Tootfinder

Open-Source LLMs Collaboration Beats Closed-Source LLMs: A Scalable Multi-Agent System
Shengji Tang, Jianjian Cao, Weihao Lin, Jiale Hong, Bo Zhang, Shuyue Hu, Lei Bai, Tao Chen, Wanli Ouyang, Peng Ye
https://arxiv.org/abs/2507.14200

Open-Source LLMs Collaboration Beats Closed-Source LLMs: A Scalable Multi-Agent System
This paper aims to demonstrate the potential and strengths of open-source collectives. It leads to a promising question: Can we harness multiple open-source LLMs to match or even beat the closed-source LLMs? To answer this, we propose SMACS, a scalable multi-agent collaboration system (MACS) framework with high performance. Specifically, for continuous integration of new LLMs and generalization to diverse questions, we first propose a Retrieval-based Prior Selection (RPS), which assigns a proxy…

From Local Interactions to Global Operators: Scalable Gaussian Process Operator for Physical Systems
Sawan Kumar, Tapas Tripura, Rajdip Nayek, Souvik Chakraborty
https://arxiv.org/abs/2506.15906

From Local Interactions to Global Operators: Scalable Gaussian Process Operator for Physical Systems
Operator learning offers a powerful paradigm for solving parametric partial differential equations (PDEs), but scaling probabilistic neural operators such as the recently proposed Gaussian Processes Operators (GPOs) to high-dimensional, data-intensive regimes remains a significant challenge. In this work, we introduce a novel, scalable GPO, which capitalizes on sparsity, locality, and structural information through judicious kernel design. Addressing the fundamental limitation of cubic computat…

Scalable Estimation of Crossed Random Effects Models via Multi-way Grouping
Shota Takeishi, Shonosuke Sugasawa
https://arxiv.org/abs/2507.15593 https://

Scalable Estimation of Crossed Random Effects Models via Multi-way Grouping
Cross-classified data frequently arise in scientific fields such as education, healthcare, and social sciences. A common modeling strategy is to introduce crossed random effects within a regression framework. However, this approach often encounters serious computational bottlenecks, particularly for non-Gaussian outcomes. In this paper, we propose a scalable and flexible method that approximates the distribution of each random effect by a discrete distribution, effectively partitioning the rand…

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A Scalable Post-Processing Pipeline for Large-Scale Free-Space Multi-Agent Path Planning with PiBT
Arjo Chakravarty, Michael X. Grey, M. A. Viraj J. Muthugala, Mohan Rajesh Elara
https://arxiv.org/abs/2506.16748

A Scalable Post-Processing Pipeline for Large-Scale Free-Space Multi-Agent Path Planning with PiBT
Free-space multi-agent path planning remains challenging at large scales. Most existing methods either offer optimality guarantees but do not scale beyond a few dozen agents, or rely on grid-world assumptions that do not generalize well to continuous space. In this work, we propose a hybrid, rule-based planning framework that combines Priority Inheritance with Backtracking (PiBT) with a novel safety-aware path smoothing method. Our approach extends PiBT to 8-connected grids and selectively appl…

DREAM: Scalable Red Teaming for Text-to-Image Generative Systems via Distribution Modeling
Boheng Li, Junjie Wang, Yiming Li, Zhiyang Hu, Leyi Qi, Jianshuo Dong, Run Wang, Han Qiu, Zhan Qin, Tianwei Zhang
https://arxiv.org/abs/2507.16329

DREAM: Scalable Red Teaming for Text-to-Image Generative Systems via Distribution Modeling
Despite the integration of safety alignment and external filters, text-to-image (T2I) generative models are still susceptible to producing harmful content, such as sexual or violent imagery. This raises serious concerns about unintended exposure and potential misuse. Red teaming, which aims to proactively identify diverse prompts that can elicit unsafe outputs from the T2I system (including the core generative model as well as potential external safety filters and other processing components), …

Global linear stability of the bubble rising in the presence of a soluble surfactant
M. A. Herrada, J. M. L\'opez-Herrera, D. Fern\'andez-Mart\'inez, M. G. Cabezas, J. M. Montanero
https://arxiv.org/abs/2506.16933

Global linear stability of the bubble rising in the presence of a soluble surfactant
We study the stability of the bubble rising in the presence of a soluble surfactant numerically and experimentally. For the surfactant concentration considered, the Marangoni stress almost immobilizes the interface. However, the non-zero surface velocity is crucial to understanding the surfactant behavior. The global linear stability analysis predicts the transition to an oblique path above the threshold of the Galilei number (the bubble radius). This transition is followed by the coexistence o…

Aethorix v1.0: AI-Driven Inverse Design of Inorganic Materials for Scalable Industrial Innovation
Yingjie Shi, Runtian Miao
https://arxiv.org/abs/2506.16609

Aethorix v1.0: AI-Driven Inverse Design of Inorganic Materials for Scalable Industrial Innovation
Artificial intelligence for Science (AI4S) is poised to transform industrial manufacturing by enabling the accelerated discovery and optimization of advanced (bio)materials, dramatically reducing development cycles, and unlocking novel high-performance solutions. We introduce Aethorix v1.0, a platform that integrates large language models for objective mining, diffusion-based generative models for zero-shot inorganic crystal design, and machine-learned interatomic potentials for rapid property …

Mean Shift for Functional Data: A Scalable Algorithm and Convergence Analysis
Ting-Li Chen, Toshinari Morimoto, Su-Yun Huang, Ruey S. Tsay
https://arxiv.org/abs/2507.14457

Mean Shift for Functional Data: A Scalable Algorithm and Convergence Analysis
This paper extends the mean shift algorithm from vector-valued data to functional data, enabling effective clustering in infinite-dimensional settings. To address the computational challenges posed by large-scale datasets, we introduce a fast stochastic variant that significantly reduces computational complexity. We provide a rigorous analysis of convergence and stability for the full functional mean shift procedure, establishing theoretical guarantees for its behavior. For the stochastic varia…