mlsanityData to 14 Oct 2026

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

arXiv 2610.17731 ↗First posted 13 Oct 2026cs.CLcited by 0

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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.

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.

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References · 22

  1. 2025Qwen3 Technical Reportcited by 8,820
  2. 2025Qwen3-VL Technical Reportcited by 2,793
  3. 2025InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiencycited by 1,510
  4. 2025Qwen3-Omni Technical Reportcited by 497
  5. 2025GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learningcited by 466
  6. 2024Justice or Prejudice? Quantifying Biases in LLM-as-a-Judgecited by 368
  7. 2024Humans or LLMs as the Judge? A Study on Judgement Biasescited by 323
  8. 2024Judging the Judges: A Systematic Study of Position Bias in LLM-as-a-Judgecited by 269
  9. 2025MiniCPM-V 4.5: Cooking Efficient MLLMs via Architecture, Data, and Training Recipecited by 186
  10. 2024LLaVA-Critic: Learning to Evaluate Multimodal Modelscited by 134
  11. 2025Learning to Plan & Reason for Evaluation with Thinking-LLM-as-a-Judgecited by 78
  12. 2025LitBench: A Benchmark and Dataset for Reliable Evaluation of Creative Writingcited by 42
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  1. 2024Foundation Models for Music: A Surveycited by 37
  2. 2025Are We on the Right Way to Assessing LLM-as-a-Judge?cited by 24
  3. 2025Music Flamingo: Scaling Music Understanding in Audio Language Modelscited by 21
  4. 2024Language Model Council: Democratically Benchmarking Foundation Models on Highly Subjective Taskscited by 18
  5. 2025Multi-Crit: Benchmarking Multimodal Judges on Pluralistic Criteria-Followingcited by 15
  6. 2025AesBiasBench: Evaluating Bias and Alignment in Multimodal Language Models for Personalized Image Aesthetic Assessmentcited by 11
  7. 2025The Reader is the Metric: How Textual Features and Reader Profiles Explain Conflicting Evaluations of AI Creative Writingcited by 8
  8. 2026Where to Look Matters: On-Policy Self-Distillation for Long-Video Understandingcited by 5
  9. 2026Visual Aesthetic Benchmark: Can Frontier Models Judge Beauty?cited by 4
  10. 2026LLMs Struggle to Measure What Distinguishes Students of Different Proficiency Levels: A Study of Item Discrimination in Reading Comprehension Assessmentcited by 3

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