Tootfinder

DiTSinger: Scaling Singing Voice Synthesis with Diffusion Transformer and Implicit Alignment
Zongcai Du, Guilin Deng, Xiaofeng Guo, Xin Gao, Linke Li, Kaichang Cheng, Fubo Han, Siyu Yang, Peng Liu, Pan Zhong, Qiang Fu
https://arxiv.org/abs/2510.09016

DiTSinger: Scaling Singing Voice Synthesis with Diffusion Transformer and Implicit Alignment
Recent progress in diffusion-based Singing Voice Synthesis (SVS) demonstrates strong expressiveness but remains limited by data scarcity and model scalability. We introduce a two-stage pipeline: a compact seed set of human-sung recordings is constructed by pairing fixed melodies with diverse LLM-generated lyrics, and melody-specific models are trained to synthesize over 500 hours of high-quality Chinese singing data. Building on this corpus, we propose DiTSinger, a Diffusion Transformer with Ro…

Scaling Transformer-Based Novel View Synthesis Models with Token Disentanglement and Synthetic Data
Nithin Gopalakrishnan Nair, Srinivas Kaza, Xuan Luo, Vishal M. Patel, Stephen Lombardi, Jungyeon Park
https://arxiv.org/abs/2509.06950

Scaling Transformer-Based Novel View Synthesis Models with Token Disentanglement and Synthetic Data
Large transformer-based models have made significant progress in generalizable novel view synthesis (NVS) from sparse input views, generating novel viewpoints without the need for test-time optimization. However, these models are constrained by the limited diversity of publicly available scene datasets, making most real-world (in-the-wild) scenes out-of-distribution. To overcome this, we incorporate synthetic training data generated from diffusion models, which improves generalization across un…

Re-assessing the stellar population scaling relations of the galaxies in the Local Universe
D. Mattolini, S. Zibetti, A. R. Gallazzi, L. Scholz-Diaz, J. Pratesi
https://arxiv.org/abs/2509.04570

Re-assessing the stellar population scaling relations of the galaxies in the Local Universe
Local galaxies follow scaling relations between mass and stellar population properties such as age and metallicity, which encode key information on their evolutionary histories. We revise these relations using the largest spectroscopic dataset from SDSS DR7 (0.005

GenPilot: A Multi-Agent System for Test-Time Prompt Optimization in Image Generation
Wen Ye, Zhaocheng Liu, Yuwei Gui, Tingyu Yuan, Yunyue Su, Bowen Fang, Chaoyang Zhao, Qiang Liu, Liang Wang
https://arxiv.org/abs/2510.07217

GenPilot: A Multi-Agent System for Test-Time Prompt Optimization in Image Generation
Text-to-image synthesis has made remarkable progress, yet accurately interpreting complex and lengthy prompts remains challenging, often resulting in semantic inconsistencies and missing details. Existing solutions, such as fine-tuning, are model-specific and require training, while prior automatic prompt optimization (APO) approaches typically lack systematic error analysis and refinement strategies, resulting in limited reliability and effectiveness. Meanwhile, test-time scaling methods opera…

ScaleDiff: Scaling Difficult Problems for Advanced Mathematical Reasoning
Qizhi Pei, Zhuoshi Pan, Honglin Lin, Xin Gao, Yu Li, Zinan Tang, Conghui He, Rui Yan, Lijun Wu
https://arxiv.org/abs/2509.21070

ScaleDiff: Scaling Difficult Problems for Advanced Mathematical Reasoning
Large Reasoning Models (LRMs) have shown impressive capabilities in complex problem-solving, often benefiting from training on difficult mathematical problems that stimulate intricate reasoning. Recent efforts have explored automated synthesis of mathematical problems by prompting proprietary models or large-scale open-source models from seed data or inherent mathematical concepts. However, scaling up these methods remains challenging due to their high computational/API cost, complexity of prom…

Scaling Generalist Data-Analytic Agents
Shuofei Qiao, Yanqiu Zhao, Zhisong Qiu, Xiaobin Wang, Jintian Zhang, Zhao Bin, Ningyu Zhang, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen
https://arxiv.org/abs/2509.25084

Scaling Generalist Data-Analytic Agents
Data-analytic agents are emerging as a key catalyst for automated scientific discovery and for the vision of Innovating AI. Current approaches, however, rely heavily on prompt engineering over proprietary models, while open-source models struggle to face diverse-format, large-scale data files and long-horizon, multi-step reasoning that real-world analytics demands. This paper introduces DataMind, a scalable data synthesis and agent training recipe designed to build generalist data-analytic agen…

Template-free precision synthesis of biphilic microdome arrays
Haobo Xu, Haonian Shu, Rong Yang
https://arxiv.org/abs/2510.00396 https://arxiv.org/pdf/2510…

Template-free precision synthesis of biphilic microdome arrays
Biphilic microdome arrays are ubiquitous in nature, but their synthetic counterparts have been scarce. To bridge that gap, we leverage condensed droplet polymerization (CDP) to enable their template-free synthesis. During CDP, monomer droplets serve as microreactors for free-radical polymerization. The droplet diameter is monitored in real time. To enable the precise prediction of the convex geometric parameters, we develop a theoretical framework that integrates geometric arguments, scaling an…

LibriQuote: A Speech Dataset of Fictional Character Utterances for Expressive Zero-Shot Speech Synthesis
Gaspard Michel, Elena V. Epure, Christophe Cerisara
https://arxiv.org/abs/2509.04072

LibriQuote: A Speech Dataset of Fictional Character Utterances for Expressive Zero-Shot Speech Synthesis
Text-to-speech (TTS) systems have recently achieved more expressive and natural speech synthesis by scaling to large speech datasets. However, the proportion of expressive speech in such large-scale corpora is often unclear. Besides, existing expressive speech corpora are typically smaller in scale and primarily used for benchmarking TTS systems. In this paper, we introduce the LibriQuote dataset, an English corpus derived from read audiobooks, designed for both fine-tuning and benchmarking exp…

MGM-Omni: Scaling Omni LLMs to Personalized Long-Horizon Speech
Chengyao Wang, Zhisheng Zhong, Bohao Peng, Senqiao Yang, Yuqi Liu, Haokun Gui, Bin Xia, Jingyao Li, Bei Yu, Jiaya Jia
https://arxiv.org/abs/2509.25131

MGM-Omni: Scaling Omni LLMs to Personalized Long-Horizon Speech
We present MGM-Omni, a unified Omni LLM for omni-modal understanding and expressive, long-horizon speech generation. Unlike cascaded pipelines that isolate speech synthesis, MGM-Omni adopts a "brain-mouth" design with a dual-track, token-based architecture that cleanly decouples multimodal reasoning from real-time speech generation. This design enables efficient cross-modal interaction and low-latency, streaming speech generation. For understanding, a unified training strategy coupled with a du…

Semantic and Visual Crop-Guided Diffusion Models for Heterogeneous Tissue Synthesis in Histopathology
Saghir Alfasly, Wataru Uegami, MD Enamul Hoq, Ghazal Alabtah, H. R. Tizhoosh
https://arxiv.org/abs/2509.17847

Semantic and Visual Crop-Guided Diffusion Models for Heterogeneous Tissue Synthesis in Histopathology
Synthetic data generation in histopathology faces unique challenges: preserving tissue heterogeneity, capturing subtle morphological features, and scaling to unannotated datasets. We present a latent diffusion model that generates realistic heterogeneous histopathology images through a novel dual-conditioning approach combining semantic segmentation maps with tissue-specific visual crops. Unlike existing methods that rely on text prompts or abstract visual embeddings, our approach preserves cri…

Red giant asteroseismic binaries in the Kepler field
Francisca Espinoza-Rojas, Nathalie Theme{\ss}l, Saskia Hekker
https://arxiv.org/abs/2509.13412 https://

Red giant asteroseismic binaries in the Kepler field
Systems in which two oscillating stars are observed in the same light curve, so-called asteroseismic binaries (ABs), arise from either chance alignments or gravitationally bound stars. In the latter case, ABs offer a novel way to find binary systems and combine asteroseismology and orbital dynamics to determine precise stellar parameters for both stars. Such systems provide valuable tests to stellar models and scaling relations. While population synthesis studies predict approximately 200 ABs i…

Yeast growth is controlled by the proportional scaling of mRNA and ribosome concentrations
Xin Gao, Michael Lanz, Rosslyn Grosely, Jonas Cremer, Joseph Puglisi, Jan M. Skotheim
https://arxiv.org/abs/2508.14997

Yeast growth is controlled by the proportional scaling of mRNA and ribosome concentrations
Despite growth being fundamental to all aspects of cell biology, we do not yet know its organizing principles in eukaryotic cells. Classic models derived from the bacteria E. coli posit that protein-synthesis rates are set by mass-action collisions between charged tRNAs produced by metabolic enzymes and mRNA-bound ribosomes. These models show that faster growth is achieved by simultaneously raising both ribosome content and peptide elongation speed. Here, we test if these models are valid for e…