Tootfinder

CP-Model-Zoo: A Natural Language Query System for Constraint Programming Models
Augustin Crespin, Ioannis Kostis, H\'el\`ene Verhaeghe, Pierre Schaus
https://arxiv.org/abs/2509.07867

CP-Model-Zoo: A Natural Language Query System for Constraint Programming Models
Constraint Programming and its high-level modeling languages have long been recognized for their potential to achieve the holy grail of problem-solving. However, the complexity of modeling languages, the large number of global constraints, and the art of creating good models have often hindered non-experts from choosing CP to solve their combinatorial problems. While generating an expert-level model from a natural-language description of a problem would be the dream, we are not yet there. We pr…

Gemma O‘Brien kicking off day two of #smashingconf Freiburg with an inspiring talk about her beautiful and powerful lettering and art work – and a super interesting interview about flow state. 👏

Singularidades para as solu\c{c}\~{o}es das Equa\c{c}\~{o}es de Navier-Stokes and Euler e o Problema do Mil\^{e}nio
Milton C. Lopes Filho, Helena J. Nussenzveig Lopes
https://arxiv.org/abs/2509.07638

Singularidades para as soluções das Equações de Navier-Stokes and Euler e o Problema do Milênio
The purpose of this note is to offer a birds-eye view on the history and the state-of-the-art in the research surrounding the Millenium Prize problem for the Navier-Stokes equations, the general problem of singularities in fluid dynamics and the corresponding problem for the Euler equations. This is the content of a plenary talk delivered at the 2024 Biannual meeting of the Brazilian Math Society by Helena Nussenzveig Lopes and it is written in portuguese.

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks
Antoine Ledent, Petr Kasalick\'y, Rodrigo Alves, Hady W. Lauw
https://arxiv.org/abs/2509.07499

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks
We introduce a new convolutional AutoEncoder architecture for user modelling and recommendation tasks with several improvements over the state of the art. Firstly, our model has the flexibility to learn a set of associations and combinations between different interaction types in a way that carries over to each user and item. Secondly, our model is able to learn jointly from both the explicit ratings and the implicit information in the sampling pattern (which we refer to as `implicit feedback')…

Improved Hamiltonian learning and sparsity testing through Bell sampling
Savar D. Sinha, Yu Tong
https://arxiv.org/abs/2509.07937 https://arxiv.org/pdf/250…

Improved Hamiltonian learning and sparsity testing through Bell sampling
We consider the problem of learning an $M$-sparse Hamiltonian and the related problem of Hamiltonian sparsity testing. Through a detailed analysis of Bell sampling, we reduce the total evolution time required by the state-of-the-art algorithm for $M$-sparse Hamiltonian learning to $\widetilde{\mathcal{O}}(M/ε)$, where $ε$ denotes the $\ell^{\infty}$ error, achieving an improvement by a factor of $M$ (ignoring the logarithmic factor) while only requiring access to forward time-evolution. We th…

Apple: “Look how good our phone cameras take pictures of minorities and marginalized groups”
Also Apple: “We’d like to present the world’s most powerful bigot an award.”

A survey on the optimal partition problem
Roberto Ognibene, Bozhidar Velichkov
https://arxiv.org/abs/2510.08241 https://arxiv.org/pdf/2510.08241

A survey on the optimal partition problem
This survey synthesizes the current state of the art on the regularity theory for solutions to the optimal partition problem. Namely, we consider non-negative, vector-valued Sobolev functions whose components have mutually disjoint support, and which are either local minimizers of the Dirichlet energy or, more generally, critical points satisfying a system of variational inequalities. This is particularly meaningful as the problem has emerged on several occasions and in diverse contexts: our ai…

CaRT: Teaching LLM Agents to Know When They Know Enough
Grace Liu, Yuxiao Qu, Jeff Schneider, Aarti Singh, Aviral Kumar
https://arxiv.org/abs/2510.08517 https://

CaRT: Teaching LLM Agents to Know When They Know Enough
Many tasks require learned models to strategically gather relevant information over multiple rounds of interaction before actually acting on a task. Strategic information gathering requires models to know not only how to effectively acquire information, but also when to stop gathering information and make a decision, in order to avoid overthinking or getting derailed when acting. In this paper, we formalize this problem and introduce Counterfactuals and Reasoning for Termination (CaRT), an appr…