Tootfinder

"The Art of Solving Impossible Problems" @ Katina Magazine: https://katinamagazine.org/content/article/future-of-work/2025/the-art-of-solving-impossible-problems
"Sometimes the best way to solve a problem is by ad…

LeanGeo: Formalizing Competitional Geometry problems in Lean
Chendong Song, Zihan Wang, Frederick Pu, Haiming Wang, Xiaohan Lin, Junqi Liu, Jia Li, Zhengying Liu
https://arxiv.org/abs/2508.14644

LeanGeo: Formalizing Competitional Geometry problems in Lean
Geometry problems are a crucial testbed for AI reasoning capabilities. Most existing geometry solving systems cannot express problems within a unified framework, thus are difficult to integrate with other mathematical fields. Besides, since most geometric proofs rely on intuitive diagrams, verifying geometry problems is particularly challenging. To address these gaps, we introduce LeanGeo, a unified formal system for formalizing and solving competition-level geometry problems within the Lean 4 …

Uncertainty Quantification for Linear Inverse Problems with Besov Prior: A Randomize-Then-Optimize Method
Andreas Horst, Babak Maboudi Afkham, Yiqiu Dong, Jakob Lemvig
https://arxiv.org/abs/2506.16888

Uncertainty Quantification for Linear Inverse Problems with Besov Prior: A Randomize-Then-Optimize Method
In this work, we investigate the use of Besov priors in the context of Bayesian inverse problems. The solution to Bayesian inverse problems is the posterior distribution which naturally enables us to interpret the uncertainties. Besov priors are discretization invariant and can promote sparsity in terms of wavelet coefficients. We propose the randomize-then-optimize method to draw samples from the posterior distribution with Besov priors under a general parameter setting and estimate the modes …

Solution to some conjectures on mobile position problems
Ethan Shallcross, James Tuite, Aoise Evans, Aditi Krishnakumar, Sumaiyah Boshar
https://arxiv.org/abs/2507.16622

Solution to some conjectures on mobile position problems
The general position problem for graphs asks for the largest number of vertices in a subset $S \subseteq V(G)$ of a graph $G$ such that for any $u,v \in S$ and any shortest $u,v$-path $P$ we have $S \cap V(P) = \{ u,v\} $, whereas the mutual visibility problem requires only that for any $u,v \in S$ there exists a shortest $u,v$-path with $S \cap V(P) = \{ u,v\} $. In the mobile versions of these problems, robots must move through the network in general position/mutual visibility such that every…

Physics-Driven Neural Network for Solving Electromagnetic Inverse Scattering Problems
Yutong Du, Zicheng Liu, Bazargul Matkerim, Changyou Li, Yali Zong, Bo Qi, Jingwei Kou
https://arxiv.org/abs/2507.16321

Physics-Driven Neural Network for Solving Electromagnetic Inverse Scattering Problems
In recent years, deep learning-based methods have been proposed for solving inverse scattering problems (ISPs), but most of them heavily rely on data and suffer from limited generalization capabilities. In this paper, a new solving scheme is proposed where the solution is iteratively updated following the updating of the physics-driven neural network (PDNN), the hyperparameters of which are optimized by minimizing the loss function which incorporates the constraints from the collected scattered…

Generalized Eshelby's inclusion and inhomogeneity problems for transient heat transfer
Chunlin Wu, Zhenhua Wei, Huiming Yin
https://arxiv.org/abs/2506.16498

Generalized Eshelby's inclusion and inhomogeneity problems for transient heat transfer
Eshelby's problems have been generalized to arbitrary shape of polygonal, polyhedral, and ellipsoidal inclusions embedded in an infinite isotropic domain under transient heat transfer, and Eshelby's tensors have been analytically derived to evaluate disturbed thermal fields caused by inclusions with a polynomial-form eigen-fields. Transformed coordinates are applied to arbitrarily shaped inclusions for domain integrals of transient fundamental solutions. This formulation is for general transien…

Tikhonov regularized second-order dynamical systems with Hessian-driven damping for solving convex optimization problems
Xiangkai Sun, Guoxiang Tian, Huan Zhang
https://arxiv.org/abs/2506.15968

Tikhonov regularized second-order dynamical systems with Hessian-driven damping for solving convex optimization problems
This paper deals with a Tikhonov regularized second-order dynamical system that incorporates time scaling, asymptotically vanishing damping and Hessian-driven damping for solving convex optimization problems. Under appropriate setting of the parameters, we first obtain fast convergence results of the function value along the trajectory generated by the dynamical system. Then, we show that the trajectory generated by the dynamical system converges weakly to a minimizer of the convex optimization…

Conditionally adaptive augmented Lagrangian method for physics-informed learning of forward and inverse problems using artificial neural networks
Qifeng Hu, Shamsulhaq Basir, Inanc Senocak
https://arxiv.org/abs/2508.15695

Conditionally adaptive augmented Lagrangian method for physics-informed learning of forward and inverse problems using artificial neural networks
We present several advances to the physics and equality constrained artificial neural networks (PECANN) framework that substantially improve its capability to learn solutions of canonical partial differential equations (PDEs). First, we generalize the augmented Lagrangian method (ALM) to support multiple independent penalty parameters, enabling simultaneous enforcement of heterogeneous constraints. Second, we reformulate pointwise constraint enforcement and Lagrange multipliers as expectations …

A Common Pool of Privacy Problems: Legal and Technical Lessons from a Large-Scale Web-Scraped Machine Learning Dataset
Rachel Hong, Jevan Hutson, William Agnew, Imaad Huda, Tadayoshi Kohno, Jamie Morgenstern
https://arxiv.org/abs/2506.17185

A Common Pool of Privacy Problems: Legal and Technical Lessons from a Large-Scale Web-Scraped Machine Learning Dataset
We investigate the contents of web-scraped data for training AI systems, at sizes where human dataset curators and compilers no longer manually annotate every sample. Building off of prior privacy concerns in machine learning models, we ask: What are the legal privacy implications of web-scraped machine learning datasets? In an empirical study of a popular training dataset, we find significant presence of personally identifiable information despite sanitization efforts. Our audit provides concr…

On using AI for EEG-based BCI applications: problems, current challenges and future trends
Thomas Barbera, Jacopo Burger, Alessandro D'Amelio, Simone Zini, Simone Bianco, Raffaella Lanzarotti, Paolo Napoletano, Giuseppe Boccignone, Jose Luis Contreras-Vidal
https://arxiv.org/abs/2506.16168

On using AI for EEG-based BCI applications: problems, current challenges and future trends
Imagine unlocking the power of the mind to communicate, create, and even interact with the world around us. Recent breakthroughs in Artificial Intelligence (AI), especially in how machines "see" and "understand" language, are now fueling exciting progress in decoding brain signals from scalp electroencephalography (EEG). Prima facie, this opens the door to revolutionary brain-computer interfaces (BCIs) designed for real life, moving beyond traditional uses to envision Brain-to-Speech, Brain-to-…

Global Lipschitz regularity in anisotropic elliptic problems with natural gradient growth
Carlo Alberto Antonini, Andrea Cianchi
https://arxiv.org/abs/2507.14606

Global Lipschitz regularity in anisotropic elliptic problems with natural gradient growth
We deal with homogeneous Dirichlet and Neumann boundary-value problems for anisotropic elliptic operators of p-Laplace type. They emerge as Euler-Lagrange equations of integral functionals of the Calculus of Variations built upon possibly anisotropic norms of the gradient of trial functions. We establish global Lipschitz regularity of solutions under the weakest possible assumption on right-hand side of the equation, which may also include the gradient term with natural growth exponent. The res…

Bicomplex Schwarz and Dirichlet Boundary Value Problems
William L. Blair
https://arxiv.org/abs/2507.15653 https://arxiv.org/pdf/2507.…

Bicomplex Schwarz and Dirichlet Boundary Value Problems
We define and solve boundary value problems of Schwarz and Dirichlet type on the complex unit disk for bicomplex-valued functions.

Defect Anomalies, a Spin-Flux Duality, and Boson-Kondo Problems
Zohar Komargodski, Fedor K. Popov, Brandon C. Rayhaun
https://arxiv.org/abs/2508.14963 https://

Defect Anomalies, a Spin-Flux Duality, and Boson-Kondo Problems
We show that the infrared phases of certain line defects in 2+1d quantum field theories are determined by anomalies, including anomalies in the space of defect coupling constants, together with a symmetry-refined corollary of the $g$-theorem. As an example, we prove that the spin-$1/2$ impurities in the 2+1d critical $O(2)$ and $O(3)$ models (known respectively as the Halon and Boson-Kondo defects) flow to non-trivial conformal line operators in the IR, and we supply evidence that the same exte…

Identifiability in Unlinked Linear Regression: Some Results and Open Problems
Fadoua Balabdaoui, Martin Slawski, Jonathan Steffani
https://arxiv.org/abs/2507.14986

Identifiability in Unlinked Linear Regression: Some Results and Open Problems
A tacit assumption in classical linear regression problems is the full knowledge of the existing link between the covariates and responses. In Unlinked Linear Regression (ULR) this link is either partially or completely missing. While the reasons causing such missingness can be different, a common challenge in statistical inference is the potential non-identifiability of the regression parameter. In this note, we review the existing literature on identifiability when the $d \ge 2$ components of…

Quantum k-SAT Related Hypergraph Problems
Simon-Luca Kremer, Dorian Rudolph, Sevag Gharibian
https://arxiv.org/abs/2506.17066 https://

Quantum k-SAT Related Hypergraph Problems
The Quantum k-SAT problem is the quantum generalization of the k-SAT problem. It is the problem whether a given local Hamiltonian is frustration-free. Frustration-free means that the ground state of the k-local Hamiltonian minimizes the energy of every local interaction term simultaneously. This is a central question in quantum physics and a canonical QMA_1-complete problem. The Quantum k-SAT problem is not as well studied as the classical k-SAT problem in terms of special tractable cases, appr…

So this power rail is interesting. It's 1.775 mV RMS and 42 mV p-p.
~5 mV p-p of 125 MHz triangular ripple (core operating frequency of the two Ethernet PHYs on the board, plus also ~500 MHz ringing on the 300 kHz switching transients reaching 42 mV p-p ( /- 21 mV).
This is probably not enough to cause any problems, all of the 1.0V supplies on the PHY are specced at /- 50 mV, but I don't like ripple :P
Like 2V5, this rail is generated by a MYMGK00504ERSR, but the s…

Fast Algorithms for Graph Arboricity and Related Problems
Ruoxu Cen, Henry Fleischmann, George Z. Li, Jason Li, Debmalya Panigrahi
https://arxiv.org/abs/2507.15598

Fast Algorithms for Graph Arboricity and Related Problems
We give an algorithm for finding the arboricity of a weighted, undirected graph, defined as the minimum number of spanning forests that cover all edges of the graph, in $\sqrt{n} m^{1+o(1)}$ time. This improves on the previous best bound of $\tilde{O}(nm)$ for weighted graphs and $\tilde{O}(m^{3/2}) $ for unweighted graphs (Gabow 1995) for this problem. The running time of our algorithm is dominated by a logarithmic number of calls to a directed global minimum cut subroutine -- if the running t…

@… Would you recommend GrapheneOS? Had any problems, e.g. certain critical apps not working?

the golang discourse reminds me of the term “balge”, short for “by and large good enough”, used to describe software that gets the job done but has misbehaviours and limitations that often cause problems
https://dotat.at/random/info.eagle.current.status

17 Daily #Productivity Tools for a #Java Engineer
https://

Neural Network Acceleration of Iterative Methods for Nonlinear Schr\"odinger Eigenvalue Problems
Daniel Peterseim, Jan-F. Pietschmann, Jonas P\"uschel, Kilian Ruess
https://arxiv.org/abs/2507.16349

Neural Network Acceleration of Iterative Methods for Nonlinear Schrödinger Eigenvalue Problems
We present a novel approach to accelerate iterative methods to solve nonlinear Schrödinger eigenvalue problems using neural networks. Nonlinear eigenvector problems are fundamental in quantum mechanics and other fields, yet conventional solvers often suffer from slow convergence in extreme parameter regimes, as exemplified by the rotating Bose- Einstein condensate (BEC) problem. Our method uses a neural network to predict and refine solution trajectories, leveraging knowledge from previous sim…

A Semi-analytic but Biased Uncertainty Assessment Method using Sample Extensions, Analysed for Nonlinear Travel Time Tomography
Xuebin Zhao, Andrew Curtis
https://arxiv.org/abs/2507.16353

A Semi-analytic but Biased Uncertainty Assessment Method using Sample Extensions, Analysed for Nonlinear Travel Time Tomography
Many geophysical problems can be cast as inverse problems that estimate a set of parameter values from observed data. Within a Bayesian framework, solutions to such problems are described probabilistically by the so-called posterior probability distribution functions (pdf's). To obtain robust inference results often requires millions of model parameter value samples to be drawn, and their simulation to be performed; this is a computationally expensive procedure. We investigate the concept of sa…

On free boundary problems for the Atlas model
Rami Atar, Amarjit Budhiraja
https://arxiv.org/abs/2507.15479 https://arxiv.org/pdf/250…

On free boundary problems for the Atlas model
For $n\in\mathbb{N}$, let $\{X^n_i\}$ be an infinite collection of Brownian particles on the real line where the leftmost particle $\min_iX^n_i(t)$ is given a drift $n$, and let $μ^n_t=n^{-1}\sum_iδ_{X^n_i(t)}$, $t\ge0$ denote the normalized configuration measure. The case where the initial particle positions follow a Poisson point process on $[0,\infty)$ of intensity $nλ$, $λ>0$ was studied where it was shown that $μ^n_t$ converge, as $n\to\infty$, to a limit characterized by a Stefan pro…

Joint Optimisation of Electric Vehicle Routing and Scheduling: A Deep Learning-Driven Approach for Dynamic Fleet Sizes
Jun Kang Yap, Vishnu Monn Baskaran, Wen Shan Tan, Ze Yang Ding, Hao Wang, David L. Dowe
https://arxiv.org/abs/2507.15307

Joint Optimisation of Electric Vehicle Routing and Scheduling: A Deep Learning-Driven Approach for Dynamic Fleet Sizes
Electric Vehicles (EVs) are becoming increasingly prevalent nowadays, with studies highlighting their potential as mobile energy storage systems to provide grid support. Realising this potential requires effective charging coordination, which are often formulated as mixed-integer programming (MIP) problems. However, MIP problems are NP-hard and often intractable when applied to time-sensitive tasks. To address this limitation, we propose a deep learning assisted approach for optimising a day-ah…

Last week I watched a new apprentice take 15 minutes to type a simple email – using the eagle-search system, eyes glued to the keyboard.
Over 12 years of #school education behind them, able to analyze Shakespeare and solve math problems, but typing as if they're seeing a computer for the first time!
HOW is it possible that in 2025 we are still producing graduates who can…

This is at the core of my depression.
For nearly all of human history, the majority of humans lived in what we today would call subsistence poverty. It was mostly unavoidable.
Sometime in the '60s we crossed over into being unequivocally able to feed everyone every year. People in the modern world only struggle to eat because of economic & political barriers designed to starve them. The world has worse famine problems right now than at any point in my lifetime.
1/x…

QUTCC: Quantile Uncertainty Training and Conformal Calibration for Imaging Inverse Problems
Cassandra Tong Ye, Shamus Li, Tyler King, Kristina Monakhova
https://arxiv.org/abs/2507.14760

QUTCC: Quantile Uncertainty Training and Conformal Calibration for Imaging Inverse Problems
Deep learning models often hallucinate, producing realistic artifacts that are not truly present in the sample. This can have dire consequences for scientific and medical inverse problems, such as MRI and microscopy denoising, where accuracy is more important than perceptual quality. Uncertainty quantification techniques, such as conformal prediction, can pinpoint outliers and provide guarantees for image regression tasks, improving reliability. However, existing methods utilize a linear consta…

Constraint satisfaction problems, compactness and non-measurable sets
Claude Tardif
https://arxiv.org/abs/2508.14838 https://arxiv.org/pdf/2508.14838

Constraint satisfaction problems, compactness and non-measurable sets
A finite relational structure A is called compact if for any infinite relational structure B of the same type, the existence of a homomorphism from B to A is equivalent to the existence of homomorphisms from all finite substructures of B to A. We show that if A has width one, then the compactness of A can be proved in the axiom system of Zermelo and Fraenkel, but otherwise, the compactness of A implies the existence of non-measurable sets in 3-space.

Any-Dimensional Polynomial Optimization via de Finetti Theorems
Eitan Levin, Venkat Chandrasekaran
https://arxiv.org/abs/2507.15632 https://

Any-Dimensional Polynomial Optimization via de Finetti Theorems
Polynomial optimization problems often arise in sequences indexed by dimension, and it is of interest to produce bounds on the optimal values of all problems in the sequence. Examples include certifying inequalities between symmetric functions or graph homomorphism densities that hold over vectors and graphs of all sizes, and computing the value of mean-field games viewed as limits of games with a growing number of players. In this paper, we study such any-dimensional polynomial problems using …

Novel Multi-Agent Action Masked Deep Reinforcement Learning for General Industrial Assembly Lines Balancing Problems
Ali Mohamed Ali, Luca Tirel, Hashim A. Hashim
https://arxiv.org/abs/2507.16635

Novel Multi-Agent Action Masked Deep Reinforcement Learning for General Industrial Assembly Lines Balancing Problems
Efficient planning of activities is essential for modern industrial assembly lines to uphold manufacturing standards, prevent project constraint violations, and achieve cost-effective operations. While exact solutions to such challenges can be obtained through Integer Programming (IP), the dependence of the search space on input parameters often makes IP computationally infeasible for large-scale scenarios. Heuristic methods, such as Genetic Algorithms, can also be applied, but they frequently …

Zebra-CoT: A Dataset for Interleaved Vision Language Reasoning
Ang Li, Charles Wang, Kaiyu Yue, Zikui Cai, Ollie Liu, Deqing Fu, Peng Guo, Wang Bill Zhu, Vatsal Sharan, Robin Jia, Willie Neiswanger, Furong Huang, Tom Goldstein, Micah Goldblum
https://arxiv.org/abs/2507.16746

Zebra-CoT: A Dataset for Interleaved Vision Language Reasoning
Humans often use visual aids, for example diagrams or sketches, when solving complex problems. Training multimodal models to do the same, known as Visual Chain of Thought (Visual CoT), is challenging due to: (1) poor off-the-shelf visual CoT performance, which hinders reinforcement learning, and (2) the lack of high-quality visual CoT training data. We introduce $\textbf{Zebra-CoT}$, a diverse large-scale dataset with 182,384 samples, containing logically coherent interleaved text-image reasoni…

Learning in Random Utility Models Via Online Decision Problems
Emerson Melo
https://arxiv.org/abs/2506.16030 https://arxiv.org/pdf/25…

Learning in Random Utility Models Via Online Decision Problems
This paper examines the Random Utility Model (RUM) in repeated stochastic choice settings where decision-makers lack full information about payoffs. We propose a gradient-based learning algorithm that embeds RUM into an online decision-making framework. Our analysis establishes Hannan consistency for a broad class of RUMs, meaning the average regret relative to the best fixed action in hindsight vanishes over time. We also show that our algorithm is equivalent to the Follow-The-Regularized-Lead…

Schr\"odingerization for quantum linear systems problems
Yin Yang, Yue Yu, Long Zhang
https://arxiv.org/abs/2508.13510 https://arxiv.org/pdf/2508.1351…

Schrödingerization for quantum linear systems problems
We develop a quantum algorithm for linear algebraic equations Ax=b from the perspective of Schrödingerization-form problems, which are characterized by a system of linear convection equations in one higher dimension. When A is positive definite, the solution x can be interpreted as the steady-state solution of linear ODEs. This ODE can be solved by using the LCHS method in [1], which serves as the continuous implementation of the Fourier transform in the Schrödingerization method from [2,3] S…

The nine model category structures on the category of sets
Omar Antol\'in-Camarena, Tobias Barthel
https://arxiv.org/abs/2508.15731 https://arxiv.org/p…

The nine model category structures on the category of sets
We give a proof of the folklore theorem, attributed to Goodwillie, that there are precisely nine model structures on the category $\mathsf{Set}$ of sets. This result is deduced from a complete study of lifting problems and the ensuing classification of all weak factorization systems on $\mathsf{Set}$. Moreover, we determine the Quillen equivalences between these model structures and exhibit an explicit example of equivalent model structures that cannot be realized by a single Quillen adjunction.

[Thread] A new US paper shows the best frontier LLM models achieve 0% on hard real-life Programming Contest problems, domains where expert humans still excel (Rohan Paul/@rohanpaul_ai)
https://x.com/rohanpaul_ai/status/1934751145400111572

Rohan Paul (@rohanpaul_ai) on X
This is really BAD news of LLM's coding skill. ☹️ The best Frontier LLM models achieve 0% on hard real-life Programming Contest problems, domains where expert humans still excel. LiveCodeBench Pro, a benchmark composed of problems from Codeforces, ICPC, and IOI (“International

A multi-strategy improved snake optimizer for three-dimensional UAV path planning and engineering problems
Genliang Li, Yaxin Cui, Jinyu Su
https://arxiv.org/abs/2507.14043

A multi-strategy improved snake optimizer for three-dimensional UAV path planning and engineering problems
Metaheuristic algorithms have gained widespread application across various fields owing to their ability to generate diverse solutions. One such algorithm is the Snake Optimizer (SO), a progressive optimization approach. However, SO suffers from the issues of slow convergence speed and susceptibility to local optima. In light of these shortcomings, we propose a novel Multi-strategy Improved Snake Optimizer (MISO). Firstly, we propose a new adaptive random disturbance strategy based on sine func…

Superlinear fractional $\Phi$-Laplacian type problems via the nonlinear Rayleigh quotient with two parameters
L. R. S. de Assis, M. L. M. Carvalho, Edcarlos D. Silva, A. Salort
https://arxiv.org/abs/2507.15514

Superlinear fractional $Φ$-Laplacian type problems via the nonlinear Rayleigh quotient with two parameters
In this work, we establish the existence and multiplicity of weak solutions for nonlocal elliptic problems driven by the fractional $Φ$-Laplacian operator, in the presence of a sign-indefinite nonlinearity. More specifically, we investigate the following nonlocal elliptic problem: \begin{equation*} \left\{\begin{array}{rcl} (-Δ_Φ)^s u +V(x)u & = & μa(x)|u|^{q-2}u-λ|u|^{p-2}u \mbox{ in }\, \mathbb{R}^N, \\ u\in W^{s,Φ}(\mathbb{R}^N),&& \end{array} \right. \end{equation*…

On Tur\'{a}n problems for Berge forests
Junpeng Zhou, D\'aniel Gerbner, Xiying Yuan
https://arxiv.org/abs/2506.16140 https://…

On Turán problems for Berge forests
For a graph $F$, an $r$-uniform hypergraph $H$ is a Berge-$F$ if there is a bijection $ϕ:E(F)\rightarrow E(H)$ such that $e\subseteq ϕ(e)$ for each $e\in E(F)$. Given a family $\mathcal{F}$ of $r$-uniform hypergraphs, an $r$-uniform hypergraph is $\mathcal{F}$-free if it does not contain any member in $\mathcal{F}$ as a subhypergraph. The Turán number of $\mathcal{F}$ is the maximum number of hyperedges in an $\mathcal{F}$-free $r$-uniform hypergraph on $n$ vertices. In this paper, some exac…

I stand by my claim that many (business) problems (and therefore cost) are caused by people not practicing interleaved style when replying to #email: https://en.wikipedia.org/wiki/Posting_styl…

SafetyFlow: An Agent-Flow System for Automated LLM Safety Benchmarking
Xiangyang Zhu, Yuan Tian, Chunyi Li, Kaiwei Zhang, Wei Sun, Guangtao Zhai
https://arxiv.org/abs/2508.15526

SafetyFlow: An Agent-Flow System for Automated LLM Safety Benchmarking
The rapid proliferation of large language models (LLMs) has intensified the requirement for reliable safety evaluation to uncover model vulnerabilities. To this end, numerous LLM safety evaluation benchmarks are proposed. However, existing benchmarks generally rely on labor-intensive manual curation, which causes excessive time and resource consumption. They also exhibit significant redundancy and limited difficulty. To alleviate these problems, we introduce SafetyFlow, the first agent-flow sys…

On Condensation Management - Learn how you can best avoid problems with condensation, damp and mould in winter. #mould #condensation #ventilation -

On Condensation Management
Learn how you can best avoid problems with condensation, damp and mould in winter. #mould #condensation #ventilation

Inside the DNC's money problems (Jessica Piper/Politico)
https://www.politico.com/news/2025/08/18/dnc-fundraising-donor-problems-midterms-00512473
http://www.memeorandum.com/250818/p9#a250818p9

What is happening? Have I gone through the looking glass?? C-suite types are saying things about AI that…actually make sense?!?
AWS CEO Matt Garman: “My view is you absolutely want to keep hiring kids out of college and teaching them the right ways to go build software and decompose problems and think about it, just as much as you ever have.” https://mastodon.social/@fromjason/115067646625547469

From PREVENTion to REACTion: Enhancing Failure Resolution in Naval Systems
Maria Teresa Rossi, Leonardo Mariani, Oliviero Riganelli
https://arxiv.org/abs/2508.15584 https://

From PREVENTion to REACTion: Enhancing Failure Resolution in Naval Systems
Complex and large industrial systems often misbehave, for instance, due to wear, misuse, or faults. To cope with these incidents, it is important to timely detect their occurrences, localize the sources of the problems, and implement the appropriate countermeasures. This paper reports our experience with a state-of-the-art failure prediction method, PREVENT, and its extension with a troubleshooting module, REACT, applied to naval systems developed by Fincantieri. Our results show how to integra…

For the first time AI systems crossed the gold-medal scoring threshold at the International Mathematical Olympiad for high-school students.
Both Google and OpenAI's models solved five out of six problems,
-- achieving the result using general-purpose “reasoning” models that processed mathematical concepts using natural language, in contrast to the previous approaches used by AI firms.
OpenAI’s breakthrough was achieved with a new experimental model centered on massively …

Reliable generation of isomorphic physics problems using ChatGPT with prompt-chaining and tool use
Zhongzhou Chen
https://arxiv.org/abs/2508.14755 https://…

Reliable generation of isomorphic physics problems using ChatGPT with prompt-chaining and tool use
We present a method for generating large numbers of isomorphic physics problems using ChatGPT through prompt chaining and tool use. This approach enables precise control over structural variations-such as numeric values and spatial relations-while supporting diverse contextual variations in the problem body. By utilizing the Python code interpreter, the method supports automatic solution validation and simple diagram generation, addressing key limitations in existing LLM-based methods. We gener…

InverTwin: Solving Inverse Problems via Differentiable Radio Frequency Digital Twin
Xingyu Chen, Jianrong Ding, Kai Zheng, Xinmin Fang, Xinyu Zhang, Chris Xiaoxuan Lu, Zhengxiong Li
https://arxiv.org/abs/2508.14204

InverTwin: Solving Inverse Problems via Differentiable Radio Frequency Digital Twin
Digital twins (DTs), virtual simulated replicas of physical scenes, are transforming various industries. However, their potential in radio frequency (RF) sensing applications has been limited by the unidirectional nature of conventional RF simulators. In this paper, we present InverTwin, an optimization-driven framework that creates RF digital twins by enabling bidirectional interaction between virtual and physical realms. InverTwin overcomes the fundamental differentiability challenges of RF o…

Yeeted the Xiaomi ROM off my phone...
Let's hope I run into less problems now cus of that dogshit ROM...

Metric Poissonian pair correlation for real sequences and energy estimates
Bryce Kerr, Hongliang Wang
https://arxiv.org/abs/2506.17031 https://

Metric Poissonian pair correlation for real sequences and energy estimates
In this paper, we establish new conditions under which a sequence of real numbers has metric Poissonian pair correlation. Our conditions improve upon results of Aistleitner, El-Baz and Munsch, and resolve one of their open problems for sequences that grow faster than a quadratic polynomial. As applications, we show that quantitatively convex and polynomial sequences are metric Poissonian.

'There Are Healthier And Better Things': Ex-Evanescence Guitarist Announces She's Quitting The Music Industry, Addresses Problems With 'Today's Attitude And Values' https://flip.it/3aSZ_j?utm_source=dlvr.it&utm_medium=mastodon

'There Are Healthier and Better Things': Ex-Evanescence Guitarist Announces She's Quitting the Music Industry, Addresses Problems With 'Today's Attitude and Values'
I just can't identify with today's attitude and values anymore.

Von Neumann was the subject of many dotty professor stories. Von Neumann
supposedly had the habit of simply writing answers to homework assignments on
the board (the method of solution being, of course, obvious) when he was asked
how to solve problems. One time one of his students tried to get more helpful
information by asking if there was another way to solve the problem. Von
Neumann looked blank for a moment, thought, and then answered, "Yes.".

Diamonds and Dominoes: Impossibility Results for Associative Modal Logics
S{\o}ren Brinck Knudstorp
https://arxiv.org/abs/2506.16366 https://

Diamonds and Dominoes: Impossibility Results for Associative Modal Logics
We show that for any class of Boolean algebras with an associative operator, if it contains the complex algebra of (P(N), U), its equational theory is undecidable. Equivalently, any associative normal modal logic valid over the frame (P(N), U) is undecidable. This settles a long-open question on the decidability of hyperboolean modal logic (Goranko and Vakarelov, 1999), and addresses several related problems.

The second variational formula of the variational problems for mappings between statistical manifolds
Hajime Urakawa
https://arxiv.org/abs/2507.13612 https…

The second variational formula of the variational problems for mappings between statistical manifolds
Recently, H. Furuhata and R. Ueno obtained the first variational formula of smooth mappings of a compact statistical manifold into another manifold. In this paper, we show the second variational formula of harmonic mappings of a statistical manifold into another one. Furthermore, we define the stability, the index and the nullity for the mappings, and we show the weakly stability for the harmonic mappings into any statistical manifolds of non-positive curvature. We also give several examples ca…

from my link log —
Thoughts on hashing in Rust.
https://purplesyringa.moe/blog/thoughts-on-rust-hashing/
saved 2024-12-13

Thoughts on Rust hashing
In languages like Python, Java, or C++, values are hashed by calling a “hash me” method on them, implemented by the type author. This fixed-hash size is then immediately used by the hash table or what have you. This design suffers from some obvious problems, like: How do you hash an integer? If you use a no-op hasher (booo), DoS attacks on hash tables are inevitable. If you hash it thoroughly, consumers that only cache hashes to optimize equality checks lose out of performance.

From Logic to Language: A Trust Index for Problem Solving with LLMs
Tehseen Rug, Felix B\"ohmer, Tessa Pfattheicher
https://arxiv.org/abs/2507.16028 h…

From Logic to Language: A Trust Index for Problem Solving with LLMs
Classical computation, grounded in formal, logical systems, has been the engine of technological progress for decades, excelling at problems that can be described with unambiguous rules. This paradigm, however, leaves a vast ocean of human problems -- those characterized by ambiguity, dynamic environments, and subjective context -- largely untouched. The advent of Large Language Models (LLMs) represents a fundamental shift, enabling computational systems to engage with this previously inaccessi…

Comparison of substructured non-overlapping domain decomposition and overlapping additive Schwarz methods for large-scale Helmholtz problems with multiple sources
Boris Martin, Pierre Jolivet, Christophe Geuzaine
https://arxiv.org/abs/2506.16875

Comparison of substructured non-overlapping domain decomposition and overlapping additive Schwarz methods for large-scale Helmholtz problems with multiple sources
Solving large-scale Helmholtz problems discretized with high-order finite elements is notoriously difficult, especially in 3D where direct factorization of the system matrix is very expensive and memory demanding, and robust convergence of iterative methods is difficult to obtain. Domain decomposition methods (DDM) constitute one of the most promising strategy so far, by combining direct and iterative approaches: using direct solvers on overlapping or non-overlapping subdomains, as a preconditi…

Asymptotic analysis on narrow tubes: narrow escape problems and diffusion processes
Wen-Tai Hsu
https://arxiv.org/abs/2508.15060 https://arxiv.org/pdf/2508…

Asymptotic analysis on narrow tubes: narrow escape problems and diffusion processes
This paper investigates a diffusion process in a narrow tubular domain with reflecting boundary conditions, where the geometry serves as a singular perturbation of an underlying graph in $\mathbb{R}^2$ or $\mathbb{R}^3$. The construction incorporates distinct scaling regimes in the neighborhoods of the graph's vertices and edges. We show that, in the limit, the projected process converges weakly to a diffusion process on the graph, with gluing conditions at the vertices that depend on the relat…

Jointly Computation- and Communication-Efficient Distributed Learning
Xiaoxing Ren, Nicola Bastianello, Karl H. Johansson, Thomas Parisini
https://arxiv.org/abs/2508.15509 https…

Jointly Computation- and Communication-Efficient Distributed Learning
We address distributed learning problems over undirected networks. Specifically, we focus on designing a novel ADMM-based algorithm that is jointly computation- and communication-efficient. Our design guarantees computational efficiency by allowing agents to use stochastic gradients during local training. Moreover, communication efficiency is achieved as follows: i) the agents perform multiple training epochs between communication rounds, and ii) compressed transmissions are used. We prove exac…

A smoothed proximal trust-region algorithm for nonconvex optimization problems with $L^p$-regularization, $p\in (0,1)$
Harbir Antil, Anna Lentz
https://arxiv.org/abs/2508.15446 …

A smoothed proximal trust-region algorithm for nonconvex optimization problems with $L^p$-regularization, $p\in (0,1)$
We investigate a trust-region algorithm to solve a nonconvex optimization problem with $L^p$-regularization for $p\in(0,1)$. The algorithm relies on descent properties of a so-called generalized Cauchy point that can be obtained efficiently by a line search along a suitable proximal path. To handle the nonconvexity and nonsmoothness of the $L^p$-pseudonorm, we replace it by a smooth approximation and construct a convex upper bound of that approximation. This enables us to use results of a trust…

Expert-Guided LLM Reasoning for Battery Discovery: From AI-Driven Hypothesis to Synthesis and Characterization
Shengchao Liu, Hannan Xu, Yan Ai, Huanxin Li, Yoshua Bengio, Harry Guo
https://arxiv.org/abs/2507.16110

Expert-Guided LLM Reasoning for Battery Discovery: From AI-Driven Hypothesis to Synthesis and Characterization
Large language models (LLMs) leverage chain-of-thought (CoT) techniques to tackle complex problems, representing a transformative breakthrough in artificial intelligence (AI). However, their reasoning capabilities have primarily been demonstrated in solving math and coding problems, leaving their potential for domain-specific applications-such as battery discovery-largely unexplored. Inspired by the idea that reasoning mirrors a form of guided search, we introduce ChatBattery, a novel agentic f…

DIO: Refining Mutual Information and Causal Chain to Enhance Machine Abstract Reasoning Ability
Ruizhuo Song, Beiming Yuan
https://arxiv.org/abs/2508.15387 https://

DIO: Refining Mutual Information and Causal Chain to Enhance Machine Abstract Reasoning Ability
Despite the outstanding performance of current deep learning models across various domains, their fundamental bottleneck in abstract reasoning remains unresolved. To address this challenge, the academic community has introduced Raven's Progressive Matrices (RPM) problems as an authoritative benchmark for evaluating the abstract reasoning capabilities of deep learning algorithms, with a focus on core intelligence dimensions such as abstract reasoning, pattern recognition, and complex problem-sol…

Quantum teleportation of an elemental silicon nanophotonic CNOT gate
Kai-Chi Chang, Xiang Cheng, Felix Ribuot-Hirsch, Murat Can Sarihan, Yujie Chen, Jaime Gonzalo Flor Flores, Mingbin Yu, Patrick Guo-Qiang Lo, Dim-Lee Kwong, Chee Wei Wong
https://arxiv.org/abs/2507.16783

Quantum teleportation of an elemental silicon nanophotonic CNOT gate
Large-scale quantum computers possess the capacity to effectively tackle practical problems that can be insurmountable for classical computers. The main challenge in building these quantum computers is to realize scalable modules for remote qubits and entanglement. By assembling small, specialized parts into a larger architecture, the modular approach mitigates complexity and uncertainty. Such a distributed architecture requires non-local quantum gate operations between remote qubits. An essent…

CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning
Pan Hu
https://arxiv.org/abs/2507.14903

CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning
Autonomous driving demands reliable and efficient solutions to closely related problems such as decision-making and motion planning. In this work, decision-making refers specifically to highway lane selection, while motion planning involves generating control commands (such as speed and steering) to reach the chosen lane. In the context of Connected Autonomous Vehicles (CAVs), achieving both flexible and safe lane selection alongside precise trajectory execution remains a significant challenge.…

How Do Community Smells Influence Self-Admitted Technical Debt in Machine Learning Projects?
Shamse Tasnim Cynthia, Nuri Almarimi, Banani Roy
https://arxiv.org/abs/2506.15884

How Do Community Smells Influence Self-Admitted Technical Debt in Machine Learning Projects?
Community smells reflect poor organizational practices that often lead to socio-technical issues and the accumulation of Self-Admitted Technical Debt (SATD). While prior studies have explored these problems in general software systems, their interplay in machine learning (ML)-based projects remains largely underexamined. In this study, we investigated the prevalence of community smells and their relationship with SATD in open-source ML projects, analyzing data at the release level. First, we ex…

Infinite horizon discounted LQ optimal control problems for mean-field switching diffusions
Kai Ding, Xun Li, Siyu Lv, Zuo Quan Xu
https://arxiv.org/abs/2506.16033

Infinite horizon discounted LQ optimal control problems for mean-field switching diffusions
This paper investigates an infinite horizon discounted linear-quadratic (LQ) optimal control problem for stochastic differential equations (SDEs) incorporating regime switching and mean-field interactions. The regime switching is modeled by a finite-state Markov chain acting as common noise, while the mean-field interactions are characterized by the conditional expectation of the state process given the history of the Markov chain. To address system stability in the infinite horizon setting, a …

A further investigation on covering systems with odd moduli
Chris Bispels, Matthew Cohen, Joshua Harrington, Joshua Lowrance, Kaelyn Pontes, Leif Schaumann, Tony W. H. Wong
https://arxiv.org/abs/2507.16135

A further investigation on covering systems with odd moduli
Erdős first introduced the idea of covering systems in 1950. Since then, much of the work in this area has concentrated on identifying covering systems that meet specific conditions on their moduli. Among the central open problems in this field is the well-known odd covering problem. In this paper, we investigate a variant of that problem, where one odd integer is permitted to appear multiple times as a modulus in the covering system, while all remaining moduli are distinct odd integers greate…

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction
Xinhe Li, Jiajun Liu, Peng Wang
https://arxiv.org/abs/2508.13037

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction
Recent studies have demonstrated that Large Language Models (LLMs) have strong mathematical reasoning abilities but rely on hundreds of billions of parameters. To tackle the challenge of poor reasoning in Small Language Models (SLMs), existing methods typically leverage LLMs to generate massive amounts of data for cramming training. In psychology, they are akin to System 1 thinking, which resolves reasoning problems rapidly based on experience and intuition. However, human learning also require…

New Algorithms for #2-SAT and #3-SAT
Junqiang Peng, Zimo Sheng, Mingyu Xiao
https://arxiv.org/abs/2507.14504 https://arxiv.org/pdf/25…

New Algorithms for #2-SAT and #3-SAT
The #2-SAT and #3-SAT problems involve counting the number of satisfying assignments (also called models) for instances of 2-SAT and 3-SAT, respectively. In 2010, Zhou et al. proposed an $\mathcal{O}^*(1.1892^m)$-time algorithm for #2-SAT and an efficient approach for #3-SAT, where $m$ denotes the number of clauses. In this paper, we show that the weighted versions of #2-SAT and #3-SAT can be solved in $\mathcal{O}^*(1.1082^m)$ and $\mathcal{O}^*(1.4423^m)$ time, respectively. These results dir…

Graph-Based Deterministic Polynomial Algorithm for NP Problems
Changryeol Lee (Department of Software, Yonsei University, Mirae Campus)
https://arxiv.org/abs/2508.13166 https://…

Graph-Based Deterministic Polynomial Algorithm for NP Problems
The P = NP problem asks whether every problem whose solution can be verified in polynomial time (NP) can also be solved in polynomial time (P). In this paper, we present a proof that P = NP, demonstrating that every NP problem can be solved deterministically in polynomial time. We introduce a new Computation Model that enables the simulation of a Turing machine, and show that NP problems can be simulated efficiently within this framework. By introducing the concept of a Feasible Graph, we ensur…

Pattern formations of coupled PDEs with transparent boundary conditions in product-type ends and applications
Huaian Diao, Hongyu Liu, Qingle Meng, Li Wang
https://arxiv.org/abs/2507.14779

Pattern formations of coupled PDEs with transparent boundary conditions in product-type ends and applications
This paper studies pattern formations in coupled elliptic PDE systems governed by transparent boundary conditions. Such systems unify diverse areas, including inverse boundary problems (via a single passive/active boundary measurement), spectral geometry of transmission eigenfunctions, and geometric characterization of invisibility phenomena and inverse shape problems in wave scattering. We uncover and rigorously characterize a novel local pattern formation, establishing a sharp quantitative re…

Structure-preserving deflation of critical eigenvalues in quadratic eigenvalue problems associated with damped mass-spring systems
Rafikul Alam, Volker Mehrmann, Ninoslav Truhar
https://arxiv.org/abs/2507.16024

Structure-preserving deflation of critical eigenvalues in quadratic eigenvalue problems associated with damped mass-spring systems
For a quadratic matrix polynomial associated with a damped mass-spring system there are three types of critical eigenvalues, the eigenvalues $\infty$ and $0$ and the eigenvalues on the imaginary axis. All these are on the boundary of the set of (robustly) stable eigenvalues. For numerical methods, but also for (robust) stability analysis, it is desirable to deflate such eigenvalues by projecting the matrix polynomial to a lower dimensional subspace before computing the other eigenvalues and eig…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/5]:
- Note on Follow-the-Perturbed-Leader in Combinatorial Semi-Bandit Problems
Botao Chen, Junya Honda

An inertial iteratively regularized extragradient method for bilevel variational inequality problems
M. Marques Alves, Kangming Chen, Ellen H. Fukuda
https://arxiv.org/abs/2507.16640

An inertial iteratively regularized extragradient method for bilevel variational inequality problems
We study a bilevel variational inequality problem where the feasible set is itself the solution set of another variational inequality. Motivated by the difficulty of computing projections onto such sets, we consider a regularized extragradient method, as proposed by Samadi and Yousefian (2025), which operates over a simpler constraint set. Building on this framework, we introduce an inertial variant (called IneIREG) that incorporates momentum through extrapolation steps. We establish iteration-…

A framework for robust quantum speedups in practical correlated electronic structure and dynamics
Jielun Chen, Garnet Kin-Lic Chan
https://arxiv.org/abs/2508.15765 https://

A framework for robust quantum speedups in practical correlated electronic structure and dynamics
Proposed quantum advantage in electronic structure has so far required significant fine-tuning to find problems where classical heuristics fail. We describe how to obtain robust quantum speedups for correlated electronic structure and dynamics precisely in the regime where widely used classical heuristics are most successful.

In-Context Iterative Policy Improvement for Dynamic Manipulation
Mark Van der Merwe, Devesh Jha
https://arxiv.org/abs/2508.15021 https://arxiv.org/pdf/2508…

In-Context Iterative Policy Improvement for Dynamic Manipulation
Attention-based architectures trained on internet-scale language data have demonstrated state of the art reasoning ability for various language-based tasks, such as logic problems and textual reasoning. Additionally, these Large Language Models (LLMs) have exhibited the ability to perform few-shot prediction via in-context learning, in which input-output examples provided in the prompt are generalized to new inputs. This ability furthermore extends beyond standard language tasks, enabling few-s…

Odd-indexed Fibonacci numbers via pattern-avoiding permutations
Juan B. Gil, Felix H. Xu, William Y. Zhu
https://arxiv.org/abs/2506.15800 https://

Odd-indexed Fibonacci numbers via pattern-avoiding permutations
In this paper, we consider several combinatorial problems whose enumeration leads to the odd-indexed Fibonacci numbers, including certain types of Dyck paths, block fountains, directed column-convex polyominoes, and set partitions with no crossings and no nestings. Our goal is to provide bijective maps to pattern-avoiding permutations and derive generating functions that track certain positional statistics at the permutation level.

AlgoTune: Can Language Models Speed Up General-Purpose Numerical Programs?
Ori Press, Brandon Amos, Haoyu Zhao, Yikai Wu, Samuel K. Ainsworth, Dominik Krupke, Patrick Kidger, Touqir Sajed, Bartolomeo Stellato, Jisun Park, Nathanael Bosch, Eli Meril, Albert Steppi, Arman Zharmagambetov, Fangzhao Zhang, David Perez-Pineiro, Alberto Mercurio, Ni Zhan, Talor Abramovich, Kilian Lieret, Hanlin Zhang, Shirley Huang, Matthias Bethge, Ofir Press

AlgoTune: Can Language Models Speed Up General-Purpose Numerical Programs?
Despite progress in language model (LM) capabilities, evaluations have thus far focused on models' performance on tasks that humans have previously solved, including in programming (Jimenez et al., 2024) and mathematics (Glazer et al., 2024). We therefore propose testing models' ability to design and implement algorithms in an open-ended benchmark: We task LMs with writing code that efficiently solves computationally challenging problems in computer science, physics, and mathematics. Our AlgoTu…

Neumann series of Bessel functions in direct and inverse spherically symmetric transmission eigenvalue problems
Vladislav V. Kravchenko, L. Estefania Murcia-Lozano, Nikolaos Pallikarakis
https://arxiv.org/abs/2507.16554

Neumann series of Bessel functions in direct and inverse spherically symmetric transmission eigenvalue problems
The transmission eigenvalue problem (TEP) plays a central role in inverse scattering theory. Despite substantial theoretical progress, the numerical solution of direct and inverse TEP in spherically symmetric domains with variable refractive index covering real and complex eigenvalues remains challenging. This study introduces a novel Neumann Series of Bessel Functions (NSBF) methodology to address this challenge. After reformulating the TEP as a Sturm-Liouville equation via a Liouville transfo…

A class of nonconvex semidefinite programming in which every KKT point is globally optimal
Akatsuki Nishioka, Yoshihiro Kanno
https://arxiv.org/abs/2506.16739

A class of nonconvex semidefinite programming in which every KKT point is globally optimal
We consider a special class of nonconvex semidefinite programming problems and show that every point satisfying the Karush--Kuhn--Tucker (KKT) conditions is globally optimal despite nonconvexity. This property is related to pseudoconvex optimization. This class of problems is motivated by an eigenfrequency topology optimization problem in structural engineering, but is presented in a more general form.

Multiplicity of dead core solutions in indefinite elliptic problems
Vladimir Bobkov, Humberto Ramos Quoirin
https://arxiv.org/abs/2507.14016 https://

Multiplicity of dead core solutions in indefinite elliptic problems
We investigate nonnegative solutions of indefinite elliptic problems which enjoy the dead core phenomenon. Our model is the subhomogeneous problem $$ -Δ_p u = (a^+(x) - μa^-(x))|u|^{q-2}u, \quad u \in W_0^{1,p}(Ω), $$ where $Ω$ is a bounded domain in $\mathbb{R}^N$, $1 0$. Thanks to a dead core formation property, we obtain multiple nonnegative dead core solutions of this problem for $μ$ large enough. This assertion, in combination with a uniqueness feature, yields…

On the Differential Topology of Expressivity of Parameterized Quantum Circuits
Johanna Barzen, Frank Leymann
https://arxiv.org/abs/2507.16401 https://

On the Differential Topology of Expressivity of Parameterized Quantum Circuits
Parameterized quantum circuits play a key role in quantum computing. Measuring the suitability of such a circuit for solving a class of problems is needed. One such promising measure is the expressivity of a circuit, which is defined in two main variants. The variant in focus of this contribution is the so-called dimensional expressivity which measures the dimension of the submanifold of states produced by the circuit. Understanding this measure needs a lot of background from differential topol…

Optimizing Force Signals from Human Demonstrations of In-Contact Motions
Johannes Hartwig, Fabian Viessmann, Dominik Henrich
https://arxiv.org/abs/2507.15608

Optimizing Force Signals from Human Demonstrations of In-Contact Motions
For non-robot-programming experts, kinesthetic guiding can be an intuitive input method, as robot programming of in-contact tasks is becoming more prominent. However, imprecise and noisy input signals from human demonstrations pose problems when reproducing motions directly or using the signal as input for machine learning methods. This paper explores optimizing force signals to correspond better to the human intention of the demonstrated signal. We compare different signal filtering methods an…

The Intrinsic Riemannian Proximal Gradient Method for Convex Optimization
Ronny Bergmann, Hajg Jasa, Paula John, Max Pfeffer
https://arxiv.org/abs/2507.16055

The Intrinsic Riemannian Proximal Gradient Method for Convex Optimization
We consider a class of (possibly strongly) geodesically convex optimization problems on Hadamard manifolds, where the objective function splits into the sum of a smooth and a possibly nonsmooth function. We introduce an intrinsic convex Riemannian proximal gradient (CRPG) method that employs the manifold proximal map for the nonsmooth step, without operating in the embedding or tangent space. A sublinear convergence rate for convex problems and a linear convergence rate for strongly convex prob…

A Unifying Framework for Semiring-Based Constraint Logic Programming With Negation (full version)
Jeroen Spaans, Jesse Heyninck
https://arxiv.org/abs/2507.16067

A Unifying Framework for Semiring-Based Constraint Logic Programming With Negation (full version)
Constraint Logic Programming (CLP) is a logic programming formalism used to solve problems requiring the consideration of constraints, like resource allocation and automated planning and scheduling. It has previously been extended in various directions, for example to support fuzzy constraint satisfaction, uncertainty, or negation, with different notions of semiring being used as a unifying abstraction for these generalizations. None of these extensions have studied clauses with negation allowe…

Efficient evaluation of forward and inverse energy-based magnetic hysteresis operators
Herbert Egger, Felix Engertsberger, Andreas Schafelner
https://arxiv.org/abs/2507.15289

Efficient evaluation of forward and inverse energy-based magnetic hysteresis operators
The energy-based vector hysteresis model of Francois-Lavet et al. establishes an implicit relation between magnetic fields and fluxes via internal magnetic polarizations which are determined by convex but non-smooth minimization problems. The systematic solution of these problems for every material point is a key ingredient for the efficient implementation of the model into standard magnetic field solvers. We propose to approximate the non-smooth terms via regularization which allows to employ …

Scaling GR(1) Synthesis via a Compositional Framework for LTL Discrete Event Control
Hern\'an Gagliardi, Victor Braberman, Sebastian Uchitel
https://arxiv.org/abs/2506.16557

Scaling GR(1) Synthesis via a Compositional Framework for LTL Discrete Event Control
We present a compositional approach to controller synthesis of discrete event system controllers with linear temporal logic (LTL) goals. We exploit the modular structure of the plant to be controlled, given as a set of labelled transition systems (LTS), to mitigate state explosion that monolithic approaches to synthesis are prone to. Maximally permissive safe controllers are iteratively built for subsets of the plant LTSs by solving weaker control problems. Observational synthesis equivalence i…

Two-dimensional greedy randomized extended Kaczmarz methods
Xin-Fang Zhang, Meng-Long Xiao, Tao Li
https://arxiv.org/abs/2506.16106 https://

Two-dimensional greedy randomized extended Kaczmarz methods
The randomized extended Kaczmarz method, proposed by Zouzias and Freris (SIAM J. Matrix Anal. Appl. 34: 773-793, 2013), is appealing for solving least-squares problems. However, its randomly selecting rows and columns of A with probability proportional to their squared norm is unattractive compared to the greedy strategy. In this paper, we first consider a novel two-dimensional greedy randomized extended Kaczmarz method for solving large linear least-squares problems. The proposed method random…

Selective Densification for Rapid Motion Planning in High Dimensions with Narrow Passages
Lu Huang, Lingxiao Meng, Jiankun Wang, Xingjian Jing
https://arxiv.org/abs/2507.15710

Selective Densification for Rapid Motion Planning in High Dimensions with Narrow Passages
Sampling-based algorithms are widely used for motion planning in high-dimensional configuration spaces. However, due to low sampling efficiency, their performance often diminishes in complex configuration spaces with narrow corridors. Existing approaches address this issue using handcrafted or learned heuristics to guide sampling toward useful regions. Unfortunately, these strategies often lack generalizability to various problems or require extensive prior training. In this paper, we propose a…

DeepThink3D: Enhancing Large Language Models with Programmatic Reasoning in Complex 3D Situated Reasoning Tasks
Jiayi Song, Rui Wan, Lipeng Ma, Weidong Yang, Qingyuan Zhou, Yixuan Li, Ben Fei
https://arxiv.org/abs/2508.15548

DeepThink3D: Enhancing Large Language Models with Programmatic Reasoning in Complex 3D Situated Reasoning Tasks
This work enhances the ability of large language models (LLMs) to perform complex reasoning in 3D scenes. Recent work has addressed the 3D situated reasoning task by invoking tool usage through large language models. Large language models call tools via APIs and integrate the generated programs through a chain of thought to solve problems based on the program results. However, due to the simplicity of the questions in the dataset, the generated program reasoning chains are relatively short. To …

R-ConstraintBench: Evaluating LLMs on NP-Complete Scheduling
Raj Jain, Marc Wetter
https://arxiv.org/abs/2508.15204 https://arxiv.org/pdf/2508.15204…

R-ConstraintBench: Evaluating LLMs on NP-Complete Scheduling
Effective scheduling under tight resource, timing, and operational constraints underpins large-scale planning across sectors such as capital projects, manufacturing, logistics, and IT fleet transitions. However, the reliability of large language models (LLMs) when reasoning under high-constraint regimes is insufficiently characterized. To address this gap, we present R-ConstraintBench, a scalable framework that evaluates models on Resource-Constrained Project Scheduling Problems (RCPSP), an NP-…

Multiphysics embedding localized orthogonal decomposition for thermomechanical coupling problems
Yuzhou Nan, Yajun Wang, Changqing Ye, Xiaofei Guan
https://arxiv.org/abs/2507.13644

Multiphysics embedding localized orthogonal decomposition for thermomechanical coupling problems
Multiscale modeling and analysis of multiphysics coupling processes in highly heterogeneous media present significant challenges. In this paper, we propose a novel multiphysics embedding localized orthogonal decomposition (ME-LOD) method for solving thermomechanical coupling problems, which also provides a systematic approach to address intricate coupling effects in multiphysical systems. Unlike the standard localized orthogonal decomposition (LOD) method that constructs separate multiscale spa…

Last-Iterate Complexity of SGD for Convex and Smooth Stochastic Problems
Guillaume Garrigos, Daniel Cortild, Lucas Ketels, Juan Peypouquet
https://arxiv.org/abs/2507.14122

Last-Iterate Complexity of SGD for Convex and Smooth Stochastic Problems
Most results on Stochastic Gradient Descent (SGD) in the convex and smooth setting are presented under the form of bounds on the ergodic function value gap. It is an open question whether bounds can be derived directly on the last iterate of SGD in this context. Recent advances suggest that it should be possible. For instance, it can be achieved by making the additional, yet unverifiable, assumption that the variance of the stochastic gradients is uniformly bounded. In this paper, we show that …

A Specialized Simplex Algorithm for Budget-Constrained Total Variation-Regularized Problems
Dominic Yang
https://arxiv.org/abs/2507.13493 https://

A Specialized Simplex Algorithm for Budget-Constrained Total Variation-Regularized Problems
We consider a class of linear programs on graphs with total variation regularization and a budgetary constraint. For these programs, we give a characterization of basic solutions in terms of rooted spanning forests with orientation on the underlying graph. This leads to an interpretation of the simplex method in terms of simple graph operations on these underlying forests. We exploit this structure to produce an accelerated simplex method and empirically show that such improvements can lead to …

Study on Control Problem of a Impulsive Neutral Integro-Differential Equations with Fading Memory
Garima Gupta, Jaydev Dabas
https://arxiv.org/abs/2507.16560

Study on Control Problem of a Impulsive Neutral Integro-Differential Equations with Fading Memory
This article addresses control problems for semilinear impulsive neutral integro-differential equations with memory in a Banach space. It investigates the approximate controllability of linear and semilinear systems and proves the establishment of mild solutions in the semilinear setting. The approach involves constructing a resolvent family for the corresponding integro-differential equation of linear type without memory. The results for the linear system are established first, then extended t…

A robust and stable phase field method for structural topology optimization
Huangxin Chen, Piaopiao Dong, Dong Wang, Xiao-Ping Wang
https://arxiv.org/abs/2507.16519

A robust and stable phase field method for structural topology optimization
This paper presents a novel phase-field-based methodology for solving minimum compliance problems in topology optimization under fixed external loads and body forces. The proposed framework characterizes the optimal structure through an order parameter function, analogous to phase-field models in materials science, where the design domain and its boundary are intrinsically represented by the order parameter function. The topology optimization problem is reformulated as a constrained minimizatio…

On the Effectiveness of Classical Regression Methods for Optimal Switching Problems
Martin Andersson, Benny Avelin, Marcus Olofsson
https://arxiv.org/abs/2506.15436

On the Effectiveness of Classical Regression Methods for Optimal Switching Problems
Simple regression methods provide robust, near-optimal solutions for optimal switching problems in dimensions ranging from 1 to 50. While the theory requires solving intractable PDE systems, the Longstaff-Schwartz algorithm with classical approaches like $k$-NN achieves excellent switching decisions without extensive hyperparameter tuning. Testing eight regression approaches on four benchmark problems, we find that simple methods maintain stable performance across diverse problem characteristic…

Information Preserving Line Search via Bayesian Optimization
Robin Labryga, Tomislav Prusina, S\"oren Laue
https://arxiv.org/abs/2507.15485 https://…

Information Preserving Line Search via Bayesian Optimization
Line search is a fundamental part of iterative optimization methods for unconstrained and bound-constrained optimization problems to determine suitable step lengths that provide sufficient improvement in each iteration. Traditional line search methods are based on iterative interval refinement, where valuable information about function value and gradient is discarded in each iteration. We propose a line search method via Bayesian optimization, preserving and utilizing otherwise discarded inform…

Asymptotic Optimality in Data-driven Decision Making
Radek Sala\v{c}, Michael Kupper, Tobias Sutter
https://arxiv.org/abs/2507.15215 https://

Asymptotic Optimality in Data-driven Decision Making
Given data generated by an abstract stochastic process, we study how to construct statistically optimal decisions for general stochastic optimization problems. Our setting encompasses non-standard data structures, including data originating from heterogeneous sources or from processes with randomly evolving data-generating mechanisms. We propose a decision-making approach that identifies optimal decisions for which a specific notion of shifted regret risk, evaluated with respect to the underlyi…

On the Convergence Rates of Iterative Regularization Algorithms for Composite Bi-Level Optimization
Shimrit Shtern, Adeolu Taiwo
https://arxiv.org/abs/2506.16382

On the Convergence Rates of Iterative Regularization Algorithms for Composite Bi-Level Optimization
This paper investigates iterative methods for solving bi-level optimization problems where both inner and outer functions have a composite structure. We provide novel theoretical results, including the first convergence rate analysis for the Iteratively REgularized Proximal Gradient (IRE-PG) method, a variant of Solodov's algorithm. Our results establish simultaneous convergence rates for the inner and outer functions, highlighting the inherent trade-offs between their respective convergence ra…

The Sweet Spot of Bound Tightening for Topology Optimization
Salvador Pineda, Juan Miguel Morales
https://arxiv.org/abs/2507.16496 https://

The Sweet Spot of Bound Tightening for Topology Optimization
Topology optimization has emerged as a powerful and increasingly relevant strategy for enhancing the flexibility and efficiency of power system operations. However, solving these problems is computationally demanding due to their combinatorial nature and the use of big-M formulations. Optimization-based bound tightening (OBBT) is a well-known strategy to improve the solution of mixed-integer linear programs (MILPs) by computing tighter bounds for continuous variables. Yet, existing OBBT approac…