potential generation of unsafe content. To mitigate this issue, researchers proposed unlearning techniques that attempt to induce the model to unlearn potentially harmful prompts. However, these methods are easily bypassed by adversarial attacks, making them unreliable for ensuring the safety of generated images. In this paper, we propose Direct Unlearning Optimization (DUO), a novel framework for removing NSFW content from T2I models while preserving their performance on unrelated topics. DUO employs a preference optimization approach using curated paired image data, ensuring that the model learns to remove unsafe visual concepts while retain unrelated features. Furthermore, we introduce an output-preserving regularization term to maintain the model's generative capabilities on safe content. Extensive experiments demonstrate that DUO can robustly defend against various state-of-the-art red teaming methods without significant performance degradation on unrelated topics, as measured by FID and CLIP scores. Our work contributes to the development of safer and more reliable T2I models, paving the way for their responsible deployment in both closed-source and open-source scenarios.