Tootfinder

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…

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…

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…

Is #AI really just dumb statistics? "Olympiad-level physics problem-solving presents a significant challenge for both humans and artificial intelligence (AI), as it requires a sophisticated integration of precise calculation, abstract reasoning, and a fundamental grasp of physical principles," says the (abstract of the) paper https://arxiv.org/abs/2511.10515: "The Chinese Physics Olympiad (CPhO), renowned for its complexity and depth, serves as an ideal and rigorous testbed for these advanced capabilities. In this paper, we introduce LOCA-R (LOgical Chain Augmentation for Reasoning), an improved version of the LOCA framework adapted for complex reasoning, and apply it to the CPhO 2025 theory examination. LOCA-R achieves a near-perfect score of 313 out of 320 points, solidly surpassing the highest-scoring human competitor and significantly outperforming all baseline methods." Oops ...?

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…

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…

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. …

Performance of Gaussian Boson Sampling on Planted Bipartite Clique Detection
Yu-Zhen Janice Chen, Laurent Massouli\'e, Don Towsley
https://arxiv.org/abs/2510.12774 https://

Performance of Gaussian Boson Sampling on Planted Bipartite Clique Detection
We investigate whether Gaussian Boson Sampling (GBS) can provide a computational advantage for solving the planted biclique problem, which is a graph problem widely believed to be classically hard when the planted structure is small. Although GBS has been heuristically and experimentally observed to favor sampling dense subgraphs, its theoretical performance on this classically hard problem remains largely unexplored. We focus on a natural statistic derived from GBS output: the frequency with w…

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 …

An Effective Method for Solving a Class of Transcendental Diophantine Equations
Zeyu Cai
https://arxiv.org/abs/2510.11753 https://arxiv.org/pdf/2510.11753

An Effective Method for Solving a Class of Transcendental Diophantine Equations
This paper investigates the exponential Diophantine equation of the form $a^x+b=c^y$, where $a, b, c$ are given positive integers with $a,c \ge 2$, and $x,y$ are positive integer unknowns. We define this form as a "Type-I transcendental diophantine equation." A general solution to this problem remains an open question; however, the ABC conjecture implies that the number of solutions for any such equation is finite. This work introduces and implements an effective algorithm designed to solve the…

Column Generation for Periodic Timetabling
Stephanie Riedm\"uller, Niels Lindner
https://arxiv.org/abs/2510.12466 https://arxiv.org/pdf/2510.12466

Column Generation for Periodic Timetabling
Periodic timetabling for public transportation networks is typically modelled as a Periodic Event Scheduling Problem (PESP). Solving instances of the benchmark library PESPlib to optimality continues to pose a challenge. As a further approach towards this goal, we remodel the problem by a time discretization of the underlying graph and consider arc-based as well as path-based integer programming formulations. For the path-based case, we use cycles on the graph expansion of the operational lines…

GAR: Generative Adversarial Reinforcement Learning for Formal Theorem Proving
Ruida Wang, Jiarui Yao, Rui Pan, Shizhe Diao, Tong Zhang
https://arxiv.org/abs/2510.11769 https://

GAR: Generative Adversarial Reinforcement Learning for Formal Theorem Proving
Solving math problems through verifiable languages such as Lean has significantly impacted both the mathematics and computer science communities. Current state-of-the-art models are often trained with expensive online Reinforcement Learning (RL) or expert iteration. However, these approaches rely on fixed problem sets, which causes inefficient training and limits the model to tackle complex problems. To overcome these limitations, we propose GAR: Generative Adversarial Reinforcement learning, a…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[8/14]:
- From Problem-Solving to Teaching Problem-Solving: Aligning LLMs with Pedagogy using Reinforcement...
David Dinucu-Jianu, Jakub Macina, Nico Daheim, Ido Hakimi, Iryna Gurevych, Mrinmaya Sachan

Exploring Artificial Intelligence and Culture: Methodology for a comparative study of AI's impact on norms, trust, and problem-solving across academic and business environments
Matthias Huemmer, Theophile Shyiramunda, Michelle J. Cummings-Koether
https://arxiv.org/abs/2510.11530

Exploring Artificial Intelligence and Culture: Methodology for a comparative study of AI's impact on norms, trust, and problem-solving across academic and business environments
This paper proposes a rigorous framework to examine the two-way relationship between artificial intelligence (AI), human cognition, problem-solving, and cultural adaptation across academic and business settings. It addresses a key gap by asking how AI reshapes cognitive processes and organizational norms, and how cultural values and institutional contexts shape AI adoption, trust, and use over time. We employ a three-wave longitudinal design that tracks AI knowledge, perceived competence, trust…

Learning Latent Energy-Based Models via Interacting Particle Langevin Dynamics
Joanna Marks, Tim Y. J. Wang, O. Deniz Akyildiz
https://arxiv.org/abs/2510.12311 https://

Learning Latent Energy-Based Models via Interacting Particle Langevin Dynamics
We develop interacting particle algorithms for learning latent variable models with energy-based priors. To do so, we leverage recent developments in particle-based methods for solving maximum marginal likelihood estimation (MMLE) problems. Specifically, we provide a continuous-time framework for learning latent energy-based models, by defining stochastic differential equations (SDEs) that provably solve the MMLE problem. We obtain a practical algorithm as a discretisation of these SDEs and pro…

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

The irony here is of course that the people who most need to see these posts won’t because of the very problem I’m giving advice about solving

A Comprehensive Survey on Benchmarks and Solutions in Software Engineering of LLM-Empowered Agentic System
Jiale Guo, Suizhi Huang, Mei Li, Dong Huang, Xingsheng Chen, Regina Zhang, Zhijiang Guo, Han Yu, Siu-Ming Yiu, Christian Jensen, Pietro Lio, Kwok-Yan Lam
https://arxiv.org/abs/2510.09721

A Comprehensive Survey on Benchmarks and Solutions in Software Engineering of LLM-Empowered Agentic System
The integration of LLMs into software engineering has catalyzed a paradigm shift from traditional rule-based systems to sophisticated agentic systems capable of autonomous problem-solving. Despite this transformation, the field lacks a comprehensive understanding of how benchmarks and solutions interconnect, hindering systematic progress and evaluation. This survey presents the first holistic analysis of LLM-empowered software engineering, bridging the critical gap between evaluation and soluti…

An Efficient Solution Method for Solving Convex Separable Quadratic Optimization Problems
Shaoze Li, Junhao Wu, Cheng Lu, Zhibin Deng, Shu-Cherng Fang
https://arxiv.org/abs/2510.11554

An Efficient Solution Method for Solving Convex Separable Quadratic Optimization Problems
Convex separable quadratic optimization problems occur in many practical applications. In this paper, based on an iterative resolution scheme of the KKT system, we develop an efficient method for solving a quadratic programming problem with a convex separable objective function subject to multiple convex separable constraints. We show that the proposed approach leads to a dual coordinate ascent algorithm and provide a convergence proof. Numerical experiments support the superior performance of …

I'm listening to the most recent episode of #YouAreNotSoSmart, David McRaney's podcast @…. It's about the Trolley problem and morality.
They keep talking about a variation of the trolley problem where, instead of pulling the lever t…

YANSS 327 – How save ourselves from ourselves by solving the trolley problem inside us all
Philosopher, neuroscientist, and psychologist, Joshua Greene tells us how the brain generates morality and how his research may have solved the infamous trolley problem, and in so doing created a w…

from my link log —
Solving the Partridge square packing problem using MiniZinc.
https://zayenz.se/blog/post/partridge-packing/
saved 2025-11-26 https:/…

Distributed clustering in partially overlapping feature spaces
Alessio Maritan, Luca Schenato
https://arxiv.org/abs/2510.09799 https://arxiv.org/pdf/2510.0…

Distributed clustering in partially overlapping feature spaces
We introduce and address a novel distributed clustering problem where each participant has a private dataset containing only a subset of all available features, and some features are included in multiple datasets. This scenario occurs in many real-world applications, such as in healthcare, where different institutions have complementary data on similar patients. We propose two different algorithms suitable for solving distributed clustering problems that exhibit this type of feature space heter…

The parabolic Dirichlet problem with continuous and H\"older boundary data, and rough coefficients
Pablo Hidalgo-Palencia, Cody Hutcheson, Joseph Kasel
https://arxiv.org/abs/2510.04833

The parabolic Dirichlet problem with continuous and Hölder boundary data, and rough coefficients
We provide very mild sufficient conditions for space-time domains (non-necessarily cylindrical) which ensure that the continuous Dirichlet problem and the Hölder Dirichlet problem are well-posed, for any parabolic operator in divergence form with merely bounded coefficients. Concretely, we show that the parabolic measure exists, even for unbounded domains, hence solving an open problem posed by Genschaw and Hofmann (2020). This problem has inherent difficulties because of its parabolic natur…

An efficient spectral Poisson solver for the nirvana-III code: the shearing-box case with vertical vacuum boundary conditions
S. Rendon Restrepo, O. Gressel
https://arxiv.org/abs/2510.10070

An efficient spectral Poisson solver for the nirvana-III code: the shearing-box case with vertical vacuum boundary conditions
The stability of a differentially rotating fluid subject to its own gravity is a problem with applications across wide areas of astrophysics--from protoplanetary discs (PPDs) to entire galaxies. The shearing box formalism offers a conceptually simple framework for studying differential rotation in the local approximation. Aimed at self-gravitating, and importantly, vertically stratified PPDs, we develop two novel methods for solving Poisson's equation in the framework of the shearing box with v…

Co-designing a Programmable RISC-V Accelerator for MPC-based Energy and Thermal Management of Many-Core HPC Processors
Alessandro Ottaviano, Andrino Meli, Paul Scheffler, Giovanni Bambini, Robert Balas, Davide Rossi, Andrea Bartolini, Luca Benini
https://arxiv.org/abs/2510.09163

Co-designing a Programmable RISC-V Accelerator for MPC-based Energy and Thermal Management of Many-Core HPC Processors
Managing energy and thermal profiles is critical for many-core HPC processors with hundreds of application-class processing elements (PEs). Advanced model predictive control (MPC) delivers state-of-the-art performance but requires solving an online optimization problem over a thousand times per second (1 kHz control bandwidth), with computational and memory demands scaling with PE count. Traditional MPC approaches execute the controller on the PEs, but operating system overheads create jitter a…

My almost 4 year old wakes us up yelling: I need to go potty, but I have a banana in my hand.
My partner: put the banana on the table and go to the potty
My almost 4 year old: ok!
Problem solving
#kidposting

D3MAS: Decompose, Deduce, and Distribute for Enhanced Knowledge Sharing in Multi-Agent Systems
Heng Zhang, Yuling Shi, Xiaodong Gu, Haochen You, Zijian Zhang, Lubin Gan, Yilei Yuan, Jin Huang
https://arxiv.org/abs/2510.10585

D3MAS: Decompose, Deduce, and Distribute for Enhanced Knowledge Sharing in Multi-Agent Systems
Multi-agent systems powered by large language models exhibit strong capabilities in collaborative problem-solving. However, these systems suffer from substantial knowledge redundancy. Agents duplicate efforts in retrieval and reasoning processes. This inefficiency stems from a deeper issue: current architectures lack mechanisms to ensure agents share minimal sufficient information at each operational stage. Empirical analysis reveals an average knowledge duplication rate of 47.3\% across agent …

Ax-Prover: A Deep Reasoning Agentic Framework for Theorem Proving in Mathematics and Quantum Physics
Marco Del Tredici, Jacob McCarran, Benjamin Breen, Javier Aspuru Mijares, Weichen Winston Yin, Jacob M. Taylor, Frank Koppens, Dirk Englund
https://arxiv.org/abs/2510.12787

Ax-Prover: A Deep Reasoning Agentic Framework for Theorem Proving in Mathematics and Quantum Physics
We present Ax-Prover, a multi-agent system for automated theorem proving in Lean that can solve problems across diverse scientific domains and operate either autonomously or collaboratively with human experts. To achieve this, Ax-Prover approaches scientific problem solving through formal proof generation, a process that demands both creative reasoning and strict syntactic rigor. Ax-Prover meets this challenge by equipping Large Language Models (LLMs), which provide knowledge and reasoning, wit…

Pseudo-MDPs: A Novel Framework for Efficiently Optimizing Last Revealer Seed Manipulations in Blockchains
Maxime Reynouard
https://arxiv.org/abs/2510.07080 https://

Pseudo-MDPs: A Novel Framework for Efficiently Optimizing Last Revealer Seed Manipulations in Blockchains
This study tackles the computational challenges of solving Markov Decision Processes (MDPs) for a restricted class of problems. It is motivated by the Last Revealer Attack (LRA), which undermines fairness in some Proof-of-Stake (PoS) blockchains such as Ethereum (\$400B market capitalization). We introduce pseudo-MDPs (pMDPs) a framework that naturally models such problems and propose two distinct problem reductions to standard MDPs. One problem reduction provides a novel, counter-intuitive per…

Information-Preserving Reformulation of Reasoning Traces for Antidistillation
Jiayu Ding, Lei Cui, Li Dong, Nanning Zheng, Furu Wei
https://arxiv.org/abs/2510.11545 https://

Information-Preserving Reformulation of Reasoning Traces for Antidistillation
Recent advances in Large Language Models (LLMs) show that extending the length of reasoning chains significantly improves performance on complex tasks. While revealing these reasoning traces helps users better follow, verify, and learn from the model's problem-solving process, it also makes them highly vulnerable to unauthorized distillation. To mitigate this risk, proprietary model providers often adopt aggressive protection strategies, such as replacing detailed reasoning with brief summaries…

Las Vegas Raiders announce 2025 Inspire Change Changemaker https://www.raiders.com/news/las-vegas-raiders-announce-2025-inspire-change-changemaker

The AI coding trap
If you ever watch someone “coding”, you might see them spending far more time staring into space than typing on their keyboard. No, they (probably) aren’t slacking off. Software development is fundamentally a practice of problem-solving, and so, as with solving a tricky crossword, most of the work is done in your head. […]
🧑‍💻

The AI coding trap | Chris Loy
If you ever watch someone “coding”, you might see them spending far more time staring into space than typing on their keyboard.

Quantum Alternating Direction Method of Multipliers for Semidefinite Programming
Hantao Nie, Dong An, Zaiwen Wen
https://arxiv.org/abs/2510.10056 https://a…

Quantum Alternating Direction Method of Multipliers for Semidefinite Programming
Semidefinite programming (SDP) is a fundamental convex optimization problem with wide-ranging applications. However, solving large-scale instances remains computationally challenging due to the high cost of solving linear systems and performing eigenvalue decompositions. In this paper, we present a quantum alternating direction method of multipliers (QADMM) for SDPs, building on recent advances in quantum computing. An inexact ADMM framework is developed, which tolerates errors in the iterates …

ProSEA: Problem Solving via Exploration Agents
William Nguyen, Vinh Luong, Christopher Nguyen
https://arxiv.org/abs/2510.07423 https://arxiv.org/pdf/2510.0…

ProSEA: Problem Solving via Exploration Agents
Large language models (LLMs) have empowered AI agents to tackle increasingly complex tasks. However, most existing agents remain limited to static planning and brittle interactions, falling short of true collaboration or adaptive reasoning. We introduce ProSEA, a modular, general-purpose multi-agent framework designed for iterative problem solving through exploration and plan evolution. ProSEA features a hierarchical architecture in which a Manager Agent orchestrates domain-specialized Expert A…

HyPlan: Hybrid Learning-Assisted Planning Under Uncertainty for Safe Autonomous Driving
Donald Pfaffmann, Matthias Klusch, Marcel Steinmetz
https://arxiv.org/abs/2510.07210 http…

HyPlan: Hybrid Learning-Assisted Planning Under Uncertainty for Safe Autonomous Driving
We present a novel hybrid learning-assisted planning method, named HyPlan, for solving the collision-free navigation problem for self-driving cars in partially observable traffic environments. HyPlan combines methods for multi-agent behavior prediction, deep reinforcement learning with proximal policy optimization and approximated online POMDP planning with heuristic confidence-based vertical pruning to reduce its execution time without compromising safety of driving. Our experimental performan…

Addressing the sign-problem in Euclidean path integrals with radial basis function neural networks
Gabor Balassa
https://arxiv.org/abs/2510.01695 https://a…

Addressing the sign-problem in Euclidean path integrals with radial basis function neural networks
Solving interacting field theories at finite densities remains a numerically and conceptually challenging task, even with modern computational capabilities. In this paper, we propose a novel approach based on an expansion of the Euclidean path integrals using radial basis function neural networks, which allows the calculation of observables at finite densities and overcomes the sign problem in a numerically very efficient manner. The method is applied to an interacting complex scalar field theo…

I hate being told what to do. I also find it hard to accept ideas from other people - my partner especially. I think it’s because I want to find solutions myself and do it in my own time. I’m often overwhelmed with things when she starts talking about another thing that needs sorting out. I feel really angry when she does that. I will try to communicate better in those situations. Sometimes I don’t need problem solving, just a hug.

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.

A Unified Deep Reinforcement Learning Approach for Close Enough Traveling Salesman Problem
Mingfeng Fan, Jiaqi Cheng, Yaoxin Wu, Yifeng Zhang, Yibin Yang, Guohua Wu, Guillaume Sartoretti
https://arxiv.org/abs/2510.03065

A Unified Deep Reinforcement Learning Approach for Close Enough Traveling Salesman Problem
In recent years, deep reinforcement learning (DRL) has gained traction for solving the NP-hard traveling salesman problem (TSP). However, limited attention has been given to the close-enough TSP (CETSP), primarily due to the challenge introduced by its neighborhood-based visitation criterion, wherein a node is considered visited if the agent enters a compact neighborhood around it. In this work, we formulate a Markov decision process (MDP) for CETSP using a discretization scheme and propose a n…

Read the Room or Lead the Room: Understanding Socio-Cognitive Dynamics in Human-AI Teaming
Jaeyoon Choi, Mohammad Amin Samadi, Spencer JaQuay, Seehee Park, Nia Nixon
https://arxiv.org/abs/2510.09944

Read the Room or Lead the Room: Understanding Socio-Cognitive Dynamics in Human-AI Teaming
Research on Collaborative Problem Solving (CPS) has traditionally examined how humans rely on one another cognitively and socially to accomplish tasks together. With the rapid advancement of AI and large language models, however, a new question emerge: what happens to team dynamics when one of the "teammates" is not human? In this study, we investigate how the integration of an AI teammate -- a fully autonomous GPT-4 agent with social, cognitive, and affective capabilities -- shapes the socio-c…

Super-resolution of 4D flow MRI through inverse problem explicit solving
Aur\'elien de Turenne, R\'emi Cart-Lamy, Denis Kouam\'e
https://arxiv.org/abs/2509.21071 htt…

Super-resolution of 4D flow MRI through inverse problem explicit solving
Four-dimensional Flow MRI (4D Flow MRI) enables non-invasive, time-resolved imaging of blood flow in three spatial dimensions, offering valuable insights into complex hemodynamics. However, its clinical utility is limited by low spatial resolution and poor signal-to-noise ratio (SNR), imposed by acquisition time constraints. In this work, we propose a novel method for super-resolution and denoising of 4D Flow MRI based on the explicit solution of an inverse problem formulated in the complex dom…

An inexact semismooth Newton-Krylov method for semilinear elliptic optimal control problem
Shiqi Chen, Xuesong Chen
https://arxiv.org/abs/2511.10058 https://arxiv.org/pdf/2511.10058 https://arxiv.org/html/2511.10058
arXiv:2511.10058v1 Announce Type: new
Abstract: An inexact semismooth Newton method has been proposed for solving semi-linear elliptic optimal control problems in this paper. This method incorporates the generalized minimal residual (GMRES) method, a type of Krylov subspace method, to solve the Newton equations and utilizes nonmonotonic line search to adjust the iteration step size. The original problem is reformulated into a nonlinear equation through variational inequality principles and discretized using a second-order finite difference scheme. By leveraging slanting differentiability, the algorithm constructs semismooth Newton directions and employs GMRES method to inexactly solve the Newton equations, significantly reducing computational overhead. A dynamic nonmonotonic line search strategy is introduced to adjust stepsizes adaptively, ensuring global convergence while overcoming local stagnation. Theoretical analysis demonstrates that the algorithm achieves superlinear convergence near optimal solutions when the residual control parameter $\eta_k$ approaches to 0. Numerical experiments validate the method's accuracy and efficiency in solving semilinear elliptic optimal control problems, corroborating theoretical insights.
toXiv_bot_toot

San Jose mayor Matt Mahan brags about "solving" the homeless problem in San Jose.
People are chased out of one area, and then they filter through neighborhoods to new spots, and the cycle repeats over and over...
#SanJose #homeless

Primer to get you started with Optimization and Mathematical Programming in R #rstats

CRAN Task View: Optimization and Mathematical Programming
This CRAN Task View contains a list of packages that offer facilities for solving optimization problems. Although every regression model in statistics solves an optimization problem, they are not part of this view. If you are looking for regression methods, the following views will also contain useful starting points: MachineLearning, Econometrics, Robust Packages are categorized according to the following sections. See also the “Related Links” and “Other Resources” sections at the end.

Hybrid Learning and Optimization methods for solving Capacitated Vehicle Routing Problem
Monit Sharma, Hoong Chuin Lau
https://arxiv.org/abs/2509.15262 https://

Hybrid Learning and Optimization methods for solving Capacitated Vehicle Routing Problem
The Capacitated Vehicle Routing Problem (CVRP) is a fundamental NP-hard problem in logistics. Augmented Lagrangian Methods (ALM) for solving CVRP performance depends heavily on well-tuned penalty parameters. In this paper, we propose a hybrid optimization approach that integrates deep reinforcement learning (RL) to automate the selection of penalty parameter values within both classical (RL-C-ALM) and quantum-enhanced (RL-Q-ALM) ALM solvers. Using Soft Actor-Critic, our approach learns penalty …

Mitigating Overthinking through Reasoning Shaping
Feifan Song, Shaohang Wei, Bofei Gao, Yejie Wang, Wen Luo, Wei Li, Linli Yao, Weimin Xiong, Liang Chen, Tianyu Liu, Houfeng Wang
https://arxiv.org/abs/2510.09535

Mitigating Overthinking through Reasoning Shaping
Large reasoning models (LRMs) boosted by Reinforcement Learning from Verifier Reward (RLVR) have shown great power in problem solving, yet they often cause overthinking: excessive, meandering reasoning that inflates computational cost. Prior designs of penalization in RLVR manage to reduce token consumption while often harming model performance, which arises from the oversimplicity of token-level supervision. In this paper, we argue that the granularity of supervision plays a crucial role in ba…

Las Vegas Raiders announce 2025 Inspire Change Changemaker https://www.raiders.com/news/las-vegas-raiders-announce-2025-inspire-change-changemaker

A New Quantum Linear System Algorithm Beyond the Condition Number and Its Application to Solving Multivariate Polynomial Systems
Jianqiang Li
https://arxiv.org/abs/2510.05588 ht…

A New Quantum Linear System Algorithm Beyond the Condition Number and Its Application to Solving Multivariate Polynomial Systems
Given a matrix $A$ of dimension $M \times N$ and a vector $\vec{b}$, the quantum linear system (QLS) problem asks for the preparation of a quantum state $|\vec{y}\rangle$ proportional to the solution of $A\vec{y} = \vec{b}$. Existing QLS algorithms have runtimes that scale linearly with the condition number $κ(A)$, the sparsity of $A$, and logarithmically with inverse precision, but often overlook structural properties of $\vec{b}$, whose alignment with $A$'s eigenspaces can greatly affec…

A space-time generalized finite difference method for solving the transient Stokes/Parabolic interface problem in the moving system
Yanan Xing, Haibiao Zheng
https://arxiv.org/abs/2509.23702

A space-time generalized finite difference method for solving the transient Stokes/Parabolic interface problem in the moving system
In this paper, a space-time generalized finite difference method (ST-GFDM) is proposed to solve the transient Stokes/Parabolic moving interface problem which is a type of fluid-structure interaction problem. The ST-GFDM considers the time dimension as the third space dimension, and the 2D time-dependent Stokes/Parabolic moving interface problem can be seen as a 3D interface problem where the interface is formed by the initial interface shape and the moving trajectory. The GFDM has an advantage …

In Massachusetts, a startup is transforming the cement industry after developing a fossil fuel-free production system, significantly reducing carbon emissions.
https://triplepundit.com/2025/sublime-systems-cement-carbon-dioxide-emissions/

Encoding Numeric Computations and Infusing Heuristic Knowledge Using Integrity Constraints in stableKanren
Xiangyu Guo, Ajay Bansal
https://arxiv.org/abs/2510.04049 https://

Encoding Numeric Computations and Infusing Heuristic Knowledge Using Integrity Constraints in stableKanren
This paper presents examples of using integrity constraints in stableKanren to encode numeric computations for problem solving. Then, we use one of the examples to introduce multiple ways to infuse heuristic knowledge and reduce solving time. stableKanren is an extension of miniKanren that supports normal logic programs under stable model semantics. stableKanren further supports numeric computation by constructing a constraint store for integrity constraints. There are three ways to extend a re…

Nystr\"om-Accelerated Primal LS-SVMs: Breaking the $O(an^3)$ Complexity Bottleneck for Scalable ODEs Learning
Weikuo Wang, Yue Liao, Huan Luo
https://arxiv.org/abs/2510.04094

Nyström-Accelerated Primal LS-SVMs: Breaking the $O(an^3)$ Complexity Bottleneck for Scalable ODEs Learning
A major problem of kernel-based methods (e.g., least squares support vector machines, LS-SVMs) for solving linear/nonlinear ordinary differential equations (ODEs) is the prohibitive $O(an^3)$ ($a=1$ for linear ODEs and 27 for nonlinear ODEs) part of their computational complexity with increasing temporal discretization points $n$. We propose a novel Nyström-accelerated LS-SVMs framework that breaks this bottleneck by reformulating ODEs as primal-space constraints. Specifically, we derive for t…

parSAT: Parallel Solving of Floating-Point Satisfiability
Markus Krahl (University of Applied Sciences Munich HM), Matthias G\"udemann (University of Applied Sciences Munich HM), Stefan Wallentowitz (University of Applied Sciences Munich HM)
https://arxiv.org/abs/2509.16237

parSAT: Parallel Solving of Floating-Point Satisfiability
Satisfiability-based verification techniques, leveraging modern Boolean satisfiability (SAT) and Satisfiability Modulo Theories (SMT) solvers, have demonstrated efficacy in addressing practical problem instances within program analysis. However, current SMT solver implementations often encounter limitations when addressing non-linear arithmetic problems, particularly those involving floating point (FP) operations. This poses a significant challenge for safety critical applications, where accur…

Invisible Saboteurs: Sycophantic LLMs Mislead Novices in Problem-Solving Tasks
Jessica Y. Bo, Majeed Kazemitabaar, Mengqing Deng, Michael Inzlicht, Ashton Anderson
https://arxiv.org/abs/2510.03667

Invisible Saboteurs: Sycophantic LLMs Mislead Novices in Problem-Solving Tasks
Sycophancy, the tendency of LLM-based chatbots to express excessive enthusiasm, agreement, flattery, and a lack of disagreement, is emerging as a significant risk in human-AI interactions. However, the extent to which this affects human-LLM collaboration in complex problem-solving tasks is not well quantified, especially among novices who are prone to misconceptions. We created two LLM chatbots, one with high sycophancy and one with low sycophancy, and conducted a within-subjects experiment (n=…

Exploring Solution Divergence and Its Effect on Large Language Model Problem Solving
Hang Li, Kaiqi Yang, Yucheng Chu, Hui Liu, Jiliang Tang
https://arxiv.org/abs/2509.22480 htt…

Exploring Solution Divergence and Its Effect on Large Language Model Problem Solving
Large language models (LLMs) have been widely used for problem-solving tasks. Most recent work improves their performance through supervised fine-tuning (SFT) with labeled data or reinforcement learning (RL) from task feedback. In this paper, we study a new perspective: the divergence in solutions generated by LLMs for a single problem. We show that higher solution divergence is positively related to better problem-solving abilities across various models. Based on this finding, we propose solut…

WAN3DNS: Weak Adversarial Networks for Solving 3D Incompressible Navier-Stokes Equations
Wenran Li, Xavier Cadet, Miloud Bessafi, C\'edric Damour, Yu Li, Alain Miranville, Peter Chin, Rong Yang, Xinguang Yang, Frederic Cadet
https://arxiv.org/abs/2509.26034

WAN3DNS: Weak Adversarial Networks for Solving 3D Incompressible Navier-Stokes Equations
The 3D incompressible Navier-Stokes equations model essential fluid phenomena, including turbulence and aerodynamics, but are challenging to solve due to nonlinearity and limited solution regularity. Despite extensive research, the full mathematical understanding of the 3D incompressible Navier-Stokes equations continues to elude scientists, highlighting the depth and difficulty of the problem. Classical solvers are costly, and neural network-based methods typically assume strong solutions, lim…

Local MAP Sampling for Diffusion Models
Shaorong Zhang, Rob Brekelmans, Greg Ver Steeg
https://arxiv.org/abs/2510.07343 https://arxiv.org/pdf/2510.07343

Local MAP Sampling for Diffusion Models
Diffusion Posterior Sampling (DPS) provides a principled Bayesian approach to inverse problems by sampling from $p(x_0 \mid y)$. However, in practice, the goal of inverse problem solving is not to cover the posterior but to recover the most accurate reconstruction, where optimization-based diffusion solvers often excel despite lacking a clear probabilistic foundation. We introduce Local MAP Sampling (LMAPS), a new inference framework that iteratively solving local MAP subproblems along the diff…

PAC Reasoning: Controlling the Performance Loss for Efficient Reasoning
Hao Zeng, Jianguo Huang, Bingyi Jing, Hongxin Wei, Bo An
https://arxiv.org/abs/2510.09133 https://…

PAC Reasoning: Controlling the Performance Loss for Efficient Reasoning
Large reasoning models (LRMs) have achieved remarkable progress in complex problem-solving tasks. Despite this success, LRMs typically suffer from high computational costs during deployment, highlighting a need for efficient inference. A popular direction of efficiency improvement is to switch the LRM between thinking and nonthinking modes dynamically. However, such approaches often introduce additional reasoning errors and lack statistical guarantees for the performance loss, which are critica…

A Localized Orthogonal Decomposition method for heterogeneous mixed-dimensional problems
Moritz Hauck, Axel M{\aa}lqvist, Malin Mosquera
https://arxiv.org/abs/2510.09442 https:/…

A Localized Orthogonal Decomposition method for heterogeneous mixed-dimensional problems
We propose a multiscale method for mixed-dimensional elliptic problems with highly heterogeneous coefficients arising, for example, in the modeling of fractured porous media. The method is based on the Localized Orthogonal Decomposition (LOD) framework and constructs locally supported, problem-adapted basis functions on a coarse mesh that does not need to resolve the coefficient oscillations. These basis functions are obtained in parallel by solving localized fine-scale problems. Our a priori e…

Proposal of method to solve a Traveling Salesman Problem using Variational Quantum Kolmogorov-Arnold Network
Hikaru Wakaura
https://arxiv.org/abs/2509.22752 https://

Proposal of method to solve a Traveling Salesman Problem using Variational Quantum Kolmogorov-Arnold Network
Traveling salesman problems (TSP) are one of the well-known combinatorial optimization problems that many groups tackle to solve. This problem appears in many types of combinational optimization, such as scheduling, route optimization, and circuit optimization. However, this problem is NP-hard, as the number of combinations increases exponentially as the number of sites increases. Quantum Annealers and Adiabatic Quantum Computers are good at solving it, and universal quantum computers are limit…

A Recipe for Efficient Sim-to-Real Transfer in Manipulation with Online Imitation-Pretrained World Models
Yilin Wang, Shangzhe Li, Haoyi Niu, Zhiao Huang, Weitong Zhang, Hao Su
https://arxiv.org/abs/2510.02538

A Recipe for Efficient Sim-to-Real Transfer in Manipulation with Online Imitation-Pretrained World Models
We are interested in solving the problem of imitation learning with a limited amount of real-world expert data. Existing offline imitation methods often struggle with poor data coverage and severe performance degradation. We propose a solution that leverages robot simulators to achieve online imitation learning. Our sim-to-real framework is based on world models and combines online imitation pretraining with offline finetuning. By leveraging online interactions, our approach alleviates the data…

From GPUs to RRAMs: Distributed In-Memory Primal-Dual Hybrid Gradient Method for Solving Large-Scale Linear Optimization Problem
Huynh Q. N. Vo, Md Tawsif Rahman Chowdhury, Paritosh Ramanan, Gozde Tutuncuoglu, Junchi Yang, Feng Qiu, Murat Yildirim
https://arxiv.org/abs/2509.21137

From GPUs to RRAMs: Distributed In-Memory Primal-Dual Hybrid Gradient Method for Solving Large-Scale Linear Optimization Problem
The exponential growth of computational workloads is surpassing the capabilities of conventional architectures, which are constrained by fundamental limits. In-memory computing (IMC) with RRAM provides a promising alternative by providing analog computations with significant gains in latency and energy use. However, existing algorithms developed for conventional architectures do not translate to IMC, particularly for constrained optimization problems where frequent matrix reprogramming remains …

CANOPI: Contingency-Aware Nodal Optimal Power Investments with High Temporal Resolution
Thomas Lee, Andy Sun
https://arxiv.org/abs/2510.03484 https://arxiv…

CANOPI: Contingency-Aware Nodal Optimal Power Investments with High Temporal Resolution
We present CANOPI, a novel algorithmic framework, for solving the Contingency-Aware Nodal Power Investments problem, a large-scale nonlinear optimization problem that jointly optimizes generation, storage, and transmission expansion. The underlying problem is nonlinear due to the impact of transmission upgrades on impedances, and the problem's large scale arises from the confluence of spatial and temporal resolutions. We propose algorithmic approaches to address these computational challenges. …

Learning to Price Bundles: A GCN Approach for Mixed Bundling
Liangyu Ding, Chenghan Wu, Guokai Li, Zizhuo Wang
https://arxiv.org/abs/2509.22557 https://arx…

Learning to Price Bundles: A GCN Approach for Mixed Bundling
Bundle pricing refers to designing several product combinations (i.e., bundles) and determining their prices in order to maximize the expected profit. It is a classic problem in revenue management and arises in many industries, such as e-commerce, tourism, and video games. However, the problem is typically intractable due to the exponential number of candidate bundles. In this paper, we explore the usage of graph convolutional networks (GCNs) in solving the bundle pricing problem. Specifically,…

Benchmarking Foundation Models with Retrieval-Augmented Generation in Olympic-Level Physics Problem Solving
Shunfeng Zheng, Yudi Zhang, Meng Fang, Zihan Zhang, Zhitan Wu, Mykola Pechenizkiy, Ling Chen
https://arxiv.org/abs/2510.00919

Benchmarking Foundation Models with Retrieval-Augmented Generation in Olympic-Level Physics Problem Solving
Retrieval-augmented generation (RAG) with foundation models has achieved strong performance across diverse tasks, but their capacity for expert-level reasoning-such as solving Olympiad-level physics problems-remains largely unexplored. Inspired by the way students prepare for competitions by reviewing past problems, we investigate the potential of RAG to enhance physics reasoning in foundation models. We introduce PhoPile, a high-quality multimodal dataset specifically designed for Olympiad-lev…

On the Strength of Linear Relaxations in Ordered Optimization
V\'ictor Blanco, Diego Laborda, Miguel Mart\'inez-Ant\'on
https://arxiv.org/abs/2510.09166 https://

On the Strength of Linear Relaxations in Ordered Optimization
We study the conditions under which the convex relaxation of a mixed-integer linear programming formulation for ordered optimization problems, where sorting is part of the decision process, yields integral optimal solutions. Thereby solving the problem exactly in polynomial time. Our analysis identifies structural properties of the input data that influence the integrality of the relaxation. We show that incorporating ordered components introduces additional layers of combinatorial complexity t…

Comparative Analysis of Ant Colony Optimization and Google OR-Tools for Solving the Open Capacitated Vehicle Routing Problem in Logistics
Assem Omar, Youssef Omar, Marwa Solayman, Hesham Mansour
https://arxiv.org/abs/2509.26216

Comparative Analysis of Ant Colony Optimization and Google OR-Tools for Solving the Open Capacitated Vehicle Routing Problem in Logistics
In modern logistics management systems, route planning requires high efficiency. The Open Capacitated Vehicle Routing Problem (OCVRP) deals with finding optimal delivery routes for a fleet of vehicles serving geographically distributed customers, without requiring the vehicles to return to the depot after deliveries. The present study is comparative in nature and speaks of two algorithms for OCVRP solution: Ant Colony Optimization (ACO), a nature-inspired metaheuristic; and Google OR-Tools, an …

Solving Currency Arbitrage Problems using D-Wave Advantage2 Quantum Annealer
Lorenzo Mazzei, Giada Beccari, Mirko Laruina, Marco Cococcioni
https://arxiv.org/abs/2509.22591 http…

Solving Currency Arbitrage Problems using D-Wave Advantage2 Quantum Annealer
Quantum annealing has emerged as a powerful tool for solving combinatorial optimization problems efficiently, making use of the principles of quantum mechanics. Companies are increasingly investing in the market of quantum computers, providing the users with the possibility to solve these optimization problems by resorting to quantum computers. This paper explores how Quantum Annealing can be applied to the Currency Arbitrage (CA) optimization problem and its comparative performance against cla…

TS-Agent: A Time Series Reasoning Agent with Iterative Statistical Insight Gathering
Penghang Liu, Elizabeth Fons, Svitlana Vyetrenko, Daniel Borrajo, Vamsi Potluru, Manuela Veloso
https://arxiv.org/abs/2510.07432

TS-Agent: A Time Series Reasoning Agent with Iterative Statistical Insight Gathering
Large language models (LLMs) have shown strong abilities in reasoning and problem solving, but recent studies reveal that they still struggle with time series reasoning tasks, where outputs are often affected by hallucination or knowledge leakage. In this work we propose TS-Agent, a time series reasoning agent that leverages LLMs strictly for what they excel at, i.e., gathering evidence and synthesizing it into conclusions through step-by-step reasoning, while delegating the extraction of stati…

Convergence analysis of inexact MBA method for constrained upper-$\mathcal{C}^2$ optimization problems
Ruyu Liu, Shaohua Pan
https://arxiv.org/abs/2511.09940 https://arxiv.org/pdf/2511.09940 https://arxiv.org/html/2511.09940
arXiv:2511.09940v1 Announce Type: new
Abstract: This paper concerns a class of constrained optimization problems in which, the objective and constraint functions are both upper-$\mathcal{C}^2$. For such nonconvex and nonsmooth optimization problems, we develop an inexact moving balls approximation (MBA) method by a workable inexactness criterion for the solving of subproblems. By leveraging a global error bound for the strongly convex program associated with parametric optimization problems, we establish the full convergence of the iterate sequence under the partial bounded multiplier property (BMP) and the Kurdyka-{\L}ojasiewicz (KL) property of the constructed potential function, and achieve the local convergence rate of the iterate and objective value sequences if the potential function satisfies the KL property of exponent $q\in[1/2,1)$. A verifiable condition is also provided to check whether the potential function satisfies the KL property of exponent $q\in[1/2,1)$ at the given critical point. To the best of our knowledge, this is the first implementable inexact MBA method with a full convergence certificate for the constrained nonconvex and nonsmooth optimization problem.
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Stochastic Gradient Descent for Incomplete Tensor Linear Systems
Anna Ma, Deanna Needell, Alexander Xue
https://arxiv.org/abs/2510.07630 https://arxiv.org/…

Stochastic Gradient Descent for Incomplete Tensor Linear Systems
Solving large tensor linear systems poses significant challenges due to the high volume of data stored, and it only becomes more challenging when some of the data is missing. Recently, Ma et al. showed that this problem can be tackled using a stochastic gradient descent-based method, assuming that the missing data follows a uniform missing pattern. We adapt the technique by modifying the update direction, showing that the method is applicable under other missing data models. We prove convergenc…

Measuring dissimilarity between convex cones by means of max-min angles
Welington de Oliveira, Valentina Sessa, David Sossa
https://arxiv.org/abs/2511.10483 https://arxiv.org/pdf/2511.10483 https://arxiv.org/html/2511.10483
arXiv:2511.10483v1 Announce Type: new
Abstract: This work introduces a novel dissimilarity measure between two convex cones, based on the max-min angle between them. We demonstrate that this measure is closely related to the Pompeiu-Hausdorff distance, a well-established metric for comparing compact sets. Furthermore, we examine cone configurations where the measure admits simplified or analytic forms. For the specific case of polyhedral cones, a nonconvex cutting-plane method is deployed to compute, at least approximately, the measure between them. Our approach builds on a tailored version of Kelley's cutting-plane algorithm, which involves solving a challenging master program per iteration. When this master program is solved locally, our method yields an angle that satisfies certain necessary optimality conditions of the underlying nonconvex optimization problem yielding the dissimilarity measure between the cones. As an application of the proposed mathematical and algorithmic framework, we address the image-set classification task under limited data conditions, a task that falls within the scope of the \emph{Few-Shot Learning} paradigm. In this context, image sets belonging to the same class are modeled as polyhedral cones, and our dissimilarity measure proves useful for understanding whether two image sets belong to the same class.
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Probing quantum advantage for solving the Fermi-Hubbard model with entropy benchmarking
Pauline Besserve, Ra\'ul Garc\'ia-Patr\'on
https://arxiv.org/abs/2510.00930 h…

Probing quantum advantage for solving the Fermi-Hubbard model with entropy benchmarking
We developed a practical quantum advantage benchmarking framework that connects the accumulation of entropy in a quantum processing unit and the degradation of the solution to a target optimization problem. The benchmark is based on approximating from below the Gibbs states boundary in the energy-entropy space for the application of interest. We believe the proposed benchmarking technique creates a powerful bridge between hardware benchmarking and application benchmarking, while remaining hardw…

Constraint Satisfaction Approaches to Wordle: Novel Heuristics and Cross-Lexicon Validation
Jahidul Arafat, Fariha Tasmin, Sanjaya Poudel, Kamrujjaman, Eftakhar Ahmed Arnob, Ahsan Habib Tareq
https://arxiv.org/abs/2510.02855

Constraint Satisfaction Approaches to Wordle: Novel Heuristics and Cross-Lexicon Validation
Wordle presents an algorithmically rich testbed for constraint satisfaction problem (CSP) solving. While existing solvers rely on information-theoretic entropy maximization or frequency-based heuristics without formal constraint treatment, we present the first comprehensive CSP formulation of Wordle with novel constraint-aware solving strategies. We introduce CSP-Aware Entropy, computing information gain after constraint propagation rather than on raw candidate sets, and a Probabilistic CSP fra…

Solving Math Word Problems Using Estimation Verification and Equation Generation
Mitchell Piehl, Dillon Wilson, Ananya Kalita, Jugal Kalita
https://arxiv.org/abs/2509.18565 http…

A System Level Approach to LQR Control of the Diffusion Equation
Addie McCurdy, Andrew Gusty, Emily Jensen
https://arxiv.org/abs/2510.05345 https://arxiv.o…

A System Level Approach to LQR Control of the Diffusion Equation
The continuous-time, infinite horizon LQR problem for the diffusion equation over the unit circle with fully distributed actuation is considered. It is well-known that the solution to this problem can be obtained from the solution to an operator-valued algebraic Riccati equation. Here, it is demonstrated that this solution can be equivalently obtained by solving an $H_2$ control problem through a closed-loop design procedure that is analogous to the "System Level Synthesis" methodology previous…

AI-Driven Grading and Moderation for Collaborative Projects in Computer Science Education
Songmei Yu, Andrew Zagula
https://arxiv.org/abs/2510.03998 https://

AI-Driven Grading and Moderation for Collaborative Projects in Computer Science Education
Collaborative group projects are integral to computer science education, as they foster teamwork, problem-solving skills, and industry-relevant competencies. However, assessing individual contributions within group settings has long been a challenge. Traditional assessment strategies, such as the equal distribution of grades or subjective peer assessments, often fall short in terms of fairness, objectivity, and scalability, particularly in large classrooms. This paper introduces a semi-automate…

HANN: Homotopy auxiliary neural network for solving nonlinear algebraic equations
Ling-Zhe Zai, Lei-Lei Guo, Zhi-Yong Zhang
https://arxiv.org/abs/2509.26358 https://

HANN: Homotopy auxiliary neural network for solving nonlinear algebraic equations
Solving nonlinear algebraic equations is a fundamental but challenging problem in scientific computations and also has many applications in system engineering. Though traditional iterative methods and modern optimization algorithms have exerted effective roles in addressing certain specific problems, there still exist certain weaknesses such as the initial value sensitivity, limited accuracy and slow convergence rate, particulary without flexible input for the neural network methods. In this pa…

TGPR: Tree-Guided Policy Refinement for Robust Self-Debugging of LLMs
Daria Ozerova, Ekaterina Trofimova
https://arxiv.org/abs/2510.06878 https://arxiv.org…

TGPR: Tree-Guided Policy Refinement for Robust Self-Debugging of LLMs
Iterative refinement has been a promising paradigm to enable large language models (LLMs) to resolve difficult reasoning and problem-solving tasks. One of the key challenges, however, is how to effectively search through the enormous search space of possible refinements. Existing methods typically fall back on predefined heuristics, which are troubled by the exploration-exploitation dilemma and cannot adapt based on past refinement outcomes. We introduce Tree-Guided Policy Refinement (TGPR), a …

Global Solutions to Non-Convex Functional Constrained Problems with Hidden Convexity
Ilyas Fatkhullin, Niao He, Guanghui Lan, Florian Wolf
https://arxiv.org/abs/2511.10626 https://arxiv.org/pdf/2511.10626 https://arxiv.org/html/2511.10626
arXiv:2511.10626v1 Announce Type: new
Abstract: Constrained non-convex optimization is fundamentally challenging, as global solutions are generally intractable and constraint qualifications may not hold. However, in many applications, including safe policy optimization in control and reinforcement learning, such problems possess hidden convexity, meaning they can be reformulated as convex programs via a nonlinear invertible transformation. Typically such transformations are implicit or unknown, making the direct link with the convex program impossible. On the other hand, (sub-)gradients with respect to the original variables are often accessible or can be easily estimated, which motivates algorithms that operate directly in the original (non-convex) problem space using standard (sub-)gradient oracles. In this work, we develop the first algorithms to provably solve such non-convex problems to global minima. First, using a modified inexact proximal point method, we establish global last-iterate convergence guarantees with $\widetilde{\mathcal{O}}(\varepsilon^{-3})$ oracle complexity in non-smooth setting. For smooth problems, we propose a new bundle-level type method based on linearly constrained quadratic subproblems, improving the oracle complexity to $\widetilde{\mathcal{O}}(\varepsilon^{-1})$. Surprisingly, despite non-convexity, our methodology does not require any constraint qualifications, can handle hidden convex equality constraints, and achieves complexities matching those for solving unconstrained hidden convex optimization.
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Computing Invariant Zeros of a MIMO Linear System Using State-Space Realization
Jhon Manuel Portella Delgado, Ankit Goel
https://arxiv.org/abs/2509.24105 https://

Computing Invariant Zeros of a MIMO Linear System Using State-Space Realization
Poles of a multi-input multi-output (MIMO) linear system can be computed by solving an eigenvalue problem; however, the problem of computing its invariant zeros is equivalent to a generalized eigenvalue problem. This paper revisits the problem of computing the invariant zeros by solving an eigenvalue problem. We introduce a realization called the invariant zero form in which the system's invariant zeros are isolated in a partition of the transformed dynamics matrix. It is shown that the invaria…

Observing Without Doing: Pseudo-Apprenticeship Patterns in Student LLM Use
Jade Hak, Nathaniel Lam Johnson, Matin Amoozadeh, Amin Alipour, Souti Chattopadhyay
https://arxiv.org/abs/2510.04986

Observing Without Doing: Pseudo-Apprenticeship Patterns in Student LLM Use
Large Language Models (LLMs) such as ChatGPT have quickly become part of student programmers' toolkits, whether allowed by instructors or not. This paper examines how introductory programming (CS1) students integrate LLMs into their problem-solving processes. We conducted a mixed-methods study with 14 undergraduates completing three programming tasks while thinking aloud and permitted to access any resources they choose. The tasks varied in open-endedness and familiarity to the participants and…

A Computationally Efficient Finite Element Method for Shape Reconstruction of Inverse Conductivity Problems
Lefu Cai, Zhixin Liu, Minghui Song, Xianchao Wang
https://arxiv.org/abs/2510.00597

A Computationally Efficient Finite Element Method for Shape Reconstruction of Inverse Conductivity Problems
The inverse conductivity problem aims at determining the unknown conductivity inside a bounded domain from boundary measurements. In practical applications, algorithms based on minimizing a regularized residual functional subject to PDE constraints have been widely used to deal with this problem. However, such approaches typically require repeated iterations and solving the forward problem at each iteration, which leads to a heavy computational cost. To address this issue, we first reformulate …

Refusal Falls off a Cliff: How Safety Alignment Fails in Reasoning?
Qingyu Yin, Chak Tou Leong, Linyi Yang, Wenxuan Huang, Wenjie Li, Xiting Wang, Jaehong Yoon, YunXing, XingYu, Jinjin Gu
https://arxiv.org/abs/2510.06036

Refusal Falls off a Cliff: How Safety Alignment Fails in Reasoning?
Large reasoning models (LRMs) with multi-step reasoning capabilities have shown remarkable problem-solving abilities, yet they exhibit concerning safety vulnerabilities that remain poorly understood. In this work, we investigate why safety alignment fails in reasoning models through a mechanistic interpretability lens. Using a linear probing approach to trace refusal intentions across token positions, we discover a striking phenomenon termed as \textbf{refusal cliff}: many poorly-aligned reason…

EngiBench: A Benchmark for Evaluating Large Language Models on Engineering Problem Solving
Xiyuan Zhou, Xinlei Wang, Yirui He, Yang Wu, Ruixi Zou, Yuheng Cheng, Yulu Xie, Wenxuan Liu, Huan Zhao, Yan Xu, Jinjin Gu, Junhua Zhao
https://arxiv.org/abs/2509.17677

EngiBench: A Benchmark for Evaluating Large Language Models on Engineering Problem Solving
Large language models (LLMs) have shown strong performance on mathematical reasoning under well-posed conditions. However, real-world engineering problems require more than mathematical symbolic computation -- they need to deal with uncertainty, context, and open-ended scenarios. Existing benchmarks fail to capture these complexities. We introduce EngiBench, a hierarchical benchmark designed to evaluate LLMs on solving engineering problems. It spans three levels of increasing difficulty (founda…

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…

Syntactic Blind Spots: How Misalignment Leads to LLMs Mathematical Errors
Dane Williamson, Yangfeng Ji, Matthew Dwyer
https://arxiv.org/abs/2510.01831 https://

Syntactic Blind Spots: How Misalignment Leads to LLMs Mathematical Errors
Large Language Models (LLMs) demonstrate strong mathematical problem-solving abilities but frequently fail on problems that deviate syntactically from their training distribution. We identify a systematic failure mode, syntactic blind spots, in which models misapply familiar reasoning strategies to problems that are semantically straightforward but phrased in unfamiliar ways. These errors are not due to gaps in mathematical competence, but rather reflect a brittle coupling between surface form …

Calibrated Reasoning: An Explanatory Verifier for Dynamic and Efficient Problem-Solving
Anisha Garg, Engin Tekin, Yash More, David Bick, Nishit Neema, Ganesh Venkatesh
https://arxiv.org/abs/2509.19681 …

Calibrated Reasoning: An Explanatory Verifier for Dynamic and Efficient Problem-Solving
Advanced test-time computing strategies are essential for scaling reasoning models, but their effectiveness is capped by the models' poor self-evaluation. We propose a pairwise Explanatory Verifier, trained via reinforcement learning (GRPO), that produces calibrated confidence scores and associated natural language reasoning for generated solutions. Our verifier improves the accuracy and efficiency of test-time strategies like best-of-n and self-reflection. Crucially, it excels at identifying c…

A first-order method for constrained nonconvex--nonconcave minimax problems under a local Kurdyka-{\L}ojasiewicz condition
Zhaosong Lu, Xiangyuan Wang
https://arxiv.org/abs/2510.01168

A first-order method for constrained nonconvex--nonconcave minimax problems under a local Kurdyka-Łojasiewicz condition
We study a class of constrained nonconvex--nonconcave minimax problems in which the inner maximization involves potentially complex constraints. Under the assumption that the inner problem of a novel lifted minimax problem satisfies a local Kurdyka-Łojasiewicz (KL) condition, we show that the maximal function of the original problem enjoys a local Hölder smoothness property. We also propose a sequential convex programming (SCP) method for solving constrained optimization problems and establis…

Calibrated Reasoning: An Explanatory Verifier for Dynamic and Efficient Problem-Solving
Anisha Garg, Engin Tekin, Yash More, David Bick, Nishit Neema, Ganesh Venkatesh
https://arxiv.org/abs/2509.19681 …

Calibrated Reasoning: An Explanatory Verifier for Dynamic and Efficient Problem-Solving
Advanced test-time computing strategies are essential for scaling reasoning models, but their effectiveness is capped by the models' poor self-evaluation. We propose a pairwise Explanatory Verifier, trained via reinforcement learning (GRPO), that produces calibrated confidence scores and associated natural language reasoning for generated solutions. Our verifier improves the accuracy and efficiency of test-time strategies like best-of-n and self-reflection. Crucially, it excels at identifying c…

AMAS: Adaptively Determining Communication Topology for LLM-based Multi-Agent System
Hui Yi Leong, Yuheng Li, Yuqing Wu, Wenwen Ouyang, Wei Zhu, Jiechao Gao
https://arxiv.org/abs/2510.01617

AMAS: Adaptively Determining Communication Topology for LLM-based Multi-Agent System
Although large language models (LLMs) have revolutionized natural language processing capabilities, their practical implementation as autonomous multi-agent systems (MAS) for industrial problem-solving encounters persistent barriers. Conventional MAS architectures are fundamentally restricted by inflexible, hand-crafted graph topologies that lack contextual responsiveness, resulting in diminished efficacy across varied academic and commercial workloads. To surmount these constraints, we introdu…

Optimization by Directional Attacks: Solving Problems with Neural Network Surrogates
Pierre-Yves Bouchet, Thibaut Vidal
https://arxiv.org/abs/2510.01461 https://

Optimization by Directional Attacks: Solving Problems with Neural Network Surrogates
This paper tackles optimization problems whose objective and constraints involve a trained Neural Network (NN), where the goal is to maximize $f(Φ(x))$ subject to $c(Φ(x)) \leq 0$, with $f$ smooth, $c$ general and non-stringent, and $Φ$ an already trained and possibly nonwhite-box NN. We address two challenges regarding this problem: identifying ascent directions for local search, and ensuring reliable convergence towards relevant local solutions. To this end, we re-purpose the notion of dir…

Latent Thinking Optimization: Your Latent Reasoning Language Model Secretly Encodes Reward Signals in its Latent Thoughts
Hanwen Du, Yuxin Dong, Xia Ning
https://arxiv.org/abs/2509.26314

Latent Thinking Optimization: Your Latent Reasoning Language Model Secretly Encodes Reward Signals in its Latent Thoughts
Large Language Models (LLMs) excel at problem solving by generating chain of thoughts in natural language, but such verbal thinking is computationally costly and prone to overthinking. Recent work instead proposes a latent thinking architecture Huggin-3.5B, which represents intermediate reasoning steps as sequence of latent representations. However, latent thoughts lack interpretability and are difficult to supervise, raising concerns about the correctness and reliability of its latent thinking…

Beyond the Final Answer: Evaluating the Reasoning Trajectories of Tool-Augmented Agents
Wonjoong Kim, Sangwu Park, Yeonjun In, Sein Kim, Dongha Lee, Chanyoung Park
https://arxiv.org/abs/2510.02837

Beyond the Final Answer: Evaluating the Reasoning Trajectories of Tool-Augmented Agents
Although recent tool-augmented benchmarks incorporate complex user requests and diverse tools, the evaluation methods for most of them remain limited to answer matching. However, as the number of steps required to resolve a user request increases, a proper evaluation of an agent's performance must go beyond the final answer to also assess the problem-solving trajectory, including previously ignored aspects such as efficiency, hallucination, and adaptivity. The most straightforward method for ev…

Expanding Computation Spaces of LLMs at Inference Time
Yoonna Jang, Kisu Yang, Isabelle Augenstein
https://arxiv.org/abs/2509.24884 https://arxiv.org/pdf/2…

Expanding Computation Spaces of LLMs at Inference Time
Chain-of-thought (CoT) rationale enables language models to use additional task-related text for problem-solving, benefiting not only from detailed reasoning steps but also from the expanded computational space of longer inputs. Prior work has trained filler or special tokens to serve as additional computation spaces. In this study, we investigate whether language models can leverage artificially inserted sequences of filler tokens solely at inference. We first identify effective token types, n…

Solving Sparse MIQCQPs: Application to the Unit Commitment Problem with ACOPF Constraints
Ignacio G\'omez-Casares, Pietro Belotti, Bissan Ghaddar, Julio Gonz\'alez-D\'iaz
https://arxiv.org/abs/2509.18911

Solving Sparse MIQCQPs: Application to the Unit Commitment Problem with ACOPF Constraints
Mixed-Integer Quadratically Constrained Quadratic Programs arise in a variety of applications, particularly in energy, water, and gas systems, where discrete decisions interact with nonconvex quadratic constraints. These problems are computationally challenging due to the combination of combinatorial complexity and nonconvexity, often rendering traditional exact methods ineffective for large-scale instances. In this paper, we propose a solution framework for sparse MIQCQPs that integrates semid…

Analysis of approximate linear programming solution to Markov decision problem with log barrier function
Donghwan Lee, Hyukjun Yang, Bum Geun Park
https://arxiv.org/abs/2509.19800

Analysis of approximate linear programming solution to Markov decision problem with log barrier function
There are two primary approaches to solving Markov decision problems (MDPs): dynamic programming based on the Bellman equation and linear programming (LP). Dynamic programming methods are the most widely used and form the foundation of both classical and modern reinforcement learning (RL). By contrast, LP-based methods have been less commonly employed, although they have recently gained attention in contexts such as offline RL. The relative underuse of the LP-based methods stems from the fact t…

Analysis of approximate linear programming solution to Markov decision problem with log barrier function
Donghwan Lee, Hyukjun Yang, Bum Geun Park
https://arxiv.org/abs/2509.19800

Analysis of approximate linear programming solution to Markov decision problem with log barrier function
There are two primary approaches to solving Markov decision problems (MDPs): dynamic programming based on the Bellman equation and linear programming (LP). Dynamic programming methods are the most widely used and form the foundation of both classical and modern reinforcement learning (RL). By contrast, LP-based methods have been less commonly employed, although they have recently gained attention in contexts such as offline RL. The relative underuse of the LP-based methods stems from the fact t…

Agile Tradespace Exploration for Space Rendezvous Mission Design via Transformers
Yuji Takubo, Daniele Gammelli, Marco Pavone, Simone D'Amico
https://arxiv.org/abs/2510.03544

Agile Tradespace Exploration for Space Rendezvous Mission Design via Transformers
Spacecraft rendezvous enables on-orbit servicing, debris removal, and crewed docking, forming the foundation for a scalable space economy. Designing such missions requires rapid exploration of the tradespace between control cost and flight time across multiple candidate targets. However, multi-objective optimization in this setting is challenging, as the underlying constraints are often highly nonconvex, and mission designers must balance accuracy (e.g., solving the full problem) with efficienc…

A note on the Davis-Yin three-operator splitting method
Maoran Wang, Xingju Cai
https://arxiv.org/abs/2509.22032 https://arxiv.org/pdf/2509.22032

A note on the Davis-Yin three-operator splitting method
This paper addresses the monotone inclusion problem in a real Hilbert space, with the objective of identifying a point \( x \in \mathcal{H} \) such that \( 0 \in Ax + Bx + Cx \), where \( A \) and \(C\) are maximal monotone operators and \( B \) is a \(β\)-cocoercive operator. The Davis-Yin three-operator splitting method constitutes a widely used algorithm for solving this problem. This method consists of two subproblems: the first entails a backward step, whereas the second comprises a backw…