Tootfinder

Riccati-ZORO: An efficient algorithm for heuristic online optimization of internal feedback laws in robust and stochastic model predictive control
Florian Messerer, Yunfan Gao, Jonathan Frey, Moritz Diehl
https://arxiv.org/abs/2511.10473 https://arxiv.org/pdf/2511.10473 https://arxiv.org/html/2511.10473
arXiv:2511.10473v1 Announce Type: new
Abstract: We present Riccati-ZORO, an algorithm for tube-based optimal control problems (OCP). Tube OCPs predict a tube of trajectories in order to capture predictive uncertainty. The tube induces a constraint tightening via additional backoff terms. This backoff can significantly affect the performance, and thus implicitly defines a cost of uncertainty. Optimizing the feedback law used to predict the tube can significantly reduce the backoffs, but its online computation is challenging.
Riccati-ZORO jointly optimizes the nominal trajectory and uncertainty tube based on a heuristic uncertainty cost design. The algorithm alternates between two subproblems: (i) a nominal OCP with fixed backoffs, (ii) an unconstrained tube OCP, which optimizes the feedback gains for a fixed nominal trajectory. For the tube optimization, we propose a cost function informed by the proximity of the nominal trajectory to constraints, prioritizing reduction of the corresponding backoffs. These ideas are developed in detail for ellipsoidal tubes under linear state feedback. In this case, the decomposition into the two subproblems yields a substantial reduction of the computational complexity with respect to the state dimension from $\mathcal{O}(n_x^6)$ to $\mathcal{O}(n_x^3)$, i.e., the complexity of a nominal OCP.
We investigate the algorithm in numerical experiments, and provide two open-source implementations: a prototyping version in CasADi and a high-performance implementation integrated into the acados OCP solver.
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Evaluating Robustness of Large Language Models Against Multilingual Typographical Errors
Yihong Liu, Raoyuan Zhao, Lena Altinger, Hinrich Sch\"utze, Michael A. Hedderich
https://arxiv.org/abs/2510.09536

Evaluating Robustness of Large Language Models Against Multilingual Typographical Errors
Large language models (LLMs) are increasingly deployed in multilingual, real-world applications with user inputs -- naturally introducing typographical errors (typos). Yet most benchmarks assume clean input, leaving the robustness of LLMs to typos across languages largely underexplored. To address this gap, we introduce MulTypo, a multilingual typo generation algorithm that simulates human-like errors based on language-specific keyboard layouts and typing behavior. We evaluate 18 open-source LL…

Colibri: A new tool for fast-flying PDF fits
Mark N. Costantini, Luca Mantani, James M. Moore, Valentina Schutze Sanchez, Maria Ubiali
https://arxiv.org/abs/2510.03391 https://

Colibri: A new tool for fast-flying PDF fits
We present Colibri, an open-source Python code that provides a general and flexible tool for PDF fits. The code is built so that users can implement their own PDF model, and use the built-in functionalities of Colibri for a fast computation of observables. It grants easy access to experimental data, several error propagation methodologies, including the Hessian method, the Monte Carlo replica method, and an efficient numerical Bayesian sampling algorithm. To demonstrate the capabilities of Coli…

GMTHRASHpy: Forward Convolutions of Crossed Molecular Beams Experiments in Python
Kazuumi Fujioka, Rui Sun
https://arxiv.org/abs/2510.05398 https://arxiv.o…

GMTHRASHpy: Forward Convolutions of Crossed Molecular Beams Experiments in Python
We introduce GMTHRASHpy, a Python-based application to do forward convolution fits of crossed molecular beams experiments. The code is designed to be easy-to-use and widely-available, so as to be of value to anyone wanting to reproduce data or fits from these experiments. GMTHRASHpy has been benchmarked to replicate the original GMTHRASH executable for a wide variety of published reactions. The inputs and algorithm guiding it are explained. The code is open-source and can be downloaded at: gith…

Spiffy: Multiplying Diffusion LLM Acceleration via Lossless Speculative Decoding
Sudhanshu Agrawal, Risheek Garrepalli, Raghavv Goel, Mingu Lee, Christopher Lott, Fatih Porikli
https://arxiv.org/abs/2509.18085

Spiffy: Multiplying Diffusion LLM Acceleration via Lossless Speculative Decoding
Diffusion LLMs (dLLMs) have recently emerged as a powerful alternative to autoregressive LLMs (AR-LLMs) with the potential to operate at significantly higher token generation rates. However, currently available open-source dLLMs often generate at much lower rates, typically decoding only a single token at every denoising timestep in order to maximize output quality. We present Spiffy, a speculative decoding algorithm that accelerates dLLM inference by $\mathbf{2.8{-}3.1\times}$ while provably p…

A Reusable Library for Second-Order Orbital Optimization Using the Trust Region Method
Jonas Greiner, Ida-Marie H{\o}yvik, Susi Lehtola, Janus J. Eriksen
https://arxiv.org/abs/2509.13931

A Reusable Library for Second-Order Orbital Optimization Using the Trust Region Method
We present a reusable, open-source software implementation of the second-order trust region algorithm in the new OpenTrustRegion library. We apply the implementation to the general-purpose optimization of molecular orbitals in various contexts within electronic-structure theory. Our permissibly licensed implementation can be included in any software package, be it free and open-source, academically licensed closed-source, or commercial. Detailing the implementation in OpenTrustRegion, we presen…