Lyrics translation requires both accurate semantic transfer and preservation of musical rhythm, syllabic structure, and poetic style.In animated musicals, the challenge intensifies due to alignment with visual and auditory cues.We introduce Multilingual Audio-Video Lyrics Benchmark for Animated Song Translation (MAVL), the first multilingual, multimodal benchmark for singable lyrics translation.By integrating text, audio, and video, MAVL enables richer and more expressive translations than textonly approaches.Building on this, we propose Syllable-Constrained Audio-Video LLM with Chain-of-Thought (SylAVL-CoT), which leverages audio-video cues and enforces syllabic constraints to produce natural-sounding lyrics.Experimental results demonstrate that SylAVL-CoT significantly outperforms textbased models in singability and contextual accuracy, emphasizing the value of multimodal, multilingual approaches for lyrics translation.

Residual connections are pivotal for deep neural networks, enabling greater depth by mitigating vanishing gradients. However, in standard residual updates, the module’s output is directly added to the input stream. This can lead to updates that predominantly reinforce or modulate the existing stream direction, potentially underutilizing the module’s capacity for learning entirely novel features. In this work, we introduce _Orthogonal Residual Update_: we decompose the module’s output relative to the input stream and add only the component orthogonal to this stream. This design aims to guide modules to contribute primarily new representa-tional directions, fostering richer feature learning while promoting more efficient training. We demonstrate that our orthogonal update strategy improves generalization accuracy and training stability across diverse architectures (ResNetV2, Vision Transformers) and datasets (CIFARs, TinyImageNet, ImageNet-1k), achieving, for instance, a +3.78 pp Acc@1 gain for ViT-B on ImageNet-1k. Code and models are available at https://github.com/BootsofLagrangian/ortho-residual.