Tianwei Zhao

Max W. Y. Lam, Yijin Xing, Weiya You et al.

Autoregressive (AR) models have demonstrated impressive capabilities in generating high-fidelity music. However, the conventional next-token prediction paradigm in AR models does not align with the human creative process in music composition, potentially compromising the musicality of generated samples. To overcome this limitation, we introduce MusiCoT, a novel chain-of-thought (CoT) prompting technique tailored for music generation. MusiCoT empowers the AR model to first outline an overall music structure before generating audio tokens, thereby enhancing the coherence and creativity of the resulting compositions. By leveraging the contrastive language-audio pretraining (CLAP) model, we establish a chain of "musical thoughts", making MusiCoT scalable and independent of human-labeled data, in contrast to conventional CoT methods. Moreover, MusiCoT allows for in-depth analysis of music structure, such as instrumental arrangements, and supports music referencing -- accepting variable-length audio inputs as optional style references. This innovative approach effectively addresses copying issues, positioning MusiCoT as a vital practical method for music prompting. Our experimental results indicate that MusiCoT consistently achieves superior performance across both objective and subjective metrics, producing music quality that rivals state-of-the-art generation models. Our samples are available at https://MusiCoT.github.io/.

Zory Zhang, Pinyuan Feng, Bingyang Wang et al.

The ability to infer what others are looking at is a critical component of a theory of mind that underpins natural human-AI interaction. We characterized this skill in 111 Vision Language Models (VLMs) and human participants (N = 65) using photos taken with manipulated difficulty and variability. We found that 94 of the 111 VLMs were not better than random guessing, while humans achieved near-ceiling accuracy. VLMs respond with each choice almost equally frequently. Are they randomly guessing? At least for five top-tier VLMs, their performance was above chance, declined with increasing task difficulty, but barely varied across different prompts and scene objects. These behavioral patterns cannot be explained by considering VLMs as random guessers. Instead, they likely utilize head orientation but not eye appearance to infer gaze direction, such that their performance is imperfect, subject to the task difficulty, but robust to superficial perceptual variations. This suggests that VLMs, lacking effective gaze inference skills, have yet to become technologies that can naturally interact with humans, but the potential remains.

Dezhi Luo, Yijiang Li, Maijunxian Wang et al.

Understanding physical transformations is fundamental for reasoning in dynamic environments. While Vision Language Models (VLMs) show promise in embodied applications, whether they genuinely understand physical transformations remains unclear. We introduce ConservationBench evaluating conservation -- whether physical quantities remain invariant under transformations. Spanning four properties with paired conserving/non-conserving scenarios, we generate 23,040 questions across 112 VLMs. Results reveal systematic failure: performance remains near chance with improvements on conservation tasks accompanied by drops on controls. Control experiments show strong textual priors favoring invariance, yet models perform worse with visual content. Neither temporal resolution, prompting, nor curated sampling helps. These findings show that current VLMs fail to maintain transformation-invariant representations of physical properties across dynamic scenes.