Tootfinder

Re-evaluating LLM-based Heuristic Search: A Case Study on the 3D Packing Problem
Guorui Quan, Mingfei Sun, Manuel L\'opez-Ib\'a\~nez
https://arxiv.org/abs/2509.02297 htt…

Re-evaluating LLM-based Heuristic Search: A Case Study on the 3D Packing Problem
The art of heuristic design has traditionally been a human pursuit. While Large Language Models (LLMs) can generate code for search heuristics, their application has largely been confined to adjusting simple functions within human-crafted frameworks, leaving their capacity for broader innovation an open question. To investigate this, we tasked an LLM with building a complete solver for the constrained 3D Packing Problem. Direct code generation quickly proved fragile, prompting us to introduce t…

Mean-field theory of the Santa Fe model revisited: a systematic derivation from an exact BBGKY hierarchy for the zero-intelligence limit-order book model
Taiki Wakatsuki, Kiyoshi Kanazawa
https://arxiv.org/abs/2510.01814

Mean-field theory of the Santa Fe model revisited: a systematic derivation from an exact BBGKY hierarchy for the zero-intelligence limit-order book model
The Santa Fe model is an established econophysics model for describing stochastic dynamics of the limit order book from the viewpoint of the zero-intelligence approach. While its foundation was studied by combining a dimensional analysis and a mean-field theory by E. Smith et al. in Quantitative Finance 2003, their arguments are rather heuristic and lack solid mathematical foundation; indeed, their mean-field equations were derived with heuristic arguments and their solutions were not explicitl…

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…

LOBE-GS: Load-Balanced and Efficient 3D Gaussian Splatting for Large-Scale Scene Reconstruction
Sheng-Hsiang Hung, Ting-Yu Yen, Wei-Fang Sun, Simon See, Shih-Hsuan Hung, Hung-Kuo Chu
https://arxiv.org/abs/2510.01767

LOBE-GS: Load-Balanced and Efficient 3D Gaussian Splatting for Large-Scale Scene Reconstruction
3D Gaussian Splatting (3DGS) has established itself as an efficient representation for real-time, high-fidelity 3D scene reconstruction. However, scaling 3DGS to large and unbounded scenes such as city blocks remains difficult. Existing divide-and-conquer methods alleviate memory pressure by partitioning the scene into blocks, but introduce new bottlenecks: (i) partitions suffer from severe load imbalance since uniform or heuristic splits do not reflect actual computational demands, and (ii) co…

Statistical Uncertainty Learning for Robust Visual-Inertial State Estimation
Seungwon Choi, Donggyu Park, Seo-Yeon Hwang, Tae-Wan Kim
https://arxiv.org/abs/2510.01648 https://…

Statistical Uncertainty Learning for Robust Visual-Inertial State Estimation
A fundamental challenge in robust visual-inertial odometry (VIO) is to dynamically assess the reliability of sensor measurements. This assessment is crucial for properly weighting the contribution of each measurement to the state estimate. Conventional methods often simplify this by assuming a static, uniform uncertainty for all measurements. This heuristic, however, may be limited in its ability to capture the dynamic error characteristics inherent in real-world data. To improve this limitatio…

Adaptive Kernel Selection for Stein Variational Gradient Descent
Moritz Melcher, Simon Weissmann, Ashia C. Wilson, Jakob Zech
https://arxiv.org/abs/2510.02067 https://

Adaptive Kernel Selection for Stein Variational Gradient Descent
A central challenge in Bayesian inference is efficiently approximating posterior distributions. Stein Variational Gradient Descent (SVGD) is a popular variational inference method which transports a set of particles to approximate a target distribution. The SVGD dynamics are governed by a reproducing kernel Hilbert space (RKHS) and are highly sensitive to the choice of the kernel function, which directly influences both convergence and approximation quality. The commonly used median heuristic o…

Chemical transport by weakly nonlinear internal gravity waves in stars
Yifeng Mao, Daniel Lecoanet
https://arxiv.org/abs/2510.02031 https://arxiv.org/pdf/2…

Chemical transport by weakly nonlinear internal gravity waves in stars
While it is well-known that internal gravity waves (IGWs) transport chemicals in the radiative zones of stars, there remains substantial uncertainty on the amount of, and physical mechanism behind, this transport. Most previous studies have relied on heuristic theories, or numerical simulations that may be hard to extrapolate to stellar parameters. In this work, we present the first rigorous asymptotic calculation of (passive) chemical transport by IGWs, in the limit of small wave amplitude. We…

Cooperative Guidance for Aerial Defense in Multiagent Systems
Shivam Bajpai, Abhinav Sinha, Shashi Ranjan Kumar
https://arxiv.org/abs/2510.02087 https://ar…

Cooperative Guidance for Aerial Defense in Multiagent Systems
This paper addresses a critical aerial defense challenge in contested airspace, involving three autonomous aerial vehicles -- a hostile drone (the pursuer), a high-value drone (the evader), and a protective drone (the defender). We present a cooperative guidance framework for the evader-defender team that guarantees interception of the pursuer before it can capture the evader, even under highly dynamic and uncertain engagement conditions. Unlike traditional heuristic, optimal control, or differ…

Dynamics of Loschmidt echoes from operator growth in noisy quantum many-body systems
Takato Yoshimura, Lucas S\'a
https://arxiv.org/abs/2509.01585 https://

Dynamics of Loschmidt echoes from operator growth in noisy quantum many-body systems
We study the dynamics of Loschmidt echoes in noisy quantum many-body systems without conservation laws. We first show that the operator Loschmidt echo in noisy unitary dynamics is equivalent to the operator norm of the corresponding dissipative dynamics upon noise averaging. We then analyze this quantity in two complementary ways, revealing universal dynamical behavior. First, we develop a heuristic picture for generic Floquet systems that connects Loschmidt echoes, out-of-time-order correlator…

Learning Admissible Heuristics for A*: Theory and Practice
Ehsan Futuhi, Nathan R. Sturtevant
https://arxiv.org/abs/2509.22626 https://arxiv.org/pdf/2509.2…

Learning Admissible Heuristics for A*: Theory and Practice
Heuristic functions are central to the performance of search algorithms such as A-star, where admissibility - the property of never overestimating the true shortest-path cost - guarantees solution optimality. Recent deep learning approaches often disregard admissibility and provide limited guarantees on generalization beyond the training data. This paper addresses both of these limitations. First, we pose heuristic learning as a constrained optimization problem and introduce Cross-Entropy Admis…

Learning Social Heuristics for Human-Aware Path Planning
Andrea Eirale, Matteo Leonetti, Marcello Chiaberge
https://arxiv.org/abs/2509.02134 https://arxiv.…

Learning Social Heuristics for Human-Aware Path Planning
Social robotic navigation has been at the center of numerous studies in recent years. Most of the research has focused on driving the robotic agent along obstacle-free trajectories, respecting social distances from humans, and predicting their movements to optimize navigation. However, in order to really be socially accepted, the robots must be able to attain certain social norms that cannot arise from conventional navigation, but require a dedicated learning process. We propose Heuristic Plann…

How Real Is AI Tutoring? Comparing Simulated and Human Dialogues in One-on-One Instruction
Ruijia Li, Yuan-Hao Jiang, Jiatong Wang, Bo Jiang
https://arxiv.org/abs/2509.01914 htt…

How Real Is AI Tutoring? Comparing Simulated and Human Dialogues in One-on-One Instruction
Heuristic and scaffolded teacher-student dialogues are widely regarded as critical for fostering students' higher-order thinking and deep learning. However, large language models (LLMs) currently face challenges in generating pedagogically rich interactions. This study systematically investigates the structural and behavioral differences between AI-simulated and authentic human tutoring dialogues. We conducted a quantitative comparison using an Initiation-Response-Feedback (IRF) coding scheme a…

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…

On Computing Top-$k$ Simple Shortest Paths from a Single Source
Mattia D'Emidio, Gabriele Di Stefano
https://arxiv.org/abs/2509.26094 https://arxiv.org…

On Computing Top-$k$ Simple Shortest Paths from a Single Source
We investigate the problem of computing the top-$k$ simple shortest paths in weighted digraphs. While the single-pair variant -- finding the top-$k$ simple shortest paths between two specified vertices -- has been extensively studied over the past decades, with Yen's algorithm and its heuristic improvements emerging as the most effective solving strategies, relatively little attention has been devoted to the more general single-source version, where the goal is determining top-$k$ simple shorte…

Efficient Cost Bounds with Linear Maps
David M Kahn, Jan Hoffmann, Thomas Reps, Jessie Grosen
https://arxiv.org/abs/2509.22982 https://arxiv.org/pdf/2509.2…

Efficient Cost Bounds with Linear Maps
The Automatic Amortized Resource Analysis (AARA) derives program-execution cost bounds using types. To do so, AARA often makes use of cost-free types, which are critical for the composition of types and cost bounds. However, inferring cost-free types using the current state-of-the-art algorithm is expensive due to recursive dependence on additional cost-free types. Furthermore, that algorithm uses a heuristic only applicable to polynomial cost bounds, and not, e.g., exponential bounds. This pap…

Navigating the Labyrinth: Path-Sensitive Unit Test Generation with Large Language Models
Dianshu Liao, Xin Yin, Shidong Pan, Chao Ni, Zhenchang Xing, Xiaoyu Sun
https://arxiv.org/abs/2509.23812

Navigating the Labyrinth: Path-Sensitive Unit Test Generation with Large Language Models
Unit testing is essential for software quality assurance, yet writing and maintaining tests remains time-consuming and error-prone. To address this challenge, researchers have proposed various techniques for automating unit test generation, including traditional heuristic-based methods and more recent approaches that leverage large language models (LLMs). However, these existing approaches are inherently path-insensitive because they rely on fixed heuristics or limited contextual information an…

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…

Metadata-Guided Adaptable Frequency Scaling across Heterogeneous Applications and Devices
Jinqi Yan, Fang He, Qianlong Sang, Bifeng Tong, Peng Sun, Yili Gong, Chuang Hu, Dazhao Cheng
https://arxiv.org/abs/2509.22707

Metadata-Guided Adaptable Frequency Scaling across Heterogeneous Applications and Devices
Dynamic Voltage and Frequency Scaling is essential for enhancing energy efficiency in mobile platforms. However, traditional heuristic-based governors are increasingly inadequate for managing the complexity of heterogeneous System-on-Chip designs and diverse application workloads. Although reinforcement learning approaches offer improved performance, their poor generalization capability and reliance on extensive retraining for each hardware and application combination leads to significant deplo…

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…

Bundle Network: a Machine Learning-Based Bundle Method
Francesca Demelas, Joseph Le Roux, Antonio Frangioni, Mathieu Lacroix, Emiliano Traversi, Roberto Wolfler Calvo
https://arxiv.org/abs/2509.24736

Bundle Network: a Machine Learning-Based Bundle Method
This paper presents Bundle Network, a learning-based algorithm inspired by the Bundle Method for convex non-smooth minimization problems. Unlike classical approaches that rely on heuristic tuning of a regularization parameter, our method automatically learns to adjust it from data. Furthermore, we replace the iterative resolution of the optimization problem that provides the search direction-traditionally computed as a convex combination of gradients at visited points-with a recurrent neural mo…

NeuSO: Neural Optimizer for Subgraph Queries
Linglin Yang, Lei Zou, Chunshan Zhao
https://arxiv.org/abs/2509.23775 https://arxiv.org/pdf/2509.23775

NeuSO: Neural Optimizer for Subgraph Queries
Subgraph query is a critical task in graph analysis with a wide range of applications across various domains. Most existing methods rely on heuristic vertex matching orderings, which may significantly degrade enumeration performance for certain queries. While learning-based optimizers have recently gained attention in the context of relational databases, they cannot be directly applied to subgraph queries due to the heterogeneous and schema-flexible nature of graph data, as well as the large nu…

A heuristic for ray class groups of quadratic number fields
Alex Bartel, Carlo Pagano
https://arxiv.org/abs/2509.20185 https://arxiv.org/pdf/2509.20185

A heuristic for ray class groups of quadratic number fields
We formulate a model for the average behaviour of ray class groups of real quadratic fields with respect to a fixed rational modulus, locally at a finite set $S$ of odd primes. To that end, we introduce Arakelov ray class groups of a number field, and postulate that, locally at $S$, the Arakelov ray class groups of real quadratic fields are distributed randomly with respect to a natural Cohen--Lenstra type probability measure. We show that our heuristics imply the Cohen--Lenstra heuristics on c…

Random Policy Valuation is Enough for LLM Reasoning with Verifiable Rewards
Haoran He, Yuxiao Ye, Qingpeng Cai, Chen Hu, Binxing Jiao, Daxin Jiang, Ling Pan
https://arxiv.org/abs/2509.24981

Random Policy Valuation is Enough for LLM Reasoning with Verifiable Rewards
RL with Verifiable Rewards (RLVR) has emerged as a promising paradigm for improving the reasoning abilities of large language models (LLMs). Current methods rely primarily on policy optimization frameworks like PPO and GRPO, which follow generalized policy iteration that alternates between evaluating the current policy's value and improving the policy based on evaluation. While effective, they often suffer from training instability and diversity collapse, requiring complex heuristic tricks and …

Lifting Cocycles: From Heuristic to Theory
Sigurd Gaukstad, Mathias Karsrud Nordal, Marius Thaule
https://arxiv.org/abs/2509.16102 https://arxiv.org/pdf/25…

Lifting Cocycles: From Heuristic to Theory
The circular coordinates algorithm, a key tool in topological data analysis, relies on a theoretically unvalidated lifting step to convert cocycles from a prime field to integer coefficients. We provide a rigorous analysis of this procedure, establishing a criterion for its success. We also introduce a novel algebraic method to reduce any lifted cocycle to a cocycle with winding number 1, ensuring feature correctness. These principles are extended to homology cycles, solidifying the theoretical…

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 …

Fourier heuristic PINNs to solve the biharmonic equations based on its coupled scheme
Yujia Huang, Xi'an Li ansd Jinran Wu
https://arxiv.org/abs/2509.15004 https://

Fourier heuristic PINNs to solve the biharmonic equations based on its coupled scheme
Physics-informed neural networks (PINNs) have been widely utilized for solving a range of partial differential equations (PDEs) in various scientific and engineering disciplines. This paper presents a Fourier heuristic-enhanced PINN (termed FCPINN) designed to address a specific class of biharmonic equations with Dirichlet and Navier boundary conditions. The method achieves this by decomposing the high-order equations into two Poisson equations. FCPINN integrates Fourier spectral theory with a …

Approximate calculation of multidimensional first passage times
James F. Lutsko
https://arxiv.org/abs/2508.21607 https://arxiv.org/pdf/2508.21607

Approximate calculation of multidimensional first passage times
The general, multidimensional barrier crossing problem for diffusive processes under the action of conservative forces is studied with the goal of developing tractable approximations. Particular attention is given to the effect of different statistical interpretations of the stochastic differential equation and to the relation between the approximations and the known, exact solutions to the one-dimensional problem. Beginning with a reasonable, but heuristic, simplifying assumption, a one-dimens…

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…

Listening, Imagining \& Refining: A Heuristic Optimized ASR Correction Framework with LLMs
Yutong Liu, Ziyue Zhang, Yongbin Yu, Xiangxiang Wang, Yuqing Cai, Nyima Tashi
https://arxiv.org/abs/2509.15095

Listening, Imagining \& Refining: A Heuristic Optimized ASR Correction Framework with LLMs
Automatic Speech Recognition (ASR) systems remain prone to errors that affect downstream applications. In this paper, we propose LIR-ASR, a heuristic optimized iterative correction framework using LLMs, inspired by human auditory perception. LIR-ASR applies a "Listening-Imagining-Refining" strategy, generating phonetic variants and refining them in context. A heuristic optimization with finite state machine (FSM) is introduced to prevent the correction process from being trapped in local optima…

Momentum-constrained Hybrid Heuristic Trajectory Optimization Framework with Residual-enhanced DRL for Visually Impaired Scenarios
Yuting Zeng, Zhiwen Zheng, You Zhou, JiaLing Xiao, Yongbin Yu, Manping Fan, Bo Gong, Liyong Ren
https://arxiv.org/abs/2509.15582

Momentum-constrained Hybrid Heuristic Trajectory Optimization Framework with Residual-enhanced DRL for Visually Impaired Scenarios
This paper proposes a momentum-constrained hybrid heuristic trajectory optimization framework (MHHTOF) tailored for assistive navigation in visually impaired scenarios, integrating trajectory sampling generation, optimization and evaluation with residual-enhanced deep reinforcement learning (DRL). In the first stage, heuristic trajectory sampling cluster (HTSC) is generated in the Frenet coordinate system using third-order interpolation with fifth-order polynomials and momentum-constrained traj…

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…

Knock-Knock: Black-Box, Platform-Agnostic DRAM Address-Mapping Reverse Engineering
Antoine Plin, Lorenzo Casalino, Thomas Rokicki, Ruben Salvador
https://arxiv.org/abs/2509.19568

Knock-Knock: Black-Box, Platform-Agnostic DRAM Address-Mapping Reverse Engineering
Modern Systems-on-Chip (SoCs) employ undocumented linear address-scrambling functions to obfuscate DRAM addressing, which complicates DRAM-aware performance optimizations and hinders proactive security analysis of DRAM-based attacks; most notably, Rowhammer. Although previous work tackled the issue of reversing physical-to-DRAM mapping, existing heuristic-based reverse-engineering approaches are partial, costly, and impractical for comprehensive recovery. This paper establishes a rigorous theor…

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

Wave Function Collapse Set Covering and the Hill Climbing Algorithm: A New, Fast Heuristic and Metaheuristic Pairing for the Minimum Set Cover Problem
David Oprea, David Perkins
https://arxiv.org/abs/2509.12236

Wave Function Collapse Set Covering and the Hill Climbing Algorithm: A New, Fast Heuristic and Metaheuristic Pairing for the Minimum Set Cover Problem
In this paper, we present a new heuristic that focuses on the optimization problem for the Minimum Set Cover Problem. Our new heuristic involves using Wave Function Collapse and is called Wave Function Collapse Set Covering (WFC-SC). This algorithm goes through observation, propagation, and collapsing, which will be explained more later on in this paper. We optimize this algorithm to quickly find optimal coverings that are close to the minimum needed. To further optimize this algorithm, we pair…

A General Optimization Framework for Movable Antenna Systems via Discrete Sampling
Changhao Liu, Weidong Mei, Zhi Chen, Jun Fang, Boyu Ning
https://arxiv.org/abs/2509.20987 http…

A General Optimization Framework for Movable Antenna Systems via Discrete Sampling
Movable antenna (MA) systems have attracted growing interest in wireless communications due to their ability to reshape wireless channels via local antenna movement within a confined region. However, optimizing antenna positions to enhance communication performance turns out to be challenging due to the highly nonlinear relationship between wireless channels and antenna positions. Existing approaches, such as gradient-based and heuristic algorithms, often suffer from high computational complexi…

Two Optimizations on the St\aa lmarck Procedure
Sergei Leonov, Liam Davis
https://arxiv.org/abs/2509.16172 https://arxiv.org/pdf/2509.16172

Two Optimizations on the Stålmarck Procedure
In this paper, we introduce StalmarckSAT, the a modern re-implementation of the Stålmarck Procedure for SAT solving, and present two novel strategies to improve the Procedure, Cardinality Driven Branching (CDB) and Deductive Priority Ordering (DPO). CDB is a heuristic to improve branching with the dilemma rule, and DPO intelligently orders simple rules based on their deductive potential. Our results demonstrate improved solve times with both strategies.

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 --…

Hierarchical Bayesian Operator-induced Symbolic Regression Trees for Structural Learning of Scientific Expressions
Somjit Roy, Pritam Dey, Debdeep Pati, Bani K. Mallick
https://arxiv.org/abs/2509.19710

Hierarchical Bayesian Operator-induced Symbolic Regression Trees for Structural Learning of Scientific Expressions
The advent of Scientific Machine Learning has heralded a transformative era in scientific discovery, driving progress across diverse domains. Central to this progress is uncovering scientific laws from experimental data through symbolic regression. However, existing approaches are dominated by heuristic algorithms or data-hungry black-box methods, which often demand low-noise settings and lack principled uncertainty quantification. Motivated by interpretable Statistical Artificial Intelligence,…

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

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

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…

Learning to Sample: Reinforcement Learning-Guided Sampling for Autonomous Vehicle Motion Planning
Korbinian Moller, Roland Stroop, Mattia Piccinini, Alexander Langmann, Johannes Betz
https://arxiv.org/abs/2509.24313

Learning to Sample: Reinforcement Learning-Guided Sampling for Autonomous Vehicle Motion Planning
Sampling-based motion planning is a well-established approach in autonomous driving, valued for its modularity and analytical tractability. In complex urban scenarios, however, uniform or heuristic sampling often produces many infeasible or irrelevant trajectories. We address this limitation with a hybrid framework that learns where to sample while keeping trajectory generation and evaluation fully analytical and verifiable. A reinforcement learning (RL) agent guides the sampling process toward…

An open-source heuristic to reboot 2D nesting research
Jeroen Gardeyn, Greet Vanden Berghe, Tony Wauters
https://arxiv.org/abs/2509.13329 https://arxiv.org…

An open-source heuristic to reboot 2D nesting research
2D nesting problems rank among the most challenging cutting and packing problems. Yet, despite their practical relevance, research over the past decade has seen remarkably little progress. One reasonable explanation could be that nesting problems are already solved to near optimality, leaving little room for improvement. However, as our paper demonstrates, we are not at the limit after all. This paper presents $\texttt{sparrow}$, an open-source heuristic approach to solving 2D irregular strip p…

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…

Minimal Data, Maximum Clarity: A Heuristic for Explaining Optimization
Amirali Rayegan, Tim Menzies
https://arxiv.org/abs/2509.08667 https://arxiv.org/pdf/…

Minimal Data, Maximum Clarity: A Heuristic for Explaining Optimization
Efficient, interpretable optimization is a critical but underexplored challenge in software engineering, where practitioners routinely face vast configuration spaces and costly, error-prone labeling processes. This paper introduces EZR, a novel and modular framework for multi-objective optimization that unifies active sampling, learning, and explanation within a single, lightweight pipeline. Departing from conventional wisdom, our Maximum Clarity Heuristic demonstrates that using less (but more…

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 …

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.

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

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

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…

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…

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 …

Neurodynamics-Driven Coupled Neural P Systems for Multi-Focus Image Fusion
Bo Li, Yunkuo Lei, Tingting Bao, Yaxian Wang, Lingling Zhang, Jun Liu
https://arxiv.org/abs/2509.17704

Neurodynamics-Driven Coupled Neural P Systems for Multi-Focus Image Fusion
Multi-focus image fusion (MFIF) is a crucial technique in image processing, with a key challenge being the generation of decision maps with precise boundaries. However, traditional methods based on heuristic rules and deep learning methods with black-box mechanisms are difficult to generate high-quality decision maps. To overcome this challenge, we introduce neurodynamics-driven coupled neural P (CNP) systems, which are third-generation neural computation models inspired by spiking mechanisms, …

DPO-Tuned Large Language Models for Segmentation in Simultaneous Speech Translation
Zeyu Yang, Satoshi Nakamura
https://arxiv.org/abs/2510.12195 https://ar…

DPO-Tuned Large Language Models for Segmentation in Simultaneous Speech Translation
Simultaneous speech translation requires accurate segmentation to balance translation quality and latency. Recent studies such as SHAS have introduced pretrained segmentation models, achieving stronger performance than heuristic rules. However, segmentation models such as SHAS, though pretrained and more robust than heuristic methods, are still constrained by supervised learning objectives and do not incorporate human preference alignment, which is crucial for natural real-time interpretation. …

Evaluating LLMs for Combinatorial Optimization: One-Phase and Two-Phase Heuristics for 2D Bin-Packing
Syed Mahbubul Huq, Daniel Brito, Daniel Sikar, Rajesh Mojumder
https://arxiv.org/abs/2509.22255

Evaluating LLMs for Combinatorial Optimization: One-Phase and Two-Phase Heuristics for 2D Bin-Packing
This paper presents an evaluation framework for assessing Large Language Models' (LLMs) capabilities in combinatorial optimization, specifically addressing the 2D bin-packing problem. We introduce a systematic methodology that combines LLMs with evolutionary algorithms to generate and refine heuristic solutions iteratively. Through comprehensive experiments comparing LLM generated heuristics against traditional approaches (Finite First-Fit and Hybrid First-Fit), we demonstrate that LLMs can pro…

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.

Efficient Heuristic Algorithms for Interleaving Distance between Merge Trees
Elena Farahbakhsh Touli, Talha Bin Masood
https://arxiv.org/abs/2509.15687 https://

Efficient Heuristic Algorithms for Interleaving Distance between Merge Trees
Merge trees are fundamental structures in topological data analysis. Interleaving distance is a widely accepted metric for comparing merge trees, with applications in visualization and scientific computing. While a greedy algorithm exists for finding the interleaving distance between labeled merge trees with overlapping labels, computing the interleaving distance between unlabeled trees or labeled trees with disjoint labels remains a significant challenge. In this work, we introduce a novel h…

Topology optimization of cathode gas channel layout in advanced proton exchange membrane fuel cells
Zahra Kazemi, Kamran Behdinan
https://arxiv.org/abs/2509.16619 https://

Topology optimization of cathode gas channel layout in advanced proton exchange membrane fuel cells
The proton exchange membrane fuel cell (PEMFC) output relies on the transport behavior within the cathode gas channels. Current designs remain inadequate as they often rely on heuristic modifications of existing layouts or designer intuition with suboptimal performance. In this study, topology optimization is proposed to redesign the PEMFC cathode gas channel layout without a priori assumptions. The optimization aims to maximize the reactant concentration and minimize power dissipation along th…

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 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…

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…

An Improved Quantum Algorithm for 3-Tuple Lattice Sieving
Lynn Engelberts, Yanlin Chen, Amin Shiraz Gilani, Maya-Iggy van Hoof, Stacey Jeffery, Ronald de Wolf
https://arxiv.org/abs/2510.08473

An Improved Quantum Algorithm for 3-Tuple Lattice Sieving
The assumed hardness of the Shortest Vector Problem in high-dimensional lattices is one of the cornerstones of post-quantum cryptography. The fastest known heuristic attacks on SVP are via so-called sieving methods. While these still take exponential time in the dimension $d$, they are significantly faster than non-heuristic approaches and their heuristic assumptions are verified by extensive experiments. $k$-Tuple sieving is an iterative method where each iteration takes as input a large numbe…

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 …

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…

NeuFACO: Neural Focused Ant Colony Optimization for Traveling Salesman Problem
Tran Thanh Dat, Tran Quang Khai, Pham Anh Khoi, Vu Van Khu, Do Duc Dong
https://arxiv.org/abs/2509.16938

NeuFACO: Neural Focused Ant Colony Optimization for Traveling Salesman Problem
This study presents Neural Focused Ant Colony Optimization (NeuFACO), a non-autoregressive framework for the Traveling Salesman Problem (TSP) that combines advanced reinforcement learning with enhanced Ant Colony Optimization (ACO). NeuFACO employs Proximal Policy Optimization (PPO) with entropy regularization to train a graph neural network for instance-specific heuristic guidance, which is integrated into an optimized ACO framework featuring candidate lists, restricted tour refinement, and sc…

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 …

Graph-Based Approximate Nearest Neighbor Search Revisited: Theoretical Analysis and Optimization
Xinran Ma, Zhaoqi Zhou, Chuan Zhou, Qi Meng, Zaijiu Shang, Guoliang Li, Zhiming Ma
https://arxiv.org/abs/2509.15531

Graph-Based Approximate Nearest Neighbor Search Revisited: Theoretical Analysis and Optimization
Graph-based approaches to approximate nearest neighbor search (ANNS) have achieved remarkable success in enabling fast, high-recall retrieval on billion-scale vector datasets. Among them, the Sparse Neighborhood Graph (SNG) has emerged as a widely adopted graph structure due to its superior search performance. However, the theoretical understanding of SNG remains limited, leading to reliance on heuristic-based and often suboptimal truncation strategies. In this work, we aim to bridge the gap be…

Smoothing Binary Optimization: A Primal-Dual Perspective
Wenbo Liu, Akang Wang, Dun Ma, Hongyi Jiang, Jianghua Wu, Wenguo Yang
https://arxiv.org/abs/2509.21064 https://

Smoothing Binary Optimization: A Primal-Dual Perspective
Binary optimization is a powerful tool for modeling combinatorial problems, yet scalable and theoretically sound solution methods remain elusive. Conventional solvers often rely on heuristic strategies with weak guarantees or struggle with large-scale instances. In this work, we introduce a novel primal-dual framework that reformulates unconstrained binary optimization as a continuous minimax problem, satisfying a strong max-min property. This reformulation effectively smooths the discrete prob…

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…

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…

Adaptive Kernel Design for Bayesian Optimization Is a Piece of CAKE with LLMs
Richard Cornelius Suwandi, Feng Yin, Juntao Wang, Renjie Li, Tsung-Hui Chang, Sergios Theodoridis
https://arxiv.org/abs/2509.17998

Adaptive Kernel Design for Bayesian Optimization Is a Piece of CAKE with LLMs
The efficiency of Bayesian optimization (BO) relies heavily on the choice of the Gaussian process (GP) kernel, which plays a central role in balancing exploration and exploitation under limited evaluation budgets. Traditional BO methods often rely on fixed or heuristic kernel selection strategies, which can result in slow convergence or suboptimal solutions when the chosen kernel is poorly suited to the underlying objective function. To address this limitation, we propose a freshly-baked Contex…

Randomized HyperSteiner: A Stochastic Delaunay Triangulation Heuristic for the Hyperbolic Steiner Minimal Tree
Aniss Aiman Medbouhi, Alejandro Garc\'ia-Castellanos, Giovanni Luca Marchetti, Daniel Pelt, Erik J Bekkers, Danica Kragic
https://arxiv.org/abs/2510.09328

Randomized HyperSteiner: A Stochastic Delaunay Triangulation Heuristic for the Hyperbolic Steiner Minimal Tree
We study the problem of constructing Steiner Minimal Trees (SMTs) in hyperbolic space. Exact SMT computation is NP-hard, and existing hyperbolic heuristics such as HyperSteiner are deterministic and often get trapped in locally suboptimal configurations. We introduce Randomized HyperSteiner (RHS), a stochastic Delaunay triangulation heuristic that incorporates randomness into the expansion process and refines candidate trees via Riemannian gradient descent optimization. Experiments on synthetic…

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…

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…

Value-Guided KV Compression for LLMs via Approximated CUR Decomposition
Ayan Sengupta, Siddhant Chaudhary, Tanmoy Chakraborty
https://arxiv.org/abs/2509.15038 https://

Value-Guided KV Compression for LLMs via Approximated CUR Decomposition
Key-value (KV) cache compression has emerged as a critical technique for reducing the memory and latency overhead of autoregressive language models during inference. Prior approaches predominantly rely on query-key attention scores to rank and evict cached tokens, assuming that attention intensity correlates with semantic importance. However, this heuristic overlooks the contribution of value vectors, which directly influence the attention output. In this paper, we propose CurDKV, a novel, valu…

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…

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 …

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 …

Heuristic Bundle Upper Bound Based Polyhedral Bundle Method for Semidefinite Programming
Zilong Cui, Ran Gu
https://arxiv.org/abs/2510.12374 https://arxiv.…

Heuristic Bundle Upper Bound Based Polyhedral Bundle Method for Semidefinite Programming
Semidefinite programming (SDP) is a fundamental class of convex optimization problems with diverse applications in mathematics, engineering, machine learning, and related disciplines. This paper investigates the application of the polyhedral bundle method to standard SDPs. The basic idea of this method is to approximate semidefinite constraints using linear constraints, and thereby transform the SDP problem into a series of quadratic programming subproblems. However, the number of linear constr…

Replaced article(s) found for cs.CG. https://arxiv.org/list/cs.CG/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}

Utilizing dynamic sparsity on pretrained DETR
Reza Sedghi, Anand Subramoney, David Kappel
https://arxiv.org/abs/2510.09380 https://arxiv.org/pdf/2510.09380…

Utilizing dynamic sparsity on pretrained DETR
Efficient inference with transformer-based models remains a challenge, especially in vision tasks like object detection. We analyze the inherent sparsity in the MLP layers of DETR and introduce two methods to exploit it without retraining. First, we propose Static Indicator-Based Sparsification (SIBS), a heuristic method that predicts neuron inactivity based on fixed activation patterns. While simple, SIBS offers limited gains due to the input-dependent nature of sparsity. To address this, we i…

Teaching According to Talents! Instruction Tuning LLMs with Competence-Aware Curriculum Learning
Yangning Li, Tingwei Lu, Yinghui Li, Yankai Chen, Wei-Chieh Huang, Wenhao Jiang, Hui Wang, Hai-Tao Zheng, Philip S. Yu
https://arxiv.org/abs/2509.13790

Teaching According to Talents! Instruction Tuning LLMs with Competence-Aware Curriculum Learning
Efficient instruction tuning aims to enhance the ultimate performance of large language models (LLMs) trained on a given instruction dataset. Curriculum learning as a typical data organization strategy has shown preliminary effectiveness in instruction tuning. However, current curriculum tuning methods suffer from the curriculum rigidity, since they rely solely on static heuristic difficulty metrics. These methods fail to adapt to the evolving capabilities of models during training, resulting i…

Fast and Optimal Incremental Parametric Procedure for the Densest Subgraph Problem: An Experimental Study
Dorit S. Hochbaum, Ayleen Irribarra-Cort\'es, Olivier Goldschmidt, Roberto As\'in-Ach\'a
https://arxiv.org/abs/2509.14993

Fast and Optimal Incremental Parametric Procedure for the Densest Subgraph Problem: An Experimental Study
The Densest Subgraph Problem (DSP) is widely used to identify community structures and patterns in networks such as bioinformatics and social networks. While solvable in polynomial time, traditional exact algorithms face computational and scalability limitations, leading to the adoption of faster, but non-optimal, heuristic methods. This work presents the first experimental study of the recently devised Incremental Parametric Cut (IPC) algorithm, which is an exact method for DSP and other "mono…

Beyond adjacency: Graph encoding with reachability and shortest paths
Shiqiang Zhang, Ruth Misener
https://arxiv.org/abs/2509.20247 https://arxiv.org/pdf/2…

Beyond adjacency: Graph encoding with reachability and shortest paths
Graph-structured data is central to many scientific and industrial domains, where the goal is often to optimize objectives defined over graph structures. Given the combinatorial complexity of graph spaces, such optimization problems are typically addressed using heuristic methods, and it remains unclear how to systematically incorporate structural constraints to effectively reduce the search space. This paper introduces explicit optimization formulations for graph search space that encode prope…

Dynamic Classifier-Free Diffusion Guidance via Online Feedback
Pinelopi Papalampidi, Olivia Wiles, Ira Ktena, Aleksandar Shtedritski, Emanuele Bugliarello, Ivana Kajic, Isabela Albuquerque, Aida Nematzadeh
https://arxiv.org/abs/2509.16131

Dynamic Classifier-Free Diffusion Guidance via Online Feedback
Classifier-free guidance (CFG) is a cornerstone of text-to-image diffusion models, yet its effectiveness is limited by the use of static guidance scales. This "one-size-fits-all" approach fails to adapt to the diverse requirements of different prompts; moreover, prior solutions like gradient-based correction or fixed heuristic schedules introduce additional complexities and fail to generalize. In this work, we challeng this static paradigm by introducing a framework for dynamic CFG scheduling. …

Leaf it to renewal: Improved predictive maintenance policies via renewal theory and decision trees
Daniel Koutas, Daniel Straub
https://arxiv.org/abs/2509.20145 https://

Leaf it to renewal: Improved predictive maintenance policies via renewal theory and decision trees
We propose a general method for deriving prognostics-based predictive maintenance policies. The method takes into account the available decision options at hand, the information on the future state of the system provided by a prognostic model, as well as the costs of the underlying renewal-reward process. It results in heuristic policies with only a few parameters, which can be determined based on theoretical considerations or by optimization from run-to-failure data. We show the potential of t…

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…

Quantum Reinforcement Learning-Guided Diffusion Model for Image Synthesis via Hybrid Quantum-Classical Generative Model Architectures
Chi-Sheng Chen, En-Jui Kuo
https://arxiv.org/abs/2509.14163

Quantum Reinforcement Learning-Guided Diffusion Model for Image Synthesis via Hybrid Quantum-Classical Generative Model Architectures
Diffusion models typically employ static or heuristic classifier-free guidance (CFG) schedules, which often fail to adapt across timesteps and noise conditions. In this work, we introduce a quantum reinforcement learning (QRL) controller that dynamically adjusts CFG at each denoising step. The controller adopts a hybrid quantum--classical actor--critic architecture: a shallow variational quantum circuit (VQC) with ring entanglement generates policy features, which are mapped by a compact multil…

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…

VRPAgent: LLM-Driven Discovery of Heuristic Operators for Vehicle Routing Problems
Andr\'e Hottung, Federico Berto, Chuanbo Hua, Nayeli Gast Zepeda, Daniel Wetzel, Michael R\"omer, Haoran Ye, Davide Zago, Michael Poli, Stefano Massaroli, Jinkyoo Park, Kevin Tierney
https://arxiv.org/abs/2510.07073

VRPAgent: LLM-Driven Discovery of Heuristic Operators for Vehicle Routing Problems
Designing high-performing heuristics for vehicle routing problems (VRPs) is a complex task that requires both intuition and deep domain knowledge. Large language model (LLM)-based code generation has recently shown promise across many domains, but it still falls short of producing heuristics that rival those crafted by human experts. In this paper, we propose VRPAgent, a framework that integrates LLM-generated components into a metaheuristic and refines them through a novel genetic search. By u…

A Variational Framework for Residual-Based Adaptivity in Neural PDE Solvers and Operator Learning
Juan Diego Toscano, Daniel T. Chen, Vivek Oommen, George Em Karniadakis
https://arxiv.org/abs/2509.14198

A Variational Framework for Residual-Based Adaptivity in Neural PDE Solvers and Operator Learning
Residual-based adaptive strategies are widely used in scientific machine learning but remain largely heuristic. We introduce a unifying variational framework that formalizes these methods by integrating convex transformations of the residual. Different transformations correspond to distinct objective functionals: exponential weights target the minimization of uniform error, while linear weights recover the minimization of quadratic error. Within this perspective, adaptive weighting is equivalen…

Shuffling Heuristic in Variational Inequalities: Establishing New Convergence Guarantees
Daniil Medyakov, Gleb Molodtsov, Grigoriy Evseev, Egor Petrov, Aleksandr Beznosikov
https://arxiv.org/abs/2509.04133

Shuffling Heuristic in Variational Inequalities: Establishing New Convergence Guarantees
Variational inequalities have gained significant attention in machine learning and optimization research. While stochastic methods for solving these problems typically assume independent data sampling, we investigate an alternative approach -- the shuffling heuristic. This strategy involves permuting the dataset before sequential processing, ensuring equal consideration of all data points. Despite its practical utility, theoretical guarantees for shuffling in variational inequalities remain une…

Crosslisted article(s) found for cs.CG. https://arxiv.org/list/cs.CG/new
[1/1]:
- Lifting Cocycles: From Heuristic to Theory
Sigurd Gaukstad, Mathias Karsrud Nordal, Marius Thaule

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…

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…

Improving Decision Trees through the Lens of Parameterized Local Search
Juha Harviainen, Frank Sommer, Manuel Sorge
https://arxiv.org/abs/2510.12726 https://

Improving Decision Trees through the Lens of Parameterized Local Search
Algorithms for learning decision trees often include heuristic local-search operations such as (1) adjusting the threshold of a cut or (2) also exchanging the feature of that cut. We study minimizing the number of classification errors by performing a fixed number of a single type of these operations. Although we discover that the corresponding problems are NP-complete in general, we provide a comprehensive parameterized-complexity analysis with the aim of determining those properties of the pr…

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…

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…

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 …

CHUCKLE -- When Humans Teach AI To Learn Emotions The Easy Way
Ankush Pratap Singh, Houwei Cao, Yong Liu
https://arxiv.org/abs/2510.09382 https://arxiv.org…

CHUCKLE -- When Humans Teach AI To Learn Emotions The Easy Way
Curriculum learning (CL) structures training from simple to complex samples, facilitating progressive learning. However, existing CL approaches for emotion recognition often rely on heuristic, data-driven, or model-based definitions of sample difficulty, neglecting the difficulty for human perception, a critical factor in subjective tasks like emotion recognition. We propose CHUCKLE (Crowdsourced Human Understanding Curriculum for Knowledge Led Emotion Recognition), a perception-driven CL frame…

Memory as Action: Autonomous Context Curation for Long-Horizon Agentic Tasks
Yuxiang Zhang, Jiangming Shu, Ye Ma, Xueyuan Lin, Shangxi Wu, Jitao Sang
https://arxiv.org/abs/2510.12635

Memory as Action: Autonomous Context Curation for Long-Horizon Agentic Tasks
Large Language Models face challenges in long-horizon agentic tasks as their constrained memory is easily overwhelmed by distracting or irrelevant context. Existing working memory methods typically rely on external, heuristic mechanisms that are decoupled from the agent's core policy. In this work, we reframe working memory management as a learnable, intrinsic capability. We propose a novel framework, Memory-as-Action, where an agent actively manages its working memory by executing explicit edi…