# Understanding Machine Taste: Aesthetic Agreement Across Foundation Models in K-pop

arXiv: https://arxiv.org/abs/2610.17731 · HTML: https://mlsanity.com/paper/2610.17731/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 30 references that are in the map.
- First posted on arXiv 13 Oct 2026.

## Builds on (most cited first, 22 of 22)

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- [Multi-Crit: Benchmarking Multimodal Judges on Pluralistic Criteria-Following](https://mlsanity.com/paper/2511.21662/) (2025)
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- [The Reader is the Metric: How Textual Features and Reader Profiles Explain Conflicting Evaluations of AI Creative Writing](https://mlsanity.com/paper/2506.03310/) (2025)
- [Where to Look Matters: On-Policy Self-Distillation for Long-Video Understanding](https://mlsanity.com/paper/2608.25356/) (2026)
- [Visual Aesthetic Benchmark: Can Frontier Models Judge Beauty?](https://mlsanity.com/paper/2605.12684/) (2026)
- [LLMs Struggle to Measure What Distinguishes Students of Different Proficiency Levels: A Study of Item Discrimination in Reading Comprehension Assessment](https://mlsanity.com/paper/2606.18709/) (2026)

## Abstract (arXiv)

As foundation models enter creative industries, understanding their aesthetic preferences becomes an important step toward AI-assisted creative decision making. In this work, we investigate how foundation models evaluate creative content and characterize their ``machine taste'' using K-pop songs and music videos as a testbed. We formulate aesthetic evaluation as a pairwise ranking problem and systematically examine pairwise inter-model agreement, group-level consensus, and cross-modal preference alignment. Our results reveal stronger pairwise inter-model agreement and group-level consensus in visual evaluation than in audio evaluation, while preferences across the two modalities remain weakly aligned. We further examine how visual consensus rankings relate to real-world popularity using historical YouTube viewership data, finding positive but varying correlations across artist groups. These findings provide initial insights into machine aesthetic preferences and lay the groundwork for future AI-assisted creative decision making that considers creative content, artist identity, audience preferences, and market dynamics.