Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
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Scientific reports
Adaptive level modification via player skill classification and large language models
Maintaining player engagement in video games requires a careful balance between challenge and player competence. Static difficulty settings fail to account for individual skill variation, while existing dynamic difficulty adjustment systems are limited to tuning low-level game parameters rather than restructuring level content. This paper presents an adaptive level modification framework that personalizes gameplay by continuously inferring player skill and applying targeted structural modifications to level content in real-time. A hybrid behavioral dataset is constructed by combining agent-generated trajectories, produced by Proximal Policy Optimization (PPO) agents, a reinforcement learning approach, trained at three distinct skill levels, with manually collected human gameplay data labeled through clustering. A classifier trained on this dataset categorizes players into expert, normal, and beginner skill levels, achieving an overall accuracy of 97.82%. The classifier output drives a two-stage large language model (LLM) pipeline guided by prompt engineering, which expands a skill-conditioned prompt into a structured modification instruction applied to the current level chunk. A physics-constrained verifier based on a graph-based shortest path method ensures all modified levels remain traversable. Evaluated on Super Mario Bros. levels, the framework achieves a post-modification playability rate of 74.1% at the full-level granularity and 83.5% at the isolated-chunk granularity, closely matching the 80.0% baseline of the original levels.
30 July 2026
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Transformer-based advertisement image layout generation and object fidelity optimization
The layout of the image and the positional distribution of the objects directly affect the audience's visual focus and the effect of the message conveyed. By setting up a specific layout structure, the viewer's attention can be more focused on the product launched or the moral of the advertisement. Existing work related to position-controllable text-to-image generation has made great progress in generating results on simple images. However, when generating images of complex scenes, the quality is often poor. This can result in the model failing to accurately convey the message of the advertisement when used to generate advertisements. To address these limitations, we propose LAYOBJ-GAN, a novel two-stage framework for layout-controllable advertisement image synthesis. Unlike prior work, our method explicitly models background layouts jointly with object layouts during the text-to-layout generation stage, enabling comprehensive spatial planning of complex scenes. Technically, we introduce a Transformer-based sequence-to-sequence layout generator that learns long-range dependencies between textual descriptions and both background and object regions, which has not been explored in previous advertisement-oriented text-to-image frameworks. In the layout-to-image stage, we further propose a fine-grained text-layout interaction normalization module (TL-Norm) that enables object knowledge transfer from a pre-trained category-to-image model, allowing object appearance to be adaptively modulated by textual context and layout constraints. Extensive experiments on MS-COCO and a high-definition advertisement dataset (AsHQ-10K) demonstrate that LAYOBJ-GAN significantly outperforms seven state-of-the-art methods in image quality, layout controllability, and semantic object accuracy. These results confirm the effectiveness of explicitly modeling background layouts and transferring object-level generative knowledge for complex advertisement image synthesis.
27 June 2026
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