arXiv:2512.11941v2 Announce Type: replace-cross Abstract: Zero-shot skeleton-based action recognition (ZS-SAR) is fundamentally constrained by prevailing approaches that rely on aligning skeleton features with static, class-level semantics. This coarse-grained alignment fails to bridge the domain shift between seen and unseen classes, thereby impeding the effective transfer of fine-grained visual knowledge. To address these limitations, we introduce \textbf{DynaPURLS}, a unified framework that establishes robust, multi-scale visual-semantic correspondences and dynamically refines them at inference time to enhance generalization. Our framework leverages a large language model to generate hierarchical textual descriptions that encompass both global movements and local body-part dynamics. Concurrently, an adaptive partitioning module produces fine-grained visual representations by semantically grouping skeleton joints. To fortify this fine-grained alignment against the train-test domain shift, DynaPURLS incorporates a dynamic refinement module. During inference, this module adapts textual features to the incoming visual stream via a lightweight learnable projection. This refinement process is stabilized by a confidence-aware, class-balanced memory bank, which mitigates error propagation from noisy pseudo-labels. Extensive experiments on three large-scale benchmark datasets, including NTU RGB+D 60/120 and PKU-MMD, demonstrate that DynaPURLS significantly outperforms prior art, setting new state-of-the-art records. The source code is made publicly available at https://github.com/Alchemist0754/DynaPURLS

arXiv:2603.29590v1 Announce Type: cross Abstract: Method illustration figures (MIFs) play a crucial role in conveying the core ideas of scientific papers, yet their generation remains a labor-intensive process. In this paper, we identify three key characteristics that substantially influence MIF generation quality, i.e., \emph{compositional complexity}, \emph{component similarity}, and \emph{design dynamics}. To handle these characteristics, we take inspiration from human authors' drawing practices and propose \textbf{FigAgent}, a novel multi-agent framework for automatically generating high-quality MIFs. Through multi-agent collaboration, our FigAgent distills drawing experiences across similar components of MIFs and encapsulates them into reusable tools that can be invoked during MIF generation, while evolving these tools to adapt to dynamic design requirements. Besides, a novel Explore-and-Select drawing strategy is introduced to mimic the human-like trial-and-error manner for gradually constructing MIFs with complex structures. Extensive experiments show the efficacy of our method. Project is available \href{https://zhuolingli.github.io/FigAgent-page-project/}{here}.