Tootfinder

Solving Integrated Periodic Railway Timetabling with Satisfiability Modulo Theories: A Scalable Approach to Routing and Vehicle Circulation
Florian Fuchs, Bernardo Martin-Iradi, Francesco Corman
https://arxiv.org/abs/2507.11489

Solving Integrated Periodic Railway Timetabling with Satisfiability Modulo Theories: A Scalable Approach to Routing and Vehicle Circulation
This paper introduces a novel approach for jointly solving the periodic Train Timetabling Problem (TTP), train routing, and Vehicle Circulation Problem (VCP) through a unified optimization model. While these planning stages are traditionally addressed sequentially, their interdependencies often lead to suboptimal vehicle usage. We propose the VCR-PESP, an integrated formulation that minimizes fleet size while ensuring feasible and infrastructure-compliant periodic timetables. We present the fir…

Solving Linear Programs with Differential Privacy
Alina Ene, Huy Le Nguyen, Ta Duy Nguyen, Adrian Vladu
https://arxiv.org/abs/2507.10946 https://

Solving Linear Programs with Differential Privacy
We study the problem of solving linear programs of the form $Ax\le b$, $x\ge0$ with differential privacy. For homogeneous LPs $Ax\ge0$, we give an efficient $(ε,δ)$-differentially private algorithm which with probability at least $1-β$ finds in polynomial time a solution that satisfies all but $O(\frac{d^{2}}ε\log^{2}\frac{d}{δβ}\sqrt{\log\frac{1}{ρ_{0}}})$ constraints, for problems with margin $ρ_{0}>0$. This improves the bound of $O(\frac{d^{5}}ε\log^{1.5}\frac{1}{ρ_{0}}\mathrm{poly…

AWorld: Dynamic Multi-Agent System with Stable Maneuvering for Robust GAIA Problem Solving
Zhitian Xie, Qintong Wu, Chengyue Yu, Chenyi Zhuang, Jinjie Gu
https://arxiv.org/abs/2508.09889

AWorld: Dynamic Multi-Agent System with Stable Maneuvering for Robust GAIA Problem Solving
The rapid advancement of large language models (LLMs) has empowered intelligent agents to leverage diverse external tools for solving complex real-world problems. However, as agents increasingly depend on multiple tools, they encounter new challenges: extended contexts from disparate sources and noisy or irrelevant tool outputs can undermine system reliability and accuracy. These challenges underscore the necessity for enhanced stability in agent-based systems. To address this, we introduce dyn…

Solving Distance-Based Optimization Problems Using Optical Hardware
Guangyao Li, Richard Zhipeng Wang, Natalia G. Berloff
https://arxiv.org/abs/2507.11378 …

Solving Distance-Based Optimization Problems Using Optical Hardware
We present a practical approach to solving distance-based optimization problems using optical computing hardware. The objective is to minimize an energy function defined as the weighted sum of squared differences between measured distances and the squared Euclidean distances of point coordinates. By representing coordinates as complex numbers, we map the optimization problem onto optical fields, enabling its solution through either the canonical transformation (CT) method or the gain-based bifu…

Error Analysis of Truncation Legendre Method for Solving Numerical Differentiation
Maksym Kyselov
https://arxiv.org/abs/2506.11529 https://

Error Analysis of Truncation Legendre Method for Solving Numerical Differentiation
We study the problem of numerical differentiation of functions from weighted Wiener classes. We construct and analyze a truncation Legendre method to recover arbitrary order derivatives. The main focus is on obtaining error estimates in integral and uniform metrics. Unlike previous studies, which predominantly focused on first-order derivatives and specific functional spaces, we conduct a comprehensive analysis across a wide spectrum of function regularity parameters and various metrics for mea…

Solving the Decision Principal Ideal Problem with Pre-processing
Jincheng Zhuang, Qi Cheng
https://arxiv.org/abs/2506.09605 https://a…

Solving the Decision Principal Ideal Problem with Pre-processing
The principal ideal problem constitutes a fundamental problem in algebraic number theory and has attracted significant attention due to its applications in ideal lattice based cryptosystems. Efficient quantum algorithm has been found to address this problem. The situation is different in the classical computational setting. In this work, we delve into the relationship between the principal ideal problem and the class field computation. We show that the decision version of the problem can be sol…

Reasoning Strategies in Large Language Models: Can They Follow, Prefer, and Optimize?
Yanjian Zhang, Guillaume Wisniewski, Nadi Tomeh, Thierry Charnois
https://arxiv.org/abs/2507.11423

Reasoning Strategies in Large Language Models: Can They Follow, Prefer, and Optimize?
Human reasoning involves different strategies, each suited to specific problems. Prior work shows that large language model (LLMs) tend to favor a single reasoning strategy, potentially limiting their effectiveness in diverse reasoning challenges. In this work, we investigate whether prompting can control LLMs reasoning strategies and assess its impact on logical problem-solving. While our experiments show that no single strategy consistently improves accuracy, performance could be enhanced if …

Solving Inverse Problems in Stochastic Self-Organising Systems through Invariant Representations
Elias Najarro, Nicolas Bessone, Sebastian Risi
https://arxiv.org/abs/2506.11796

Solving Inverse Problems in Stochastic Self-Organising Systems through Invariant Representations
Self-organising systems demonstrate how simple local rules can generate complex stochastic patterns. Many natural systems rely on such dynamics, making self-organisation central to understanding natural complexity. A fundamental challenge in modelling such systems is solving the inverse problem: finding the unknown causal parameters from macroscopic observations. This task becomes particularly difficult when observations have a strong stochastic component, yielding diverse yet equivalent patter…

Teaching Problem Solving in Undergraduate Physics Courses: An Endorsement for Deliberate Practice
Kelly Miller, Olivia Miller, Georgia Lawrence
https://arxiv.org/abs/2508.08133 …

Teaching Problem Solving in Undergraduate Physics Courses: An Endorsement for Deliberate Practice
Developing expert-like problem-solving skills is a central goal of undergraduate physics education. In this study, we investigate the impact of teaching explicit problem-solving frameworks, combined with deliberate practice, on students' problem-solving approaches. Using multidimensional scaling to analyze students' decision-making patterns, we compare the similarity of students taught with these methods to physics experts and to students taught with traditional repeated practice. Our results s…

When CP requires $\bar\theta=0$, not $\bar\theta=\pi$
Luca Vecchi
https://arxiv.org/abs/2507.10680 https://arxiv.org/pdf/2507.10680…

When CP requires $\barθ=0$, not $\barθ=π$
Imposing CP or P forces the QCD topological angle to be either $\barθ=0$ or $\barθ=π$. However, only the former is phenomenologically viable. This implies that the assumption of CP or P alone cannot provide a framework for unambiguously solving the Strong CP problem. We show that $\barθ=0$ is naturally selected when the assumption of CP is combined with the hypothesis that the Standard Model is embedded in a suitable gauge group.

Solving the Market Split Problem with Lattice Enumeration
Alfred Wassermann
https://arxiv.org/abs/2508.08702 https://arxiv.org/pdf/2508.08702

Solving the Market Split Problem with Lattice Enumeration
The market split problem was proposed by Cornuéjols and Dawande in 1998 as benchmark problem for algorithms solving linear systems with binary variables. The recent (2025) Quantum Optimization Benchmark Library (QOBLIB) contains a set of feasible instances of the market split problem. The market split problem seems to be difficult to solve with the conventional branch-and-bound approach of integer linear programming software which reportedly can handle QOBLIB instances up to $m=7$. In contra…

Solving Random Planted CSPs below the $n^{k/2}$ Threshold
Arpon Basu, Jun-Ting Hsieh, Andrew D. Lin, Peter Manohar
https://arxiv.org/abs/2507.10833 https:/…

Solving Random Planted CSPs below the $n^{k/2}$ Threshold
We present a family of algorithms to solve random planted instances of any $k$-ary Boolean constraint satisfaction problem (CSP). A randomly planted instance of a Boolean CSP is generated by (1) choosing an arbitrary planted assignment $x^*$, and then (2) sampling constraints from a particular "planting distribution" designed so that $x^*$ will satisfy every constraint. Given an $n$ variable instance of a $k$-ary Boolean CSP with $m$ constraints, our algorithm runs in time $n^{O(\ell)}$ for a c…

Metacognition and self-regulated learning in manipulative robotic problem-solving task
Margarida Romero (UniCA, UIC, LINE), George Kalmpourtzis
https://arxiv.org/abs/2508.05112 …

Metacognition and self-regulated learning in manipulative robotic problem-solving task
Metacognition is an important aspect in creative problem solving (CPS) and through this chapter we analyse the meta-reasoning aspects applied in the different processes of monitoring the progress of learners' reasoning and CPS activities. Meta-reasoning monitors the way that problem-solving processes advance and regulate time and efforts towards a solution. In the context of an ill-defined problem, exploration is required to develop a better-defined problem space and advance towards the solutio…

Should we teach vibe coding? Here's why not.
Should AI coding be taught in undergrad CS education?
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I teach undergraduate computer science labs, including for intro and more-advanced core courses. I don't publish (non-negligible) scholarly work in the area, but I've got years of craft expertise in course design, and I do follow the academic literature to some degree. In other words, In not the world's leading expert, but I have spent a lot of time thinking about course design, and consider myself competent at it, with plenty of direct experience in what knowledge & skills I can expect from students as they move through the curriculum.
I'm also strongly against most uses of what's called "AI" these days (specifically, generative deep neutral networks as supplied by our current cadre of techbro). There are a surprising number of completely orthogonal reasons to oppose the use of these systems, and a very limited number of reasonable exceptions (overcoming accessibility barriers is an example). On the grounds of environmental and digital-commons-pollution costs alone, using specifically the largest/newest models is unethical in most cases.
But as any good teacher should, I constantly question these evaluations, because I worry about the impact on my students should I eschew teaching relevant tech for bad reasons (and even for his reasons). I also want to make my reasoning clear to students, who should absolutely question me on this. That inspired me to ask a simple question: ignoring for one moment the ethical objections (which we shouldn't, of course; they're very stark), at what level in the CS major could I expect to teach a course about programming with AI assistance, and expect students to succeed at a more technically demanding final project than a course at the same level where students were banned from using AI? In other words, at what level would I expect students to actually benefit from AI coding "assistance?"
To be clear, I'm assuming that students aren't using AI in other aspects of coursework: the topic of using AI to "help you study" is a separate one (TL;DR it's gross value is not negative, but it's mostly not worth the harm to your metacognitive abilities, which AI-induced changes to the digital commons are making more important than ever).
So what's my answer to this question?
If I'm being incredibly optimistic, senior year. Slightly less optimistic, second year of a masters program. Realistic? Maybe never.
The interesting bit for you-the-reader is: why is this my answer? (Especially given that students would probably self-report significant gains at lower levels.) To start with, [this paper where experienced developers thought that AI assistance sped up their work on real tasks when in fact it slowed it down] (https://arxiv.org/abs/2507.09089) is informative. There are a lot of differences in task between experienced devs solving real bugs and students working on a class project, but it's important to understand that we shouldn't have a baseline expectation that AI coding "assistants" will speed things up in the best of circumstances, and we shouldn't trust self-reports of productivity (or the AI hype machine in general).
Now we might imagine that coding assistants will be better at helping with a student project than at helping with fixing bugs in open-source software, since it's a much easier task. For many programming assignments that have a fixed answer, we know that many AI assistants can just spit out a solution based on prompting them with the problem description (there's another elephant in the room here to do with learning outcomes regardless of project success, but we'll ignore this over too, my focus here is on project complexity reach, not learning outcomes). My question is about more open-ended projects, not assignments with an expected answer. Here's a second study (by one of my colleagues) about novices using AI assistance for programming tasks. It showcases how difficult it is to use AI tools well, and some of these stumbling blocks that novices in particular face.
But what about intermediate students? Might there be some level where the AI is helpful because the task is still relatively simple and the students are good enough to handle it? The problem with this is that as task complexity increases, so does the likelihood of the AI generating (or copying) code that uses more complex constructs which a student doesn't understand. Let's say I have second year students writing interactive websites with JavaScript. Without a lot of care that those students don't know how to deploy, the AI is likely to suggest code that depends on several different frameworks, from React to JQuery, without actually setting up or including those frameworks, and of course three students would be way out of their depth trying to do that. This is a general problem: each programming class carefully limits the specific code frameworks and constructs it expects students to know based on the material it covers. There is no feasible way to limit an AI assistant to a fixed set of constructs or frameworks, using current designs. There are alternate designs where this would be possible (like AI search through adaptation from a controlled library of snippets) but those would be entirely different tools.
So what happens on a sizeable class project where the AI has dropped in buggy code, especially if it uses code constructs the students don't understand? Best case, they understand that they don't understand and re-prompt, or ask for help from an instructor or TA quickly who helps them get rid of the stuff they don't understand and re-prompt or manually add stuff they do. Average case: they waste several hours and/or sweep the bugs partly under the rug, resulting in a project with significant defects. Students in their second and even third years of a CS major still have a lot to learn about debugging, and usually have significant gaps in their knowledge of even their most comfortable programming language. I do think regardless of AI we as teachers need to get better at teaching debugging skills, but the knowledge gaps are inevitable because there's just too much to know. In Python, for example, the LLM is going to spit out yields, async functions, try/finally, maybe even something like a while/else, or with recent training data, the walrus operator. I can't expect even a fraction of 3rd year students who have worked with Python since their first year to know about all these things, and based on how students approach projects where they have studied all the relevant constructs but have forgotten some, I'm not optimistic seeing these things will magically become learning opportunities. Student projects are better off working with a limited subset of full programming languages that the students have actually learned, and using AI coding assistants as currently designed makes this impossible. Beyond that, even when the "assistant" just introduces bugs using syntax the students understand, even through their 4th year many students struggle to understand the operation of moderately complex code they've written themselves, let alone written by someone else. Having access to an AI that will confidently offer incorrect explanations for bugs will make this worse.
To be sure a small minority of students will be able to overcome these problems, but that minority is the group that has a good grasp of the fundamentals and has broadened their knowledge through self-study, which earlier AI-reliant classes would make less likely to happen. In any case, I care about the average student, since we already have plenty of stuff about our institutions that makes life easier for a favored few while being worse for the average student (note that our construction of that favored few as the "good" students is a large part of this problem).
To summarize: because AI assistants introduce excess code complexity and difficult-to-debug bugs, they'll slow down rather than speed up project progress for the average student on moderately complex projects. On a fixed deadline, they'll result in worse projects, or necessitate less ambitious project scoping to ensure adequate completion, and I expect this remains broadly true through 4-6 years of study in most programs (don't take this as an endorsement of AI "assistants" for masters students; we've ignored a lot of other problems along the way).
There's a related problem: solving open-ended project assignments well ultimately depends on deeply understanding the problem, and AI "assistants" allow students to put a lot of code in their file without spending much time thinking about the problem or building an understanding of it. This is awful for learning outcomes, but also bad for project success. Getting students to see the value of thinking deeply about a problem is a thorny pedagogical puzzle at the best of times, and allowing the use of AI "assistants" makes the problem much much worse. This is another area I hope to see (or even drive) pedagogical improvement in, for what it's worth.
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A comparative study of data- and image- domain LSRTM under velocity-impedance parametrization
Pengliang Yang, Zhengyu Ji
https://arxiv.org/abs/2508.10405 https://

A comparative study of data- and image- domain LSRTM under velocity-impedance parametrization
Least-squares reverse time migration (LSRTM) is one of the classic seismic imaging methods to reconstruct model perturbations within a known reference medium. It can be computed in either data or image domain using different methods by solving a linear inverse problem, whereas a careful comparison analysis of them is lacking in the literature. In this article, we present a comparative study for multiparameter LSRTM in data- and image- domain in the framework of SMIwiz open software. Different f…

This https://arxiv.org/abs/2503.09605 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

QWENDY: Gene Regulatory Network Inference Enhanced by Large Language Model and Transformer
Knowing gene regulatory networks (GRNs) is important for understanding various biological mechanisms. In this paper, we present a method, QWENDY, that uses single-cell gene expression data measured at four time points to infer GRNs. Based on a linear gene expression model, it solves the transformation for the covariance matrices. Unlike its predecessor WENDY, QWENDY avoids solving a non-convex optimization problem and produces a unique solution. Then we enhance QWENDY by the transformer neural …

EvoCurr: Self-evolving Curriculum with Behavior Code Generation for Complex Decision-making
Yang Cheng, Zilai Wang, Weiyu Ma, Wenhui Zhu, Yue Deng, Jian Zhao
https://arxiv.org/abs/2508.09586

EvoCurr: Self-evolving Curriculum with Behavior Code Generation for Complex Decision-making
Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse domains, including programming, planning, and decision-making. However, their performance often degrades when faced with highly complex problem instances that require deep reasoning over long horizons. In such cases, direct problem-solving approaches can lead to inefficiency or failure due to the lack of structured intermediate guidance. To address this, we propose a novel self-evolve framework, EvoCurr, in wh…

Triple-S: A Collaborative Multi-LLM Framework for Solving Long-Horizon Implicative Tasks in Robotics
Zixi Jia, Hongbin Gao, Fashe Li, Jiqiang Liu, Hexiao Li, Qinghua Liu
https://arxiv.org/abs/2508.07421

Triple-S: A Collaborative Multi-LLM Framework for Solving Long-Horizon Implicative Tasks in Robotics
Leveraging Large Language Models (LLMs) to write policy code for controlling robots has gained significant attention. However, in long-horizon implicative tasks, this approach often results in API parameter, comments and sequencing errors, leading to task failure. To address this problem, we propose a collaborative Triple-S framework that involves multiple LLMs. Through In-Context Learning, different LLMs assume specific roles in a closed-loop Simplification-Solution-Summary process, effectivel…

Solving partial differential equations in participating media
Bailey Miller, Rohan Sawhney, Keenan Crane, Ioannis Gkioulekas
https://arxiv.org/abs/2506.08237

Solving partial differential equations in participating media
We consider the problem of solving partial differential equations (PDEs) in domains with complex microparticle geometry that is impractical, or intractable, to model explicitly. Drawing inspiration from volume rendering, we propose tackling this problem by treating the domain as a participating medium that models microparticle geometry stochastically, through aggregate statistical properties (e.g., particle density). We first introduce the problem setting of PDE simulation in participating medi…

Efficient Branch-and-Bound for Submodular Function Maximization under Knapsack Constraint
Yimin Hao, Yi Zhou, Chao Xu, Zhang-Hua Fu
https://arxiv.org/abs/2507.11107

Efficient Branch-and-Bound for Submodular Function Maximization under Knapsack Constraint
The submodular knapsack problem (SKP), which seeks to maximize a submodular set function by selecting a subset of elements within a given budget, is an important discrete optimization problem. The majority of existing approaches to solving the SKP are approximation algorithms. However, in domains such as health-care facility location and risk management, the need for optimal solutions is still critical, necessitating the use of exact algorithms over approximation methods. In this paper, we pres…

Enhancing Decision Space Diversity in Multi-Objective Evolutionary Optimization for the Diet Problem
Gustavo V. Nascimento, Ivan R. Meneghini, Val\'eria Santos, Eduardo Luz, Gladston Moreira
https://arxiv.org/abs/2508.07077

Enhancing Decision Space Diversity in Multi-Objective Evolutionary Optimization for the Diet Problem
Multi-objective evolutionary algorithms (MOEAs) are essential for solving complex optimization problems, such as the diet problem, where balancing conflicting objectives, like cost and nutritional content, is crucial. However, most MOEAs focus on optimizing solutions in the objective space, often neglecting the diversity of solutions in the decision space, which is critical for providing decision-makers with a wide range of choices. This paper introduces an approach that directly integrates a H…

The Integrality Gap of the Traveling Salesman Problem is $4/3$ if the LP Solution Has at Most $n 6$ Non-zero Components
Tullio Villa, Eleonora Vercesi, Janos Barta, Monaldo Mastrolilli
https://arxiv.org/abs/2507.07003

The Integrality Gap of the Traveling Salesman Problem is $4/3$ if the LP Solution Has at Most $n+6$ Non-zero Components
In this paper, we address the classical Dantzig-Fulkerson-Johnson formulation of the metric Traveling Salesman Problem and study the integrality gap of its linear relaxation, namely the Subtour Elimination Problem (SEP). This integrality gap is conjectured to be $4/3$. We prove that, when solving a problem on $n$ nodes, if the optimal SEP solution has at most $n+6$ non-zero components, then the conjecture is true. To establish this result, we consider, for a given integer $k$, the infinite fami…

SHREC'25 Track on Multiple Relief Patterns: Report and Analysis
Gabriele Paolini, Claudio Tortorici, Stefano Berretti, Ahmed Hazem Youssef, Halim Benhabiles, Adnane Cabani, Ruiwen He, Karim Hammoudi, Iyyakutti Iyappan Ganapathi, Syed Sadaf Ali, Divya Velayudhan, Maregu Assefa, Naoufel Werghi
https://arxiv.org/abs/2508.09909

SHREC'25 Track on Multiple Relief Patterns: Report and Analysis
This SHREC 2025 track focuses on the recognition and segmentation of relief patterns embedded on the surface of a set of synthetically generated triangle meshes. We report the methods proposed by the participants, whose performance highlights the inherent complexity of solving the problem, which is still open. Then, we discuss the critical aspects of the proposed tasks, highlight the limitations of current techniques, and outline possible directions for future research. All resources and track …

Constrained free energy minimization for the design of thermal states and stabilizer thermodynamic systems
Michele Minervini, Madison Chin, Jacob Kupperman, Nana Liu, Ivy Luo, Meghan Ly, Soorya Rethinasamy, Kathie Wang, Mark M. Wilde
https://arxiv.org/abs/2508.09103

Constrained free energy minimization for the design of thermal states and stabilizer thermodynamic systems
A quantum thermodynamic system is described by a Hamiltonian and a list of conserved, non-commuting charges, and a fundamental goal is to determine the minimum energy of the system subject to constraints on the charges. Recently, [Liu et al., arXiv:2505.04514] proposed first- and second-order classical and hybrid quantum-classical algorithms for solving a dual chemical potential maximization problem, and they proved that these algorithms converge to global optima by means of gradient-ascent app…

AgentsNet: Coordination and Collaborative Reasoning in Multi-Agent LLMs
Florian Gr\"otschla, Luis M\"uller, Jan T\"onshoff, Mikhail Galkin, Bryan Perozzi
https://arxiv.org/abs/2507.08616

AgentsNet: Coordination and Collaborative Reasoning in Multi-Agent LLMs
Large-language models (LLMs) have demonstrated powerful problem-solving capabilities, in particular when organized in multi-agent systems. However, the advent of such systems also raises several questions on the ability of a complex network of agents to effectively self-organize and collaborate. While measuring performance on standard reasoning benchmarks indicates how well multi-agent systems can solve reasoning tasks, it is unclear whether these systems are able to leverage their topology eff…

For many of the days of my career, the most important task I’ve had was to say “no”.
It’s really more like “No, not that way, but let’s find a solution.”
Saying “yes, here’s some code to do the thing you asked, even though you should DEFINITELY not be doing that thing because it’s dangerous for you and your users” is how someone (or some machine) who is not [yet] good at this job acts.

GPU-Accelerated Distributed QAOA on Large-scale HPC Ecosystems
Zhihao Xu, Srikar Chundury, Seongmin Kim, Amir Shehata, Xinyi Li, Ang Li, Tengfei Luo, Frank Mueller, In-Saeng Suh
https://arxiv.org/abs/2506.10531

GPU-Accelerated Distributed QAOA on Large-scale HPC Ecosystems
Quantum computing holds great potential to accelerate the process of solving complex combinatorial optimization problems. The Distributed Quantum Approximate Optimization Algorithm (DQAOA) addresses high-dimensional, dense problems using current quantum computing techniques and high-performance computing (HPC) systems. In this work, we improve the scalability and efficiency of DQAOA through advanced problem decomposition and parallel execution using message passing on the Frontier CPU/GPU super…

Weighted and unweighted enrichment strategies for solving the Poisson problem with Dirichlet boundary conditions
Francesco Dell'Accio, Luca Desiderio, Allal Guessab, Federico Nudo
https://arxiv.org/abs/2508.07238

Weighted and unweighted enrichment strategies for solving the Poisson problem with Dirichlet boundary conditions
In this paper, we propose weighted and unweighted enrichment strategies to enhance the accuracy of the linear lagrangian finite element for solving the Poisson problem with Dirichlet boundary conditions. We first recall key examples of admissible enrichment functions, specifically designed to overcome the limitations of the linear lagrangian finite element in capturing solution features such as sharp gradients and boundary-layer phenomena. We then introduce two novel three-parameter families of…

Evaluation of a deliberate-practice informed supplemental intervention in graduate Quantum Mechanics
Michael E. Robbins, Guillaume M. Laurent, Eric W. Burkholder
https://arxiv.org/abs/2508.09917

Evaluation of a deliberate-practice informed supplemental intervention in graduate Quantum Mechanics
Despite the prevalence of physics education research literature related to problem solving, recent studies have illustrated that opportunities for ``authentic'' problem solving -- conceptualized as making decisions with limited information using one's physics knowledge -- are limited at both the graduate and undergraduate levels in physics curricula. Building on one of these studies, we designed a supplemental intervention for a graduate-level quantum mechanics course which scaffolded the pract…

Physics-Informed Neural Networks with Hard Nonlinear Equality and Inequality Constraints
Ashfaq Iftakher, Rahul Golder, M. M. Faruque Hasan
https://arxiv.org/abs/2507.08124 https://arxiv.org/pdf/2507.08124 https://arxiv.org/html/2507.08124
arXiv:2507.08124v1 Announce Type: new
Abstract: Traditional physics-informed neural networks (PINNs) do not guarantee strict constraint satisfaction. This is problematic in engineering systems where minor violations of governing laws can significantly degrade the reliability and consistency of model predictions. In this work, we develop KKT-Hardnet, a PINN architecture that enforces both linear and nonlinear equality and inequality constraints up to machine precision. It leverages a projection onto the feasible region through solving Karush-Kuhn-Tucker (KKT) conditions of a distance minimization problem. Furthermore, we reformulate the nonlinear KKT conditions using log-exponential transformation to construct a general sparse system with only linear and exponential terms, thereby making the projection differentiable. We apply KKT-Hardnet on both test problems and a real-world chemical process simulation. Compared to multilayer perceptrons and PINNs, KKT-Hardnet achieves higher accuracy and strict constraint satisfaction. This approach allows the integration of domain knowledge into machine learning towards reliable hybrid modeling of complex systems.
toXiv_bot_toot

Mathematical Computation and Reasoning Errors by Large Language Models
Liang Zhang, Edith Aurora Graf
https://arxiv.org/abs/2508.09932 https://arxiv.org/pd…

Mathematical Computation and Reasoning Errors by Large Language Models
Large Language Models (LLMs) are increasingly utilized in AI-driven educational instruction and assessment, particularly within mathematics education. The capability of LLMs to generate accurate answers and detailed solutions for math problem-solving tasks is foundational for ensuring reliable and precise feedback and assessment in math education practices. Our study focuses on evaluating the accuracy of four LLMs (OpenAI GPT-4o and o1, DeepSeek-V3 and DeepSeek-R1) solving three categories of m…

Complexity of normalized stochastic first-order methods with momentum under heavy-tailed noise
Chuan He, Zhaosong Lu, Defeng Sun, Zhanwang Deng
https://arxiv.org/abs/2506.11214

Complexity of normalized stochastic first-order methods with momentum under heavy-tailed noise
In this paper, we propose practical normalized stochastic first-order methods with Polyak momentum, multi-extrapolated momentum, and recursive momentum for solving unconstrained optimization problems. These methods employ dynamically updated algorithmic parameters and do not require explicit knowledge of problem-dependent quantities such as the Lipschitz constant or noise bound. We establish first-order oracle complexity results for finding approximate stochastic stationary points under heavy-t…

A Concept for Autonomous Problem-Solving in Intralogistics Scenarios
Johannes Sigel, Daniel Dittler, Nasser Jazdi, Michael Weyrich
https://arxiv.org/abs/2507.03534

A Concept for Autonomous Problem-Solving in Intralogistics Scenarios
Achieving greater autonomy in automation systems is crucial for handling unforeseen situations effectively. However, this remains challenging due to technological limitations and the complexity of real-world environments. This paper examines the need for increased autonomy, defines the problem, and outlines key enabling technologies. A structured concept is proposed, consisting of three main steps: context enrichment, situation analysis, and generation of solution strategies. By following this …

Exploring Societal Concerns and Perceptions of AI: A Thematic Analysis through the Lens of Problem-Seeking
Naomi Omeonga wa Kayembe
https://arxiv.org/abs/2505.23930

Exploring Societal Concerns and Perceptions of AI: A Thematic Analysis through the Lens of Problem-Seeking
This study introduces a novel conceptual framework distinguishing problem-seeking from problem-solving to clarify the unique features of human intelligence in contrast to AI. Problem-seeking refers to the embodied, emotionally grounded process by which humans identify and set goals, while problem-solving denotes the execution of strategies aimed at achieving such predefined objectives. The framework emphasizes that while AI excels at efficiency and optimization, it lacks the orientation derived…

"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…

Replaced article(s) found for q-bio.NC. https://arxiv.org/list/q-bio.NC/new
[1/1]:
- From Transformer to Biology: A Hierarchical Model for Attention in Complex Problem-Solving
Zhongqiao Lin, Yunwei Li, Tianming Yang

"Two final considerations include (1) the necessity of being both deliberate and strategic and (2) the importance of being flexible and even whimsical about your future."
https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.…

Learning Deliberately, Acting Intuitively: Unlocking Test-Time Reasoning in Multimodal LLMs
Yahan Yu, Yuyang Dong, Masafumi Oyamada
https://arxiv.org/abs/2507.06999

Learning Deliberately, Acting Intuitively: Unlocking Test-Time Reasoning in Multimodal LLMs
Reasoning is a key capability for large language models (LLMs), particularly when applied to complex tasks such as mathematical problem solving. However, multimodal reasoning research still requires further exploration of modality alignment and training costs. Many of these approaches rely on additional data annotation and relevant rule-based rewards to enhance the understanding and reasoning ability, which significantly increases training costs and limits scalability. To address these challeng…

Been using this for a while & it's excellent on providing accurate, thorough, fast but SAFE output... without needing use hardcore reasoning of o3-mini.
✅ Available today: GPT-5 in Microsoft 365 Copilot | Microsoft 365 Blog
https://www.microsoft.co…

Available today: GPT-5 in Microsoft 365 Copilot | Microsoft 365 Blog
Microsoft has launched GPT-5 in Microsoft 365 Copilot and Copilot Studio, featuring a system for more efficient problem-solving.

Human-AI Schema Discovery and Application for Creative Problem Solving
Sitong Wang
https://arxiv.org/abs/2508.05045 https://arxiv.org/pdf/2508.05045…

Human-AI Schema Discovery and Application for Creative Problem Solving
Humans often rely on underlying structural patterns-schemas-to create, whether by writing stories, designing software, or composing music. Schemas help organize ideas and guide exploration, but they are often difficult to discover and apply, especially in complex or unfamiliar domains. My Ph.D. research develops a framework for human-AI schema discovery and application to support creative problem solving. I design systems that support users in sensemaking over examples to abstract schemas, and …

An Introduction to Solving the Least-Squares Problem in Variational Data Assimilation
I. Dau\v{z}ickait\.e, M. A. Freitag, S. G\"urol, A. S. Lawless, A. Ramage, J. A. Scott, J. M. Tabeart
https://arxiv.org/abs/2506.09211

An Introduction to Solving the Least-Squares Problem in Variational Data Assimilation
Variational data assimilation is a technique for combining measured data with dynamical models. It is a key component of Earth system state estimation and is commonly used in weather and ocean forecasting. The approach involves a large-scale generalized nonlinear least-squares problem. Solving the resulting sequence of sparse linear subproblems requires the use of sophisticated numerical linear algebra methods. In practical applications, the computational demands severely limit the number of it…

Study of Robust Features in Formulating Guidance for Heuristic Algorithms for Solving the Vehicle Routing Problem
Bachtiar Herdianto, Romain Billot, Flavien Lucas, Marc Sevaux
https://arxiv.org/abs/2508.06129

Study of Robust Features in Formulating Guidance for Heuristic Algorithms for Solving the Vehicle Routing Problem
The Vehicle Routing Problem (VRP) is a complex optimization problem with numerous real-world applications, mostly solved using metaheuristic algorithms due to its $\mathcal{NP}$-Hard nature. Traditionally, these metaheuristics rely on human-crafted designs developed through empirical studies. However, recent research shows that machine learning methods can be used the structural characteristics of solutions in combinatorial optimization, thereby aiding in designing more efficient algorithms, pa…

Optimizing Optimizations: Case Study on Detecting Specific Types of Mathematical Optimization Constraints with E-Graphs in JijModeling
Hiromi Ishii (Jij, Inc), Taro Shimizu (Jij, Inc), Toshiki Teramura (Jij, Inc)
https://arxiv.org/abs/2506.06495

Optimizing Optimizations: Case Study on Detecting Specific Types of Mathematical Optimization Constraints with E-Graphs in JijModeling
In solving mathematical optimization problems efficiently, it is crucial to make use of information about specific types of constraints, such as the one-hot or Special-Ordered Set (SOS) constraints. In many cases, exploiting such information gives asymptotically better execution time. JijModeling, an industrial-strength mathematical optimization modeller, achieves this by separating the symbolic representation of an optimization problem from the input data. In this paper, we will report a real-…

A matheuristic for solving the single row facility layout problem
Thomas Pammer, Markus Sinnl
https://arxiv.org/abs/2506.09793 https://

A matheuristic for solving the single row facility layout problem
The single row facility layout problem (SRFLP) is a well-studied NP-hard combinatorial optimization problem with applications in manufacturing and logistics systems. In the SRFLP, a set of facilities with lengths is given, as well as weights between each pair of facilities. The facilities must be arranged on a line, such that the sum of the weighted center-to-center distances is minimized. In this work, we introduce a novel matheuristic approach that integrates exact optimization into a m…

Cascaded Information Disclosure for Generalized Evaluation of Problem Solving Capabilities
Yunxiang Yan, Tomohiro Sawada, Kartik Goyal
https://arxiv.org/abs/2507.23776 https://

Cascaded Information Disclosure for Generalized Evaluation of Problem Solving Capabilities
While question-answering~(QA) benchmark performance is an automatic and scalable method to compare LLMs, it is an indirect method of evaluating their underlying problem-solving capabilities. Therefore, we propose a holistic and generalizable framework based on \emph{cascaded question disclosure} that provides a more accurate estimate of the models' problem-solving capabilities while maintaining the scalability and automation. This approach collects model responses in a stagewise manner with eac…

Does using machine learning solve our problem of p-hacking and HARKing or do we have the same problems as with statistical tests and models?
https://digiresacademy.kit.com/posts/is-machine-learning-and-ai-solving-the-problem-of-p-hacking

Experimental search for neutron-antineutron oscillation with use of ultra-cold neutrons revisited
Tatsushi Shima
https://arxiv.org/abs/2508.07525 https://a…

Experimental search for neutron-antineutron oscillation with use of ultra-cold neutrons revisited
Neutron-antineutron oscillation (nnbar-osc) is a baryon-number-violating process and a sensitive probe for physics beyond the Standard Model. Ultra-cold neutrons (UCNs) are attractive for nnbar-osc searches because of their long storage time, but earlier analyses indicated that phase shifts on wall reflection differ for neutron and antineutron, leading to severe decoherence and loss of sensitivity. Here we revisit this problem by numerically solving the time-dependent Schroedinger equation for …

Thinking Out of the Box: Hybrid SAT Solving by Unconstrained Continuous Optimization
Zhiwei Zhang, Samy Wu Fung, Anastasios Kyrillidis, Stanley Osher, Moshe Y. Vardi
https://arxiv.org/abs/2506.00674

Thinking Out of the Box: Hybrid SAT Solving by Unconstrained Continuous Optimization
The Boolean satisfiability (SAT) problem lies at the core of many applications in combinatorial optimization, software verification, cryptography, and machine learning. While state-of-the-art solvers have demonstrated high efficiency in handling conjunctive normal form (CNF) formulas, numerous applications require non-CNF (hybrid) constraints, such as XOR, cardinality, and Not-All-Equal constraints. Recent work leverages polynomial representations to represent such hybrid constraints, but it re…

Polarized Element-pair Code Based FFMA over a Gaussian Multiple-access Channel
Zhang-li-han Liu, Qi-yue Yu
https://arxiv.org/abs/2506.06796 https://…

Polarized Element-pair Code Based FFMA over a Gaussian Multiple-access Channel
This paper presents polarized element-pair (EP) codes for polarization-adjusted finite-field multiple-access (PA-FFMA) systems. The core innovation of FFMA systems lies in their unique processing order that exchanges the conventional sequence of channel coding and multiplexing operations, effectively solving the multiuser finite-blocklength (FBL) problem while enhancing error performance. In this architecture, EPs serve as virtual resources for user separation, where different EP codes provide …

Forward and Inverse Problems for a Langevin-Type Fractional Equation Involving Non-Local Time Condition
Fayziev Yusuf, Jumaeva Shakhnoza
https://arxiv.org/abs/2507.07446

Forward and Inverse Problems for a Langevin-Type Fractional Equation Involving Non-Local Time Condition
The paper addresses the formulation and analysis of direct and inverse problems for a Langevin-type fractional differential equation under a non-local condition imposed on the time variable. An additional condition for solving the inverse problem has the form $u(t_0)=ω$. The existence and uniqueness of a solution to the problems are established, and stability inequalities are derived. Criteria ensuring the uniqueness of the solution are identified.

Warm-starting outer approximation for parametrized convex MINLP
Erik Tamm, Gabriele Eichfelder, Jan Kronqvist
https://arxiv.org/abs/2507.08595 https://

Warm-starting outer approximation for parametrized convex MINLP
We address the challenge of efficiently solving parametrized sequences of convex Mixed-Integer Nonlinear Programming (MINLP) problems through warm-starting techniques. We focus on an outer approximation (OA) approach, for which we develop the theoretical foundation and present two warm-starting techniques for solving sequences of convex MINLPs. These types of problem sequences arise in several important applications, such as, multiobjective MINLPs using scalarization techniques, sparse linear r…

Bridging the Silos of Digitalization and Sustainability by Twin Transition: A Multivocal Literature Review
Baran Shajari, Istvan David
https://arxiv.org/abs/2506.04267

Bridging the Silos of Digitalization and Sustainability by Twin Transition: A Multivocal Literature Review
Twin transition is the method of parallel digital and sustainability transitions in a mutually supporting way or, in common terms, "greening of and by IT and data." Twin transition reacts to the growing problem of unsustainable digitalization, particularly in the ecological sense. Ignoring this problem will eventually limit the digital adeptness of society and the problem-solving capacity of humankind. Information systems engineering must find ways to support twin transition journeys through it…

Solving the Pod Repositioning Problem with Deep Reinforced Adaptive Large Neighborhood Search
Lin Xie, Hanyi Li
https://arxiv.org/abs/2506.02746 https://…

Solving the Pod Repositioning Problem with Deep Reinforced Adaptive Large Neighborhood Search
The Pod Repositioning Problem (PRP) in Robotic Mobile Fulfillment Systems (RMFS) involves selecting optimal storage locations for pods returning from pick stations. This work presents an improved solution method that integrates Adaptive Large Neighborhood Search (ALNS) with Deep Reinforcement Learning (DRL). A DRL agent dynamically selects destroy and repair operators and adjusts key parameters such as destruction degree and acceptance thresholds during the search. Specialized heuristics for bo…

Multimodal Late Fusion Model for Problem-Solving Strategy Classification in a Machine Learning Game
Clemens Witt, Thiemo Leonhardt, Nadine Bergner, Mareen Grillenberger
https://arxiv.org/abs/2507.22426

Multimodal Late Fusion Model for Problem-Solving Strategy Classification in a Machine Learning Game
Machine learning models are widely used to support stealth assessment in digital learning environments. Existing approaches typically rely on abstracted gameplay log data, which may overlook subtle behavioral cues linked to learners' cognitive strategies. This paper proposes a multimodal late fusion model that integrates screencast-based visual data and structured in-game action sequences to classify students' problem-solving strategies. In a pilot study with secondary school students (N=149) p…

Mirror Symmetry of Spencer-Hodge Decompositions in Constrained Geometric Systems
Dongzhe Zheng
https://arxiv.org/abs/2506.05816 https://

Mirror Symmetry of Spencer-Hodge Decompositions in Constrained Geometric Systems
This paper systematically investigates the interaction mechanism between metric structures and mirror transformations in Spencer complexes of compatible pairs. Our core contribution is the establishment of mirror symmetry for Spencer-Hodge decomposition theory, solving the key technical problem of analyzing the behavior of metric geometry under sign transformations. Through precise operator difference analysis, we prove that the perturbation $\mathcal{R}^k = -2(-1)^k ω\otimes δ^λ_{\mathfrak{…

This https://arxiv.org/abs/2503.08262 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDS_…

Cost-driven prunings for iterative solving of constrained routing problem with SRLG-disjoint protection
The search for the optimal pair of active and protection paths in a network with Shared Risk Link Groups (SRLG) is a challenging but high-value problem in the industry that is inevitable in ensuring reliable connections on the modern Internet. We propose a new approach to solving this problem, with a novel use of statistical analysis of the distribution of paths with respect to their cost, which is an integral part of our innovation. The key idea in our algorithm is to employ iterative updates …

Solving a real-world modular logistic scheduling problem with a quantum-classical metaheuristics
Florian Krellner, Abhishek Awasthi, Nico Kraus, Sarah Braun, Michael Poppel, Daniel Porawski
https://arxiv.org/abs/2507.21701

Solving a real-world modular logistic scheduling problem with a quantum-classical metaheuristics
This study evaluates the performance of a quantum-classical metaheuristic and a traditional classical mathematical programming solver, applied to two mathematical optimization models for an industry-relevant scheduling problem with autonomous guided vehicles (AGVs). The two models are: (1) a time-indexed mixed-integer linear program, and (2) a novel binary optimization problem with linear and quadratic constraints and a linear objective. Our experiments indicate that optimization methods are ve…

Stability analysis of the free-surface Stokes problem and an unconditionally stable explicit scheme
Igor Tominec, Lukas Lundgren, Andr\'e L\"ofgren, Josefin Ahlkrona
https://arxiv.org/abs/2506.10447

Stability analysis of the free-surface Stokes problem and an unconditionally stable explicit scheme
Accurate simulations of ice sheet dynamics, mantle convection, lava flow, and other highly viscous free-surface flows involve solving the coupled Stokes/free-surface equations. In this paper, we theoretically analyze the stability and conservation properties of the weak form of this system for Newtonian fluids and non-Newtonian fluids, at both the continuous and discrete levels. We perform the fully discrete stability analysis for finite element methods used in space with explicit and implicit …

Structured Prompts, Better Outcomes? Exploring the Effects of a Structured Interface with ChatGPT in a Graduate Robotics Course
Jerome Brender, Laila El-Hamamsy, Kim Uittenhove, Francesco Mondada, Engin Bumbacher
https://arxiv.org/abs/2507.07767

Structured Prompts, Better Outcomes? Exploring the Effects of a Structured Interface with ChatGPT in a Graduate Robotics Course
Prior research shows that how students engage with Large Language Models (LLMs) influences their problem-solving and understanding, reinforcing the need to support productive LLM-uses that promote learning. This study evaluates the impact of a structured GPT platform designed to promote 'good' prompting behavior with data from 58 students in a graduate-level robotics course. The students were assigned to either an intervention group using the structured platform or a control group using ChatGPT…

An Exploration of Internal States in Collaborative Problem Solving
Sifatul Anindho, Videep Venkatesha, Mariah Bradford, Anne M. Cleary, Nathaniel Blanchard
https://arxiv.org/abs/2507.02229

An Exploration of Internal States in Collaborative Problem Solving
Collaborative problem solving (CPS) is a complex cognitive, social, and emotional process that is increasingly prevalent in educational and professional settings. This study investigates the emotional states of individuals during CPS using a mixed-methods approach. Teams of four first completed a novel CPS task. Immediately after, each individual was placed in an isolated room where they reviewed the video of their group performing the task and self-reported their internal experiences throughou…

GPU accelerated variant of Schroeppel-Shamir's algorithm for solving the market split problem
Nils-Christian Kempke, Thorsten Koch
https://arxiv.org/abs/2507.05045

GPU accelerated variant of Schroeppel-Shamir's algorithm for solving the market split problem
The market split problem (MSP), introduced by Cornuejols and Dawande (1998), is a challenging binary optimization problem that performs poorly on state-of-the-art linear programming-based branch-and-cut solvers. We present a novel algorithm for solving the feasibility version of this problem, derived from Schroeppel-Shamir's algorithm for the one-dimensional subset sum problem. Our approach is based on exhaustively enumerating one-dimensional solutions of MSP and utilizing GPUs to evaluate cand…

Computational algorithm for downward continuation of gravity anomalies
D. K. Ivanov, L. N. Temirbekova, P. N. Vabishchevich
https://arxiv.org/abs/2507.08506

Computational algorithm for downward continuation of gravity anomalies
The downward continuation of potential fields from the Earth's surface into the subsurface is a critical task in gravity exploration, as it helps to identify the sources of gravity anomalies. This problem is often addressed by solving a first-kind integral equation using regularization techniques to stabilize an inherently unstable process. A similar approach is used in our work, where the continued field is represented as the potential of a simple layer or its vertical derivative. The constanc…

PyVision: Agentic Vision with Dynamic Tooling
Shitian Zhao, Haoquan Zhang, Shaoheng Lin, Ming Li, Qilong Wu, Kaipeng Zhang, Chen Wei
https://arxiv.org/abs/2507.07998

PyVision: Agentic Vision with Dynamic Tooling
LLMs are increasingly deployed as agents, systems capable of planning, reasoning, and dynamically calling external tools. However, in visual reasoning, prior approaches largely remain limited by predefined workflows and static toolsets. In this report, we present PyVision, an interactive, multi-turn framework that enables MLLMs to autonomously generate, execute, and refine Python-based tools tailored to the task at hand, unlocking flexible and interpretable problem-solving. We develop a taxonom…

Determining unit groups and $\mathrm{K}_1$ of finite rings
Tommy Hofmann
https://arxiv.org/abs/2506.00266 https://arxiv.org/pdf/2506.…

Determining unit groups and $\mathrm{K}_1$ of finite rings
We consider the computational problem of determining the unit group of a finite ring, by which we mean the computation of a finite presentation together with an algorithm to express units as words in the generators. We show that the problem is equivalent to the number theoretic problems of factoring integers and solving discrete logarithms in finite fields. A similar equivalence is shown for the problem of determining the abelianization of the unit group or the first $K$-group of finite rings.

A Tree-Shaped Tableau for Checking the Satisfiability of Signal Temporal Logic with Bounded Temporal Operators
Beatrice Melani (Politecnico di Milano), Ezio Bartocci (TU Wien), Michele Chiari (TU Wien)
https://arxiv.org/abs/2507.21598

A Tree-Shaped Tableau for Checking the Satisfiability of Signal Temporal Logic with Bounded Temporal Operators
Signal Temporal Logic (STL) is a widely recognized formal specification language to express rigorous temporal requirements on mixed analog signals produced by cyber-physical systems (CPS). A relevant problem in CPS design is how to efficiently and automatically check whether a set of STL requirements is logically consistent. This problem reduces to solving the STL satisfiability problem, which is decidable when we assume that our system operates in discrete time steps dictated by an embedded sy…

An Effective Two-Phase Genetic Algorithm for Solving the Resource Constrained Project Scheduling Problem (RCPSP)
D. Sun, S. Zhou
https://arxiv.org/abs/2506.21915

An Effective Two-Phase Genetic Algorithm for Solving the Resource Constrained Project Scheduling Problem (RCPSP)
This note presents a simple and effective variation of genetic algorithm (GA) for solving RCPSP, denoted as 2-Phase Genetic Algorithm (2PGA). The 2PGA implements GA parent selection in two phases: Phase-1 includes the best current solutions in the parent pool, and Phase-2 excludes the best current solutions from the parent pool. The 2PGA carries out the GA evolution by alternating the two phases iteratively. In exploring a solution space, the Phase-1 emphasizes intensification in current neighb…

Simple Algorithms for Fully Dynamic Edge Connectivity
Yotam Kenneth-Mordoch, Robert Krauthgamer
https://arxiv.org/abs/2508.07783 https://arxiv.org/pdf/2508…

Simple Algorithms for Fully Dynamic Edge Connectivity
In the fully dynamic edge connectivity problem, the input is a simple graph $G$ undergoing edge insertions and deletions, and the goal is to maintain its edge connectivity, denoted $λ_G$. We present two simple randomized algorithms solving this problem. The first algorithm maintains the edge connectivity in worst-case update time $\tilde{O}(n)$ per edge update, matching the known bound but with simpler analysis. Our second algorithm achieves worst-case update time $\tilde{O}(n/λ_G)$ and worst…

CNFs and DNFs with Exactly $k$ Solutions
L. Sunil Chandran, Rishikesh Gajjala, Kuldeep S. Meel
https://arxiv.org/abs/2506.07268 https://

CNFs and DNFs with Exactly $k$ Solutions
Model counting is a fundamental problem that consists of determining the number of satisfying assignments for a given Boolean formula. The weighted variant, which computes the weighted sum of satisfying assignments, has extensive applications in probabilistic reasoning, network reliability, statistical physics, and formal verification. A common approach for solving weighted model counting is to reduce it to unweighted model counting, which raises an important question: {\em What is the minimum …

A doubly nonlinear elliptic problem with variable exponents, homogeneous Neumann conditions and generalized logistic source
Bogdan Maxim
https://arxiv.org/abs/2506.23660

A doubly nonlinear elliptic problem with variable exponents, homogeneous Neumann conditions and generalized logistic source
The aim of this work is to prove existence and uniqueness results for a doubly nonlinear elliptic problem that is essential for solving the associated parabolic problem using Rothe's method (discretizing time). We work under very weak assumptions, dropping the commonly used condition that the source term is locally Lipschitz, which appears frequently in the literature. Instead, we rely on the continuity of the Nemytskii operator between two Lebesgue spaces with variable exponents. All results p…

Phoenix: A Novel Context-Aware Voice-Powered Math Equation Workspace and Editor
Kenneth Ge, Ryan Paul, Priscilla Zhang, JooYoung Seo
https://arxiv.org/abs/2508.07576 https://

Phoenix: A Novel Context-Aware Voice-Powered Math Equation Workspace and Editor
Writing mathematical notation requires substantial effort, diverting cognitive resources from conceptual understanding to documentation mechanics, significantly impacting individuals with fine motor disabilities (FMDs). Current limits of speech-based math technologies rely on precise dictation of math symbols and unintuitive command-based interfaces. We present a novel voice-powered math workspace, applying neuroscience insights to create an intuitive problem-solving environment. To minimize co…

Collab-Solver: Collaborative Solving Policy Learning for Mixed-Integer Linear Programming
Siyuan Li, Yifan Yu, Yanchen Deng, Zhihao Zhang, Mengjing Chen, Fangzhou Zhu, Tao Zhong, Jianye Hao, Peng Liu, Bo An
https://arxiv.org/abs/2508.03030

Collab-Solver: Collaborative Solving Policy Learning for Mixed-Integer Linear Programming
Mixed-integer linear programming (MILP) has been a fundamental problem in combinatorial optimization. Previous works have designed a plethora of hard-coded heuristics to accomplish challenging MILP solving with domain knowledge. Driven by the high capability of neural networks, recent research is devoted to replacing manually designed heuristics with learned policies. Although learning-based MILP methods have shown great promise, existing worksindependentlytreatthepolicylearningineachmoduleofMI…

Designing lattice proteins with variational quantum algorithms
Hanna Linn, Lucas Knuthson, Anders Irb\"ack, Sandipan Mohanty, Laura Garc\'ia-\'Alvarez, G\"oran Johansson
https://arxiv.org/abs/2508.02369

Designing lattice proteins with variational quantum algorithms
Quantum heuristics have shown promise in solving various optimization problems, including lattice protein folding. Equally relevant is the inverse problem, protein design, where one seeks sequences that fold to a given target structure. The latter problem is often split into two steps: (i) searching for sequences that minimize the energy in the target structure, and (ii) testing whether the generated sequences fold to the desired structure. Here, we investigate the utility of variational quantu…

On Solving the Assignment Problem with Conflicts
Roberto Montemanni, Derek H. Smith
https://arxiv.org/abs/2506.04274 https://arxiv.or…

On Solving the Assignment Problem with Conflicts
A variant of the well-known Assignment Problem is studied in this paper, where pairs of assignments are conflicting, and cannot be selected at the same time. This configures a set of hard constraints. The problem, which models real applications, looks for a complete assignment that minimizes the total cost, while no conflict is violated. In this paper, we consider a previously known mixed integer linear program representing the problem and we solve it with the open-source solver CP-SAT, part of…

GeoLaux: A Benchmark for Evaluating MLLMs' Geometry Performance on Long-Step Problems Requiring Auxiliary Lines
Yumeng Fu, Jiayin Zhu, Lingling Zhang, Bo Zhao, Shaoxuan Ma, Yushun Zhang, Yanrui Wu, Wenjun Wu
https://arxiv.org/abs/2508.06226

GeoLaux: A Benchmark for Evaluating MLLMs' Geometry Performance on Long-Step Problems Requiring Auxiliary Lines
Geometry problem solving (GPS) requires models to master diagram comprehension, logical reasoning, knowledge application, numerical computation, and auxiliary line construction. This presents a significant challenge for Multimodal Large Language Models (MLLMs). However, existing benchmarks for evaluating MLLM geometry skills overlook auxiliary line construction and lack fine-grained process evaluation, making them insufficient for assessing MLLMs' long-step reasoning abilities. To bridge these …

CR-BLEA: Contrastive Ranking for Adaptive Resource Allocation in Bilevel Evolutionary Algorithms
Dejun Xu, Jijia Chen, Gary G. Yen, Min Jiang
https://arxiv.org/abs/2506.06362

CR-BLEA: Contrastive Ranking for Adaptive Resource Allocation in Bilevel Evolutionary Algorithms
Bilevel optimization poses a significant computational challenge due to its nested structure, where each upper-level candidate solution requires solving a corresponding lower-level problem. While evolutionary algorithms (EAs) are effective at navigating such complex landscapes, their high resource demands remain a key bottleneck -- particularly the redundant evaluation of numerous unpromising lower-level tasks. Despite recent advances in multitasking and transfer learning, resource waste persis…

MultiObjectiveAlgorithms.jl: a Julia package for solving multi-objective optimization problems
Oscar Dowson, Xavier Gandibleux, G\"okhan Kof
https://arxiv.org/abs/2507.05501 …

MultiObjectiveAlgorithms.jl: a Julia package for solving multi-objective optimization problems
We present MultiObjectiveAlgorithms.jl, an open-source Julia library for solving multi-objective optimization problems written in JuMP. MultiObjectiveAlgorithms.jl implements a number of different solution algorithms, which all rely on an iterative scalarization of the problem from a multi-objective optimization problem to a sequence of single-objective subproblems. As part of this work, we extended JuMP to support vector-valued objective functions. Because it is based on JuMP, MultiObjectiveAl…

MateInfoUB: A Real-World Benchmark for Testing LLMs in Competitive, Multilingual, and Multimodal Educational Tasks
Dumitran Adrian Marius, Theodor-Pierre Moroianu, Buca Mihnea-Vicentiu
https://arxiv.org/abs/2507.03162

MateInfoUB: A Real-World Benchmark for Testing LLMs in Competitive, Multilingual, and Multimodal Educational Tasks
The rapid advancement of Large Language Models (LLMs) has transformed various domains, particularly computer science (CS) education. These models exhibit remarkable capabilities in code-related tasks and problem-solving, raising questions about their potential and limitations in advanced CS contexts. This study presents a novel bilingual (English-Romanian) multimodal (text and image) dataset of multiple-choice questions derived from a high-level computer science competition. A particularity of …

Exploring Communication Strategies for Collaborative LLM Agents in Mathematical Problem-Solving
Liang Zhang, Xiaoming Zhai, Jionghao Lin, Jionghao Lin, Jennifer Kleiman, Diego Zapata-Rivera, Carol Forsyth, Yang Jiang, Xiangen Hu, Arthur C. Graesser
https://arxiv.org/abs/2507.17753

Exploring Communication Strategies for Collaborative LLM Agents in Mathematical Problem-Solving
Large Language Model (LLM) agents are increasingly utilized in AI-aided education to support tutoring and learning. Effective communication strategies among LLM agents improve collaborative problem-solving efficiency and facilitate cost-effective adoption in education. However, little research has systematically evaluated the impact of different communication strategies on agents' problem-solving. Our study examines four communication modes, \textit{teacher-student interaction}, \textit{peer-to…

Learning to control inexact Benders decomposition via reinforcement learning
Zhe Li, Bernard T. Agyeman, Ilias Mitrai, Prodromos Daoutidis
https://arxiv.org/abs/2508.06700 https…

Learning to control inexact Benders decomposition via reinforcement learning
Benders decomposition (BD), along with its generalized version (GBD), is a widely used algorithm for solving large-scale mixed-integer optimization problems that arise in the operation of process systems. However, the off-the-shelf application to online settings can be computationally inefficient due to the repeated solution of the master problem. An approach to reduce the solution time is to solve the master problem to local optimality. However, identifying the level of suboptimality at each i…

Heterogeneous optimized Schwarz Methods for heat conduction in composites with thermal contact resistance
Huan Zhang, Hui Zhang, Yan Wang, Yingxiang Xu
https://arxiv.org/abs/2508.06408

Heterogeneous optimized Schwarz Methods for heat conduction in composites with thermal contact resistance
Heat transfer in composites is critical in engineering, where imperfect layer contact causes thermal contact resistance (TCR), leading to interfacial temperature discontinuity. We propose solving this numerically using the optimized Schwarz method (OSM), which decouples the heterogeneous problem into homogeneous subproblems. This avoids ill-conditioned systems from monolithic solving due to high contrast and interface jumps. Both energy estimate and Fourier analysis are used to prove the conver…

A Grover-Based Quantum Algorithm for Solving Perfect Mazes via Fitness-Guided Search
Michelle L. Wu
https://arxiv.org/abs/2507.21937 https://arxiv.org/pdf/…

A Grover-Based Quantum Algorithm for Solving Perfect Mazes via Fitness-Guided Search
We present a quantum algorithm for solving perfect mazes by casting the pathfinding task as a structured search problem. Building on Grover's amplitude amplification, the algorithm encodes all candidate paths in superposition and evaluates their proximity to the goal using a reversible fitness operator based on quantum arithmetic. A Grover-compatible oracle marks high-fitness states, and an adaptive cutoff strategy refines the search iteratively. We provide formal definitions, unitary construct…

Bregman proximal gradient method for linear optimization under entropic constraints
Luis M. Brice\~no-Arias, Ma\"el Le Treust
https://arxiv.org/abs/2506.10849

Bregman proximal gradient method for linear optimization under entropic constraints
In this paper, we present an efficient algorithm for solving a linear optimization problem with entropic constraints -- a class of problems that arises in game theory and information theory. Our analysis distinguishes between the cases of active and inactive constraints, addressing each using a Bregman proximal gradient method with entropic Legendre functions, for which we establish an ergodic convergence rate of $O(1/n)$ in objective values. For a specific cost structure, our framework provide…

Evaluation of LLMs for mathematical problem solving
Ruonan Wang, Runxi Wang, Yunwen Shen, Chengfeng Wu, Qinglin Zhou, Rohitash Chandra
https://arxiv.org/abs/2506.00309

Evaluation of LLMs for mathematical problem solving
Large Language Models (LLMs) have shown impressive performance on a range of educational tasks, but are still understudied for their potential to solve mathematical problems. In this study, we compare three prominent LLMs, including GPT-4o, DeepSeek-V3, and Gemini-2.0, on three mathematics datasets of varying complexities (GSM8K, MATH500, and UNSW datasets). We take a five-dimensional approach based on the Structured Chain-of-Thought (SCoT) framework to assess final answer correctness, step com…

RCP-Merging: Merging Long Chain-of-Thought Models with Domain-Specific Models by Considering Reasoning Capability as Prior
Junyao Yang, Jianwei Wang, Huiping Zhuang, Cen Chen, Ziqian Zeng
https://arxiv.org/abs/2508.03140

RCP-Merging: Merging Long Chain-of-Thought Models with Domain-Specific Models by Considering Reasoning Capability as Prior
Large Language Models (LLMs) with long chain-of-thought (CoT) capability, termed Reasoning Models, demonstrate superior intricate problem-solving abilities through multi-step long CoT reasoning. To create a dual-capability model with long CoT capability and domain-specific knowledge without substantial computational and data costs, model merging emerges as a highly resource-efficient method. However, significant challenges lie in merging domain-specific LLMs with long CoT ones since nowadays me…

A discontinuous Galerkin plane wave neural network method for Helmholtz equation and Maxwell's equations
Long Yuan, Menghui Wu, Qiya Hu
https://arxiv.org/abs/2506.09309

A discontinuous Galerkin plane wave neural network method for Helmholtz equation and Maxwell's equations
In this paper we propose a discontinuous Galerkin plane wave neural network (DGPWNN) method for approximately solving Helmholtz equation and Maxwell's equations. In this method, we define an elliptic-type variational problem as in the plane wave least square method with $h-$refinement and introduce the adaptive construction of recursively augmented discontinuous Galerkin subspaces whose basis functions are realizations of element-wise neural network functions with $hp-$refinement, where the act…

This https://arxiv.org/abs/2410.08850 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Learning to Stop: Deep Learning for Mean Field Optimal Stopping
Optimal stopping is a fundamental problem in optimization with applications in risk management, finance, robotics, and machine learning. We extend the standard framework to a multi-agent setting, named multi-agent optimal stopping (MAOS), where agents cooperate to make optimal stopping decisions in a finite-space, discrete-time environment. Since solving MAOS becomes computationally prohibitive as the number of agents is very large, we study the mean-field optimal stopping (MFOS) problem, obtai…

Dude, where's my utterance? Evaluating the effects of automatic segmentation and transcription on CPS detection
Videep Venkatesha, Mariah Bradford, Nathaniel Blanchard
https://arxiv.org/abs/2507.04454

Dude, where's my utterance? Evaluating the effects of automatic segmentation and transcription on CPS detection
Collaborative Problem-Solving (CPS) markers capture key aspects of effective teamwork, such as staying on task, avoiding interruptions, and generating constructive ideas. An AI system that reliably detects these markers could help teachers identify when a group is struggling or demonstrating productive collaboration. Such a system requires an automated pipeline composed of multiple components. In this work, we evaluate how CPS detection is impacted by automating two critical components: transcr…

Efficient randomized algorithms for the fixed Tucker-rank problem of Tucker decomposition with adaptive shifts
Maolin Che, Yimin Wei, Chong Wu, Hong Yan
https://arxiv.org/abs/2506.04840

Efficient randomized algorithms for the fixed Tucker-rank problem of Tucker decomposition with adaptive shifts
Randomized numerical linear algebra is proved to bridge theoretical advancements to offer scalable solutions for approximating tensor decomposition. This paper introduces fast randomized algorithms for solving the fixed Tucker-rank problem of Tucker decomposition, through the integration of adaptive shifted power iterations. The proposed algorithms enhance randomized variants of truncated high-order singular value decomposition (T-HOSVD) and sequentially T-HOSVD (ST-HOSVD) by incorporating dyna…

ToolVQA: A Dataset for Multi-step Reasoning VQA with External Tools
Shaofeng Yin, Ting Lei, Yang Liu
https://arxiv.org/abs/2508.03284 https://arxiv.org/pdf…

ToolVQA: A Dataset for Multi-step Reasoning VQA with External Tools
Integrating external tools into Large Foundation Models (LFMs) has emerged as a promising approach to enhance their problem-solving capabilities. While existing studies have demonstrated strong performance in tool-augmented Visual Question Answering (VQA), recent benchmarks reveal significant gaps in real-world tool-use proficiency, particularly in functionally diverse multimodal settings requiring multi-step reasoning. In this work, we introduce ToolVQA, a large-scale multimodal dataset compri…

A derivative-free regularization algorithm for equality constrained nonlinear least squares problems
Xi Chen, Jinyan Fan
https://arxiv.org/abs/2507.05623 h…

A derivative-free regularization algorithm for equality constrained nonlinear least squares problems
In this paper, we study the equality constrained nonlinear least squares problem, where the Jacobian matrices of the objective function and constraints are unavailable or expensive to compute. We approximate the Jacobian matrices via orthogonal spherical smoothing and propose a derivative-free regularization algorithm for solving the problem. At each iteration, a regularized augmented Lagrangian subproblem is solved to obtain a Newton-like step. If a sufficient decrease in the merit function of…

A fixed-time stable dynamical model for solving EVLCPs
Yufei Wei, Shiping Lin, Cairong Chen, Dongmei Yu, Deren Han
https://arxiv.org/abs/2507.20652 https://

A fixed-time stable dynamical model for solving EVLCPs
A fixed-time stable dynamical system for solving the extended vertical linear complementarity problem (EVLCP) is developed. The system is based on the reformulation of EVLCP as a special case of a new kind of generalized absolute value equations. Some properties of the new kind of generalized absolute value equations are explored which are useful for developing a fixed-time stable dynamical system for solving it. Without using any smoothing technique, we develop a dynamical system for solving t…

This https://arxiv.org/abs/2312.00272 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Stochastic forward-backward-half forward splitting algorithm with variance reduction
In this paper, we present a stochastic forward-backward-half forward splitting algorithm with variance reduction for solving the structured monotone inclusion problem composed of a maximally monotone operator, a maximally monotone operator and a cocoercive operator in a separable real Hilbert space. By deffining a Lyapunov function, we establish the weak almost sure convergence of the proposed algorithm, and obtain the linear convergence when one of the maximally monotone operators is strongly …

An Efficient Augmented Lagrangian Method for Dynamic Optimal Transport on Surfaces Based on Second-Order Cone Programming
Liang Chen, Youyicun Lin, Yuxuan Zhou
https://arxiv.org/abs/2506.08988

An Efficient Augmented Lagrangian Method for Dynamic Optimal Transport on Surfaces Based on Second-Order Cone Programming
This paper proposes an efficient numerical optimization approach for solving dynamic optimal transport (DOT) problems on general smooth surfaces, computing both the quadratic Wasserstein distance and the associated transportation path. Building on the convex DOT model of Benamou and Brenier, we first properly reformulate its dual problem, discretized on a triangular mesh for space together with a staggered grid for time, to a linear second-order cone programming. Then the resulting finite-dimen…

Computing stabilizing feedback gains for stochastic linear systems via policy iteration method
Xinpei Zhang, Guangyan Jia
https://arxiv.org/abs/2508.05214 https://

Computing stabilizing feedback gains for stochastic linear systems via policy iteration method
In recent years, stabilizing unknown dynamical systems has became a critical problem in control systems engineering. Addressing this for linear time-invariant (LTI) systems is an essential fist step towards solving similar problems for more complex systems. In this paper, we develop a model-free reinforcement learning algorithm to compute stabilizing feedback gains for stochastic LTI systems with unknown system matrices. This algorithm proceeds by solving a series of discounted stochastic linea…

Was Residual Penalty and Neural Operators All We Needed for Solving Optimal Control Problems?
Oliver G. S. Lundqvist, Fabricio Oliveira
https://arxiv.org/abs/2506.04742

Was Residual Penalty and Neural Operators All We Needed for Solving Optimal Control Problems?
Neural networks have been used to solve optimal control problems, typically by training neural networks using a combined loss function that considers data, differential equation residuals, and objective costs. We show that including cost functions in the training process is unnecessary, advocating for a simpler architecture and streamlined approach by decoupling the optimal control problem from the training process. Thus, our work shows that a simple neural operator architecture, such as DeepON…

On the Complexity of Finding Stationary Points in Nonconvex Simple Bilevel Optimization
Jincheng Cao, Ruichen Jiang, Erfan Yazdandoost Hamedani, Aryan Mokhtari
https://arxiv.org/abs/2507.23155

On the Complexity of Finding Stationary Points in Nonconvex Simple Bilevel Optimization
In this paper, we study the problem of solving a simple bilevel optimization problem, where the upper-level objective is minimized over the solution set of the lower-level problem. We focus on the general setting in which both the upper- and lower-level objectives are smooth but potentially nonconvex. Due to the absence of additional structural assumptions for the lower-level objective-such as convexity or the Polyak-Łojasiewicz (PL) condition-guaranteeing global optimality is generally intrac…

Convex Approximations of Random Constrained Markov Decision Processes
V Varagapriya, Vikas Vikram Singh, Abdel Lisser
https://arxiv.org/abs/2505.24815 http…

Convex Approximations of Random Constrained Markov Decision Processes
Constrained Markov decision processes (CMDPs) are used as a decision-making framework to study the long-run performance of a stochastic system. It is well-known that a stationary optimal policy of a CMDP problem under discounted cost criterion can be obtained by solving a linear programming problem when running costs and transition probabilities are exactly known. In this paper, we consider a discounted cost CMDP problem where the running costs and transition probabilities are defined using ran…

The Generalized Matrix Separation Problem: Algorithms
Xuemei Chen, Owen Deen
https://arxiv.org/abs/2507.17069 https://arxiv.org/pdf/2507.17069

The Generalized Matrix Separation Problem: Algorithms
When given a generalized matrix separation problem, which aims to recover a low rank matrix $L_0$ and a sparse matrix $S_0$ from $M_0=L_0+HS_0$, the work \cite{CW25} proposes a novel convex optimization problem whose objective function is the sum of the $\ell_1$-norm and nuclear norm. In this paper we detail the iterative algorithms and its associated computations for solving this convex optimization problem. We present various efficient implementation strategies, with attention to practical ca…

Regularized Extragradient Methods for Solving Equilibrium Problems on Hadamard Manifolds
Shikher Sharmaa, Pankaj Gautam, Simeon Reich
https://arxiv.org/abs/2506.22391

Regularized Extragradient Methods for Solving Equilibrium Problems on Hadamard Manifolds
Employing two distinct types of regularization terms, we propose two regularized extragradient methods for solving equilibrium problems on Hadamard manifolds. The sequences generated by these extragradient algorithms converge to a solution of the equilibrium problem without requiring the Lipschitz continuity of the bifunction or imposing additional conditions on the parameters. We establish convergence results for both algorithms and derive global error bounds along with $R$-linear convergence …

This https://arxiv.org/abs/2411.13111 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Optimal investment problem in a renewal risk model with generalized Erlang distributed interarrival times
This paper explores the optimal investment problem of a renewal risk model with generalized Erlang distributed interarrival times. The phases of the Erlang interarrival time is assumed to be observable. The price of the risky asset is driven by the constant elasticity of variance model (CEV) and the insurer aims to maximize the exponential utility of the terminal wealth by asset allocation. By solving the corresponding Hamilton-Jacobi-Bellman (HJB) equation, we establish the concavity of the va…