Text-driven 3D human motion generation models face significant challenges in responding to diverse and unconstrained textual prompts, primarily due to the limited availability of 3D motion training data. To address this, we introduce MoVT, a novel framework that effectively leverages the extensive range of human action...
Bei-Bei Jing, Tian-Le Guo, You-Jia Zhang et al.· 0 citations
This work proposes Ontology-Masked Reasoning Autoencoding (OMRA), a controlled intervention that masks and reconstructs over-explained spans while preserving stance, factual content, and coherence and reduces the measured gap between AI preference and real user engagement.
Xing-Lang Zhang, Yuan-Meng Xiang, Yun-Yao Zhang et al.· 0 citations
HexLogicAgent is proposed, a framework that first organizes the meaning of natural-language statements and then guides logical reasoning through structured verification, supported by a logical hexagon theory, which explains why a complete structure of opposing meanings is necessary for reliable reasoning.
Emotion understanding in discourse requires reasoning beyond surface sentiment because speakers often convey affect through indirect, implicit, polite, ironic, or deliberately mismatched expressions. Existing emotion benchmarks mainly annotate surface polarity or final emotion categories, while lacking a structured acc...
Zhen-Yan Zheng, Yun-Yao Zhang, Jun Sheng et al.· 0 citations
VAKE (Verifiable Activation of Parametric KnowledgE), a two-stage reinforcement-learning framework that externalizes latent parametric knowledge through explicit Priming and transfers the acquired elicitation capability to implicit Reasoning, is proposed.
Zuocheng Ying, Yang Yang, Yumou Wu et al.· 0 citations
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