Tootfinder

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…

Instruction and Solution Probabilities as Heuristics for Inductive Programming
Edward McDaid, Sarah McDaid
https://arxiv.org/abs/2506.13804 https://…

Instruction and Solution Probabilities as Heuristics for Inductive Programming
Instruction subsets (ISs) are heuristics that can shrink the size of the inductive programming (IP) search space by tens of orders of magnitude. Here, we extend the IS approach by introducing instruction and solution probabilities as additional heuristics. Instruction probability reflects the expectation of an instruction occurring in a solution, based on the frequency of instruction occurrence in a large code sample. The solution probability for a partial or complete program is simply the prod…

Whistlejacket is a Brand, Design and Communications Agency from the UK. They commissioned me to create illustrations and iconography, and build an interactive digital handbook for the Waste and Resources Action Programme (WRAP).
#Design #Illustration #Portfolio

Selective Densification for Rapid Motion Planning in High Dimensions with Narrow Passages
Lu Huang, Lingxiao Meng, Jiankun Wang, Xingjian Jing
https://arxiv.org/abs/2507.15710

Selective Densification for Rapid Motion Planning in High Dimensions with Narrow Passages
Sampling-based algorithms are widely used for motion planning in high-dimensional configuration spaces. However, due to low sampling efficiency, their performance often diminishes in complex configuration spaces with narrow corridors. Existing approaches address this issue using handcrafted or learned heuristics to guide sampling toward useful regions. Unfortunately, these strategies often lack generalizability to various problems or require extensive prior training. In this paper, we propose a…

caida_as: CAIDA AS graphs (2004-2007)
A sequence of 122 network snapshots denoting Autonomous System (AS) relationships on the Internet, from 2004-2007, inferred using the Serial-1 method from RouteViews BGP table snapshots and a set of heuristics.
This network has 23072 nodes and 78684 edges.
Tags: Technological, Communication, Unweighted, Temporal

Evolutionary Caching to Accelerate Your Off-the-Shelf Diffusion Model
Anirud Aggarwal, Abhinav Shrivastava, Matthew Gwilliam
https://arxiv.org/abs/2506.15682

Evolutionary Caching to Accelerate Your Off-the-Shelf Diffusion Model
Diffusion-based image generation models excel at producing high-quality synthetic content, but suffer from slow and computationally expensive inference. Prior work has attempted to mitigate this by caching and reusing features within diffusion transformers across inference steps. These methods, however, often rely on rigid heuristics that result in limited acceleration or poor generalization across architectures. We propose Evolutionary Caching to Accelerate Diffusion models (ECAD), a genetic a…

Excitation Amplitude Sampling for Low Variance Electronic Structure on Quantum Computers
Connor Lenihan, Oliver J. Backhouse, Tom W. A. Montgomery, Phalgun Lolur, M. J. Bhaseen, George H. Booth
https://arxiv.org/abs/2506.15438

Excitation Amplitude Sampling for Low Variance Electronic Structure on Quantum Computers
We combine classical heuristics with partial shadow tomography to enable efficient protocols for extracting information from correlated ab initio electronic systems encoded on quantum devices. By proposing the use of a correlation energy functional and sampling of a polynomial set of excitation amplitudes of the quantum state, we can demonstrate an almost two order of magnitude reduction in required number of shots for a given statistical error in the energy estimate, as well as observing a lin…

This https://arxiv.org/abs/2504.09546 has been replaced.
initial toot: https://mastoxiv.page/@arXi…

A simulation-heuristics dual-process model for intuitive physics
The role of mental simulation in human physical reasoning is widely acknowledged, but whether it is employed across scenarios with varying simulation costs and where its boundary lies remains unclear. Using a pouring-marble task, our human study revealed two distinct error patterns when predicting pouring angles, differentiated by simulation time. While mental simulation accurately captured human judgments in simpler scenarios, a linear heuristic model better matched human predictions when simu…

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…

caida_as: CAIDA AS graphs (2004-2007)
A sequence of 122 network snapshots denoting Autonomous System (AS) relationships on the Internet, from 2004-2007, inferred using the Serial-1 method from RouteViews BGP table snapshots and a set of heuristics.
This network has 22965 nodes and 94386 edges.
Tags: Technological, Communication, Unweighted, Temporal

Measuring CEX-DEX Extracted Value and Searcher Profitability: The Darkest of the MEV Dark Forest
Fei Wu, Danning Sui, Thomas Thiery, Mallesh Pai
https://arxiv.org/abs/2507.13023

Measuring CEX-DEX Extracted Value and Searcher Profitability: The Darkest of the MEV Dark Forest
This paper provides a comprehensive empirical analysis of the economics and dynamics behind arbitrages between centralized and decentralized exchanges (CEX-DEX) on Ethereum. We refine heuristics to identify arbitrage transactions from on-chain data and introduce a robust empirical framework to estimate arbitrage revenue without knowing traders' actual behaviors on CEX. Leveraging an extensive dataset spanning 19 months from August 2023 to March 2025, we estimate a total of 233.8M USD extracted …

On tangible user interfaces, humans and spatiality
Ehud Sharlin, Benjamin Watson, Yoshifumi Kitamura, Fumio Kishino, Yuichi Itoh
https://arxiv.org/abs/2507.13167

On tangible user interfaces, humans and spatiality
Like the prehistoric twig and stone, tangible user interfaces (TUIs) are objects manipulated by humans. TUI success will depend on how well they exploit spatiality, the intuitive spatial skills humans have with the objects they use. In this paper we carefully examine the relationship between humans and physical objects, and related previous research. From this examination we distill a set of observations, and turn these into heuristics for incorporation of spatiality into TUI application design…

from my link log —
When do pattern match compilation heuristics matter?
https://www.cs.tufts.edu/~nr/pubs/match-abstract.html
saved 2025-06-02

Efficiently Constructing Sparse Navigable Graphs
Alex Conway, Laxman Dhulipala, Martin Farach-Colton, Rob Johnson, Ben Landrum, Christopher Musco, Yarin Shechter, Torsten Suel, Richard Wen
https://arxiv.org/abs/2507.13296

Efficiently Constructing Sparse Navigable Graphs
Graph-based nearest neighbor search methods have seen a surge of popularity in recent years, offering state-of-the-art performance across a wide variety of applications. Central to these methods is the task of constructing a sparse navigable search graph for a given dataset endowed with a distance function. Unfortunately, doing so is computationally expensive, so heuristics are universally used in practice. In this work, we initiate the study of fast algorithms with provable guarantees for se…

POPQC: Parallel Optimization for Quantum Circuits (Extended Version)
Pengyu Liu, Jatin Arora, Mingkuan Xu, Umut A. Acar
https://arxiv.org/abs/2506.13720 ht…

POPQC: Parallel Optimization for Quantum Circuits (Extended Version)
Optimization of quantum programs or circuits is a fundamental problem in quantum computing and remains a major challenge. State-of-the-art quantum circuit optimizers rely on heuristics and typically require superlinear, and even exponential, time. Recent work proposed a new approach that pursues a weaker form of optimality called local optimality. Parameterized by a natural number $Ω$, local optimality insists that each and every $Ω$-segment of the circuit is optimal with respect to an extern…

Heuristics for (ir)reducibility of $p$-rank strata of the moduli space of hyperelliptic curves
Thomas Bouchet, Erik Davis, Steven R. Groen, Zachary Porat, Benjamin York
https://arxiv.org/abs/2506.06457

Heuristics for (ir)reducibility of $p$-rank strata of the moduli space of hyperelliptic curves
Let $\mathcal{H}_g$ denote the moduli space of smooth hyperelliptic curves of genus $g$ in characteristic $p\geq 3$, and let $\mathcal{H}_g^f$ denote the $p$-rank $f$ stratum of $\mathcal{H}_g$ for $0 \leq f \leq g$. Achter and Pries note in their 2011 work that determining the number of irreducible components of $\mathcal{H}_g^f$ would lead to several intriguing corollaries. In this paper, we present a computational approach for estimating the number of irreducible components in various $p$-ra…

cAST: Enhancing Code Retrieval-Augmented Generation with Structural Chunking via Abstract Syntax Tree
Yilin Zhang, Xinran Zhao, Zora Zhiruo Wang, Chenyang Yang, Jiayi Wei, Tongshuang Wu
https://arxiv.org/abs/2506.15655

cAST: Enhancing Code Retrieval-Augmented Generation with Structural Chunking via Abstract Syntax Tree
Retrieval-Augmented Generation (RAG) has become essential for large-scale code generation, grounding predictions in external code corpora to improve actuality. However, a critical yet underexplored aspect of RAG pipelines is chunking -- the process of dividing documents into retrievable units. Existing line-based chunking heuristics often break semantic structures, splitting functions or merging unrelated code, which can degrade generation quality. We propose chunking via Abstract Syntax Trees …

Feedforward Ordering in Neural Connectomes via Feedback Arc Minimization
Soroush Vahidi
https://arxiv.org/abs/2506.13799 https://arxi…

Feedforward Ordering in Neural Connectomes via Feedback Arc Minimization
We present a suite of scalable algorithms for minimizing feedback arcs in large-scale weighted directed graphs, with the goal of revealing biologically meaningful feedforward structure in neural connectomes. Using the FlyWire Connectome Challenge dataset, we demonstrate the effectiveness of our ranking strategies in maximizing the total weight of forward-pointing edges. Our methods integrate greedy heuristics, gain-aware local refinements, and global structural analysis based on strongly connec…

Synergizing Reinforcement Learning and Genetic Algorithms for Neural Combinatorial Optimization
Shengda Gu, Kai Li, Junliang Xing, Yifan Zhang, Jian Cheng
https://arxiv.org/abs/2506.09404

Synergizing Reinforcement Learning and Genetic Algorithms for Neural Combinatorial Optimization
Combinatorial optimization problems are notoriously challenging due to their discrete structure and exponentially large solution space. Recent advances in deep reinforcement learning (DRL) have enabled the learning heuristics directly from data. However, DRL methods often suffer from limited exploration and susceptibility to local optima. On the other hand, evolutionary algorithms such as Genetic Algorithms (GAs) exhibit strong global exploration capabilities but are typically sample inefficien…

caida_as: CAIDA AS graphs (2004-2007)
A sequence of 122 network snapshots denoting Autonomous System (AS) relationships on the Internet, from 2004-2007, inferred using the Serial-1 method from RouteViews BGP table snapshots and a set of heuristics.
This network has 20344 nodes and 81882 edges.
Tags: Technological, Communication, Unweighted, Temporal

Assessing the Quality of Binomial Samplers: A Statistical Distance Framework
Uddalok Sarkar, Sourav Chakraborty, Kuldeep S. Meel
https://arxiv.org/abs/2506.12061

Assessing the Quality of Binomial Samplers: A Statistical Distance Framework
Randomized algorithms depend on accurate sampling from probability distributions, as their correctness and performance hinge on the quality of the generated samples. However, even for common distributions like Binomial, exact sampling is computationally challenging, leading standard library implementations to rely on heuristics. These heuristics, while efficient, suffer from approximation and system representation errors, causing deviations from the ideal distribution. Although seemingly minor,…

This https://arxiv.org/abs/2106.12331 has been replaced.
link: https://scholar.google.com/scholar?q=a

Optimal Transmission Switching: Improving Solver Performance Using Heuristics
The optimal transmission switching problem (OTSP) is an established problem of changing a power grid's topology to obtain an improved operation by controlling the switching status of transmission lines. This problem was proven to be NP-hard. Proposed solution techniques based on mixed-integer formulations can guarantee globally optimal solutions but are potentially intractable in realistic power grids. Heuristics methods cannot guarantee global optimality but can provide tractable solution appr…

Benchmarking Quantum Heuristics: Non-Variational QWOA for Weighted Maxcut
Tavis Bennett, Aidan Smith, Edric Matwiejew, Jingbo Wang
https://arxiv.org/abs/2505.24191

Benchmarking Quantum Heuristics: Non-Variational QWOA for Weighted Maxcut
We present benchmarking results for the non-variational Quantum Walk Optimisation Algorithm (non-variational QWOA) applied to the weighted maxcut problem, using classical simulations for problem sizes up to $n = 31$. The amplified quantum state, prepared using a quadratic number of alternating unitaries, achieves a constant average-case measurement probability for globally optimal solutions across these problem sizes. This behaviour contrasts with that of classical heuristics, which, for NP-har…

Game Theory Meets LLM and Agentic AI: Reimagining Cybersecurity for the Age of Intelligent Threats
Quanyan Zhu
https://arxiv.org/abs/2507.10621 https://

Game Theory Meets LLM and Agentic AI: Reimagining Cybersecurity for the Age of Intelligent Threats
Protecting cyberspace requires not only advanced tools but also a shift in how we reason about threats, trust, and autonomy. Traditional cybersecurity methods rely on manual responses and brittle heuristics. To build proactive and intelligent defense systems, we need integrated theoretical frameworks and software tools. Game theory provides a rigorous foundation for modeling adversarial behavior, designing strategic defenses, and enabling trust in autonomous systems. Meanwhile, software tools p…

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

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

Improving elliptic curve rank classification using multi-value and learned Mestre-Nagao sums
Zvonimir Bujanovi\'c, Matija Kazalicki, Domagoj Vlah
https://arxiv.org/abs/2506.07967

Improving elliptic curve rank classification using multi-value and learned Mestre-Nagao sums
Determining the rank of an elliptic curve E/Q remains a central challenge in number theory. Heuristics such as Mestre--Nagao sums are widely used to estimate ranks, but there is considerable room for improving their predictive power. This paper introduces two novel methods for enhancing rank classification using Mestre--Nagao sums. First, we propose a ``multi-value'' approach that simultaneously uses two distinct sums, S_0 and S_5, evaluated over multiple ranges. This multi-sum perspective sign…

"During his time at IBM, he wrote some of the first artificial intelligence software—his "geometry theorem machine" was the first advanced AI program, and the third AI program ever. It is a logical AI system that can prove theorems in planar geometry about parallel lines, congruence, and equality and inequality of segments and angles. Like Logic Theorist, it uses heuristics."

caida_as: CAIDA AS graphs (2004-2007)
A sequence of 122 network snapshots denoting Autonomous System (AS) relationships on the Internet, from 2004-2007, inferred using the Serial-1 method from RouteViews BGP table snapshots and a set of heuristics.
This network has 19267 nodes and 77860 edges.
Tags: Technological, Communication, Unweighted, Temporal

PFCS: Prime Factorization Cache System for Deterministic Data Relationship Discovery
Duy Le
https://arxiv.org/abs/2507.03919 https://…

PFCS: Prime Factorization Cache System for Deterministic Data Relationship Discovery
Cache systems fundamentally limit modern computing performance due to their inability to precisely capture data relationships. While achieving 85-92% hit rates, traditional systems rely on statistical heuristics that cannot guarantee relationship discovery, leading to suboptimal prefetching and resource waste. We present PFCS (Prime Factorization Cache System), which leverages the mathematical uniqueness of prime factorization to achieve deterministic relationship discovery with zero false posi…

This https://arxiv.org/abs/2403.05322 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Direct-search methods in the year 2025: Theoretical guarantees and algorithmic paradigms
Optimizing a function without using derivatives is a challenging paradigm, that precludes from using classical algorithms from nonlinear optimization, and may thus seem intractable other than by using heuristics. Nevertheless, the field of derivative-free optimization has succeeded in producing algorithms that do not rely on derivatives and yet are endowed with convergence guarantees. One class of such methods, called direct-search methods, is particularly popular thanks to its simplicity of im…

Dynamic Chunking for End-to-End Hierarchical Sequence Modeling
Sukjun Hwang, Brandon Wang, Albert Gu
https://arxiv.org/abs/2507.07955 https://arxiv.org/pdf/2507.07955 https://arxiv.org/html/2507.07955
arXiv:2507.07955v1 Announce Type: new
Abstract: Despite incredible progress in language models (LMs) in recent years, largely resulting from moving away from specialized models designed for specific tasks to general models based on powerful architectures (e.g. the Transformer) that learn everything from raw data, pre-processing steps such as tokenization remain a barrier to true end-to-end foundation models. We introduce a collection of new techniques that enable a dynamic chunking mechanism which automatically learns content -- and context -- dependent segmentation strategies learned jointly with the rest of the model. Incorporating this into an explicit hierarchical network (H-Net) allows replacing the (implicitly hierarchical) tokenization-LM-detokenization pipeline with a single model learned fully end-to-end. When compute- and data- matched, an H-Net with one stage of hierarchy operating at the byte level outperforms a strong Transformer language model operating over BPE tokens. Iterating the hierarchy to multiple stages further increases its performance by modeling multiple levels of abstraction, demonstrating significantly better scaling with data and matching a token-based Transformer of twice its size. H-Nets pretrained on English show significantly increased character-level robustness, and qualitatively learn meaningful data-dependent chunking strategies without any heuristics or explicit supervision. Finally, the H-Net's improvement over tokenized pipelines is further increased in languages and modalities with weaker tokenization heuristics, such as Chinese and code, or DNA sequences (nearly 4x improvement in data efficiency over baselines), showing the potential of true end-to-end models that learn and scale better from unprocessed data.
toXiv_bot_toot

From Theory to Practice: Advancing Multi-Robot Path Planning Algorithms and Applications
Teng Guo
https://arxiv.org/abs/2506.09914 https://

From Theory to Practice: Advancing Multi-Robot Path Planning Algorithms and Applications
The labeled MRPP (Multi-Robot Path Planning) problem involves routing robots from start to goal configurations efficiently while avoiding collisions. Despite progress in solution quality and runtime, its complexity and industrial relevance continue to drive research. This dissertation introduces scalable MRPP methods with provable guarantees and practical heuristics. First, we study dense MRPP on 2D grids, relevant to warehouse and parcel systems. We propose the Rubik Table method, achieving …

This https://arxiv.org/abs/2506.05096 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Astraea: A GPU-Oriented Token-wise Acceleration Framework for Video Diffusion Transformers
Video diffusion transformers (vDiTs) have made impressive progress in text-to-video generation, but their high computational demands present major challenges for practical deployment. While existing acceleration methods reduce workload at various granularities, they often rely on heuristics, limiting their applicability. We introduce ASTRAEA, an automatic framework that searches for near-optimal configurations for vDiT-based video generation. At its core, ASTRAEA proposes a lightweight token …

I usually don't bother with writing about #AI, but after seeing an interesting post about "The Era of the AI Idiot", it made me think of the "Expert systems" #AI boom in the 1980's.

Quality Diversity Genetic Programming for Learning Scheduling Heuristics
Meng Xu, Frank Neumann, Aneta Neumann, Yew Soon Ong
https://arxiv.org/abs/2507.02235

Quality Diversity Genetic Programming for Learning Scheduling Heuristics
Real-world optimization often demands diverse, high-quality solutions. Quality-Diversity (QD) optimization is a multifaceted approach in evolutionary algorithms that aims to generate a set of solutions that are both high-performing and diverse. QD algorithms have been successfully applied across various domains, providing robust solutions by exploring diverse behavioral niches. However, their application has primarily focused on static problems, with limited exploration in the context of dynami…

Higher-Order Tensor-Based Deferral of Gaussian Splitting for Orbit Uncertainty Propagation
G. Andrew Siciliano, Keith A. LeGrand, Jackson Kulik
https://arxiv.org/abs/2507.01771

Higher-Order Tensor-Based Deferral of Gaussian Splitting for Orbit Uncertainty Propagation
Accurate propagation of orbital uncertainty is essential for a range of applications within space domain awareness. Adaptive Gaussian mixture-based approaches offer tractable nonlinear uncertainty propagation through splitting mixands to increase resolution in areas of stronger nonlinearities, as well as by reducing mixands to prevent unnecessary computational effort. Recent work introduced principled heuristics that incorporate information from the system dynamics and initial uncertainty to de…

This https://arxiv.org/abs/2505.22086 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAR_…

iDSE: Navigating Design Space Exploration in High-Level Synthesis Using LLMs
High-Level Synthesis (HLS) serves as an agile hardware development tool that streamlines the circuit design by abstracting the register transfer level into behavioral descriptions, while allowing designers to customize the generated microarchitectures through optimization directives. However, the combinatorial explosion of possible directive configurations yields an intractable design space. Traditional design space exploration (DSE) methods, despite adopting heuristics or constructing predicti…

An experiment in price perception error
Shawn Berry
https://arxiv.org/abs/2506.19953 https://arxiv.org/pdf/2506.19953

An experiment in price perception error
The process of consumer decision-making is multidimensional, and price perception is a very important but still not well-understood dimension for both marketers and consumers. Although heuristics or mental shortcuts are seen as biased and can cause decision errors, consumers tend to use price knowledge heuristics for purchase decisions, sometimes relying on old information. This study examined the effects of individual attitudinal and demographic factors on the ability of 351 respondents to cor…

ARAG: Agentic Retrieval Augmented Generation for Personalized Recommendation
Reza Yousefi Maragheh, Pratheek Vadla, Priyank Gupta, Kai Zhao, Aysenur Inan, Kehui Yao, Jianpeng Xu, Praveen Kanumala, Jason Cho, Sushant Kumar
https://arxiv.org/abs/2506.21931

ARAG: Agentic Retrieval Augmented Generation for Personalized Recommendation
Retrieval-Augmented Generation (RAG) has shown promise in enhancing recommendation systems by incorporating external context into large language model prompts. However, existing RAG-based approaches often rely on static retrieval heuristics and fail to capture nuanced user preferences in dynamic recommendation scenarios. In this work, we introduce ARAG, an Agentic Retrieval-Augmented Generation framework for Personalized Recommendation, which integrates a multi-agent collaboration mechanism int…

W2W: A Simulated Exploration of IMU Placement Across the Human Body for Designing Smarter Wearable
Lala Shakti Swarup Ray, Bo Zhou, Paul Lukowicz
https://arxiv.org/abs/2507.05532 …

W2W: A Simulated Exploration of IMU Placement Across the Human Body for Designing Smarter Wearable
Inertial measurement units (IMUs) are central to wearable systems for activity recognition and pose estimation, but sensor placement remains largely guided by heuristics and convention. In this work, we introduce Where to Wear (W2W), a simulation-based framework for systematic exploration of IMU placement utility across the body. Using labeled motion capture data, W2W generates realistic synthetic IMU signals at 512 anatomically distributed surface patches, enabling high-resolution, task-specif…

The second moment of the size of the $2$-class group of monogenized cubic fields
Manjul Bhargava, Arul Shankar, Ashvin Swaminathan
https://arxiv.org/abs/2506.05539

The second moment of the size of the $2$-class group of monogenized cubic fields
We prove that when totally real (resp., complex) monogenized cubic number fields are ordered by height, the second moment of the size of the $2$-class group is at most $3$ (resp., at most $6$). In the totally real case, we further prove that the second moment of the size of the narrow $2$-class group is at most $9$. This result gives further evidence in support of the general observation, first made in work of Bhargava--Hanke--Shankar and recently formalized into a set of heuristics in work of …

Q-learning-based Hierarchical Cooperative Local Search for Steelmaking-continuous Casting Scheduling Problem
Yang Lv, Rong Hu, Bin Qian, Jian-Bo Yang
https://arxiv.org/abs/2506.08608

Q-learning-based Hierarchical Cooperative Local Search for Steelmaking-continuous Casting Scheduling Problem
The steelmaking continuous casting scheduling problem (SCCSP) is a critical and complex challenge in modern steel production, requiring the coordinated assignment and sequencing of steel charges across multiple production stages. Efficient scheduling not only enhances productivity but also significantly reduces energy consumption. However, both traditional heuristics (e.g., two-stage local search) and recent metaheuristics often struggle to adapt to the dynamic characteristics of practical SCCS…

caida_as: CAIDA AS graphs (2004-2007)
A sequence of 122 network snapshots denoting Autonomous System (AS) relationships on the Internet, from 2004-2007, inferred using the Serial-1 method from RouteViews BGP table snapshots and a set of heuristics.
This network has 20906 nodes and 85988 edges.
Tags: Technological, Communication, Unweighted, Temporal

Out of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback
Rex Chen, Karen Wu, John McCartney, Norman Sadeh, Fei Fang
https://arxiv.org/abs/2505.21349

Out of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback
How can a traffic simulation be designed to faithfully reflect real-world traffic conditions? Past data-driven approaches to traffic simulation in the literature have relied on unrealistic or suboptimal heuristics. They also fail to adequately account for the effects of uncertainty and multimodality in the data on simulation outcomes. In this work, we integrate advances in AI to construct a three-step, end-to-end pipeline for generating a traffic simulation from detector data: computer vision f…

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

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

Disa: Accurate Learning-based Static Disassembly with Attentions
Peicheng Wang, Monika Santra, Mingyu Liu, Cong Sun, Dongrui Zeng, Gang Tan
https://arxiv.org/abs/2507.07246

Disa: Accurate Learning-based Static Disassembly with Attentions
For reverse engineering related security domains, such as vulnerability detection, malware analysis, and binary hardening, disassembly is crucial yet challenging. The fundamental challenge of disassembly is to identify instruction and function boundaries. Classic approaches rely on file-format assumptions and architecture-specific heuristics to guess the boundaries, resulting in incomplete and incorrect disassembly, especially when the binary is obfuscated. Recent advancements of disassembly ha…

This https://arxiv.org/abs/2412.13664 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

A Skeleton-Based Topological Planner for Exploration in Complex Unknown Environments
The capability of autonomous exploration in complex, unknown environments is important in many robotic applications. While recent research on autonomous exploration have achieved much progress, there are still limitations, e.g., existing methods relying on greedy heuristics or optimal path planning are often hindered by repetitive paths and high computational demands. To address such limitations, we propose a novel exploration framework that utilizes the global topology information of observed …

This https://arxiv.org/abs/2503.10503 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Sample Compression for Continual Learning
Continual learning algorithms aim to learn from a sequence of tasks, making the training distribution non-stationary. The majority of existing continual learning approaches in the literature rely on heuristics and do not provide learning guarantees. In this paper, we present a new method called Continual Pick-to-Learn (CoP2L), which is able to retain the most representative samples for each task in an efficient way. CoP2L combines the Pick-to-Learn algorithm (rooted in the sample compression th…

Proof of The TAP Free Energy for High-Dimensional Linear Regression with Spherical Priors at All Temperatures
Zhiyuan Yu, Jingbo Liu
https://arxiv.org/abs/2506.20768

Proof of The TAP Free Energy for High-Dimensional Linear Regression with Spherical Priors at All Temperatures
Variational inference (VI) has emerged as an efficient framework for approximating intractable posteriors. Traditional mean-field VI methods fail to capture the complex dependencies and can be inconsistent in high-dimensional settings. In contrast, it has been conjectured, based on statistical physics heuristics and conditions for the stationary points, that the more refined Thouless-Anderson-Palmer (TAP) approximation is consistent in the high-dimensional regimes. Existing results, however, ha…

This https://arxiv.org/abs/2505.01214 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

A Warm-start QAOA based approach using a swap-based mixer for the TSP: theoretical considerations,implementation and experiments
This paper investigates quantum heuristics based on Mixer Hamiltonians, which allow the search to be restricted to a specific subspace and enable warm-start strategies for solving the Traveling Salesman Problem (TSP). Approaches involving Mixer Hamiltonians can be integrated into the Quantum Approximate Optimization Algorithm (QAOA), where the Mixer acts as a mapping function that transforms qubit strings into feasible solution sets. We first introduce a swap-based mixer tailored to the TSP, wh…

SOF: Sorted Opacity Fields for Fast Unbounded Surface Reconstruction
Lukas Radl, Felix Windisch, Thomas Deixelberger, Jozef Hladky, Michael Steiner, Dieter Schmalstieg, Markus Steinberger
https://arxiv.org/abs/2506.19139

SOF: Sorted Opacity Fields for Fast Unbounded Surface Reconstruction
Recent advances in 3D Gaussian representations have significantly improved the quality and efficiency of image-based scene reconstruction. Their explicit nature facilitates real-time rendering and fast optimization, yet extracting accurate surfaces - particularly in large-scale, unbounded environments - remains a difficult task. Many existing methods rely on approximate depth estimates and global sorting heuristics, which can introduce artifacts and limit the fidelity of the reconstructed mesh.…

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…

Greedy Selection under Independent Increments: A Toy Model Analysis
Huitao Yang
https://arxiv.org/abs/2506.17941 https://arxiv.org/pd…

Greedy Selection under Independent Increments: A Toy Model Analysis
We study an iterative selection problem over N i.i.d. discrete-time stochastic processes with independent increments. At each stage, a fixed number of processes are retained based on their observed values. Under this simple model, we prove that the optimal strategy for selecting the final maximum-value process is to apply greedy selection at each stage. While the result relies on strong independence assumptions, it offers a clean justification for greedy heuristics in multi-stage elimination se…

This https://arxiv.org/abs/2503.07792 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Efficient Neural Clause-Selection Reinforcement
Clause selection is arguably the most important choice point in saturation-based theorem proving. Framing it as a reinforcement learning (RL) task is a way to challenge the human-designed heuristics of state-of-the-art provers and to instead automatically evolve -- just from prover experiences -- their potentially optimal replacement. In this work, we present a neural network architecture for scoring clauses for clause selection that is powerful yet efficient to evaluate. Following RL principle…

AI Agent Smart Contract Exploit Generation
Arthur Gervais, Liyi Zhou
https://arxiv.org/abs/2507.05558 https://arxiv.org/pdf/2507.0555…

AI Agent Smart Contract Exploit Generation
We present A1, an agentic execution driven system that transforms any LLM into an end-to-end exploit generator. A1 has no hand-crafted heuristics and provides the agent with six domain-specific tools that enable autonomous vulnerability discovery. The agent can flexibly leverage these tools to understand smart contract behavior, generate exploit strategies, test them on blockchain states, and refine approaches based on execution feedback. All outputs are concretely validated to eliminate false …

Beyond C/C : Probabilistic and LLM Methods for Next-Generation Software Reverse Engineering
Zhuo Zhuo, Xiangyu Zhang
https://arxiv.org/abs/2506.03504 http…

Beyond C/C++: Probabilistic and LLM Methods for Next-Generation Software Reverse Engineering
This proposal discusses the growing challenges in reverse engineering modern software binaries, particularly those compiled from newer system programming languages such as Rust, Go, and Mojo. Traditional reverse engineering techniques, developed with a focus on C and C++, fall short when applied to these newer languages due to their reliance on outdated heuristics and failure to fully utilize the rich semantic information embedded in binary programs. These challenges are exacerbated by the limi…

Parallel Point-to-Point Shortest Paths and Batch Queries
Xiaojun Dong, Andy Li, Yan Gu, Yihan Sun
https://arxiv.org/abs/2506.16488 https://

Parallel Point-to-Point Shortest Paths and Batch Queries
We propose Orionet, efficient parallel implementations of Point-to-Point Shortest Paths (PPSP) queries using bidirectional search (BiDS) and other heuristics, with an additional focus on batch PPSP queries. We present a framework for parallel PPSP built on existing single-source shortest paths (SSSP) frameworks by incorporating pruning conditions. As a result, we develop efficient parallel PPSP algorithms based on early termination, bidirectional search, A$^*$ search, and bidirectional A$^*$ al…

iDSE: Navigating Design Space Exploration in High-Level Synthesis Using LLMs
Runkai Li, Jia Xiong, Xi Wang
https://arxiv.org/abs/2505.22086 https://…

iDSE: Navigating Design Space Exploration in High-Level Synthesis Using LLMs
High-Level Synthesis (HLS) serves as an agile hardware development tool that streamlines the circuit design by abstracting the register transfer level into behavioral descriptions, while allowing designers to customize the generated microarchitectures through optimization directives. However, the combinatorial explosion of possible directive configurations yields an intractable design space. Traditional design space exploration (DSE) methods, despite adopting heuristics or constructing predicti…

This https://arxiv.org/abs/2505.21119 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Universal Value-Function Uncertainties
Estimating epistemic uncertainty in value functions is a crucial challenge for many aspects of reinforcement learning (RL), including efficient exploration, safe decision-making, and offline RL. While deep ensembles provide a robust method for quantifying value uncertainty, they come with significant computational overhead. Single-model methods, while computationally favorable, often rely on heuristics and typically require additional propagation mechanisms for myopic uncertainty estimates. In …

This https://arxiv.org/abs/2202.13250 has been replaced.
link: https://scholar.google.com/scholar?q=a

TabID: Automatic Identification and Tabulation of Subproblems in Constraint Models
The performance of a constraint model can often be improved by converting a subproblem into a single table constraint (referred to as tabulation). Finding subproblems to tabulate is traditionally a manual and time-intensive process, even for expert modellers. This paper presents TabID, an entirely automated method to identify promising subproblems for tabulation in constraint programming. We introduce a diverse set of heuristics designed to identify promising candidates for tabulation, aiming t…

caida_as: CAIDA AS graphs (2004-2007)
A sequence of 122 network snapshots denoting Autonomous System (AS) relationships on the Internet, from 2004-2007, inferred using the Serial-1 method from RouteViews BGP table snapshots and a set of heuristics.
This network has 19489 nodes and 79718 edges.
Tags: Technological, Communication, Unweighted, Temporal

MoR: Better Handling Diverse Queries with a Mixture of Sparse, Dense, and Human Retrievers
Jushaan Singh Kalra, Xinran Zhao, To Eun Kim, Fengyu Cai, Fernando Diaz, Tongshuang Wu
https://arxiv.org/abs/2506.15862

MoR: Better Handling Diverse Queries with a Mixture of Sparse, Dense, and Human Retrievers
Retrieval-augmented Generation (RAG) is powerful, but its effectiveness hinges on which retrievers we use and how. Different retrievers offer distinct, often complementary signals: BM25 captures lexical matches; dense retrievers, semantic similarity. Yet in practice, we typically fix a single retriever based on heuristics, which fails to generalize across diverse information needs. Can we dynamically select and integrate multiple retrievers for each individual query, without the need for manual…

Phase-based Nonlinear Model Predictive Control for Humanoid Walking Stabilization with Single and Double Support Time Adjustments
Kwanwoo Lee, Gyeongjae Park, Jaeheung Park
https://arxiv.org/abs/2506.03856

Phase-based Nonlinear Model Predictive Control for Humanoid Walking Stabilization with Single and Double Support Time Adjustments
Balance control for humanoid robots has been extensively studied to enable robots to navigate in real-world environments. However, balance controllers that explicitly optimize the durations of both the single support phase, also known as step timing, and the Double Support Phase (DSP) have not been widely explored due to the inherent nonlinearity of the associated optimization problem. Consequently, many recent approaches either ignore the DSP or adjust its duration based on heuristics or on li…

Meta-Fair: AI-Assisted Fairness Testing of Large Language Models
Miguel Romero-Arjona, Jos\'e A. Parejo, Juan C. Alonso, Ana B. S\'anchez, Aitor Arrieta, Sergio Segura
https://arxiv.org/abs/2507.02533

Meta-Fair: AI-Assisted Fairness Testing of Large Language Models
Fairness--the absence of unjustified bias--is a core principle in the development of Artificial Intelligence (AI) systems, yet it remains difficult to assess and enforce. Current approaches to fairness testing in large language models (LLMs) often rely on manual evaluation, fixed templates, deterministic heuristics, and curated datasets, making them resource-intensive and difficult to scale. This work aims to lay the groundwork for a novel, automated method for testing fairness in LLMs, reducin…

Fantastic Flips and Where to Find Them: A General Framework for Parameterized Local Search on Partitioning Problem
Niels Gr\"uttemeier, Nils Morawietz, Frank Sommer
https://arxiv.org/abs/2506.24001

Fantastic Flips and Where to Find Them: A General Framework for Parameterized Local Search on Partitioning Problem
Parameterized local search combines classic local search heuristics with the paradigm of parameterized algorithmics. While most local search algorithms aim to improve given solutions by performing one single operation on a given solution, the parameterized approach aims to improve a solution by performing $k$ simultaneous operations. Herein, $k$ is a parameter called search radius for which the value can be chosen by a user. One major goal in the field of parameterized local search is to outlin…

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…

Replaced article(s) found for cs.HC. https://arxiv.org/list/cs.HC/new
[1/1]:
- Heuristics for AI-driven Graphical Asset Generation Tools in Game Design and Development Pipeline...
Kaisei Fukaya, Damon Daylamani-Zad, Harry Agius

Solving the Pod Repositioning Problem with Deep Reinforced Adaptive Large Neighborhood Search
Lin Xie, Hanyi Li
https://arxiv.org/abs/2506.02746 https://…

Solving the Pod Repositioning Problem with Deep Reinforced Adaptive Large Neighborhood Search
The Pod Repositioning Problem (PRP) in Robotic Mobile Fulfillment Systems (RMFS) involves selecting optimal storage locations for pods returning from pick stations. This work presents an improved solution method that integrates Adaptive Large Neighborhood Search (ALNS) with Deep Reinforcement Learning (DRL). A DRL agent dynamically selects destroy and repair operators and adjusts key parameters such as destruction degree and acceptance thresholds during the search. Specialized heuristics for bo…

SysLLMatic: Large Language Models are Software System Optimizers
Huiyun Peng, Arjun Gupte, Ryan Hasler, Nicholas John Eliopoulos, Chien-Chou Ho, Rishi Mantri, Leo Deng, Konstantin L\"aufer, George K. Thiruvathukal, James C. Davis
https://arxiv.org/abs/2506.01249

SysLLMatic: Large Language Models are Software System Optimizers
Automatic software system optimization can improve software speed, reduce operating costs, and save energy. Traditional approaches to optimization rely on manual tuning and compiler heuristics, limiting their ability to generalize across diverse codebases and system contexts. Recent methods using Large Language Models (LLMs) offer automation to address these limitations, but often fail to scale to the complexity of real-world software systems and applications. We present SysLLMatic, a system th…

CODS : A Theoretical Model for Computational Design Based on Design Space
Nan Cao, Xiaoyu Qi, Chuer Chen, Xiaoke Yan
https://arxiv.org/abs/2506.18455 https…

CODS : A Theoretical Model for Computational Design Based on Design Space
We introduce CODS (Computational Optimization in Design Space), a theoretical model that frames computational design as a constrained optimization problem over a structured, multi-dimensional design space. Unlike existing methods that rely on handcrafted heuristics or domain-specific rules, CODS provides a generalizable and interpretable framework that supports diverse design tasks. Given a user requirement and a well-defined design space, CODS automatically derives soft and hard constraints us…