Tootfinder

A-MHA*: Anytime Multi-Heuristic A*
Ramkumar Natarajan, Muhammad Suhail Saleem, William Xiao, Sandip Aine, Howie Choset, Maxim Likhachev
https://arxiv.org/abs/2508.21637 https://…

A-MHA*: Anytime Multi-Heuristic A*
Designing good heuristic functions for graph search requires adequate domain knowledge. It is often easy to design heuristics that perform well and correlate with the underlying true cost-to-go values in certain parts of the search space but these may not be admissible throughout the domain thereby affecting the optimality guarantees of the search. Bounded suboptimal search using several such partially good but inadmissible heuristics was developed in Multi-Heuristic A* (MHA*). Although MHA* le…

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…

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…

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…

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

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…

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…

A goal-driven ruin and recreate heuristic for the 2D variable-sized bin packing problem with guillotine constraints
Jeroen Gardeyn, Tony Wauters
https://arxiv.org/abs/2508.19306

A goal-driven ruin and recreate heuristic for the 2D variable-sized bin packing problem with guillotine constraints
This paper addresses the two-dimensional bin packing problem with guillotine constraints. The problem requires a set of rectangular items to be cut from larger rectangles, known as bins, while only making use of edge-to-edge (guillotine) cuts. The goal is to minimize the total bin area needed to cut all required items. This paper also addresses variants of the problem which permit 90° rotation of items and/or a heterogeneous set of bins. A novel heuristic is introduced which is based on the ru…

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…

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…

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…

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…

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…

The Goldbach Conjecture as an Informational Economy Principle: A Heuristic Framework from Computational Physics
Ricardo Adonis Caraccioli Abrego
https://arxiv.org/abs/2508.18273

The Goldbach Conjecture as an Informational Economy Principle: A Heuristic Framework from Computational Physics
This paper presents a heuristic framework for analyzing the Goldbach Conjecture (GC) from the perspective of the physics of information. Through empirical analysis, we propose an Informational Economy Principle (IEP), which posits that differences between prime numbers tend to be resolved by pairs with a minimal sum with over- whelmingly high probability. We argue that this tendency is analogous to least-action principles in physics and is consistent with the fundamental physical limits of comp…

A Scalable Heuristic for Molecular Docking on Neutral-Atom Quantum Processors
Mathieu Garrigues, Victor Onofre, Wesley Coelho, S. Acheche
https://arxiv.org/abs/2508.18147 https:…

A Scalable Heuristic for Molecular Docking on Neutral-Atom Quantum Processors
Molecular docking is a critical computational method in drug discovery used to predict the binding conformation and orientation of a ligand within a protein's binding site. Mapping this challenge onto a graph-based problem, specifically the Maximum Weighted Independent Set (MWIS) problem, allows it to be addressed by specialized hardware such as neutral-atom quantum processors. However, a significant bottleneck has been the size mismatch between biologically relevant molecular systems and the l…

Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Sikuan Yan, Xiufeng Yang, Zuchao Huang, Ercong Nie, Zifeng Ding, Zonggen Li, Xiaowen Ma, Hinrich Sch\"utze, Volker Tresp, Yunpu Ma
https://arxiv.org/abs/2508.19828

Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of NLP tasks, but they remain fundamentally stateless, constrained by limited context windows that hinder long-horizon reasoning. Recent efforts to address this limitation often augment LLMs with an external memory bank, yet most existing pipelines are static and heuristic-driven, lacking any learned mechanism for deciding what to store, update, or retrieve. We present Memory-R1, a reinforcement learning …

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.

OLMoASR: Open Models and Data for Training Robust Speech Recognition Models
Huong Ngo, Matt Deitke, Martijn Bartelds, Sarah Pratt, Josh Gardner, Matt Jordan, Ludwig Schmidt
https://arxiv.org/abs/2508.20869

OLMoASR: Open Models and Data for Training Robust Speech Recognition Models
Improvements in training data scale and quality have led to significant advances, yet its influence in speech recognition remains underexplored. In this paper, we present a large-scale dataset, OLMoASR-Pool, and series of models, OLMoASR, to study and develop robust zero-shot speech recognition models. Beginning from OLMoASR-Pool, a collection of 3M hours of English audio and 17M transcripts, we design text heuristic filters to remove low-quality or mistranscribed data. Our curation pipeline pr…

MAAdvisor: Zero-Shot Index Advisor using Multi-Agent LLMs
Zhaodonghui Li, Haitao Yuan, Jiachen Shi, Hao Zhang, Yu Rong, Gao Cong
https://arxiv.org/abs/2508.16044 https://…

MAAdvisor: Zero-Shot Index Advisor using Multi-Agent LLMs
Index recommendation is one of the most important problems in database management system (DBMS) optimization. Given queries and certain index-related constraints, traditional methods rely on heuristic optimization or learning-based models to select effective indexes and improve query performance. However, heuristic optimization suffers from high computation time, and learning-based models lose generalisability due to training for different workloads and database schemas. With the recent rapid d…

Minimizing the Weighted Number of Tardy Jobs: Data-Driven Heuristic for Single-Machine Scheduling
Nikolai Antonov, Pr\v{e}mysl \v{S}\r{u}cha, Mikol\'a\v{s} Janota, Jan H\r{u}la
https://arxiv.org/abs/2508.13703

Minimizing the Weighted Number of Tardy Jobs: Data-Driven Heuristic for Single-Machine Scheduling
Existing research on single-machine scheduling is largely focused on exact algorithms, which perform well on typical instances but can significantly deteriorate on certain regions of the problem space. In contrast, data-driven approaches provide strong and scalable performance when tailored to the structure of specific datasets. Leveraging this idea, we focus on a single-machine scheduling problem where each job is defined by its weight, duration, due date, and deadline, aiming to minimize the …

Direction Informed Trees (DIT*): Optimal Path Planning via Direction Filter and Direction Cost Heuristic
Liding Zhang, Kejia Chen, Kuanqi Cai, Yu Zhang, Yixuan Dang, Yansong Wu, Zhenshan Bing, Fan Wu, Sami Haddadin, Alois Knoll
https://arxiv.org/abs/2508.19168

Direction Informed Trees (DIT*): Optimal Path Planning via Direction Filter and Direction Cost Heuristic
Optimal path planning requires finding a series of feasible states from the starting point to the goal to optimize objectives. Popular path planning algorithms, such as Effort Informed Trees (EIT*), employ effort heuristics to guide the search. Effective heuristics are accurate and computationally efficient, but achieving both can be challenging due to their conflicting nature. This paper proposes Direction Informed Trees (DIT*), a sampling-based planner that focuses on optimizing the search di…

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…

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…

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…

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…

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method
Matteo Ba\`u (Ricerca sul Sistema Energetico), Luca Perbellini (Politecnico di Milano), Samuele Grillo (Politecnico di Milano)
https://arxiv.org/abs/2508.19083

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method
Several methods have been proposed in the literature to improve the quality of AC optimal power flow (AC-OPF) datasets used in machine learning (ML) models. Yet, scalability to large power systems remains unaddressed and comparing generation approaches is still hindered by the absence of widely accepted metrics quantifying AC-OPF dataset quality. In this work, we tackle both these limitations. We provide a simple heuristic that samples load setpoints uniformly in total load active power, rather…

A first-order condition for discrete-time distribution steering
Alberto Dom\'inguez Corella, David Gonz\'alez-S\'anchez
https://arxiv.org/abs/2508.21026 https://

A first-order condition for discrete-time distribution steering
We study a class of distribution-steering problems from a variational point of view. Under some differentiability assumptions, we derive necessary conditions for optimal Markov policies in the spirit of the Lagrange multiplier approach. We also provide a heuristic gradient-based method derived from the variational principle.

WKB structure in a scalar model of flat bands
Semyon Dyatlov, Henry Zeng, Maciej Zworski
https://arxiv.org/abs/2508.18021 https://arxiv.org/pdf/2508.18021

WKB structure in a scalar model of flat bands
We consider a family of periodic scalar operators for which one can define flat bands in the sense of Floquet--Bloch theory. One puzzling question originating in recent physics literature is a quantisation rule for the values of parameters at which these flat bands occur. We present a general theorem about the structure of solutions to the corresponding equation and a heuristic argument explaining their WKB structure in a specific case. That structure also explains the quantisation condition --…

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…

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…

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 …

HiFo-Prompt: Prompting with Hindsight and Foresight for LLM-based Automatic Heuristic Design
Chentong Chen, Mengyuan Zhong, Jianyong Sun, Ye Fan, Jialong Shi
https://arxiv.org/abs/2508.13333

HiFo-Prompt: Prompting with Hindsight and Foresight for LLM-based Automatic Heuristic Design
LLM-based Automatic Heuristic Design (AHD) within Evolutionary Computation (EC) frameworks has shown promising results. However, its effectiveness is hindered by the use of static operators and the lack of knowledge accumulation mechanisms. We introduce HiFo-Prompt, a framework that guides LLMs with two synergistic prompting strategies: Foresight and Hindsight. Foresight-based prompts adaptively steer the search based on population dynamics, managing the exploration-exploitation trade-off. In a…

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,…

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…

A principled approach for comparing Variable Importance
Angel Reyero-Lobo, Pierre Neuvial, Bertrand Thirion
https://arxiv.org/abs/2507.17306 https://arxiv.…

A principled approach for comparing Variable Importance
Variable importance measures (VIMs) aim to quantify the contribution of each input covariate to the predictability of a given output. With the growing interest in explainable AI, numerous VIMs have been proposed, many of which are heuristic in nature. This is often justified by the inherent subjectivity of the notion of importance. This raises important questions regarding usage: What makes a good VIM? How can we compare different VIMs? In this paper, we address these questions by: (1) propos…

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…

Skeptik: A Hybrid Framework for Combating Potential Misinformation in Journalism
Arlen Fan, Fan Lei, Steven R. Corman, Ross Maciejewski
https://arxiv.org/abs/2508.18499 https://…

Skeptik: A Hybrid Framework for Combating Potential Misinformation in Journalism
The proliferation of misinformation in journalism, often stemming from flawed reasoning and logical fallacies, poses significant challenges to public understanding and trust in news media. Traditional fact-checking methods, while valuable, are insufficient for detecting the subtle logical inconsistencies that can mislead readers within seemingly factual content. To address this gap, we introduce Skeptik, a hybrid framework that integrates Large Language Models (LLMs) with heuristic approaches t…

Probably faster multiplication of sparse polynomials
Joris van der Hoeven
https://arxiv.org/abs/2508.16164 https://arxiv.org/pdf/2508.16164

Probably faster multiplication of sparse polynomials
In this paper, we present a probabilistic algorithm to multiply two sparse polynomials almost as efficiently as two dense univariate polynomials with a result of approximately the same size. The algorithm depends on unproven heuristics that will be made precise. Non-heuristic versions that are a constant times slower are also presented.

Disentangling the Drivers of LLM Social Conformity: An Uncertainty-Moderated Dual-Process Mechanism
Huixin Zhong, Yanan Liu, Qi Cao, Shijin Wang, Zijing Ye, Zimu Wang, Shiyao Zhang
https://arxiv.org/abs/2508.14918

Disentangling the Drivers of LLM Social Conformity: An Uncertainty-Moderated Dual-Process Mechanism
As large language models (LLMs) integrate into collaborative teams, their social conformity -- the tendency to align with majority opinions -- has emerged as a key concern. In humans, conformity arises from informational influence (rational use of group cues for accuracy) or normative influence (social pressure for approval), with uncertainty moderating this balance by shifting from purely analytical to heuristic processing. It remains unclear whether these human psychological mechanisms apply …

Genetic Informed Trees (GIT*): Path Planning via Reinforced Genetic Programming Heuristics
Liding Zhang, Kuanqi Cai, Zhenshan Bing, Chaoqun Wang, Alois Knoll
https://arxiv.org/abs/2508.20871

Genetic Informed Trees (GIT*): Path Planning via Reinforced Genetic Programming Heuristics
Optimal path planning involves finding a feasible state sequence between a start and a goal that optimizes an objective. This process relies on heuristic functions to guide the search direction. While a robust function can improve search efficiency and solution quality, current methods often overlook available environmental data and simplify the function structure due to the complexity of information relationships. This study introduces Genetic Informed Trees (GIT*), which improves upon Effort …

Harnessing the edge of chaos for combinatorial optimization
Hayato Goto, Ryo Hidaka, Kosuke Tatsumura
https://arxiv.org/abs/2508.17655 https://arxiv.org/pd…

Harnessing the edge of chaos for combinatorial optimization
Nonlinear dynamical systems with continuous variables can be used for solving combinatorial optimization problems with discrete variables.In particular, numerical simulations of them can be used as heuristic algorithms with a desirable property, namely, parallelizability, which allows us to execute them in a massively parallel manner using cutting-edge many-core processors, leading to ultrafast performance. However, the dynamical-system approaches with continuous variables are usually less accu…

Coarse Addition and the St. Petersburg Paradox: A Heuristic Perspective
Takashi Izumo
https://arxiv.org/abs/2507.12475 https://arxiv.…

Coarse Addition and the St. Petersburg Paradox: A Heuristic Perspective
The St. Petersburg paradox presents a longstanding challenge in decision theory. It describes a game whose expected value is infinite, yet for which no rational finite stake can be determined. Traditional solutions introduce auxiliary assumptions, such as diminishing marginal utility, temporal discounting, or extended number systems. These methods often involve mathematical refinements that may not correspond to how people actually perceive or process numerical information. This paper explores …

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.…

Crosslisted article(s) found for math.CO. https://arxiv.org/list/math.CO/new
[1/1]:
- Random-projector quantum diagnostics of Ramsey numbers and a prime-factor heuristic for $R(5,5)=45$
Fabrizio Tamburini

Computation of Optimal Type-II Progressing Censoring Scheme Using Genetic Algorithm Approach
Ujjwal Roy, Ritwik Bhattacharya
https://arxiv.org/abs/2507.20001 https://

Computation of Optimal Type-II Progressing Censoring Scheme Using Genetic Algorithm Approach
The experimenter must perform a legitimate search in the entire set of feasible censoring schemes to identify the optimal type II progressive censoring scheme, when applied to a life-testing experiment. Current recommendations are limited to small sample sizes. Exhaustive search strategies are not practically feasible for large sample sizes. This paper proposes a meta-heuristic algorithm based on the genetic algorithm for large sample sizes. The algorithm is found to provide optimal or near-opt…

Semi-discrete Active Flux as a Petrov-Galerkin method
Wasilij Barsukow
https://arxiv.org/abs/2508.15017 https://arxiv.org/pdf/2508.15017

Semi-discrete Active Flux as a Petrov-Galerkin method
Active Flux (AF) is a recent numerical method for hyperbolic conservation laws, whose degrees of freedom are averages/moments and (shared) point values at cell interfaces. It has been noted previously in a heuristic fashion that it thus combines ideas from Finite Volume/Discontinuous Galerkin (DG) methods with a continuous approximation common in continuous Finite Element (CG) methods. This work shows that the semi-discrete Active Flux method on Cartesian meshes can be obtained from a variation…

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…

Hyperparameter-free minimum-lengthscale constraints for topology optimization
Rodrigo Arrieta, Giuseppe Romano, Steven G. Johnson
https://arxiv.org/abs/2507.16108

Hyperparameter-free minimum-lengthscale constraints for topology optimization
The geometric constraints of Zhou et al. (2015) are a widely used technique in topology/freeform optimization to impose minimum lengthscales for manufacturability. However, its efficacy degrades as design binarization is increased, and it requires heuristic tuning of multiple hyperparameters. In this work, we derive analytical hyperparameters from first principles, depending only on the target lengthscale. We present results for both conic and PDE-based filtering schemes, showing that the latte…

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…

LLM Agent for Hyper-Parameter Optimization
Wanzhe Wang, Jianqiu Peng, Menghao Hu, Weihuang Zhong, Tong Zhang, Shuai Wang, Yixin Zhang, Mingjie Shao, Wanli Ni
https://arxiv.org/abs/2506.15167

LLM Agent for Hyper-Parameter Optimization
Hyper-parameters are essential and critical for the performance of communication algorithms. However, current hyper-parameters tuning methods for warm-start particles swarm optimization with cross and mutation (WS-PSO-CM) algortihm for radio map-enabled unmanned aerial vehicle (UAV) trajectory and communication are primarily heuristic-based, exhibiting low levels of automation and unsatisfactory performance. In this paper, we design an large language model (LLM) agent for automatic hyper-parame…

@… 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 …

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…

Traversing the Narrow Path: A Two-Stage Reinforcement Learning Framework for Humanoid Beam Walking
TianChen Huang, Wei Gao, Runchen Xu, Shiwu Zhang
https://arxiv.org/abs/2508.20661

Traversing the Narrow Path: A Two-Stage Reinforcement Learning Framework for Humanoid Beam Walking
Traversing narrow beams is challenging for humanoids due to sparse, safety-critical contacts and the fragility of purely learned policies. We propose a physically grounded, two-stage framework that couples an XCoM/LIPM footstep template with a lightweight residual planner and a simple low-level tracker. Stage-1 is trained on flat ground: the tracker learns to robustly follow footstep targets by adding small random perturbations to heuristic footsteps, without any hand-crafted centerline locking…

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)-…

Can AI Keep a Secret? Contextual Integrity Verification: A Provable Security Architecture for LLMs
Aayush Gupta
https://arxiv.org/abs/2508.09288 https://ar…

Can AI Keep a Secret? Contextual Integrity Verification: A Provable Security Architecture for LLMs
Large language models (LLMs) remain acutely vulnerable to prompt injection and related jailbreak attacks; heuristic guardrails (rules, filters, LLM judges) are routinely bypassed. We present Contextual Integrity Verification (CIV), an inference-time security architecture that attaches cryptographically signed provenance labels to every token and enforces a source-trust lattice inside the transformer via a pre-softmax hard attention mask (with optional FFN/residual gating). CIV provides determin…

STARec: An Efficient Agent Framework for Recommender Systems via Autonomous Deliberate Reasoning
Chenghao Wu, Ruiyang Ren, Junjie Zhang, Ruirui Wang, Zhongrui Ma, Qi Ye, Wayne Xin Zhao
https://arxiv.org/abs/2508.18812

STARec: An Efficient Agent Framework for Recommender Systems via Autonomous Deliberate Reasoning
While modern recommender systems are instrumental in navigating information abundance, they remain fundamentally limited by static user modeling and reactive decision-making paradigms. Current large language model (LLM)-based agents inherit these shortcomings through their overreliance on heuristic pattern matching, yielding recommendations prone to shallow correlation bias, limited causal inference, and brittleness in sparse-data scenarios. We introduce STARec, a slow-thinking augmented agent …

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…

Characterizing Visualization Perception with Psychological Phenomena: Uncovering the Role of Subitizing in Data Visualization
Arran Zeyu Wang, Ghulam Jilani Quadri, Mengyuan Zhu, Chin Tseng, Danielle Albers Szafir
https://arxiv.org/abs/2508.17460

Characterizing Visualization Perception with Psychological Phenomena: Uncovering the Role of Subitizing in Data Visualization
Understanding how people perceive visualizations is crucial for designing effective visual data representations; however, many heuristic design guidelines are derived from specific tasks or visualization types, without considering the constraints or conditions under which those guidelines hold. In this work, we aimed to assess existing design heuristics for categorical visualization using well-established psychological knowledge. Specifically, we examine the impact of the subitizing phenomenon …

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…

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…

Cyber Orbits of Large Scale Network Traffic
Jeremy Kepner, Hayden Jananthan, Chasen Milner, Michael Houle, Michael Jones, Peter Michaleas, Alex Pentland
https://arxiv.org/abs/2508.16847

Cyber Orbits of Large Scale Network Traffic
The advent of high-performance graph libraries, such as the GraphBLAS, has enabled the analysis of massive network data sets and revealed new models for their behavior. Physical analogies for complicated network behavior can be a useful aid to understanding these newly discovered network phenomena. Prior work leveraged the canonical Gull's Lighthouse problem and developed a computational heuristic for modeling large scale network traffic using this model. A general solution using this approach …

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…

Detecting Multiple Targets with Distributed Sensing and Communication in Cell-Free Massive MIMO
Zinat Behdad, Ozlem Tugfe Demir, Ki Won Sung, Cicek Cavdar
https://arxiv.org/abs/2507.17441

Detecting Multiple Targets with Distributed Sensing and Communication in Cell-Free Massive MIMO
This paper investigates multi-target detection in an integrated sensing and communication (ISAC) system within a cell-free massive MIMO (CF-mMIMO) framework. We adopt a user-centric approach for communication user equipments (UEs) and a distributed sensing approach for multi-target detection. A heuristic access point (AP) mode selection algorithm and a channel-aware distributed sensing scheme are proposed, where local measurements at receive APs (RX-APs) are weighted based on the received signa…

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…

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…

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…

Innovative Distributed Maintenance Concept: From the design to cost optimisation
Maria Di Mascolo (G-SCOP, G-SCOP\_DOME2S), Zineb Simeu-Abazi (G-SCOP, G-SCOP\_DOME2S), Rony Ars\`ene Djeunang Mezafack (G-SCOP, G-SCOP\_DOME2S)
https://arxiv.org/abs/2508.16160

Innovative Distributed Maintenance Concept: From the design to cost optimisation
This study proposes an integrated heuristic framework for the strategic optimization of distributed maintenance operations in geo-distributed production systems (GDPS). It introduces a dual-entity maintenance structure comprising a Centralized Maintenance Workshop (CMW) and a Mobile Maintenance Workshop (MMW), aimed at minimizing total long-term maintenance costs. The cost function incorporates transport, operations, and downtime penalties, optimized via a two-stage algorithmic approach: a Main…

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…

Code Generation for Near-Roofline Finite Element Actions on GPUs from Symbolic Variational Forms
Kaushik Kulkarni, Andreas Kl\"ockner
https://arxiv.org/abs/2506.17471

Code Generation for Near-Roofline Finite Element Actions on GPUs from Symbolic Variational Forms
We present a novel parallelization strategy for evaluating Finite Element Method (FEM) variational forms on GPUs, focusing on those that are expressible through the Unified Form Language (UFL) on simplex meshes. We base our approach on code transformations, wherein we construct a space of scheduling candidates and rank them via a heuristic cost model to effectively handle the large diversity of computational workloads that can be expressed in this way. We present a design of a search space to w…

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 …

On the Modern Structure of the Gauss-Landau Theorem
Manuel M. Aguilera
https://arxiv.org/abs/2506.15101 https://arxiv.org/pdf/2506.15…

On the Modern Structure of the Gauss-Landau Theorem
We formalize the Gauss-Landau theorem, providing a unified prime factorization approach to computing the GCD and LCM of finite nonzero integer sets. Although commonly used as a heuristic or technique in elementary number theory education, these theorems have not been explicitly formalized or named in the literature. This formalization aims to enhance understanding and facilitate adoption in mathematical instruction and research.

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…

ADMIRE-BayesOpt: Accelerated Data MIxture RE-weighting for Language Models with Bayesian Optimization
Shengzhuang Chen, Xu Ouyang, Michael Arthur Leopold Pearce, Thomas Hartvigsen, Jonathan Richard Schwarz
https://arxiv.org/abs/2508.11551

ADMIRE-BayesOpt: Accelerated Data MIxture RE-weighting for Language Models with Bayesian Optimization
Determining the optimal data mixture for large language model training remains a challenging problem with an outsized impact on performance. In practice, language model developers continue to rely on heuristic exploration since no learning-based approach has emerged as a reliable solution. In this work, we propose to view the selection of training data mixtures as a black-box hyperparameter optimization problem, for which Bayesian Optimization is a well-established class of appropriate algorith…

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…

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…

Delta: A Learned Mixed Cost-based Query Optimization Framework
Jiazhen Peng, Zheng Qu, Xiaoye Miao, Rong Zhu
https://arxiv.org/abs/2506.15848 https://

Delta: A Learned Mixed Cost-based Query Optimization Framework
Query optimizer is a crucial module for database management systems. Existing optimizers exhibit two flawed paradigms: (1) cost-based optimizers use dynamic programming with cost models but face search space explosion and heuristic pruning constraints; (2) value-based ones train value networks to enable efficient beam search, but incur higher training costs and lower accuracy. They also lack mechanisms to detect queries where they may perform poorly. To determine more efficient plans, we propos…

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…

Analysis-Aware Defeaturing of Dirichlet Features
Philipp Weder, Annalisa Buffa
https://arxiv.org/abs/2508.13886 https://arxiv.org/pdf/2508.13886

Analysis-Aware Defeaturing of Dirichlet Features
Feature removal from computational geometries, or defeaturing, is an integral part of industrial simulation pipelines. Defeaturing simplifies the otherwise costly or even impossible meshing process, speeds up the simulation, and lowers its memory footprint. Current defeaturing operators are often based on heuristic criteria and ignore the impact of the simplifications on the PDE solution. This work extends the mathematically rigorous framework developed by Buffa, Chanon, and Vázquez (2022) to …

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…

Practical colinear chaining on sequences revisited
Nicola Rizzo, Manuel C\'aceres, Veli M\"akinen
https://arxiv.org/abs/2506.11750 https://…

Practical colinear chaining on sequences revisited
Colinear chaining is a classical heuristic for sequence alignment and is widely used in modern practical aligners. Jain et al. (J. Comput. Biol. 2022) proposed an $O(n \log^3 n)$ time algorithm to chain a set of $n$ anchors so that the chaining cost matches the edit distance of the input sequences, when anchors are maximal exact matches. Moreover, assuming a uniform and sparse distribution of anchors, they provided a practical solution ($\mathtt{ChainX}$) working in $O(n \cdot \mathsf{SOL} + n …

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 …

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…

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…

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.…

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…

Newfluence: Boosting Model interpretability and Understanding in High Dimensions
Haolin Zou, Arnab Auddy, Yongchan Kwon, Kamiar Rahnama Rad, Arian Maleki
https://arxiv.org/abs/2507.11895

Newfluence: Boosting Model interpretability and Understanding in High Dimensions
The increasing complexity of machine learning (ML) and artificial intelligence (AI) models has created a pressing need for tools that help scientists, engineers, and policymakers interpret and refine model decisions and predictions. Influence functions, originating from robust statistics, have emerged as a popular approach for this purpose. However, the heuristic foundations of influence functions rely on low-dimensional assumptions where the number of parameters $p$ is much smaller than the …

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…

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 …

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 Quantum Annealing Approach for Solving Optimal Feature Selection and Next Release Problems
Shuchang Wang, Xiaopeng Qiu, Yingxing Xue, Yanfu Li, Wei Yang
https://arxiv.org/abs/2506.14129

A Quantum Annealing Approach for Solving Optimal Feature Selection and Next Release Problems
Search-based software engineering (SBSE) addresses critical optimization challenges in software engineering, including the next release problem (NRP) and feature selection problem (FSP). While traditional heuristic approaches and integer linear programming (ILP) methods have demonstrated efficacy for small to medium-scale problems, their scalability to large-scale instances remains unknown. Here, we introduce quantum annealing (QA) as a subroutine to tackling multi-objective SBSE problems, leve…

Adaptive Network Selection for Latency-Aware V2X Systems under Varying Network and Vehicle Densities
Muhammad Z. Haq, Nadia N. Qadri, Omer Chughtai, Sadiq A. Ahmad, Waqas Khalid, Heejung Yu
https://arxiv.org/abs/2508.14471

Adaptive Network Selection for Latency-Aware V2X Systems under Varying Network and Vehicle Densities
This paper presents ANS-V2X, an Adaptive Network Selection framework tailored for latency-aware V2X systems operating under varying vehicle densities and heterogeneous network conditions. Modern vehicular environments demand low-latency and high-throughput communication, yet real-time network selection is hindered by diverse application requirements and the coexistence of multiple Radio Access Technologies (RATs) such as 4G, 5G, and ad hoc links. ANS-V2X employs a heuristic-driven approach to a…

Optimization-Free Fast Optimal Control: Bang-Ride Property, Monotonicity, and Applications to Fast Battery Charging
Shengling Shi, Jacob Sass, Jiaen Wu, Minsu Kim, Yingjie Ma, Sungho Shin, Richard D. Braatz
https://arxiv.org/abs/2508.09010

Optimization-Free Fast Optimal Control: Bang-Ride Property, Monotonicity, and Applications to Fast Battery Charging
Single-input fast optimal control problems, which aim to achieve the optimal objective as fast as possible, occur in various real-world applications. In the case of fast battery charging, the associated optimal control problem becomes computationally challenging when detailed battery models are used. A recent heuristic optimization-free algorithm can significantly reduce the computational cost and provide an approximate solution, consistent with many heuristic input profiles in practice. These …

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…

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…

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…

Efficient Quantum Information-Inspired Ansatz for Variational Quantum Eigensolver Algorithm: Applications to Atomic Systems
Abdul Kalam, Prasenjit Deb, Akitada Sakurai, B. K. Sahoo, V. S. Prasannaa, B. P. Das
https://arxiv.org/abs/2508.10593

Efficient Quantum Information-Inspired Ansatz for Variational Quantum Eigensolver Algorithm: Applications to Atomic Systems
We present a quantum information-inspired ansatz for the variational quantum eigensolver (VQE) and demonstrate its efficacy in calculating ground-state energies of atomic systems. Instead of adopting a heuristic approach, we start with an approximate multi-qubit target state and utilize two quantum information-theoretic quantities, i.e., von Neumann entropy and quantum mutual information, to construct our ansatz. The quantum information encoded in the target state helps us to design unique bloc…

Crosslisted article(s) found for cs.NE. https://arxiv.org/list/cs.NE/new
[1/1]:
- HiFo-Prompt: Prompting with Hindsight and Foresight for LLM-based Automatic Heuristic Design
Chentong Chen, Mengyuan Zhong, Jianyong Sun, Ye Fan, Jialong Shi

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

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

Replaced article(s) found for math.OC. https://arxiv.org/list/math.OC/new
[1/2]:
- AILS-II: An Adaptive Iterated Local Search Heuristic for the Large-scale Capacitated Vehicle Rout...
Vin\'icius R. M\'aximo, Jean-Fran\c{c}ois Cordeau, Mari\'a C. V. Nascimento
…

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. …