Tootfinder

The Tournament Tree Method for preference elicitation in Multi-criteria decision-making
Diego Garc\'ia-Zamora, \'Alvaro Labella, Jos\'e Rui Figueira
https://arxiv.org/abs/2510.08197

The Tournament Tree Method for preference elicitation in Multi-criteria decision-making
Pairwise comparison methods, such as Fuzzy Preference Relations and Saaty's Multiplicative Preference Relations, are widely used to model expert judgments in multi-criteria decision-making. However, their application is limited by the high cognitive load required to complete $m(m-1)/2$ comparisons, the risk of inconsistency, and the computational complexity of deriving consistent value scales. This paper proposes the Tournament Tree Method (TTM), a novel elicitation and evaluation framework tha…

AImoclips: A Benchmark for Evaluating Emotion Conveyance in Text-to-Music Generation
Gyehun Go, Satbyul Han, Ahyeon Choi, Eunjin Choi, Juhan Nam, Jeong Mi Park
https://arxiv.org/abs/2509.00813

AImoclips: A Benchmark for Evaluating Emotion Conveyance in Text-to-Music Generation
Recent advances in text-to-music (TTM) generation have enabled controllable and expressive music creation using natural language prompts. However, the emotional fidelity of TTM systems remains largely underexplored compared to human preference or text alignment. In this study, we introduce AImoclips, a benchmark for evaluating how well TTM systems convey intended emotions to human listeners, covering both open-source and commercial models. We selected 12 emotion intents spanning four quadrants …

On the Discretization Error of the Discrete Generalized Quantum Master Equation
Ruojing Peng, Lachlan P. Lindoy, Joonho Lee
https://arxiv.org/abs/2507.19323 https://

On the Discretization Error of the Discrete Generalized Quantum Master Equation
The transfer tensor method (TTM) [Cerrillo and Cao, Phys. Rev. Lett. 2014, 112, 110401] can be considered a discrete-time formulation of the Nakajima-Zwanzig quantum master equation (NZ-QME) for modeling non-Markovian quantum dynamics. A recent paper [Makri, J. Chem. Theory Comput. 2025, 21, 5037] raised concerns regarding the consistency of the TTM discretization, particularly a spurious term at the initial time \( t=0 \). This Communication presents a detailed analysis of the discretization s…

Music Arena: Live Evaluation for Text-to-Music
Yonghyun Kim, Wayne Chi, Anastasios N. Angelopoulos, Wei-Lin Chiang, Koichi Saito, Shinji Watanabe, Yuki Mitsufuji, Chris Donahue
https://arxiv.org/abs/2507.20900

Music Arena: Live Evaluation for Text-to-Music
We present Music Arena, an open platform for scalable human preference evaluation of text-to-music (TTM) models. Soliciting human preferences via listening studies is the gold standard for evaluation in TTM, but these studies are expensive to conduct and difficult to compare, as study protocols may differ across systems. Moreover, human preferences might help researchers align their TTM systems or improve automatic evaluation metrics, but an open and renewable source of preferences does not cur…

AI-Assisted Music Production: A User Study on Text-to-Music Models
Francesca Ronchini, Luca Comanducci, Simone Marcucci, Fabio Antonacci
https://arxiv.org/abs/2509.23364 https:/…

AI-Assisted Music Production: A User Study on Text-to-Music Models
Text-to-music models have revolutionized the creative landscape, offering new possibilities for music creation. Yet their integration into musicians workflows remains underexplored. This paper presents a case study on how TTM models impact music production, based on a user study of their effect on producers creative workflows. Participants produce tracks using a custom tool combining TTM and source separation models. Semi-structured interviews and thematic analysis reveal key challenges, opport…