Recent diffusion- and conditional flow matching (CFM)-based emotional voice conversion (EVC) systems achieve strong conversion quality. However, they typically rely on multiple sampling steps, because the conditioning specifies which emotion to impose while its acoustic realization is left to the sampler. In this paper...
Yu Pan, Xiao-Juan Cai, Liang Zhang et al.· 0 citations
Recent Vision-Language-Action (VLA) models increasingly use natural-language instructions to condition autonomous driving behavior. Yet existing closed-loop benchmarks primarily measure route completion, safety, and rule compliance, leaving a fundamental question unresolved: does language actually determine which feasi...
Zi-Han You, Ze-Hua Fan, Wenqi Lyu et al.· 0 citations
A rubric-based audio-grounded evaluation framework that verifies event realization, acoustic attributes, and speech content through fine-grained semantic criteria, and human validation demonstrates that the benchmark achieves stronger alignment with human semantic judgments than conventional global similarity metrics.
Jin-Ting Wang, Yuguang Yang, Shengyu Li et al.· 0 citations
Extensive experiments demonstrate that AnyAudio-Judge not only significantly enhances zero-shot alignment detection compared to state-of-the-art baselines, but also provides precise and interpretable reward signals that substantially improve instruction alignment in downstream reinforcement learning for audio generatio...
Hai-Tao Li, Tian Tan, Yuguang Yang et al.· arXiv.org· 3 citations
Squeeze10-LLM is a staged mixed-precision post-training quantization (PTQ) framework and achieves state-of-the-art performance for sub-2bit weight-only quantization, improving average accuracy from 43% to 56% on six zero-shot classification tasks--a significant boost over existing PTQ methods.
Qingcheng Zhu, Yang-Yang Ren, Lin-Lin Yang et al.· arXiv.org· 1 citation
StageWAM is introduced, which augments a Motus-based World Action Model with Stage-JEPA, a goal-conditioned Joint-Embedding Predictive Architecture (JEPA) predictor, which uses a frozen V-JEPA2 encoder to extract the current-state representation and predicts the latent target of the next inferred stage.
Xiao Liu, Yuguang Yang, Xi Wang et al.· 2 citations· ⚡1
Adaptive-WAM is introduced, a quality-aware multi-exit planner built on a Wan2.2-5B backbone that avoids the iterative classifier-free denoising loop and VAE decoding required for future-video synthesis, while dynamically allocating backbone depth according to trajectory quality.
Sining Ang, Yuguang Yang, Yan Wang· 2 citations
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