Tootfinder

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

AI Agents with Human-Like Collaborative Tools: Adaptive Strategies for Enhanced Problem-Solving
Harper Reed, Michael Sugimura, Angelo Zangari
https://arxiv.org/abs/2509.13547 ht…

AI Agents with Human-Like Collaborative Tools: Adaptive Strategies for Enhanced Problem-Solving
We investigate whether giving LLM agents the collaborative tools and autonomy that humans naturally use for problem solving can improve their performance. We equip Claude Code agents with MCP-based social media and journaling tools and allow them to use these tools as they see fit. Across 34 Aider Polyglot Python programming challenges, collaborative tools substantially improve performance on the hardest problems, delivering 15-40% lower cost, 12-27% fewer turns, and 12-38% faster completion th…

Queen Domination by SAT Solving
Taha Rostami, Curtis Bright
https://arxiv.org/abs/2508.11945 https://arxiv.org/pdf/2508.11945

Queen Domination by SAT Solving
The queen domination problem asks for the minimum number of queens needed to attack all squares on an $n\times n$ chessboard. Once this optimal number is known, determining the number of distinct solutions up to isomorphism has also attracted considerable attention. Previous work has introduced specialized and highly optimized search procedures to address open instances of the problem. While efficient in terms of runtime, these approaches have not provided proofs that can be independently verif…

NP-Completeness of Multicast Beamforming in Wireless Communication
Sagar Shrestha
https://arxiv.org/abs/2508.12241 https://arxiv.org/pdf/2508.12241

NP-Completeness of Multicast Beamforming in Wireless Communication
In this work, the classical problem of multi-cast beamforming in wireless communication is reconsidered. An existing work has shown that the multi-cast beamforming problem is NP-hard. In this project, we show that the corresponding decision problem for real channel matrices and beamformers is NP-complete. Finally, we carry out simulations to reveal the computational complexity of solving the NP-complete beamforming problem in various setting using SAT/SMT solvers.

FlexMind: Scaffolding Flexible Ideation Workflows with AI in Creative Problem-Solving
Yaqing Yang, Vikram Mohanty, Nikolas Martelaro, Aniket Kittur, Yan-Ying Chen, Matthew K. Hong
https://arxiv.org/abs/2509.12408

FlexMind: Scaffolding Flexible Ideation Workflows with AI in Creative Problem-Solving
Divergent thinking in the ideation stage of creative problem-solving demands that individuals explore a broad design space. Yet this exploration rarely follows a neat, linear sequence; problem-solvers constantly shift among searching, creating, and evaluating ideas. Existing interfaces either impose rigid, step-by-step workflows or permit unguided free-form exploration. To strike a balance between flexibility and guidance for augmenting people's efficiency and creativity, we introduce a human-A…

OpenAI says its reasoning system solved all 12 problems at the 2025 ICPC World Finals, with GPT-5 solving 11 and an experimental model solving the last (Maximilian Schreiner/The Decoder)
https://the-decoder.com/openai-outperforms…

OpenAI outperforms humans and Google at the world's top collegiate programming contest
An OpenAI system has solved every problem at the world’s most prestigious collegiate programming championship, outperforming not only human competitors but also Google's Deepmind model.

On Solving Asymmetric Diagonally Dominant Linear Systems in Sublinear Time
Tsz Chiu Kwok, Zhewei Wei, Mingji Yang
https://arxiv.org/abs/2509.13891 https://…

On Solving Asymmetric Diagonally Dominant Linear Systems in Sublinear Time
We initiate a study of solving a row/column diagonally dominant (RDD/CDD) linear system $Mx=b$ in sublinear time, with the goal of estimating $t^{\top}x^*$ for a given vector $t\in R^n$ and a specific solution $x^*$. This setting naturally generalizes the study of sublinear-time solvers for symmetric diagonally dominant (SDD) systems [AKP19] to the asymmetric case. Our first contributions are characterizations of the problem's mathematical structure. We express a solution $x^*$ via a Neumann …

Heterogeneous and anisotropic elastic parameter estimation using a novel semi-analytical forward solver
Xiaopeng Zhu, Zhongyi Huang
https://arxiv.org/abs/2506.15185

Heterogeneous and anisotropic elastic parameter estimation using a novel semi-analytical forward solver
An efficient procedure using a novel semi-analytical forward solver for identifying heterogeneous and anisotropic elastic parameters from only one full-field measurement is proposed and explored. We formulate the inverse problem as an special energy functional minimization with total variation(TV) regularization. The minimization problem is solved by Adam algorithm, which only requires solving one forward problem and no adjoint problem in each iteration. In order to deal with the irregularity o…

Modified security analysis of device-independent quantum key distribution with random key basis
Sawan Bhattacharyya, Turbasu Chatterjee, Pankaj Agrawal, Prasenjit Deb
https://arxiv.org/abs/2508.12938

Modified security analysis of device-independent quantum key distribution with random key basis
Security analysis is a critical part in any cryptographic protocol, may it be classical or quantum. Without security analysis, one cannot ensure the secrecy of the distributed keys. To perform a conclusive security analysis, it is very often necessary to frame the problem as an optimization problem. However, solving such optimization problems is quite challenging. In this article, we focus on the security analysis of device-independent quantum key distribution (DIQKD) with random key basis prot…

An Adaptive CMSA for Solving the Longest Filled Common Subsequence Problem with an Application in Audio Querying
Marko Djukanovic, Christian Blum, Aleksandar Kartelj, Ana Nikolikj, Guenther Raidl
https://arxiv.org/abs/2509.12261

An Adaptive CMSA for Solving the Longest Filled Common Subsequence Problem with an Application in Audio Querying
This paper addresses the Longest Filled Common Subsequence (LFCS) problem, a challenging NP-hard problem with applications in bioinformatics, including gene mutation prediction and genomic data reconstruction. Existing approaches, including exact, metaheuristic, and approximation algorithms, have primarily been evaluated on small-sized instances, which offer limited insights into their scalability. In this work, we introduce a new benchmark dataset with significantly larger instances and demons…

Beyond Solving Math Quiz: Evaluating the Ability of Large Reasoning Models to Ask for Information
Youcheng Huang, Bowen Qin, Chen Huang, Duanyu Feng, Xi Yang, Wenqiang Lei
https://arxiv.org/abs/2508.11252

Beyond Solving Math Quiz: Evaluating the Ability of Large Reasoning Models to Ask for Information
Large Reasoning Models (LRMs) have demonstrated remarkable problem-solving abilities in mathematics, as evaluated by existing benchmarks exclusively on well-defined problems. However, such evaluation setup constitutes a critical gap, since a genuine intelligent agent should not only solve problems (as a math quiz solver), but also be able~to ask for information when the problems lack sufficient information, enabling proactivity in responding users' requests. To bridge such gap, we proposes a ne…

Designing Algorithms for Entropic Optimal Transport from an Optimisation Perspective
Vishwak Srinivasan, Qijia Jiang
https://arxiv.org/abs/2507.12246 https…

Designing Algorithms for Entropic Optimal Transport from an Optimisation Perspective
In this work, we develop a collection of novel methods for the entropic-regularised optimal transport problem, which are inspired by existing mirror descent interpretations of the Sinkhorn algorithm used for solving this problem. These are fundamentally proposed from an optimisation perspective: either based on the associated semi-dual problem, or based on solving a non-convex constrained problem over subset of joint distributions. This optimisation viewpoint results in non-asymptotic rates of …

Atom-Searcher: Enhancing Agentic Deep Research via Fine-Grained Atomic Thought Reward
Yong Deng, Guoqing Wang, Zhenzhe Ying, Xiaofeng Wu, Jinzhen Lin, Wenwen Xiong, Yuqin Dai, Shuo Yang, Zhanwei Zhang, Qiwen Wang, Yang Qin, Changhua Meng
https://arxiv.org/abs/2508.12800

Atom-Searcher: Enhancing Agentic Deep Research via Fine-Grained Atomic Thought Reward
Large language models (LLMs) exhibit remarkable problem-solving abilities, but struggle with complex tasks due to static internal knowledge. Retrieval-Augmented Generation (RAG) enhances access to external information, yet remains limited in multi-hop reasoning and strategic search due to rigid workflows. Recent advancements in agentic deep research empower LLMs to autonomously reason, search, and synthesize information. However, current approaches relying on outcome-based reinforcement learnin…

Double Machine Learning for Conditional Moment Restrictions: IV regression, Proximal Causal Learning and Beyond
Daqian Shao, Ashkan Soleymani, Francesco Quinzan, Marta Kwiatkowska
https://arxiv.org/abs/2506.14950

Double Machine Learning for Conditional Moment Restrictions: IV regression, Proximal Causal Learning and Beyond
Solving conditional moment restrictions (CMRs) is a key problem considered in statistics, causal inference, and econometrics, where the aim is to solve for a function of interest that satisfies some conditional moment equalities. Specifically, many techniques for causal inference, such as instrumental variable (IV) regression and proximal causal learning (PCL), are CMR problems. Most CMR estimators use a two-stage approach, where the first-stage estimation is directly plugged into the second st…

Simultaneous Contact Sequence and Patch Planning for Dynamic Locomotion
Victor Dh\'edin, Haizhou Zhao, Majid Khadiv
https://arxiv.org/abs/2508.12928 https://

Simultaneous Contact Sequence and Patch Planning for Dynamic Locomotion
Legged robots have the potential to traverse highly constrained environments with agile maneuvers. However, planning such motions requires solving a highly challenging optimization problem with a mixture of continuous and discrete decision variables. In this paper, we present a full pipeline based on Monte-Carlo tree search (MCTS) and whole-body trajectory optimization (TO) to perform simultaneous contact sequence and patch selection on highly challenging environments. Through extensive simulat…

Solving the Trans-Planckian Censorship Problem with a Power-law Tail in $R^2$ Inflation: A Dynamical System Approach
S. D. Odintsov, V. K. Oikonomou, Eleni I. Manouri, Asterios T. Papadopoulos
https://arxiv.org/abs/2509.11387

Solving the Trans-Planckian Censorship Problem with a Power-law Tail in $R^2$ Inflation: A Dynamical System Approach
In this work we elaborate on solving the trans-Planckian censorship problem of standard slow-roll inflation by using a power-law inflationary tail generated by a scalar field with an exponential potential. We use a quantitative approach by studying in detail the phase space of a combined $F(R,ϕ)$ cosmological system, focusing on the de Sitter and power-law subspaces of the total phase space. As we show, the de Sitter subspace of the $F(R,ϕ)$ system shares the same fixed points as the vacuum $…

Algorithmic Perspective on Toda's Theorem
Dror Fried, Etay Segal, Gad E. Yaron
https://arxiv.org/abs/2509.13871 https://arxiv.org/pdf/2509.13871…

Algorithmic Perspective on Toda's Theorem
Toda's Theorem is a fundamental result in computational complexity theory, whose proof relies on a reduction from a QBF problem with a constant number of quantifiers to a model counting problem. While this reduction, henceforth called Toda's reduction, is of a purely theoretical nature, the recent progress of model counting tools raises the question of whether the reduction can be utilized to an efficient algorithm for solving QBF. In this work, we address this question by looking at Toda's red…

Energy Conversion and Electron Acceleration and Transport in 3D Simulations of Solar Flares
Xiaocan Li, Chengcai Shen, Xiaoyan Xie, Fan Guo, Bin Chen, Ivan Oparin, Yuqian Wei, Sijie Yu, Jeongbhin Seo
https://arxiv.org/abs/2508.12990

Energy Conversion and Electron Acceleration and Transport in 3D Simulations of Solar Flares
Recent observations and simulations indicate that solar flares undergo extremely complex three-dimensional (3D) evolution, making 3D particle transport models essential for understanding electron acceleration and interpreting flare emissions. In this study, we investigate this problem by solving Parker's transport equation with 3D MHD simulations of solar flares. By examining energy conversion in the 3D system, we evaluate the roles of different acceleration mechanisms, including reconnection c…

Uniform stability for the matrix inverse Sturm-Liouville problems
Natalia P. Bondarenko
https://arxiv.org/abs/2506.15300 https://arxi…

Uniform stability for the matrix inverse Sturm-Liouville problems
In this paper, the uniform stability of the inverse spectral problem is proved for the matrix Sturm-Liouville operator on a finite interval. Namely, we describe the sets of spectral data, on which the inverse spectral mapping is bounded and, consequently, the uniform estimates hold for the differences of the matrix potentials and of the corresponding coefficients of the boundary conditions. Our approach is based on a constructive procedure for solving the inverse problem by developing ideas of …

MUSIC algorithm for locating point-like scatterers with multiple interactions
Nana Meng
https://arxiv.org/abs/2509.13738 https://arxiv.org/pdf/2509.13738…

MUSIC algorithm for locating point-like scatterers with multiple interactions
We study the inverse problem of locating point sources from far-field data under plane wave incidence. A direct computational method is developed based on multiple scattering theory, using a novel indicator function to avoid iterative solving. The approach is efficient, noise-stable, and capable of resolving closely spaced or multiple sources. Numerical results validate its accuracy and robustness in various configurations. This work provides a practical computational tool for wave-based imagin…

Deformation Quantization on $\mathbb{R}^d$
Haiqi Wu
https://arxiv.org/abs/2509.14235 https://arxiv.org/pdf/2509.14235…

Deformation Quantization on $\mathbb{R}^d$
The primary aim of this essay, drawn from the author's MMath dissertation at Oxford, is to present and explain Kontsevich's formality theorem. The first two sections introduce the main topic. Sections 3 and 4 discuss Hochschild (co)homology, DGLA, and Maurer-Cartan elements. HKR theorem is stated in section 4, while a proof is deferred to appendix. In Section 5 we talk about L-infinity morphisms. The central result of section 5 gives a penultimate step for solving the problem. We prove the main…

Collaborative Preferences for Learning Mathematics: A Scale Validation Study
Sang Hyun Kim, Tanya Evans
https://arxiv.org/abs/2508.12199 https://arxiv.org/…

Collaborative Preferences for Learning Mathematics: A Scale Validation Study
Collaboration within mathematics has been established as being effective in providing students with crucial opportunities to develop critical thinking, effective communication, and teamwork skills. By engaging in group problem-solving and shared learning experiences, students may gain deeper insights into mathematical concepts and learn to approach challenges from multiple perspectives. However, there remains a need for a reliable instrument to capture students' preferences for collaboration. T…

e-boost: Boosted E-Graph Extraction with Adaptive Heuristics and Exact Solving
Jiaqi Yin, Zhan Song, Chen Chen, Yaohui Cai, Zhiru Zhang, Cunxi Yu
https://arxiv.org/abs/2508.13020

e-boost: Boosted E-Graph Extraction with Adaptive Heuristics and Exact Solving
E-graphs have attracted growing interest in many fields, particularly in logic synthesis and formal verification. E-graph extraction is a challenging NP-hard combinatorial optimization problem. It requires identifying optimal terms from exponentially many equivalent expressions, serving as the primary performance bottleneck in e-graph based optimization tasks. However, traditional extraction methods face a critical trade-off: heuristic approaches offer speed but sacrifice optimality, while exac…

Efficient Compilation of Algorithms into Compact Linear Programs
Shermin Khosravi, David Bremner
https://arxiv.org/abs/2509.13006 https://arxiv.org/pdf/250…

Efficient Compilation of Algorithms into Compact Linear Programs
Linear Programming (LP) is widely applied in industry and is a key component of various other mathematical problem-solving techniques. Recent work introduced an LP compiler translating polynomial-time, polynomial-space algorithms into polynomial-size LPs using intuitive high-level programming languages, offering a promising alternative to manually specifying each set of constraints through Algebraic Modeling Languages (AMLs). However, the resulting LPs, while polynomial in size, are often extre…

An explicit computational approach for a three-dimensional system of nonlinear elastodynamic sine-Gordon problem
Eric Ngondiep
https://arxiv.org/abs/2506.14807

An explicit computational approach for a three-dimensional system of nonlinear elastodynamic sine-Gordon problem
This paper proposes an explicit computational method for solving a three-dimensional system of nonlinear elastodynamic sine-Gordon equations subject to appropriate initial and boundary conditions. The time derivative is approximated by interpolation technique whereas the finite element approach is used to approximate the space derivatives. The developed numerical scheme is so-called, high-order explicit computational technique. The new algorithm efficiently treats the time derivative term and p…

Orthogonal Polynomials for the Gaussian Weight with a Jump and Discrete Painlev\'e Equations
Anton Dzhamay, Elizaveta Trunina
https://arxiv.org/abs/2508.12202 https://

Orthogonal Polynomials for the Gaussian Weight with a Jump and Discrete Painlevé Equations
We describe a refined version of the discrete Painlevé identification problem that emphasizes the importance on going beyond just the surface type in describing a discrete Painlevé dynamic. We give an example of solving such identification problem for a particular recurrence relation that previously appeared in studying orthogonal polynomials for the Gaussian weight with a single jump.

Click, Watch, Learn: The Impact of Student Self-Study Materials on Physics E&M Course Outcomes
James K. Hirons, Jonathan D. Perry, Dawson T. Nodurft, Scott Crawford, William Bassichis, Tatiana L. Erukhimova
https://arxiv.org/abs/2508.12143

Click, Watch, Learn: The Impact of Student Self-Study Materials on Physics E&M Course Outcomes
Performance in introductory courses, particularly physics, is often crucial for student success in STEM majors and can impact an individual's tendency to persist in their chosen field. To enhance students' individual learning experiences, faculty at many universities have worked to develop open-access, self-study materials to help build conceptual understanding and problem-solving skills. Faculty at Texas A&M University have contributed to these efforts, creating more than 200 online video reso…

TinyTim: A Family of Language Models for Divergent Generation
Christopher J. Agostino
https://arxiv.org/abs/2508.11607 https://arxiv.org/pdf/2508.11607

TinyTim: A Family of Language Models for Divergent Generation
This work introduces TinyTim, a family of large language models fine-tuned on James Joyce's `Finnegans Wake'. Through quantitative evaluation against baseline models, we demonstrate that TinyTim V1 produces a statistically distinct generative profile characterized by high lexical diversity and low semantic coherence. These findings are interpreted through theories of creativity and complex problem-solving, arguing that such specialized models can function as divergent knowledge sources within m…

Sampled-Based Guided Quantum Walk: Non-variational quantum algorithm for combinatorial optimization
Ugo Nzongani, Dylan Laplace Mermoud, Giuseppe Di Molfetta, Andrea Simonetto
https://arxiv.org/abs/2509.15138

Sampled-Based Guided Quantum Walk: Non-variational quantum algorithm for combinatorial optimization
We introduce SamBa-GQW, a novel quantum algorithm for solving binary combinatorial optimization problems of arbitrary degree with no use of any classical optimizer. The algorithm is based on a continuous-time quantum walk on the solution space represented as a graph. The walker explores the solution space to find its way to vertices that minimize the cost function of the optimization problem. The key novelty of our algorithm is an offline classical sampling protocol that gives information about…

VLMgineer: Vision Language Models as Robotic Toolsmiths
George Jiayuan Gao, Tianyu Li, Junyao Shi, Yihan Li, Zizhe Zhang, Nadia Figueroa, Dinesh Jayaraman
https://arxiv.org/abs/2507.12644

VLMgineer: Vision Language Models as Robotic Toolsmiths
Tool design and use reflect the ability to understand and manipulate the physical world through creativity, planning, and foresight. As such, these capabilities are often regarded as measurable indicators of intelligence across biological species. While much of today's research on robotic intelligence focuses on generating better controllers, inventing smarter tools offers a complementary form of physical intelligence: shifting the onus of problem-solving onto the tool's design. Given the vast …

Human-AI collaboration or obedient and often clueless AI in instruct, serve, repeat dynamics?
Mohammed Saqr, Kamila Misiejuk, Sonsoles L\'opez-Pernas
https://arxiv.org/abs/2508.10919

Human-AI collaboration or obedient and often clueless AI in instruct, serve, repeat dynamics?
While research on human-AI collaboration exists, it mainly examined language learning and used traditional counting methods with little attention to evolution and dynamics of collaboration on cognitively demanding tasks. This study examines human-AI interactions while solving a complex problem. Student-AI interactions were qualitatively coded and analyzed with transition network analysis, sequence analysis and partial correlation networks as well as comparison of frequencies using chi-square an…

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…

LearnLens: An AI-Enhanced Dashboard to Support Teachers in Open-Ended Classrooms
Namrata Srivastava, Shruti Jain, Clayton Cohn, Naveeduddin Mohammed, Umesh Timalsina, Gautam Biswas
https://arxiv.org/abs/2509.10582

LearnLens: An AI-Enhanced Dashboard to Support Teachers in Open-Ended Classrooms
Exploratory learning environments (ELEs), such as simulation-based platforms and open-ended science curricula, promote hands-on exploration and problem-solving but make it difficult for teachers to gain timely insights into students' conceptual understanding. This paper presents LearnLens, a generative AI (GenAI)-enhanced teacher-facing dashboard designed to support problem-based instruction in middle school science. LearnLens processes students' open-ended responses from digital assessments to…

SSNCVX: A primal-dual semismooth Newton method for convex composite optimization problem
Zhanwang Deng, Tao Wei, Jirui Ma, Zaiwen Wen
https://arxiv.org/abs/2509.11995 https://…

SSNCVX: A primal-dual semismooth Newton method for convex composite optimization problem
In this paper, we propose a uniform semismooth Newton-based algorithmic framework called SSNCVX for solving a broad class of convex composite optimization problems. By exploiting the augmented Lagrangian duality, we reformulate the original problem into a saddle point problem and characterize the optimality conditions via a semismooth system of nonlinear equations. The nonsmooth structure is handled internally without requiring problem specific transformation or introducing auxiliary variable…

A near-complete resolution of the exponential-time complexity of k-opt for the traveling salesman problem
Sophia Heimann, Hung P. Hoang, Stefan Hougardy
https://arxiv.org/abs/2507.12304

A near-complete resolution of the exponential-time complexity of k-opt for the traveling salesman problem
The $k$-opt algorithm is one of the simplest and most widely used heuristics for solving the traveling salesman problem. Starting from an arbitrary tour, the $k$-opt algorithm improves the current tour in each iteration by exchanging up to $k$ edges. The algorithm continues until no further improvement of this kind is possible. For a long time, it remained an open question how many iterations the $k$-opt algorithm might require for small values of $k$, assuming the use of an optimal pivot rule.…

SuperUROP: An FPGA-Based Spatial Accelerator for Sparse Matrix Operations
Rishab Parthasarathy
https://arxiv.org/abs/2509.11529 https://arxiv.org/pdf/2509.…

SuperUROP: An FPGA-Based Spatial Accelerator for Sparse Matrix Operations
Solving sparse systems of linear equations is a fundamental problem in the field of numerical methods, with applications spanning from circuit design to urban planning. These problems can have millions of constraints, such as when laying out transistors on a circuit, or trying to optimize traffic light timings, making fast sparse solvers extremely important. However, existing state-of-the-art software-level solutions for solving sparse linear systems, termed iterative solvers, are extremely ine…

Foundational theory for optimal decision tree problems. II. Optimal hypersurface decision tree algorithm
Xi He
https://arxiv.org/abs/2509.12057 https://arx…

Foundational theory for optimal decision tree problems. II. Optimal hypersurface decision tree algorithm
Decision trees are a ubiquitous model for classification and regression tasks due to their interpretability and efficiency. However, solving the optimal decision tree (ODT) problem remains a challenging combinatorial optimization task. Even for the simplest splitting rules--axis-parallel hyperplanes--it is NP-hard to optimize. In Part I of this series, we rigorously defined the proper decision tree model through four axioms and, based on these, introduced four formal definitions of the ODT prob…

The existence and uniqueness of infinite combinatorial Yamabe flows
Bohao Ji
https://arxiv.org/abs/2507.12355 https://arxiv.org/pdf/2…

The existence and uniqueness of infinite combinatorial Yamabe flows
In this paper, we study the combinatorial Yamabe flow on infinite triangulated surfaces in Euclidean background geometry, aiming for solving discrete Yamabe problem on noncompact surfaces. Under suitable conditions, we establish the short-time existence and uniqueness of the flow. We further introduce an extended version of the flow and prove its long-time existence. As an application, we prove the convergence result of the Yamabe flow in the case of hexagonal triangulations of the plane.

Should we teach vibe coding? Here's why not.
Should AI coding be taught in undergrad CS education?
1/2
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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Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining
Ya\c{s}ar Utku Al\c{c}alar, Junno Yun, Mehmet Ak\c{c}akaya
https://arxiv.org/abs/2509.09880

Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining
Diffusion/score-based models have recently emerged as powerful generative priors for solving inverse problems, including accelerated MRI reconstruction. While their flexibility allows decoupling the measurement model from the learned prior, their performance heavily depends on carefully tuned data fidelity weights, especially under fast sampling schedules with few denoising steps. Existing approaches often rely on heuristics or fixed weights, which fail to generalize across varying measurement …

Overcoming Knowledge Discrepancies: Structuring Reasoning Threads through Knowledge Balancing in Interactive Scenarios
Daniel Burkhardt, Xiangwei Cheng
https://arxiv.org/abs/2508.12100

Overcoming Knowledge Discrepancies: Structuring Reasoning Threads through Knowledge Balancing in Interactive Scenarios
Reasoning in interactive problem solving scenarios requires models to construct reasoning threads that reflect user understanding and align with structured domain knowledge. However, current reasoning models often lack explicit semantic hierarchies, user-domain knowledge alignment, and principled mechanisms to prune reasoning threads for effectiveness. These limitations result in lengthy generic output that does not guide users through goal-oriented reasoning steps. To address this, we propose …

Generation and Evaluation in the Human Invention Process through the Lens of Game Design
Katherine M. Collins, Graham Todd, Cedegao E. Zhang, Adrian Weller, Julian Togelius, Junyi Chu, Lionel Wong, Thomas L. Griffiths, Joshua B. Tenenbaum
https://arxiv.org/abs/2508.10914

Generation and Evaluation in the Human Invention Process through the Lens of Game Design
The human ability to learn rules and solve problems has been a central concern of cognitive science research since the field's earliest days. But we do not just follow rules and solve problems given to us by others: we modify those rules, create new problems, and set new goals and tasks for ourselves and others. Arguably, even more than rule following and problem solving, human intelligence is about creatively breaking and stretching the rules, changing the game, and inventing new problems wort…

Threshold sensing yields optimal path formation in Physarum polycephalum -- but the mould does not know
Daniele Proverbio, Giulia Giordano
https://arxiv.org/abs/2507.12347

Threshold sensing yields optimal path formation in Physarum polycephalum -- but the mould does not know
A circuital network model for the foraging behaviour of Physarum polycephalum proves that threshold sensing yields the emergence of optimal paths that connect food sources and solve mazes. These findings are in agreement with the experimental evidence of emergent problem solving by P. polycephalum and provide insight into the evolution of primitive intelligence. Our results can also inspire the development of threshold-based algorithms for computing applications.

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…

LLM-Based Instance-Driven Heuristic Bias In the Context of a Biased Random Key Genetic Algorithm
Camilo Chac\'on Sartori, Mart\'in Isla Pino, Pedro Pinacho-Davidson, Christian Blum
https://arxiv.org/abs/2509.09707

LLM-Based Instance-Driven Heuristic Bias In the Context of a Biased Random Key Genetic Algorithm
Integrating Large Language Models (LLMs) within metaheuristics opens a novel path for solving complex combinatorial optimization problems. While most existing approaches leverage LLMs for code generation to create or refine specific heuristics, they often overlook the structural properties of individual problem instances. In this work, we introduce a novel framework that integrates LLMs with a Biased Random-Key Genetic Algorithm (BRKGA) to solve the NP-hard Longest Run Subsequence problem. Our …

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.

Developer-LLM Conversations: An Empirical Study of Interactions and Generated Code Quality
Suzhen Zhong, Ying Zou, Bram Adams
https://arxiv.org/abs/2509.10402 https://

Developer-LLM Conversations: An Empirical Study of Interactions and Generated Code Quality
Large Language Models (LLMs) are becoming integral to modern software development workflows, assisting developers with code generation, API explanation, and iterative problem-solving through natural language conversations. Despite widespread adoption, there is limited understanding of how developers interact with LLMs in practice and how these conversational dynamics influence task outcomes, code quality, and software engineering workflows. To address this, we leverage CodeChat, a large dataset…

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.

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…

Learn to Relax with Large Language Models: Solving Nonlinear Combinatorial Optimization Problems via Bidirectional Coevolution
Beidan Liu, Zhengqiu Zhu, Chen Gao, Yong Zhao, Wei Qi, Quanjun Yin
https://arxiv.org/abs/2509.12643

Learn to Relax with Large Language Models: Solving Nonlinear Combinatorial Optimization Problems via Bidirectional Coevolution
Nonlinear Combinatorial Optimization Problems (NCOPs) present a formidable computational hurdle in practice, as their nonconvex nature gives rise to multi-modal solution spaces that defy efficient optimization. Traditional constraint relaxation approaches rely heavily on expert-driven, iterative design processes that lack systematic automation and scalable adaptability. While recent Large Language Model (LLM)-based optimization methods show promise for autonomous problem-solving, they predomina…

Solving Skolem problem for negative indexed $k-$generalized Pell numbers
Monalisa Mohapatra, Pritam Kumar Bhoi, Gopal Krishna Panda
https://arxiv.org/abs/2509.04503 https://

Solving Skolem problem for negative indexed $k-$generalized Pell numbers
In this paper, we address the Skolem problem for the $k-$Generalized Pell numbers ($P_n^{(k)}$) extended to negative indices. We focus on identifying and bounding the indices $n<0$ for which $P_n^{(k)}=0.$ In particular, we establish that the zero multiplicity is $ χ_k = 1 + (k(k-2)/4),$ if $k$ is even and $1 + ((k - 1)^2/4)$, if $k$ is odd for all $k \in [4, 500].$

High Performance Parallel Solvers for the time-harmonic Maxwell Equations
Elise Fressart (CMAP), S\'ebastien Dubois (CMAP), Lo\"ic Gouarin (X, CNRS), Marc Massot (CMAP), Michel Nowak (CMAP), Nicole Spillane (CMAP)
https://arxiv.org/abs/2507.13066

High Performance Parallel Solvers for the time-harmonic Maxwell Equations
We consider the numerical solution of large scale time-harmonic Maxwell equations. To this day, this problem remains difficult, in particular because the equations are neither Hermitian nor semi-definite. Our approach is to compare different strategies for solving this set of equations with preconditioners that are available either in PETSc, MUMPS, or in hypre. Four different preconditioners are considered. The first is the sparse approximate inverse, which is often applied to electromagnetic p…

Prompting the Professoriate: A Qualitative Study of Instructor Perspectives on LLMs in Data Science Education
Ana Elisa Lopez-Miranda, Tiffany Timbers, Rohan Alexander
https://arxiv.org/abs/2509.12283 …

Prompting the Professoriate: A Qualitative Study of Instructor Perspectives on LLMs in Data Science Education
Large Language Models (LLMs) have shifted in just a few years from novelty to ubiquity, raising fundamental questions for data science education. Tasks once used to teach coding, writing, and problem-solving can now be completed by LLMs, forcing educators to reconsider both pedagogy and assessment. To understand how instructors are adapting, we conducted semi-structured interviews with 42 instructors from 33 institutions in 10 countries in June and July 2025. Our qualitative analysis reveals a …

"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…

Crosslisted article(s) found for eess.AS. https://arxiv.org/list/eess.AS/new
[1/1]:
- An Adaptive CMSA for Solving the Longest Filled Common Subsequence Problem with an Application in...
Marko Djukanovic, Christian Blum, Aleksandar Kartelj, Ana Nikolikj, Guenther Raidl

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…

On the factorization of matrices into products of positive definite ones
Mahmoud Abdelgalil, Tryphon T. Georgiou
https://arxiv.org/abs/2507.12560 https://

On the factorization of matrices into products of positive definite ones
The present work revisits and provides a new approach on a result by Charles Ballantine, on the factorization of a square matrix with positive determinant into a product of positive definite factors. {\em Ballantine-type} factorizations, that bound the number of positive definite factors, proved central in solving a basic, yet elusive control problem--the strong controllability of a linear system via control in the form of state feedback. Ballantine's result transcends control engineering, and …

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…

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 …

Scaling Agents via Continual Pre-training
Liangcai Su, Zhen Zhang, Guangyu Li, Zhuo Chen, Chenxi Wang, Maojia Song, Xinyu Wang, Kuan Li, Jialong Wu, Xuanzhong Chen, Zile Qiao, Zhongwang Zhang, Huifeng Yin, Shihao Cai, Runnan Fang, Zhengwei Tao, Wenbiao Yin, Chenxiong Qian, Yong Jiang, Pengjun Xie, Fei Huang, Jingren Zhou
https://arxiv.org/…

Scaling Agents via Continual Pre-training
Large language models (LLMs) have evolved into agentic systems capable of autonomous tool use and multi-step reasoning for complex problem-solving. However, post-training approaches building upon general-purpose foundation models consistently underperform in agentic tasks, particularly in open-source implementations. We identify the root cause: the absence of robust agentic foundation models forces models during post-training to simultaneously learn diverse agentic behaviors while aligning them…

Monopole and Seniority Truncations in the Large-Scale Configuration Interaction Shell Model Approach
Priyanka Choudhary, Chong Qi
https://arxiv.org/abs/2507.11796

Monopole and Seniority Truncations in the Large-Scale Configuration Interaction Shell Model Approach
This paper addresses the challenges of solving the quantum many-body problem, particularly within nuclear physics, through the configuration interaction (CI) method. Large-scale shell model calculations often become computationally infeasible for systems with a large number of valence particles, requiring truncation techniques. We propose truncation methods for the nuclear shell model, in which angular momentum is conserved and rotational symmetry is restored. We introduce the monopole-interact…

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…

Variational data assimilation for the wave equation in heterogeneous media: Numerical investigation of stability
Erik Burman, Janosch Preuss, Tim van Beeck
https://arxiv.org/abs/2509.13108

Variational data assimilation for the wave equation in heterogeneous media: Numerical investigation of stability
In recent years, several numerical methods for solving the unique continuation problem for the wave equation in a homogeneous medium with given data on the lateral boundary of the space-time cylinder have been proposed. This problem enjoys Lipschitz stability if the geometric control condition is fulfilled, which allows devising optimally convergent numerical methods. In this article, we investigate whether these results carry over to the case in which the medium exhibits a jump discontinuity. …

Towards solving industrial integer linear programs with Decoded Quantum Interferometry
Francesc Sabater, Ouns El Harzli, Geert-Jan Besjes, Marvin Erdmann, Johannes Klepsch, Jonas Hiltrop, Jean-Francois Bobier, Yudong Cao, Carlos A. Riofrio
https://arxiv.org/abs/2509.08328

Towards solving industrial integer linear programs with Decoded Quantum Interferometry
Optimization via decoded quantum interferometry (DQI) has recently gained a great deal of attention as a promising avenue for solving optimization problems using quantum computers. In this paper, we apply DQI to an industrial optimization problem in the automotive industry: the vehicle option-package pricing problem. Our main contributions are 1) formulating the industrial problem as an integer linear program (ILP), 2) converting the ILP into instances of max-XORSAT, and 3) developing a detaile…

On the complex moment problem as a dynamic inverse problem for a discrete system
A. S. Mikhaylov, V. S. Mikhaylov
https://arxiv.org/abs/2509.02443 https://…

On the complex moment problem as a dynamic inverse problem for a discrete system
We consider the complex moment problem, that is the problem of constructing a positive Borel measure on $\mathbb{C}$ from a given set of moments. We relate this problem to the dynamic inverse problem for the discrete system associated with the complex Jacobi matrix. We show how the characterization of dynamic inverse data in solving the inverse problem provides sufficient conditions for solving the complex moment problem.

A Dichotomy Theorem for Multi-Pass Streaming CSPs
Yumou Fei, Dor Minzer, Shuo Wang
https://arxiv.org/abs/2509.11399 https://arxiv.org/pdf/2509.11399…

A Dichotomy Theorem for Multi-Pass Streaming CSPs
We show a dichotomy result for $p$-pass streaming algorithms for all CSPs and for up to polynomially many passes. More precisely, we prove that for any arity parameter $k$, finite alphabet $Σ$, collection $\mathcal{F}$ of $k$-ary predicates over $Σ$ and any $c\in (0,1)$, there exists $00$ there is a constant pass, $O_{\varepsilon}(\log n)$-space randomized streaming algorithm solving the promise problem $\text{MaxCSP}(\mathcal{F})[c,s-\varepsilon…

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…

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…

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…

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…

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…

Optimal Spectral Approximation in the Overlaps for Generalized Finite Element Methods
Christian Alber, Peter Bastian, Moritz Hauck, Robert Scheichl
https://arxiv.org/abs/2507.12226

Optimal Spectral Approximation in the Overlaps for Generalized Finite Element Methods
In this paper, we study a generalized finite element method for solving second-order elliptic partial differential equations with rough coefficients. The method uses local approximation spaces computed by solving eigenvalue problems on rings around the boundary of local subdomains. Compared to the corresponding method that solves eigenvalue problems on the whole subdomains, the problem size and the bandwidth of the resulting system matrices are substantially reduced, resulting in faster spectra…

OpenCoderRank: AI-Driven Technical Assessments Made Easy
Hridoy Sankar Dutta, Sana Ansari, Swati Kumari, Shounak Ravi Bhalerao
https://arxiv.org/abs/2509.06774 https://

OpenCoderRank: AI-Driven Technical Assessments Made Easy
Organizations and educational institutions use time-bound assessment tasks to evaluate coding and problem-solving skills. These assessments measure not only the correctness of the solutions, but also their efficiency. Problem setters (educator/interviewer) are responsible for crafting these challenges, carefully balancing difficulty and relevance to create meaningful evaluation experiences. Conversely, problem solvers (student/interviewee) apply coding efficiency and logical thinking to arrive …

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…

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 …

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…

Collisional model with dissipative and dephasing baths: Nonadditive effects at strong coupling
Alessandro Prositto, Carlos Ramon-Escandell, Dvira Segal
https://arxiv.org/abs/2509.10988

Collisional model with dissipative and dephasing baths: Nonadditive effects at strong coupling
The repeated interaction model provides a framework for emulating and analyzing the dynamics of open quantum systems. We explore here the dynamics generated by this protocol in a system that is simultaneously coupled to two baths through noncommuting system operators. One bath is made to couple to nondiagonal elements of the system, thus it induces dissipative dynamics, while the other couples to diagonal elements, and by itself it generates pure dephasing. By solving the problem analytically e…

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…

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…

Replaced article(s) found for cs.CY. https://arxiv.org/list/cs.CY/new
[1/1]:
- Prompt Programming: A Platform for Dialogue-based Computational Problem Solving with Generative A...
Victor-Alexandru P\u{a}durean, Paul Denny, Alkis Gotovos, Adish Singla

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…

Online Block Packing
Ariel Ben Eliezer, Noam Nisan
https://arxiv.org/abs/2507.12357 https://arxiv.org/pdf/2507.12357

Online Block Packing
We consider the algorithmic challenge that is faced by blockchains that have multidimensional block constraints and serve quasi-patient bidders. We provide online approximation algorithms for this problem, thus solving open problems left by [Babaioff and Nisan, EC 2025].

RefactorCoderQA: Benchmarking LLMs for Multi-Domain Coding Question Solutions in Cloud and Edge Deployment
Shadikur Rahman, Aroosa Hameed, Gautam Srivastava, Syed Muhammad Danish
https://arxiv.org/abs/2509.10436

RefactorCoderQA: Benchmarking LLMs for Multi-Domain Coding Question Solutions in Cloud and Edge Deployment
To optimize the reasoning and problem-solving capabilities of Large Language Models (LLMs), we propose a novel cloud-edge collaborative architecture that enables a structured, multi-agent prompting framework. This framework comprises three specialized components: GuideLLM, a lightweight model deployed at the edge to provide methodological guidance; SolverLLM, a more powerful model hosted in the cloud responsible for generating code solutions; and JudgeLLM, an automated evaluator for assessing s…

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 …

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…

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…

A Role-Aware Multi-Agent Framework for Financial Education Question Answering with LLMs
Andy Zhu, Yingjun Du
https://arxiv.org/abs/2509.09727 https://arxiv…

A Role-Aware Multi-Agent Framework for Financial Education Question Answering with LLMs
Question answering (QA) plays a central role in financial education, yet existing large language model (LLM) approaches often fail to capture the nuanced and specialized reasoning required for financial problem-solving. The financial domain demands multistep quantitative reasoning, familiarity with domain-specific terminology, and comprehension of real-world scenarios. We present a multi-agent framework that leverages role-based prompting to enhance performance on domain-specific QA. Our framew…

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…

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…

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…

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…

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…

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…

Near-Optimal Recovery Performance of PhaseLift for Phase Retrieval from Coded Diffraction Patterns
Meng Huang, Jinming Wen, Ran Zhang
https://arxiv.org/abs/2509.10300 https://…

Near-Optimal Recovery Performance of PhaseLift for Phase Retrieval from Coded Diffraction Patterns
The PhaseLift algorithm is an effective convex method for solving the phase retrieval problem from Fourier measurements with coded diffraction patterns (CDP). While exact reconstruction guarantees are well-established in the noiseless case, the stability of recovery under noise remains less well understood. In particular, when the measurements are corrupted by an additive noise vector $\mathbf{w} \in \mathbb{R}^m$, existing recovery bounds scale on the order of $\|\mathbf{w}\|_2$, whi…

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…

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…

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…

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…

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

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

Freeze and Conquer: Reusable Ansatz for Solving the Traveling Salesman Problem
Fabrizio Fagiolo, Nicolo' Vescera
https://arxiv.org/abs/2508.21730 https://

Freeze and Conquer: Reusable Ansatz for Solving the Traveling Salesman Problem
In this paper we present a variational algorithm for the Traveling Salesman Problem (TSP) that combines (i) a compact encoding of permutations, which reduces the qubit requirement too, (ii) an optimize-freeze-reuse strategy: where the circuit topology (``Ansatz'') is first optimized on a training instance by Simulated Annealing (SA), then ``frozen'' and re-used on novel instances, limited to a rapid re-optimization of only the circuit parameters. This pipeline eliminates costly structural resea…