Ruihao Li

Yunshu Bai, RuiHao Li, Hao Zhang et al.

Game UI implementation requires translating stylized mockups into interactive engine entities. However, current "Screenshot-to-Code" tools often struggle with the irregular geometries and deep visual hierarchies typical of game interfaces. To bridge this gap, we introduce SPRITE, a pipeline that transforms static screenshots into editable engine assets. By integrating Vision-Language Models (VLMs) with a structured YAML intermediate representation, SPRITE explicitly captures complex container relationships and non-rectangular layouts. We evaluated SPRITE against a curated Game UI benchmark and conducted expert reviews with professional developers to assess reconstruction fidelity and prototyping efficiency. Our findings demonstrate that SPRITE streamlines development by automating tedious coding and resolving complex nesting. By facilitating rapid in-engine iteration, SPRITE effectively blurs the boundaries between artistic design and technical implementation in game development. Project page: https://baiyunshu.github.io/sprite.github.io/

Ruihao Li, Andrew Jacob, Neeraja J. Yadwadkar et al.

Specialized accelerators dominate AI workloads, but CPUs remain critical for orchestrating these accelerators and running datacenter services. As a result, CPU performance increasingly shapes end-to-end system efficiency, making it necessary for benchmarks to reflect modern workloads and bottlenecks. However, it remains unclear how emerging CPU benchmark suites reflect these shifts. To address this, we present the first comprehensive characterization of SPEC CPU2026 across nine platforms spanning recent Intel, AMD, Ampere, and Nvidia processors. We find that, compared to SPEC CPU2017, SPEC CPU2026 increases instruction volume and memory footprint, and shifts pressure toward emerging bottlenecks, most notably higher instruction-cache stress. We next examine whether the full suite is necessary for architectural evaluation. Using clustering-based representativeness analysis, we identify that compact subsets of 4-5 workloads per group preserve 96.4-99.9% of full-suite behavior, substantially reducing evaluation costs without sacrificing fidelity. To better position SPEC CPU2026, we compare it against SPEC CPU2017, DCPerf, and MLPerf using cross-suite microarchitectural metrics. SPEC CPU2026 remains a general-purpose suite with complementary characteristics: it is less vector-intensive than MLPerf and has lower frontend pressure than DCPerf, yet moves closer to real-world CPU behavior than prior SPEC CPU generations. Finally, we show that SPEC CPU2026 supports practical architectural studies beyond aggregate scores through case studies on page sizes and allocators, prefetching, compiler optimizations, ISA sensitivity, and many-core scaling. The new round-robin stagger mode generates proxy workloads that approximate DCPerf, reducing the IPC gap to 13.7%. Overall, SPEC CPU2026 sets a new foundation for rigorous and cost-effective CPU evaluation.

Ming Yan, Ruihao Li, Hao Zhang et al.

Language agents have shown impressive problem-solving skills within defined settings and brief timelines. Yet, with the ever-evolving complexities of open-world simulations, there's a pressing need for agents that can flexibly adapt to complex environments and consistently maintain a long-term memory to ensure coherent actions. To bridge the gap between language agents and open-world games, we introduce Language Agent for Role-Playing (LARP), which includes a cognitive architecture that encompasses memory processing and a decision-making assistant, an environment interaction module with a feedback-driven learnable action space, and a postprocessing method that promotes the alignment of various personalities. The LARP framework refines interactions between users and agents, predefined with unique backgrounds and personalities, ultimately enhancing the gaming experience in open-world contexts. Furthermore, it highlights the diverse uses of language models in a range of areas such as entertainment, education, and various simulation scenarios. The project page is released at https://miao-ai-lab.github.io/LARP/.

Lim Chien Her, Ming Yan, Yunshu Bai et al.

Procedural Content Generation (PCG) offers scalable methods for algorithmically creating complex, customizable worlds. However, controlling these pipelines requires the precise configuration of opaque technical parameters. We propose a training-free architecture that utilizes LLM agents for zero-shot PCG parameter configuration. While Large Language Models (LLMs) promise a natural language interface for PCG tools, off-the-shelf models often fail to bridge the semantic gap between abstract user instructions and strict parameter specifications. Our system pairs an Actor agent with a Critic agent, enabling an iterative workflow where the system autonomously reasons over tool parameters and refines configurations to progressively align with human design preferences. We validate this approach on the generation of various 3D maps, establishing a new benchmark for instruction-following in PCG. Experiments demonstrate that our approach outperforms single-agent baselines, producing diverse and structurally valid environments from natural language descriptions. These results demonstrate that off-the-shelf LLMs can be effectively repurposed as generalized agents for arbitrary PCG tools. By shifting the burden from model training to architectural reasoning, our method offers a scalable framework for mastering complex software without task-specific fine-tuning.