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arXiv2026

Music Hallucination in Audio-Language Models: A Hierarchical Formulation and Empirical Study

Audio-language models increasingly generate confident music descriptions that are unsupported by the input audio. We present, to our knowledge, the first music-specific, layer-wise, multi-paradigm empirical study of hallucination in audio-language models and formulate it as a hierarchical perceptual grounding failure across five layers: sound events, temporal properties, tonal attributes, style, and emotion. We introduce MuseDiag, a multi-paradigm diagnostic framework with contradiction-based verification, and evaluate nine models (four open-source and five closed-source). We find that (1) vocal misperception is a universal weakness across all nine models, tonal perception is a major axis of architectural differentiation, and Audio-Flamingo-3 remains the stable leader while substantial reordering below it reveals paradigm-specific vulnerability profiles; (2) affirmative bias, generation-mode effects, and layer-specific perceptual limitations are each empirically associated with the observed patterns, with convergent evidence from multiple analyses rather than strict causal attribution; and (3) our two training-free mitigation methods, Audio-Dependency-Aware Decoding for Music (ADD-M) and Taxonomy-Guided Perceptual Anchoring (TPA), can reduce hallucination in probing, but their gains vary by model and often do not carry over to free-form generation, showing that music hallucination mitigation must be evaluated across paradigms.

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arXiv2023

MS-UNet-v2: Adaptive Denoising Method and Training Strategy for Medical Image Segmentation with Small Training Data

Models based on U-like structures have improved the performance of medical image segmentation. However, the single-layer decoder structure of U-Net is too "thin" to exploit enough information, resulting in large semantic differences between the encoder and decoder parts. Things get worse if the number of training sets of data is not sufficiently large, which is common in medical image processing tasks where annotated data are more difficult to obtain than other tasks. Based on this observation, we propose a novel U-Net model named MS-UNet for the medical image segmentation task in this study. Instead of the single-layer U-Net decoder structure used in Swin-UNet and TransUnet, we specifically design a multi-scale nested decoder based on the Swin Transformer for U-Net. The proposed multi-scale nested decoder structure allows the feature mapping between the decoder and encoder to be semantically closer, thus enabling the network to learn more detailed features. In addition, we propose a novel edge loss and a plug-and-play fine-tuning Denoising module, which not only effectively improves the segmentation performance of MS-UNet, but could also be applied to other models individually. Experimental results show that MS-UNet could effectively improve the network performance with more efficient feature learning capability and exhibit more advanced performance, especially in the extreme case with a small amount of training data, and the proposed Edge loss and Denoising module could significantly enhance the segmentation performance of MS-UNet.

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