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cs.CL2025
ArtifactsBench: Bridging the Visual-Interactive Gap in LLM Code Generation Evaluation
Chenchen Zhang, Yuhang Li, Can Xu +17
The generative capabilities of Large Language Models (LLMs) are rapidly expanding from static code to dynamic, interactive visual artifacts. This progress is bottlenecked by a crit…
cs.CL2025
Adaptive Deep Reasoning: Triggering Deep Thinking When Needed
Yunhao Wang, Yuhao Zhang, Tinghao Yu +3
Large language models (LLMs) have shown impressive capabilities in handling complex tasks through long-chain reasoning. However, the extensive reasoning steps involved can signific…