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…

Prompting Test-Time Scaling Is A Strong LLM Reasoning Data Augmentation
Sondos Mahmoud Bsharat, Zhiqiang Shen
https://arxiv.org/abs/2510.09599 https://arxi…

Prompting Test-Time Scaling Is A Strong LLM Reasoning Data Augmentation
Large language models (LLMs) have demonstrated impressive reasoning capabilities when provided with chain-of-thought exemplars, but curating large reasoning datasets remains laborious and resource-intensive. In this work, we introduce Prompting Test-Time Scaling (P-TTS), a simple yet effective inference-time data augmentation strategy for enhancing LLM reasoning through finetuning. Rather than collecting thousands or even millions of examples, P-TTS leverages a small pool of only 90 manually se…

"...and I said 'it sounds like you're just giving iPhones to phone thieves with more steps' and the shareholders started cheering..."

A Tauberian approach to metric scaling limits of random discrete structures, with an application to random planar maps
William Fleurat
https://arxiv.org/abs/2510.05078 https://

A Tauberian approach to metric scaling limits of random discrete structures, with an application to random planar maps
We prove sandwich theorems and a Tauberian theorem in the space of compact metric measure spaces, endowed with the Gromov-Hausdorff-Prokhorov (GHP) topology. These results hold with respect to a close relative of Gromov's Lipschitz order. As a proof-of-concept of a general method to prove metric scaling limits of random discrete structures, we give an application to the theory of random planar maps: the Brownian sphere is the scaling limit in the GHP topology of irreducible quandrangulations. O…

“Protect free speech,” Trump said.
Then he silenced Colbert and Kimmel.
Banning books, removing images of slavery, “examining” Smithsonian exhibits, threatening FCC licenses…
BULLSHIT. This is censorship!
We won’t let him erase history, muzzle critics, or rewrite truth.
We stand with Jimmy.
https://

Congresswoman Chellie Pingree (@pingree.house.gov)
“Protect free speech,” Trump said. Then he silenced Colbert and Kimmel. Banning books, removing images of slavery, “examining” Smithsonian exhibits, threatening FCC licenses… BULLSHIT. This is censorship! We won’t let him erase history, muzzle critics, or rewrite truth. We stand with Jimmy.

From the article: "He added: “It’s something where it’s moving very quickly and people don’t necessarily have time to absorb it or figure out what to do.”"
That impersonal, natural "it" is moving - Not the people developing the models and selling them (or inflicting them), not the wealthy investors demanding market share, not the sci-fi addled techbros (Muskrats?) imagining that if only we get AGI all problems will be solved tomorrow, - all wanting to be first. Oh…

From the article: "He added: “It’s something where it’s moving very quickly and people don’t necessarily have time to absorb it or figure out what to do.”"
That impersonal, natural "it" is moving - Not the people developing the models and selling them (or inflicting them), not the wealthy investors demanding market share, not the sci-fi addled techbros (Muskrats?) imagining that if only we get AGI all problems will be solved tomorrow, - all wanting to be first. Oh…

Smart copolymer microgels with high volume phase transition temperature: Composition, swelling, and morphology
Aditi Gujare, Stefanie Uredat, Jonas Runge, Felix Morgenstern, Domenico Truzzolillo, Thomas Hellweg, Julian Oberdisse
https://arxiv.org/abs/2509.19942

Smart copolymer microgels with high volume phase transition temperature: Composition, swelling, and morphology
The thermosensitivity and microstructure of microgels made by copolymerizing standard microgel-forming monomers with more hydrophilic comonomers is investigated, with the aim of increasing the volume phase transition temperature (VPTT). We precisely determine the incorporation of N-(hydroxymethyl)acrylamide (HMAM) and purpose-synthesized N-(2-hydroxyisopropyl)acrylamide (HIPAM) into microgels -- neither of which forms microgels on its own by precipitation polymerization. The swelling properties…

When Silence Matters: The Impact of Irrelevant Audio on Text Reasoning in Large Audio-Language Models
Chen-An Li, Tzu-Han Lin, Hung-yi Lee
https://arxiv.org/abs/2510.00626 https…

When Silence Matters: The Impact of Irrelevant Audio on Text Reasoning in Large Audio-Language Models
Large audio-language models (LALMs) unify speech and text processing, but their robustness in noisy real-world settings remains underexplored. We investigate how irrelevant audio, such as silence, synthetic noise, and environmental sounds, affects text reasoning tasks where audio is unnecessary. Across three text-based benchmarks, we find that even non-informative audio reduces accuracy and increases prediction volatility; the severity of interference scales with longer durations, higher amplit…

Layer-wise Minimal Pair Probing Reveals Contextual Grammatical-Conceptual Hierarchy in Speech Representations
Linyang He, Qiaolin Wang, Xilin Jiang, Nima Mesgarani
https://arxiv.org/abs/2509.15655

Layer-wise Minimal Pair Probing Reveals Contextual Grammatical-Conceptual Hierarchy in Speech Representations
Transformer-based speech language models (SLMs) have significantly improved neural speech recognition and understanding. While existing research has examined how well SLMs encode shallow acoustic and phonetic features, the extent to which SLMs encode nuanced syntactic and conceptual features remains unclear. By drawing parallels with linguistic competence assessments for large language models, this study is the first to systematically evaluate the presence of contextual syntactic and semantic f…