This study implements a hybrid-structure-based deep learning framework to generate monthly 2 m specific humidity, 2 m temperature and surface pressure at 1/30° × 1/30° horizontal resolution during 1901–2023, and reproduces learned terrain-related spatial gradients without explicitly enforcing physical equations or terr...
Zheng Jin, Ze-Zhou Chen, Qinglong You et al.· Earth System Science Data· 0 citations
Hindsight Entropy-Assisted Learning (HEAL), an RL-free framework designed to bridge this reasoning gap, is proposed and extensive experiments demonstrate that HEAL significantly outperforms traditional SFT distillation and other baselines.
A unified taxonomy that isolates drafter-side parallelism from orthogonal design choices such as tree construction and verification strategies is introduced, and a comprehensive empirical comparison of existing methods under varying degrees of parallelism across standardized multimodal benchmarks, including OCR, VQA, v...
Yantao Li, Huanlin Gao, Fang Zhao et al.· 0 citations
Modern text-to-image (T2I) models often have similar total scores but different strengths, making practical selection difficult. Fine-grained benchmarks decompose prompts into questions, yet often return them to prompt scores and fixed categories, weakening attribution and ignoring complexity. Related requirements are...
Shao-An Zhao, Fang Zhao, Xueqiang Guo et al.· 0 citations
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