Tootfinder

Constructing Optimal Kobon Triangle Arrangements via Table Encoding, SAT Solving, and Heuristic Straightening
Pavlo Savchuk
https://arxiv.org/abs/2507.07951

Constructing Optimal Kobon Triangle Arrangements via Table Encoding, SAT Solving, and Heuristic Straightening
We present new methods and results for constructing optimal Kobon triangle arrangements. First, we introduce a compact table notation for describing arrangements of pseudolines, enabling the representation and analysis of complex cases, including symmetrical arrangements, arrangements with parallel lines, and arrangements with multiple-line intersection points. Building on this, we provide a simple heuristic method and tools for recovering straight-line arrangements from a given table, with the…

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…

Causal Negative Sampling via Diffusion Model for Out-of-Distribution Recommendation
Chu Zhao, Eneng Yang, Yizhou Dang, Jianzhe Zhao, Guibing Guo, Xingwei Wang
https://arxiv.org/abs/2508.07243

Causal Negative Sampling via Diffusion Model for Out-of-Distribution Recommendation
Heuristic negative sampling enhances recommendation performance by selecting negative samples of varying hardness levels from predefined candidate pools to guide the model toward learning more accurate decision boundaries. However, our empirical and theoretical analyses reveal that unobserved environmental confounders (e.g., exposure or popularity biases) in candidate pools may cause heuristic sampling methods to introduce false hard negatives (FHNS). These misleading samples can encourage the …

Quantum Optimization on Rydberg Atom Arrays with Arbitrary Connectivity: Gadgets Limitations and a Heuristic Approach
Pierre Cazals, Amalia Sorondo, Victor Onofre, Constantin Dalyac, Wesley da Silva Coelho, Vittorio Vitale
https://arxiv.org/abs/2508.06130

Quantum Optimization on Rydberg Atom Arrays with Arbitrary Connectivity: Gadgets Limitations and a Heuristic Approach
Programmable quantum systems based on Rydberg atom arrays have recently emerged as a promising testbed for combinatorial optimization. Indeed, the Maximum Weighted Independent Set problem on unit-disk graphs can be efficiently mapped to such systems due to their geometric constraints. However, extending this capability to arbitrary graph instances typically necessitates the use of reduction gadgets, which introduce additional experimental overhead and complexity. Here, we analyze the complexity…

HyColor: An Efficient Heuristic Algorithm for Graph Coloring
Enqiang Zhu, Yu Zhang, Haopeng Sun, Ziqi Wei, Witold Pedrycz, Chanjuan Liu, Jin Xu
https://arxiv.org/abs/2506.07373

HyColor: An Efficient Heuristic Algorithm for Graph Coloring
The graph coloring problem (GCP) is a classic combinatorial optimization problem that aims to find the minimum number of colors assigned to vertices of a graph such that no two adjacent vertices receive the same color. GCP has been extensively studied by researchers from various fields, including mathematics, computer science, and biological science. Due to the NP-hard nature, many heuristic algorithms have been proposed to solve GCP. However, existing GCP algorithms focus on either small hard …

A proposal and assessment of an improved heuristic for the Eager Test smell detection
Huynh Khanh Vi Tran, Nauman bin Ali, Michael Unterkalmsteiner, J\"urgen B\"orstler
https://arxiv.org/abs/2507.06354

A proposal and assessment of an improved heuristic for the Eager Test smell detection
Context: The evidence for the prevalence of test smells at the unit testing level has relied on the accuracy of detection tools, which have seen intense research in the last two decades. The Eager Test smell, one of the most prevalent, is often identified using simplified detection rules that practitioners find inadequate. Objective: We aim to improve the rules for detecting the Eager Test smell. Method: We reviewed the literature on test smells to analyze the definitions and detection rules of…

Heuristic approaches for a new variant of the Team Orienteering Problem
Alberto Guastalla, Roberto Aringhieri, Pierre Hosteins
https://arxiv.org/abs/2507.06012

Heuristic approaches for a new variant of the Team Orienteering Problem
In this paper we tackle the Team Orienteering Problem with Service Times, Mandatory Nodes and Incompatibilities, introduced in~\cite{Guastalla2024} and arising from two real-world healthcare applications. We propose two heuristic algorithms in the form of a Variable Descent Neighbourhood algorithm and a matheuristic based on a Cuts Separation approach. For the former, we also provide a multi-thread version exploiting its intrinsic capability to be parallelised. Both algorithms include a specifi…

Learning to Optimize Package Picking for Large-Scale, Real-World Robot Induction
Shuai Li, Azarakhsh Keipour, Sicong Zhao, Srinath Rajagopalan, Charles Swan, Kostas E. Bekris
https://arxiv.org/abs/2506.09765

Learning to Optimize Package Picking for Large-Scale, Real-World Robot Induction
Warehouse automation plays a pivotal role in enhancing operational efficiency, minimizing costs, and improving resilience to workforce variability. While prior research has demonstrated the potential of machine learning (ML) models to increase picking success rates in large-scale robotic fleets by prioritizing high-probability picks and packages, these efforts primarily focused on predicting success probabilities for picks sampled using heuristic methods. Limited attention has been given, howev…

A Heuristic Study of Temperature: Quantum Circuitry in Thermal Systems
HongZheng Liu, YiNuo Tian, Zhiyue Wu
https://arxiv.org/abs/2506.06994 https://

A Heuristic Study of Temperature: Quantum Circuitry in Thermal Systems
Classical thermodynamic singularities at phase transitions and in negative-temperature regimes arise because the equal-a-priori probability postulate overlooks the quantum dynamical complexity required to prepare microstates. We promote bounded dynamical complexity to a postulate and develop Complexity Windowed Thermodynamics (CWT). Introducing a finite complexity budget yields the windowed entropy, an entropy of ignorance that is smooth in energy and monotonically non-increasing in the budget.…

Chain-of-Alpha: Unleashing the Power of Large Language Models for Alpha Mining in Quantitative Trading
Lang Cao, Zekun Xi, Long Liao, Ziwei Yang, Zheng Cao
https://arxiv.org/abs/2508.06312

Chain-of-Alpha: Unleashing the Power of Large Language Models for Alpha Mining in Quantitative Trading
Alpha factor mining is a fundamental task in quantitative trading, aimed at discovering interpretable signals that can predict asset returns beyond systematic market risk. While traditional methods rely on manual formula design or heuristic search with machine learning, recent advances have leveraged Large Language Models (LLMs) for automated factor discovery. However, existing LLM-based alpha mining approaches remain limited in terms of automation, generality, and efficiency. In this paper, we…

EoH-S: Evolution of Heuristic Set using LLMs for Automated Heuristic Design
Fei Liu, Yilu Liu, Qingfu Zhang, Xialiang Tong, Mingxuan Yuan
https://arxiv.org/abs/2508.03082 https:…

EoH-S: Evolution of Heuristic Set using LLMs for Automated Heuristic Design
Automated Heuristic Design (AHD) using Large Language Models (LLMs) has achieved notable success in recent years. Despite the effectiveness of existing approaches, they only design a single heuristic to serve all problem instances, often inducing poor generalization across different distributions or settings. To address this issue, we propose Automated Heuristic Set Design (AHSD), a new formulation for LLM-driven AHD. The aim of AHSD is to automatically generate a small-sized complementary heur…

Optimizing Districting Plans to Maximize Majority-Minority Districts via IPs and Local Search
Daniel Brous, David Shmoys
https://arxiv.org/abs/2508.07446 https://

Optimizing Districting Plans to Maximize Majority-Minority Districts via IPs and Local Search
In redistricting litigation, effective enforcement of the Voting Rights Act has often involved providing the court with districting plans that display a larger number of majority-minority districts than the current proposal (as was true, for example, in what followed Allen v. Milligan concerning the congressional districting plan for Alabama in 2023). Recent work by Cannon et al. proposed a heuristic algorithm for generating plans to optimize majority-minority districts, which they called short…

Behaviour Space Analysis of LLM-driven Meta-heuristic Discovery
Niki van Stein, Haoran Yin, Anna V. Kononova, Thomas B\"ack, Gabriela Ochoa
https://arxiv.org/abs/2507.03605

Behaviour Space Analysis of LLM-driven Meta-heuristic Discovery
We investigate the behaviour space of meta-heuristic optimisation algorithms automatically generated by Large Language Model driven algorithm discovery methods. Using the Large Language Evolutionary Algorithm (LLaMEA) framework with a GPT o4-mini LLM, we iteratively evolve black-box optimisation heuristics, evaluated on 10 functions from the BBOB benchmark suite. Six LLaMEA variants, featuring different mutation prompt strategies, are compared and analysed. We log dynamic behavioural metrics in…

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning
Kuo Yang, Xingjie Yang, Linhui Yu, Qing Xu, Yan Fang, Xu Wang, Zhengyang Zhou, Yang Wang
https://arxiv.org/abs/2506.08507

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning
Large Language Model (LLM)-driven Multi-agent systems (Mas) have recently emerged as a powerful paradigm for tackling complex real-world tasks. However, existing Mas construction methods typically rely on manually crafted interaction mechanisms or heuristic rules, introducing human biases and constraining the autonomous ability. Even with recent advances in adaptive Mas construction, existing systems largely remain within the paradigm of semi-autonomous patterns. In this work, we propose MasHos…

A Constructive Heuristic Sieve for the Twin Prime Problem
Yuhang Shi
https://arxiv.org/abs/2507.03107 https://arxiv.org/pdf/2507.03107

A Constructive Heuristic Sieve for the Twin Prime Problem
The quantitative distribution of twin primes remains a central open problem in number theory. This paper develops a heuristic model grounded in the principles of sieve theory, with the goal of constructing an analytical approximation for the twin prime constant from first principles. The core of this method, which we term ``$f(t; z)$ function analysis'', involves representing the sieve's density product as a ratio of infinite series involving $f(t;z)$, the elementary symmetric polynomials of pr…

Synthetic Heuristic Evaluation: A Comparison between AI- and Human-Powered Usability Evaluation
Ruican Zhong, David W. McDonald, Gary Hsieh
https://arxiv.org/abs/2507.02306

Synthetic Heuristic Evaluation: A Comparison between AI- and Human-Powered Usability Evaluation
Usability evaluation is crucial in human-centered design but can be costly, requiring expert time and user compensation. In this work, we developed a method for synthetic heuristic evaluation using multimodal LLMs' ability to analyze images and provide design feedback. Comparing our synthetic evaluations to those by experienced UX practitioners across two apps, we found our evaluation identified 73% and 77% of usability issues, which exceeded the performance of 5 experienced human evaluators (5…

A Heuristic Method for Simplified Resource Allocation based on Comparative Advantage in Wireless Access Systems
Lin Cheng, Bernardo A. Huberman
https://arxiv.org/abs/2508.01824 …

A Heuristic Method for Simplified Resource Allocation based on Comparative Advantage in Wireless Access Systems
This paper presents a heuristic method for simplifying resource allocation in access systems, leveraging the concept of comparative advantage to reduce computational complexity while maintaining near-optimal performance. Using power-division non-orthogonal multiple access (PD-NOMA) as an example, we demonstrate how this approach mitigates the challenge of power allocation in multi-cell networks. Our method reduces the search space for optimization, significantly decreasing computational overhea…

ALE-Bench: A Benchmark for Long-Horizon Objective-Driven Algorithm Engineering
Yuki Imajuku, Kohki Horie, Yoichi Iwata, Kensho Aoki, Naohiro Takahashi, Takuya Akiba
https://arxiv.org/abs/2506.09050

ALE-Bench: A Benchmark for Long-Horizon Objective-Driven Algorithm Engineering
How well do AI systems perform in algorithm engineering for hard optimization problems in domains such as package-delivery routing, crew scheduling, factory production planning, and power-grid balancing? We introduce ALE-Bench, a new benchmark for evaluating AI systems on score-based algorithmic programming contests. Drawing on real tasks from the AtCoder Heuristic Contests, ALE-Bench presents optimization problems that are computationally hard and admit no known exact solution. Unlike short-du…

@… Yes, so much yes.
I have started using “a checklist / comparison table where none was necessary” as an LLM heuristic now, because there are so many terrible articles (many written before mainstream LLMs were released) which use a checklist as a replacement for any actual content, and now the machines have been trained to regurgitate them.
My thinking …

Performance of the Extended Ising Machine for the Quadratic Knapsack Problem
Haruka Akishima, Hirotaka Tamura, Kazue Kudo
https://arxiv.org/abs/2508.06909 https://

Performance of the Extended Ising Machine for the Quadratic Knapsack Problem
The extended Ising machine (EIM) enhances conventional Ising models, which handle only binary quadratic forms by allowing constraints through real-valued dependent variables. We address the quadratic knapsack problem (QKP), hard to solve using Ising machines when formulated as a quadratic unconstrained binary optimization (QUBO). We demonstrated the EIM's superiority by comparing it with the conventional Ising model-based approach, a commercial exact solver, and a state-of-the-art heuristic sol…

Pairwise similarity method for majority domination problem
N. I. Shushko, D. V. Lemtyuzhnikova
https://arxiv.org/abs/2506.08886 https://

Pairwise similarity method for majority domination problem
The paper considers the problem of finding the number of dominant voters in two-level voting procedures. At the first stage, voting is conducted among local groups of voters, and at the second stage, the results are aggregated to form a final decision. The goal is to determine the minimum proportion of voters supporting a proposal for it to be accepted. The paper uses the method of pairwise comparisons to analyze the structure of the problem and develop heuristic algorithms with guaranteed accu…

Bayesian symbolic regression: Automated equation discovery from a physicists' perspective
Roger Guimera, Marta Sales-Pardo
https://arxiv.org/abs/2507.19540 https://

Bayesian symbolic regression: Automated equation discovery from a physicists' perspective
Symbolic regression automates the process of learning closed-form mathematical models from data. Standard approaches to symbolic regression, as well as newer deep learning approaches, rely on heuristic model selection criteria, heuristic regularization, and heuristic exploration of model space. Here, we discuss the probabilistic approach to symbolic regression, an alternative to such heuristic approaches with direct connections to information theory and statistical physics. We show how the prob…

Empirical Analysis Of Heuristic and Approximation Algorithms for the The Mutual-Visibility Problem
Vanja Stojanovi\'c, Bor Panger\v{s}i\v{c}
https://arxiv.org/abs/2507.01076

Empirical Analysis Of Heuristic and Approximation Algorithms for the The Mutual-Visibility Problem
The NP-complete mutual-visibility (MV) problem currently lacks empirical analysis on its practical behaviour despite theoretical studies. This paper addresses this gap by implementing and evaluating three distinct algorithms - a direct greedy heuristic, a hypergraph-based approximation, and a genetic algorithm - on diverse synthetic graph datasets, including those with analytically known $μ(G)$ values and general graph models. Our results demonstrate that for smaller graphs, the algorithms con…

Robust Bandwidth Estimation for Real-Time Communication with Offline Reinforcement Learning
Jian Kai, Tianwei Zhang, Zihan Ling, Yang Cao, Can Shen
https://arxiv.org/abs/2507.05785

Robust Bandwidth Estimation for Real-Time Communication with Offline Reinforcement Learning
Accurate bandwidth estimation (BWE) is critical for real-time communication (RTC) systems. Traditional heuristic approaches offer limited adaptability under dynamic networks, while online reinforcement learning (RL) suffers from high exploration costs and potential service disruptions. Offline RL, which leverages high-quality data collected from real-world environments, offers a promising alternative. However, challenges such as out-of-distribution (OOD) actions, policy extraction from behavior…

Exact and Heuristic Algorithms for Constrained Biclustering
Antonio M. Sudoso
https://arxiv.org/abs/2508.05493 https://arxiv.org/pdf/2508.05493

Exact and Heuristic Algorithms for Constrained Biclustering
Biclustering, also known as co-clustering or two-way clustering, simultaneously partitions the rows and columns of a data matrix to reveal submatrices with coherent patterns. Incorporating background knowledge into clustering to enhance solution quality and interpretability has attracted growing interest in mathematical optimization and machine learning research. Extending this paradigm to biclustering enables prior information to guide the joint grouping of rows and columns. We study constrain…

Improved lower bounds on the maximum size of graphs with girth 5
Jan Goedgebeur, Jorik Jooken, Gwena\"el Joret, Tibo Van den Eede
https://arxiv.org/abs/2508.05562 https://

Improved lower bounds on the maximum size of graphs with girth 5
We present a new algorithm for improving lower bounds on $ex(n;\{C_3,C_4\})$, the maximum size (number of edges) of an $n$-vertex graph of girth at least 5. The core of our algorithm is a variant of a hill-climbing heuristic introduced by Exoo, McKay, Myrvold and Nadon (2011) to find small cages. Our algorithm considers a range of values of $n$ in multiple passes. In each pass, the hill-climbing heuristic for a specific value of $n$ is initialized with a few graphs obtained by modifying near-ex…

A Heuristic Algorithm for Shortest Path Search
Huashan Yu, Xiaolin Wang, Yingwei Luo
https://arxiv.org/abs/2506.19349 https://arxiv.o…

A Heuristic Algorithm for Shortest Path Search
The Single-Source Shortest Path (SSSP) problem is well-known for the challenges in developing fast, practical, and work-efficient parallel algorithms. This work introduces a novel shortest path search method. It allows paths with different lengths to be extended in parallel at the cost of almost negligible repeated relaxations. A dynamic-stepping heuristic is proposed for the method to efficiently reduce the extended paths and the synchronizations. A traversal-optimization heuristic is proposed…

Heuristic Bounded Prime Gaps via a Chaotic Multidimensional Sieve and Random Matrix Theory
Milad Ghadimi
https://arxiv.org/abs/2507.17986 https://arxiv.org…

Heuristic Bounded Prime Gaps via a Chaotic Multidimensional Sieve and Random Matrix Theory
We present the Enhanced Multidimensional Chaotic Heuristic Sieve (EMCHS), a novel probabilistic framework that integrates chaotic perturbations and random matrix theory (RMT) to suggest improved bounds on prime gaps. Building upon the foundational sieves of Goldston-Pintz-Yildirim and Maynard, EMCHS heuristically suggests unconditional gaps of at most 180 and conditional gaps of at most 8 under a partial Elliott-Halberstam conjecture (EHC) with delta = 0.3. These heuristic suggestions surpass M…

An End-to-End Multi-objective Ensemble Ranking Framework for Video Recommendation
Tiantian He, Minzhi Xie, Runtong Li, Xiaoxiao Xu, Jiaqi Yu, Zixiu Wang, Lantao Hu, Han Li, Kun Gai
https://arxiv.org/abs/2508.05093

An End-to-End Multi-objective Ensemble Ranking Framework for Video Recommendation
We propose a novel End-to-end Multi-objective Ensemble Ranking framework (EMER) for the multi-objective ensemble ranking module, which is the most critical component of the short video recommendation system. EMER enhances personalization by replacing manually-designed heuristic formulas with an end-to-end modeling paradigm. EMER introduces a meticulously designed loss function to address the fundamental challenge of defining effective supervision for ensemble ranking, where no single ground-tru…

Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance
Luciano Ribeiro Galv\~ao, Rafael de Andrade Moral
https://arxiv.org/abs/2506.05609

Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance
In this study, machine learning models were tested to predict whether or not a customer of an insurance company would purchase a travel insurance product. For this purpose, secondary data provided by an open-source website that compiles databases from statistical modeling competitions were used. The dataset used presents approximately 2,700 records from an unidentified company in the tourism insurance sector. Initially, the feature engineering stage was carried out, which were selected through …

Replaced article(s) found for cs.PF. https://arxiv.org/list/cs.PF/new
[1/1]:
- Empirical Analysis Of Heuristic and Approximation Algorithms for the The Mutual-Visibility Problem
Vanja Stojanovi\'c, Bor Panger\v{s}i\v{c}

Wearable Music2Emotion : Assessing Emotions Induced by AI-Generated Music through Portable EEG-fNIRS Fusion
Sha Zhao, Song Yi, Yangxuan Zhou, Jiadong Pan, Jiquan Wang, Jie Xia, Shijian Li, Shurong Dong, Gang Pan
https://arxiv.org/abs/2508.04723

Wearable Music2Emotion : Assessing Emotions Induced by AI-Generated Music through Portable EEG-fNIRS Fusion
Emotions critically influence mental health, driving interest in music-based affective computing via neurophysiological signals with Brain-computer Interface techniques. While prior studies leverage music's accessibility for emotion induction, three key limitations persist: \textbf{(1) Stimulus Constraints}: Music stimuli are confined to small corpora due to copyright and curation costs, with selection biases from heuristic emotion-music mappings that ignore individual affective profiles. \text…

LLM-Driven Instance-Specific Heuristic Generation and Selection
Shaofeng Zhang, Shengcai Liu, Ning Lu, Jiahao Wu, Ji Liu, Ke Tang
https://arxiv.org/abs/2506.00490

LLM-Driven Instance-Specific Heuristic Generation and Selection
Combinatorial optimization problems are widely encountered in real-world applications. Designing high-quality heuristic algorithms that efficiently approximate optimal solutions within reasonable time is a critical research challenge. In recent years, many works have explored integrating Large Language Models (LLMs) with Evolutionary Algorithms to automate heuristic algorithm design through prompt engineering. However, these approaches generally adopt a problem-specific paradigm, applying a sin…

Meta-heuristic Hypergraph-Assisted Robustness Optimization for Higher-order Complex Systems
Xilong Qu, Wenbin Pei, Haifang Li, Qiang Zhang, Bing Xue, Mengjie Zhang, del Genio Charo, Stefano Boccaletti
https://arxiv.org/abs/2505.24123

Meta-heuristic Hypergraph-Assisted Robustness Optimization for Higher-order Complex Systems
In complex systems (e.g., communication, transportation, and biological networks), high robustness ensures sustained functionality and stability even when resisting attacks. However, the inherent structure complexity and the unpredictability of attacks make robustness optimization challenging. Hypergraphs provide a framework for modeling complicated higher-order interactions in complex systems naturally, but their potential has not been systematically investigated. Therefore, we propose an effe…

This https://arxiv.org/abs/2502.01907 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Robust Cislunar Low-Thrust Trajectory Optimization under Uncertainties via Sequential Covariance Steering
Spacecraft operations are influenced by uncertainties such as dynamics modeling, navigation, and maneuver execution errors. Although mission design has traditionally incorporated heuristic safety margins to mitigate the effect of uncertainties, particularly before/after crucial events, it is yet unclear whether this practice will scale in the cislunar region, which features locally chaotic nonlinear dynamics and involves frequent lunar flybys. This paper applies chance-constrained covariance st…

Dynamically Adaptive Reasoning via LLM-Guided MCTS for Efficient and Context-Aware KGQA
Yingxu Wang, Shiqi Fan, Mengzhu Wang, Siwei Liu
https://arxiv.org/abs/2508.00719 https://…

Dynamically Adaptive Reasoning via LLM-Guided MCTS for Efficient and Context-Aware KGQA
Knowledge Graph Question Answering (KGQA) aims to interpret natural language queries and perform structured reasoning over knowledge graphs by leveraging their relational and semantic structures to retrieve accurate answers. Recent KGQA methods primarily follow either retrieve-then-reason paradigm, relying on GNNs or heuristic rules for static paths extraction, or dynamic path generation strategies that use large language models (LLMs) with prompting to jointly perform retrieval and reasoning. …

PlanTUS: A heuristic tool for prospective planning of transcranial ultrasound transducer placements
Maximilian Lueckel, Suhas Vijayakumar, Til Ole Bergmann
https://arxiv.org/abs/2506.22563

PlanTUS: A heuristic tool for prospective planning of transcranial ultrasound transducer placements
Low-intensity focused transcranial ultrasonic stimulation (TUS) offers unique depth and precision in non-invasive brain stimulation. Effective administration of TUS, however, requires precise placement of transducers to ensure selective neural target exposure and engagement. This process is constrained by individual skull anatomy, hardware limitations, as well as computational and time costs of running exhaustive acoustic simulations to optimize transducer placement. To address these challenges…

The symbolic partition with generalized Koopman analysis
Haipeng Li, Pengfei Guo, Yueheng Lan
https://arxiv.org/abs/2506.06973 https://

The symbolic partition with generalized Koopman analysis
Symbolic dynamics serves as a crucial tool in the study of chaotic systems, prompting extensive research into various methods for symbolic partitioning. The limitations of these methods are heuristic and empirical for the partition the multivariate chaotic state space. Notably, the use of operator theory in partitioning the multivariable chaotic series into precise symbolic cells has been underexplored. In this paper, we propose a novel symbolic partition method, referred to as Koopman Analysis…

A Hierarchical Bin Packing Framework with Dual Manipulators via Heuristic Search and Deep Reinforcement Learning
Beomjoon Lee, Changjoo Nam
https://arxiv.org/abs/2506.01628

A Hierarchical Bin Packing Framework with Dual Manipulators via Heuristic Search and Deep Reinforcement Learning
We address the bin packing problem (BPP), which aims to maximize bin utilization when packing a variety of items. The offline problem, where the complete information about the item set and their sizes is known in advance, is proven to be NP-hard. The semi-online and online variants are even more challenging, as full information about incoming items is unavailable. While existing methods have tackled both 2D and 3D BPPs, the 2D BPP remains underexplored in terms of fully maximizing utilization. …

Bayesian inference of neutron star crust properties using an ab initio-benchmarked meta-model
S. Burrello, F. Gulminelli, M. Antonelli, M. Colonna, A. Fantina
https://arxiv.org/abs/2506.05603

Bayesian inference of neutron star crust properties using an ab initio-benchmarked meta-model
Accurate modeling of the neutron star crust is essential for interpreting multimessenger observations and constraining the nuclear equation of state (EoS). However, standard phenomenological EoS models often rely on heuristic extrapolations in the low-density regime, which are inconsistent with microscopic predictions. In this work, we refine a unified meta-modeling framework for the EoS by incorporating low-density corrections based on energy density functionals constrained by ab initio neutro…

Critical Design Strategy: a Method for Heuristically Evaluating Visualisation Designs
Jonathan C. Roberts, Hanan Alnjar, Aron E. Owen, Panagiotis D. Ritsos
https://arxiv.org/abs/2508.05325

Critical Design Strategy: a Method for Heuristically Evaluating Visualisation Designs
We present the Critical Design Strategy (CDS) - a structured method designed to facilitate the examination of visualisation designs through reflection and critical thought. The CDS helps designers think critically and make informed improvements using heuristic evaluation. When developing a visual tool or pioneering a novel visualisation approach, identifying areas for enhancement can be challenging. Critical thinking is particularly crucial for visualisation designers and tool developers, espec…

This https://arxiv.org/abs/2409.14202 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

Mining Causality: AI-Assisted Search for Instrumental Variables
The instrumental variables (IVs) method is a leading empirical strategy for causal inference. Finding IVs is a heuristic and creative process, and justifying its validity -- especially exclusion restrictions -- is largely rhetorical. We propose using large language models (LLMs) to search for new IVs through narratives and counterfactual reasoning, similar to how a human researcher would. The stark difference, however, is that LLMs can dramatically accelerate this process and explore an extreme…

A Frank-Wolfe-based primal heuristic for quadratic mixed-integer optimization
Gioni Mexi, Deborah Hendrych, S\'ebastien Designolle, Mathieu Besan\c{c}on, Sebastian Pokutta
https://arxiv.org/abs/2508.01299

A Frank-Wolfe-based primal heuristic for quadratic mixed-integer optimization
We propose a primal heuristic for quadratic mixed-integer problems. Our method extends the Boscia framework -- originally a mixed-integer convex solver leveraging a Frank-Wolfe-based branch-and-bound approach -- to address nonconvex quadratic objective and constraints. We reformulate nonlinear constraints, introduce preprocessing steps, and a suite of heuristics including rounding strategies, gradient-guided selection, and large neighborhood search techniques that exploit integer-feasible verti…

Meta-heuristic design of a light-weight homologous backup structure of the primary reflector for the Large Submillimeter Telescope
Chihiro Imamura, Yoichi Tamura, Hiroaki Kawamura, Toshiaki Kimura, Akio Taniguchi, Mikio Kurita
#toXiv_bot_toot

An Efficient Detector for Faulty GNSS Measurements Detection With Non-Gaussian Noises
Penggao Yan, Baoshan Song, Xiao Xia, Weisong Wen, Li-Ta Hsu
https://arxiv.org/abs/2507.03987 …

An Efficient Detector for Faulty GNSS Measurements Detection With Non-Gaussian Noises
Fault detection is crucial to ensure the reliability of navigation systems. However, mainstream fault detection methods are developed based on Gaussian assumptions on nominal errors, while current attempts at non-Gaussian fault detection are either heuristic or lack rigorous statistical properties. The performance and reliability of these methods are challenged in real-world applications. This paper proposes the jackknife detector, a fault detection method tailored for linearized pseudorange-ba…

This https://arxiv.org/abs/2506.00490 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNE_…

LLM-Driven Instance-Specific Heuristic Generation and Selection
Combinatorial optimization problems are widely encountered in real-world applications. Designing high-quality heuristic algorithms that efficiently approximate optimal solutions within reasonable time is a critical research challenge. In recent years, many works have explored integrating Large Language Models (LLMs) with Evolutionary Algorithms to automate heuristic algorithm design through prompt engineering. However, these approaches generally adopt a problem-specific paradigm, applying a sin…

This https://arxiv.org/abs/2410.17704 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hept…

Fermion Masses and Mixing in String-Inspired Models
We study a class of supersymmetric Froggatt-Nielsen (FN) models with multiple U(1) symmetries and Standard Model (SM) singlets inspired by heterotic string compactifications on Calabi-Yau threefolds. The string-theoretic origin imposes a particular charge pattern on the SM fields and FN singlets, dividing the latter into perturbative and non-perturbative types. Employing systematic and heuristic search strategies, such as genetic algorithms, we identify charge assignments and singlet VEVs that …

LLM Meets the Sky: Heuristic Multi-Agent Reinforcement Learning for Secure Heterogeneous UAV Networks
Lijie Zheng, Ji He, Shih Yu Chang, Yulong Shen, Dusit Niyato
https://arxiv.org/abs/2507.17188

LLM Meets the Sky: Heuristic Multi-Agent Reinforcement Learning for Secure Heterogeneous UAV Networks
This work tackles the physical layer security (PLS) problem of maximizing the secrecy rate in heterogeneous UAV networks (HetUAVNs) under propulsion energy constraints. Unlike prior studies that assume uniform UAV capabilities or overlook energy-security trade-offs, we consider a realistic scenario where UAVs with diverse payloads and computation resources collaborate to serve ground terminals in the presence of eavesdroppers. To manage the complex coupling between UAV motion and communication,…

Gauge theory approach to describe ice crystals habit evolution in ice clouds
Gianluca Di Natale, Francesco Pio De Cosmo, Leandro Cieri
https://arxiv.org/abs/2508.02472 https://

Gauge theory approach to describe ice crystals habit evolution in ice clouds
Ice clouds, particularly cirrus clouds, significantly influence Earth's radiative balance but remain poorly characterized in current climate models. A major uncertainty arises from the variability of their microphysical properties, especially the evolution of ice crystal habits under depositional growth. We propose a heuristic method to describe habit evolution based on four fundamental shapes identified in the literature and from in situ observations: droxtals, plates, columns, and rosettes. T…

Automated anatomy-based post-processing reduces false positives and improved interpretability of deep learning intracranial aneurysm detection
Jisoo Kim, Chu-Hsuan Lin, Alberto Ceballos-Arroyo, Ping Liu, Huaizu Jiang, Shrikanth Yadav, Qi Wan, Lei Qin, Geoffrey S Young
https://arxiv.org/abs/2507.00832

Automated anatomy-based post-processing reduces false positives and improved interpretability of deep learning intracranial aneurysm detection
Introduction: Deep learning (DL) models can help detect intracranial aneurysms on CTA, but high false positive (FP) rates remain a barrier to clinical translation, despite improvement in model architectures and strategies like detection threshold tuning. We employed an automated, anatomy-based, heuristic-learning hybrid artery-vein segmentation post-processing method to further reduce FPs. Methods: Two DL models, CPM-Net and a deformable 3D convolutional neural network-transformer hybrid (3D-CN…

Greedy recursion parameter selection for the One-Way Navier-Stokes (OWNS) equations
Michael K. Sleeman, Tim Colonius
https://arxiv.org/abs/2506.02320 https…

Greedy recursion parameter selection for the One-Way Navier-Stokes (OWNS) equations
The One-Way Navier-Stokes (OWNS) equations use recursive filtering to construct efficient, well-posed one-way approximations to linear hyperbolic systems. The recursion parameters are critical to the accuracy and stability of the method, and have previously been chosen based on heuristic estimates of key eigenvalues (or their branches), which converges slowly and requires trial-and-error tuning for new systems. We review the projection and recursive OWNS formulations (OWNS-P and OWNS-R) for inh…

This https://arxiv.org/abs/2504.21131 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

A Formalism for Optimal Search with Dynamic Heuristics (Extended Version)
While most heuristics studied in heuristic search depend only on the state, some accumulate information during search and thus also depend on the search history. Various existing approaches use such dynamic heuristics in $\mathrm{A}^*$-like algorithms and appeal to classic results for $\mathrm{A}^*$ to show optimality. However, doing so ignores the complexities of searching with a mutable heuristic. In this paper we formalize the idea of dynamic heuristics and use them in a generic algorithm fr…

Deep Learning Framework Testing via Heuristic Guidance Based on Multiple Model Measurements
Yinglong Zou, Juan Zhai, Chunrong Fang, Yanzhou Mu, Jiawei Liu, Zhenyu Chen
https://arxiv.org/abs/2507.15181

Deep Learning Framework Testing via Heuristic Guidance Based on Multiple Model Measurements
Deep learning frameworks serve as the foundation for developing and deploying deep learning applications. To enhance the quality of deep learning frameworks, researchers have proposed numerous testing methods using deep learning models as test inputs. However, existing methods predominantly measure model bug detection effectiveness as heuristic indicators, presenting three critical limitations: Firstly, existing methods fail to quantitatively measure model's operator combination variety, potent…

Block: Balancing Load in LLM Serving with Context, Knowledge and Predictive Scheduling
Wei Da, Evangelia Kalyvianaki
https://arxiv.org/abs/2508.03611 https://

Block: Balancing Load in LLM Serving with Context, Knowledge and Predictive Scheduling
This paper presents Block, a distributed scheduling framework designed to optimize load balancing and auto-provisioning across instances in large language model serving frameworks by leveraging contextual information from incoming requests. Unlike popular model serving systems that rely on monolithic and heuristic task schedulers, Block operates as a fully distributed, stateless, and predictive scheduling system to achieve low overhead, reliability, and scalability. It leverages the determinist…

Can GPT-4o Evaluate Usability Like Human Experts? A Comparative Study on Issue Identification in Heuristic Evaluation
Guilherme Guerino, Luiz Rodrigues, Bruna Capeleti, Rafael Ferreira Mello, Andr\'e Freire, Luciana Zaina
https://arxiv.org/abs/2506.16345

Can GPT-4o Evaluate Usability Like Human Experts? A Comparative Study on Issue Identification in Heuristic Evaluation
Heuristic evaluation is a widely used method in Human-Computer Interaction (HCI) to inspect interfaces and identify issues based on heuristics. Recently, Large Language Models (LLMs), such as GPT-4o, have been applied in HCI to assist in persona creation, the ideation process, and the analysis of semi-structured interviews. However, considering the need to understand heuristics and the high degree of abstraction required to evaluate them, LLMs may have difficulty conducting heuristic evaluation…

A Hybrid Heuristic Framework for Resource-Efficient Querying of Scientific Experiments Data
Mayank Patel, Minal Bhise
https://arxiv.org/abs/2506.10422 http…

A Hybrid Heuristic Framework for Resource-Efficient Querying of Scientific Experiments Data
Scientific experiments and modern applications are generating large amounts of data every day. Most organizations utilize In-house servers or Cloud resources to manage application data and workload. The traditional database management system (DBMS) and HTAP systems spend significant time & resources to load the entire dataset into DBMS before starting query execution. On the other hand, in-situ engines may reparse required data multiple times, increasing resource utilization and data processing…

Context-Aware Behavior Learning with Heuristic Motion Memory for Underwater Manipulation
Markus Buchholz, Ignacio Carlucho, Michele Grimaldi, Maria Koskinopoulou, Yvan R. Petillot
https://arxiv.org/abs/2507.14099

Context-Aware Behavior Learning with Heuristic Motion Memory for Underwater Manipulation
Autonomous motion planning is critical for efficient and safe underwater manipulation in dynamic marine environments. Current motion planning methods often fail to effectively utilize prior motion experiences and adapt to real-time uncertainties inherent in underwater settings. In this paper, we introduce an Adaptive Heuristic Motion Planner framework that integrates a Heuristic Motion Space (HMS) with Bayesian Networks to enhance motion planning for autonomous underwater manipulation. Our appr…

High dimensional convergence rates for sparse precision estimators for matrix-variate data
Hongqiang Sun, Kshitij Khare
https://arxiv.org/abs/2507.01741 ht…

High dimensional convergence rates for sparse precision estimators for matrix-variate data
In several applications, the underlying structure of the data allows for the samples to be organized into a matrix variate form. In such settings, the underlying row and column covariance matrices are fundamental quantities of interest. We focus our attention on two popular estimators that have been proposed in the literature: a penalized sparse estimator called SMGM and a heuristic sample covariance estimator. We establish convergence rates for these estimators in relevant high-dimensional set…

High-Layer Attention Pruning with Rescaling
Songtao Liu, Peng Liu
https://arxiv.org/abs/2507.01900 https://arxiv.org/pdf/2507.01900…

High-Layer Attention Pruning with Rescaling
Pruning is a highly effective approach for compressing large language models (LLMs), significantly reducing inference latency. However, conventional training-free structured pruning methods often employ a heuristic metric that indiscriminately removes some attention heads across all pruning layers, without considering their positions within the network architecture. In this work, we propose a novel pruning algorithm that strategically prunes attention heads in the model's higher layers. Since t…

Improved belief propagation is sufficient for real-time decoding of quantum memory
Tristan M\"uller, Thomas Alexander, Michael E. Beverland, Markus B\"uhler, Blake R. Johnson, Thilo Maurer, Drew Vandeth
https://arxiv.org/abs/2506.01779

Improved belief propagation is sufficient for real-time decoding of quantum memory
We introduce a new heuristic decoder, Relay-BP, targeting real-time quantum circuit decoding for large-scale quantum computers. Relay-BP achieves high accuracy across circuit-noise decoding problems: significantly outperforming BP+OSD+CS-10 for bivariate-bicycle codes and comparable to min-weight-matching for surface codes. As a lightweight message-passing decoder, Relay-BP is inherently parallel, enabling rapid low-footprint decoding with FPGA or ASIC real-time implementations, similar to stan…

This https://arxiv.org/abs/2303.17475 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Learning distributed representations with efficient SoftMax normalization
Learning distributed representations, or embeddings, that encode the relational similarity patterns among objects is a relevant task in machine learning. A popular method to learn the embedding matrices $X, Y$ is optimizing a loss function of the term ${\rm SoftMax}(XY^T)$. The complexity required to calculate this term, however, runs quadratically with the problem size, making it a computationally heavy solution. In this article, we propose a linear-time heuristic approximation to compute the …

Replaced article(s) found for cs.SI. https://arxiv.org/list/cs.SI/new
[1/1]:
- Design of A* based heuristic algorithm for efficient interdiction in multi-Layer networks
Sukanya Samanta

Modelling Nonstationary Time Series using Trend-Stationary Hypothesis
Zhandos Abdikhadir
https://arxiv.org/abs/2506.07987 https://arx…

Modelling Nonstationary Time Series using Trend-Stationary Hypothesis
This paper challenges the prevalence of unit root models by introducing the Linear Trend-Stationary Trigonometric ARMA (LTSTA), a novel framework for modelling nonstationary time series under the trend-stationary hypothesis. LTSTA decomposes series into three components: (1) a deterministic trend (modelled via continuous piecewise linear functions with structural breaks), (2) a Fourier-based deterministic seasonality component, and (3) a stochastic ARMA error term. We propose a heuristic approa…

Eulerian-Lagrangian scaling of the Lyapunov exponent in homogeneous turbulence
Jin Ge, Joran Rolland, John Christos Vassilicos
https://arxiv.org/abs/2507.19972 https://

Eulerian-Lagrangian scaling of the Lyapunov exponent in homogeneous turbulence
We present a heuristic derivation of the maximal Lyapunov exponent $γ$ of homogeneous turbulence which yields two new relations, a sweeping relation and the scaling of the uncertainty field's integral length $L_Δ$. These relations and the maximal Lyapunov exponent's scaling that they imply are confirmed by periodic turbulence simulations. As the Reynolds number $Re_\lammbda$ increases, $L_Δ$ and $γ^{-1}$ decrease towards values smaller than the Kolmogorov length and time scales.

A Novel Hybrid Grey Wolf Differential Evolution Algorithm
Ioannis D. Bougas, Pavlos Doanis, Maria S. Papadopoulou, Achilles D. Boursianis, Sotirios P. Sotiroudis, Zaharias D. Zaharis, George Koudouridis, Panagiotis Sarigiannidis, Mohammad Abdul Matint, George Karagiannidis, Sotirios K. Goudos
https://arxiv.org/abs/2507.03022

A Novel Hybrid Grey Wolf Differential Evolution Algorithm
Grey wolf optimizer (GWO) is a nature-inspired stochastic meta-heuristic of the swarm intelligence field that mimics the hunting behavior of grey wolves. Differential evolution (DE) is a popular stochastic algorithm of the evolutionary computation field that is well suited for global optimization. In this part, we introduce a new algorithm based on the hybridization of GWO and two DE variants, namely the GWO-DE algorithm. We evaluate the new algorithm by applying various numerical benchmark fun…

Graph Contrastive Learning for Optimizing Sparse Data in Recommender Systems with LightGCL
Aravinda Jatavallabha, Prabhanjan Bharadwaj, Ashish Chander
https://arxiv.org/abs/2506.00048

Graph Contrastive Learning for Optimizing Sparse Data in Recommender Systems with LightGCL
Graph Neural Networks (GNNs) are powerful tools for recommendation systems, but they often struggle under data sparsity and noise. To address these issues, we implemented LightGCL, a graph contrastive learning model that uses Singular Value Decomposition (SVD) for robust graph augmentation, preserving semantic integrity without relying on stochastic or heuristic perturbations. LightGCL enables structural refinement and captures global collaborative signals, achieving significant gains over stat…

VISCA: Inferring Component Abstractions for Automated End-to-End Testing
Parsa Alian, Martin Tang, Ali Mesbah
https://arxiv.org/abs/2506.04161 https://

VISCA: Inferring Component Abstractions for Automated End-to-End Testing
Providing optimal contextual input presents a significant challenge for automated end-to-end (E2E) test generation using large language models (LLMs), a limitation that current approaches inadequately address. This paper introduces Visual-Semantic Component Abstractor (VISCA), a novel method that transforms webpages into a hierarchical, semantically rich component abstraction. VISCA starts by partitioning webpages into candidate segments utilizing a novel heuristic-based segmentation method. Th…

Evaluating the Efficacy of LLM-Based Reasoning for Multiobjective HPC Job Scheduling
Prachi Jadhav, Hongwei Jin, Ewa Deelman, Prasanna Balaprakash
https://arxiv.org/abs/2506.02025

Evaluating the Efficacy of LLM-Based Reasoning for Multiobjective HPC Job Scheduling
High-Performance Computing (HPC) job scheduling involves balancing conflicting objectives such as minimizing makespan, reducing wait times, optimizing resource use, and ensuring fairness. Traditional methods, including heuristic-based (e.g., First-Come-First-Served) or intensive optimization techniques, often lack adaptability to dynamic workloads and heterogeneous HPC systems. To address this, we propose a novel Large Language Model (LLM)-based scheduler using a ReAct-style framework (Reason +…

Exploiting Euclidean Distance Field Properties for Fast and Safe 3D planning with a modified Lazy Theta*
Jose A. Cobano, L. Merino, F. Caballero
https://arxiv.org/abs/2505.24024

Exploiting Euclidean Distance Field Properties for Fast and Safe 3D planning with a modified Lazy Theta*
Graph search planners have been widely used for 3D path planning in the literature, and Euclidean Distance Fields (EDFs) are increasingly being used as a representation of the environment. However, to the best of our knowledge, the integration of EDFs into heuristic planning has been carried out in a loosely coupled fashion, dismissing EDF properties that can be used to accelerate/improve the planning process and enhance the safety margins of the resultant trajectories. This paper presents a fa…

Exact operator inference with minimal data
Henrik Rosenberger, Benjamin Sanderse, Giovanni Stabile
https://arxiv.org/abs/2506.01244 https://

Exact operator inference with minimal data
This work introduces a novel method to generate snapshot data for operator inference that guarantees the exact reconstruction of intrusive projection-based reduced-order models (ROMs). To ensure exact reconstruction, the operator inference least squares matrix must have full rank, without regularization. Existing works have achieved this full rank using heuristic strategies to generate snapshot data and a-posteriori checks on full rank, but without a guarantee of success. Our novel snapshot dat…

An Improved Grey Wolf Optimizer Inspired by Advanced Cooperative Predation for UAV Shortest Path Planning
Zuhao Teng, Qian Dong, Ze Zhang, Shuangyao Huang, Wenzhang Zhang, Jingchen Wang, Ji Li, Xi Chen
https://arxiv.org/abs/2506.03663

An Improved Grey Wolf Optimizer Inspired by Advanced Cooperative Predation for UAV Shortest Path Planning
With the widespread application of Unmanned Aerial Vehicles (UAVs) in domains like military reconnaissance, emergency rescue, and logistics delivery, efficiently planning the shortest flight path has become a critical challenge. Traditional heuristic-based methods often suffer from the inability to escape from local optima, which limits their effectiveness in finding the shortest path. To address these issues, a novel Improved Grey Wolf Optimizer (IGWO) is presented in this study. The proposed …

A High-Performance Multilevel Framework for Quantum Layout Synthesis
Shuohao Ping, Naren Sathishkumar, Wan-Hsuan Lin, Hanyu Wang, Jason Cong
https://arxiv.org/abs/2505.24169

A High-Performance Multilevel Framework for Quantum Layout Synthesis
Quantum Layout Synthesis (QLS) is a critical compilation stage that adapts quantum circuits to hardware constraints with an objective of minimizing the SWAP overhead. While heuristic tools demonstrate good efficiency, they often produce suboptimal solutions, and exact methods suffer from limited scalability. In this work, we propose ML-SABRE, a high-performance multilevel framework for QLS that improves both solution quality and compilation time through a hierarchical optimization approach. We …

Simple Approximations for General Spanner Problems
Fritz B\"okler, Markus Chimani, Henning Jasper
https://arxiv.org/abs/2506.23638 https://

Simple Approximations for General Spanner Problems
Consider a graph with n nodes and m edges, independent edge weights and lengths, and arbitrary distance demands for node pairs. The spanner problem asks for a minimum-weight subgraph that satisfies these demands via sufficiently short paths w.r.t. the edge lengths. For multiplicative alpha-spanners (where demands equal alpha times the original distances) and assuming that each edge's weight equals its length, the simple Greedy heuristic by Althöfer et al. (1993) is known to yield strong soluti…

This https://arxiv.org/abs/2506.02576 has been replaced.
link: https://scholar.google.com/scholar?q=a

ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting
Passenger demand forecasting helps optimize vehicle scheduling, thereby improving urban efficiency. Recently, attention-based methods have been used to adequately capture the dynamic nature of spatio-temporal data. However, existing methods that rely on heuristic masking strategies cannot fully adapt to the complex spatio-temporal correlations, hindering the model from focusing on the right context. These works also overlook the high-level correlations that exist in the real world. Effectively …

AdapSCA-PSO: An Adaptive Localization Algorithm with AI-Based Hybrid SCA-PSO for IoT WSNs
Ze Zhang, Qian Dong, Wenhan Wang
https://arxiv.org/abs/2507.22317 https://

AdapSCA-PSO: An Adaptive Localization Algorithm with AI-Based Hybrid SCA-PSO for IoT WSNs
The accurate localization of sensor nodes is a fundamental requirement for the practical application of the Internet of Things (IoT). To enable robust localization across diverse environments, this paper proposes a hybrid meta-heuristic localization algorithm. Specifically, the algorithm integrates the Sine Cosine Algorithm (SCA), which is effective in global search, with Particle Swarm Optimization (PSO), which excels at local search. An adaptive switching module is introduced to dynamically s…

Design of A* based heuristic algorithm for efficient interdiction in multi-Layer networks
Sukanya Samanta
https://arxiv.org/abs/2506.10017 https://

Design of A* based heuristic algorithm for efficient interdiction in multi-Layer networks
Intercepting a criminal using limited police resources presents a significant challenge in dynamic crime environments, where the criminal's location continuously changes over time. The complexity is further heightened by the vastness of the transportation network. To tackle this problem, we propose a layered graph representation, in which each time step is associated with a duplicate of the transportation network. For any given set of attacker strategies, a near-optimal defender strategy is com…

A Hierarchical Constructive Heuristic for Large-Scale Survivable Traffic Grooming Problem under Double-Link Failures
Silong Zhang, Jixuan Feng, Junyan Liu, Yu Liu, Zhou Xu, Fan Zhang
https://arxiv.org/abs/2506.13074

A Hierarchical Constructive Heuristic for Large-Scale Survivable Traffic Grooming Problem under Double-Link Failures
This paper studies a survivable traffic grooming problem in large-scale optical transport networks under double-link failures (STG2). Each communication demand must be assigned a route for every possible scenario involving zero, one, or two failed fiber links. Protection against double-link failures is crucial for ensuring reliable telecommunications services while minimizing equipment costs, making it essential for telecommunications companies today. However, this significantly complicates the…

This https://arxiv.org/abs/2403.18213 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Long-Term Open-Pit Mine Planning with Large Neighbourhood Search
We present a Large Neighbourhood Search based approach for solving complex long-term open-pit mine planning problems. An initial feasible solution, generated by a sliding windows heuristic, is improved through repeated solves of a restricted mixed-integer program. Each iteration leaves only a subset of the variables in the planning model free to take on new values. We form these subsets through the use of neighbourhood formation strategies that exploit model structure. We show that our approach…

Learnable Adaptive Time-Frequency Representation via Differentiable Short-Time Fourier Transform
Maxime Leiber, Yosra Marnissi, Axel Barrau, Sylvain Meignen, Laurent Massouli\'e
https://arxiv.org/abs/2506.21440

Learnable Adaptive Time-Frequency Representation via Differentiable Short-Time Fourier Transform
The short-time Fourier transform (STFT) is widely used for analyzing non-stationary signals. However, its performance is highly sensitive to its parameters, and manual or heuristic tuning often yields suboptimal results. To overcome this limitation, we propose a unified differentiable formulation of the STFT that enables gradient-based optimization of its parameters. This approach addresses the limitations of traditional STFT parameter tuning methods, which often rely on computationally intensi…

Context-Aware Code Wiring Recommendation with LLM-based Agent
Taiming Wang, Yanjie Jiang, Chunhao Dong, Yuxia Zhang, Hui Liu
https://arxiv.org/abs/2507.01315

Context-Aware Code Wiring Recommendation with LLM-based Agent
Copy-paste-modify is a widespread and pragmatic practice in software development, where developers adapt reused code snippets, sourced from platforms such as Stack Overflow, GitHub, or LLM outputs, into their local codebase. A critical yet underexplored aspect of this adaptation is code wiring, which involves substituting unresolved variables in the pasted code with suitable ones from the surrounding context. Existing solutions either rely on heuristic rules or historical templates, often faili…

Quantum Imaginary-Time Evolution with Polynomial Resources in Time
Lei Zhang, Jizhe Lai, Xian Wu, Xin Wang
https://arxiv.org/abs/2507.00908 https://…

Quantum Imaginary-Time Evolution with Polynomial Resources in Time
Imaginary-time evolution is fundamental to analyzing quantum many-body systems, yet classical simulation requires exponentially growing resources in both system size and evolution time. While quantum approaches reduce the system-size scaling, existing methods rely on heuristic techniques with measurement precision or success probability that deteriorates as evolution time increases. We present a quantum algorithm that prepares normalized imaginary-time evolved states using an adaptive normaliza…

COIN: Uncertainty-Guarding Selective Question Answering for Foundation Models with Provable Risk Guarantees
Zhiyuan Wang, Jinhao Duan, Qingni Wang, Xiaofeng Zhu, Tianlong Chen, Xiaoshuang Shi, Kaidi Xu
https://arxiv.org/abs/2506.20178

COIN: Uncertainty-Guarding Selective Question Answering for Foundation Models with Provable Risk Guarantees
Uncertainty quantification (UQ) for foundation models is essential to identify and mitigate potential hallucinations in automatically generated text. However, heuristic UQ approaches lack formal guarantees for key metrics such as the false discovery rate (FDR) in selective prediction. Previous work adopts the split conformal prediction (SCP) framework to ensure desired coverage of admissible answers by constructing prediction sets, but these sets often contain incorrect candidates, limiting the…

Route Optimization Over Scheduled Services For Large-Scale Package Delivery Networks
Mohammed Faisal Ahmed, Pascal Van Hentenryck, Ahmed El Nashar
https://arxiv.org/abs/2507.20844

Route Optimization Over Scheduled Services For Large-Scale Package Delivery Networks
This paper introduces the Trailer Path Optimization with Schedule Services Problem (TPOSSP) and proposes a column- generation heuristic (CG-heuristic) to find high-quality solutions to large-scale instances. The TPOSSP aims at determining trailer routes over a time-dependent network using existing scheduled services, while considering tractor capacity constraints and time windows for trailer pickups and deliveries. The objective is to minimize both the number of schedules used and the total mil…

Prompt Smart, Pay Less: Cost-Aware APO for Real-World Applications
Jayesh Choudhari, Piyush Kumar Singh, Douglas McIlwraith, Snehal Nair
https://arxiv.org/abs/2507.15884

Prompt Smart, Pay Less: Cost-Aware APO for Real-World Applications
Prompt design is a critical factor in the effectiveness of Large Language Models (LLMs), yet remains largely heuristic, manual, and difficult to scale. This paper presents the first comprehensive evaluation of Automatic Prompt Optimization (APO) methods for real-world, high-stakes multiclass classification in a commercial setting, addressing a critical gap in the existing literature where most of the APO frameworks have been validated only on benchmark classification tasks of limited complexity…

Speeding Up Hyper-Heuristics With Markov-Chain Operator Selection and the Only-Worsening Acceptance Operator
Abderrahim Bendahi, Benjamin Doerr, Adrien Fradin, Johannes F. Lutzeyer
https://arxiv.org/abs/2506.01107

Speeding Up Hyper-Heuristics With Markov-Chain Operator Selection and the Only-Worsening Acceptance Operator
The move-acceptance hyper-heuristic was recently shown to be able to leave local optima with astonishing efficiency (Lissovoi et al., Artificial Intelligence (2023)). In this work, we propose two modifications to this algorithm that demonstrate impressive performances on a large class of benchmarks including the classic Cliff$_d$ and Jump$_m$ function classes. (i) Instead of randomly choosing between the only-improving and any-move acceptance operator, we take this choice via a simple two-state…

iPLAN: Redefining Indoor Wireless Network Planning Through Large Language Models
Jinbo Hou, Stefanos Bakirtzis, Kehai Qiu, Sichong Liao, Hui Song, Haonan Hu, Kezhi Wang, Jie Zhang
https://arxiv.org/abs/2507.19096

iPLAN: Redefining Indoor Wireless Network Planning Through Large Language Models
Efficient indoor wireless network (IWN) planning is crucial for providing high-quality 5G in-building services. However, traditional meta-heuristic and artificial intelligence-based planning methods face significant challenges due to the intricate interplay between indoor environments (IEs) and IWN demands. In this article, we present an indoor wireless network Planning with large LANguage models (iPLAN) framework, which integrates multi-modal IE representations into large language model (LLM)-…

TRPrompt: Bootstrapping Query-Aware Prompt Optimization from Textual Rewards
Andreea Nica, Ivan Zakazov, Nicolas Mario Baldwin, Saibo Geng, Robert West
https://arxiv.org/abs/2507.18618

TRPrompt: Bootstrapping Query-Aware Prompt Optimization from Textual Rewards
Prompt optimization improves the reasoning abilities of large language models (LLMs) without requiring parameter updates to the target model. Following heuristic-based "Think step by step" approaches, the field has evolved in two main directions: while one group of methods uses textual feedback to elicit improved prompts from general-purpose LLMs in a training-free way, a concurrent line of research relies on numerical rewards to train a special prompt model, tailored for providing optimal prom…

DeepGo: Predictive Directed Greybox Fuzzing
Peihong Lin, Pengfei Wang, Xu Zhou, Wei Xie, Gen Zhang, Kai Lu
https://arxiv.org/abs/2507.21952 https://arxiv.o…

DeepGo: Predictive Directed Greybox Fuzzing
The state-of-the-art DGF techniques redefine and optimize the fitness metric to reach the target sites precisely and quickly. However, optimizations for fitness metrics are mainly based on heuristic algorithms, which usually rely on historical execution information and lack foresight on paths that have not been exercised yet. Thus, those hard-to-execute paths with complex constraints would hinder DGF from reaching the targets, making DGF less efficient. In this paper, we propose DeepGo, a predi…

Optimization of Robotic Liquid Handling as a Capacitated Vehicle Routing Problem
Guangqi Wu, Runzhong Wang, Connor W. Coley
https://arxiv.org/abs/2506.02795

Optimization of Robotic Liquid Handling as a Capacitated Vehicle Routing Problem
We present an optimization strategy to reduce the execution time of liquid handling operations in the context of an automated chemical laboratory. By formulating the task as a capacitated vehicle routing problem (CVRP), we leverage heuristic solvers traditionally used in logistics and transportation planning to optimize task execution times. As exemplified using an 8-channel pipette with individually controllable tips, our approach demonstrates robust optimization performance across different l…

Multi-robot LiDAR SLAM: a practical case study in underground tunnel environments
Federica Di Lauro, Domenico G. Sorrenti, Miguel Angel Sotelo
https://arxiv.org/abs/2507.21553 h…

Multi-robot LiDAR SLAM: a practical case study in underground tunnel environments
Multi-robot SLAM aims at localizing and building a map with multiple robots, interacting with each other. In the work described in this article, we analyze the pipeline of a decentralized LiDAR SLAM system to study the current limitations of the state of the art, and we discover a significant source of failures, i.e., that the loop detection is the source of too many false positives. We therefore develop and propose a new heuristic to overcome these limitations. The environment taken as referen…

Speeding up Local Optimization in Vehicle Routing with Tensor-based GPU Acceleration
Zhenyu Lei, Jin-Kao Hao, Qinghua Wu
https://arxiv.org/abs/2506.17357 h…

Speeding up Local Optimization in Vehicle Routing with Tensor-based GPU Acceleration
Local search plays a central role in many effective heuristic algorithms for the vehicle routing problem (VRP) and its variants. However, neighborhood exploration is known to be computationally expensive and time consuming, especially for large instances or problems with complex constraints. In this study, we explore a promising direction to address this challenge by introducing an original tensor-based GPU acceleration method designed to speed up the commonly used local search operators in veh…

DHEvo: Data-Algorithm Based Heuristic Evolution for Generalizable MILP Solving
Zhihao Zhang, Siyuan Li, Chenxi Li, Feifan Liu, Mengjing Chen, Kai Li, Tao Zhong, Bo An, Peng Liu
https://arxiv.org/abs/2507.15615

DHEvo: Data-Algorithm Based Heuristic Evolution for Generalizable MILP Solving
Primal heuristics play a critical role in improving the efficiency of mixed integer programming (MILP) solvers. As large language models (LLMs) have demonstrated superior code generation abilities, recent MILP works are devoted to leveraging the evolutionary computation approaches with LLMs to generate effective primal heuristics. Although the generated heuristics have achieved better solving performance than the hand-crafted ones with little adaptability, the advantage of current LLM-based met…

This https://arxiv.org/abs/2502.03701 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

First-ish Order Methods: Hessian-aware Scalings of Gradient Descent
Gradient descent is the primary workhorse for optimizing large-scale problems in machine learning. However, its performance is highly sensitive to the choice of the learning rate. A key limitation of gradient descent is its lack of natural scaling, which often necessitates expensive line searches or heuristic tuning to determine an appropriate step size. In this paper, we address this limitation by incorporating Hessian information to scale the gradient direction. By accounting for the curvatur…

When Engineering Outruns Intelligence: A Re-evaluation of Instruction-Guided Navigation
Matin Aghaei, Mohammad Ali Alomrani, Yingxue Zhang, Mahdi Biparva
https://arxiv.org/abs/2507.20021

When Engineering Outruns Intelligence: A Re-evaluation of Instruction-Guided Navigation
Large language models (LLMs) are often credited with recent leaps in ObjectGoal Navigation, yet the extent to which they improve planning remains unclear. We revisit this question on the HM3D-v1 validation split. First, we strip InstructNav of its Dynamic Chain-of-Navigation prompt, open-vocabulary GLEE detector and Intuition saliency map, and replace them with a simple Distance-Weighted Frontier Explorer (DWFE). This geometry-only heuristic raises Success from 58.0% to 61.1% and lifts SPL from…

A Unified Toolbox for Multipartite Entanglement Certification
Ye-Chao Liu, Jannis Halbey, Sebastian Pokutta, S\'ebastien Designolle
https://arxiv.org/abs/2507.17435 https://…

A Unified Toolbox for Multipartite Entanglement Certification
We present a unified framework for multipartite entanglement characterization based on the conditional gradient (CG) method, incorporating both fast heuristic detection and rigorous witness construction with numerical error control. Our method enables entanglement certification in quantum systems of up to ten qubits and applies to arbitrary entanglement structures. We demonstrate its power by closing the gap between entanglement and separability bounds in white noise robustness benchmarks for a…

A Fisher's exact test justification of the TF-IDF term-weighting scheme
Paul Sheridan, Zeyad Ahmed, Aitazaz A. Farooque
https://arxiv.org/abs/2507.15742

A Fisher's exact test justification of the TF-IDF term-weighting scheme
Term frequency-inverse document frequency, or TF-IDF for short, is arguably the most celebrated mathematical expression in the history of information retrieval. Conceived as a simple heuristic quantifying the extent to which a given term's occurrences are concentrated in any one given document out of many, TF-IDF and its many variants are routinely used as term-weighting schemes in diverse text analysis applications. There is a growing body of scholarship dedicated to placing TF-IDF on a sound …

Online Planning for Cooperative Air-Ground Robot Systems with Unknown Fuel Requirements
Ritvik Agarwal, Behnoushsadat Hatami, Alvika Gautam, Parikshit Maini
https://arxiv.org/abs/2506.20804

Online Planning for Cooperative Air-Ground Robot Systems with Unknown Fuel Requirements
We consider an online variant of the fuel-constrained UAV routing problem with a ground-based mobile refueling station (FCURP-MRS), where targets incur unknown fuel costs. We develop a two-phase solution: an offline heuristic-based planner computes initial UAV and UGV paths, and a novel online planning algorithm that dynamically adjusts rendezvous points based on real-time fuel consumption during target processing. Preliminary Gazebo simulations demonstrate the feasibility of our approach in ma…

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

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

PSALM-V: Automating Symbolic Planning in Interactive Visual Environments with Large Language Models
Wang Bill Zhu, Miaosen Chai, Ishika Singh, Robin Jia, Jesse Thomason
https://arxiv.org/abs/2506.20097

PSALM-V: Automating Symbolic Planning in Interactive Visual Environments with Large Language Models
We propose PSALM-V, the first autonomous neuro-symbolic learning system able to induce symbolic action semantics (i.e., pre- and post-conditions) in visual environments through interaction. PSALM-V bootstraps reliable symbolic planning without expert action definitions, using LLMs to generate heuristic plans and candidate symbolic semantics. Previous work has explored using large language models to generate action semantics for Planning Domain Definition Language (PDDL)-based symbolic planners.…

Experimental Setup and Software Pipeline to Evaluate Optimization based Autonomous Multi-Robot Search Algorithms
Aditya Bhatt, Mary Katherine Corra, Franklin Merlo, Prajit KrisshnaKumar, Souma Chowdhury
https://arxiv.org/abs/2506.16710

Experimental Setup and Software Pipeline to Evaluate Optimization based Autonomous Multi-Robot Search Algorithms
Signal source localization has been a problem of interest in the multi-robot systems domain given its applications in search \& rescue and hazard localization in various industrial and outdoor settings. A variety of multi-robot search algorithms exist that usually formulate and solve the associated autonomous motion planning problem as a heuristic model-free or belief model-based optimization process. Most of these algorithms however remains tested only in simulation, thereby losing the opportu…