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# 1 INTRODUCTION
In today’s world, data and knowledge are stored, managed, and queried through databases, which are accessed using query languages such as SQL (for relational databases) or Cypher (for graph databases). Recent advancements in large language models (LLMs)
Simple Query
MASimple Query
WMAMedium Query WFM... | Database query languages such as SQL for relational databases and Cypher for graph databases have been widely adopted. Recent advancements in large language models (LLMs) enable natural language interactions with databases through models like Text2SQL and Text2Cypher. Fine-tuning these models typically requires large, ... | [
"cs.DB",
"cs.LG"
] |
# 1. Introduction
Inflammation is a key response of biological organisms to harmful stimuli, like pathogens or tissue damage. This mechanism is layered across different scales, ranging from macroscopic swelling of the 3D tissue down to the release of sub-cellular biochemical molecules. At the microscopic level, inflam... | Label-free imaging has gained broad interest because of its potential to omit elaborate staining procedures which is especially relevant for in vivo use. Label-free multiphoton microscopy (MPM), for instance, exploits two-photon excitation of natural autofluorescence (AF) from native, metabolic proteins, making it idea... | [
"cs.LG",
"physics.optics"
] |
# 1 Introduction
Human mobility—the movement of individuals across space and time—is a fundamental aspect of human behaviour, shaping urban dynamics, transportation systems, and public policies [1]. Over the past decade, computational approaches, especially those based on artificial intelligence (AI) techniques such a... | Predicting individuals' next locations is a core task in human mobility modelling, with wide-ranging implications for urban planning, transportation, public policy and personalised mobility services. Traditional approaches largely depend on location embeddings learned from historical mobility patterns, limiting their a... | [
"cs.AI"
] |
# 1 Introduction
As scientific software becomes increasingly central to research activities, metadata describing these tools is scattered across a growing number of registries, repositories, package managers, and publication platforms. These platforms differ in scope, stability, and longevity—some emerge or disappear ... | Software is an essential component of research. However, little attention has been paid to it compared with that paid to research data. Recently, there has been an increase in efforts to acknowledge and highlight the importance of software in research activities.
Structured metadata from platforms like bio.tools, Bio... | [
"cs.SE",
"cs.CL",
"cs.DL"
] |
# 1 Introduction
# 2 Related Works 4
2.1 Markup Languages (XML, HTML) 4
2.2 Lightweight Data Interchange Formats (JSON, YAML) 5
2.2.1 JSON (JavaScript Object Notation) 5
2.2.2 YAML (YAML Ain’t Markup Language) 5
2.3 Tabular Data Formats (CSV) . 6
2.4 Comparison with Binary Formats . 6
# 3 Methods
# 7
3.1 Serializa... | Text serialization is a fundamental concept in modern computing, enabling the conversion of complex data structures into a format that can be easily stored, transmitted, and reconstructed. This paper provides an extensive overview of text serialization, exploring its importance, prevalent formats, underlying methods, a... | [
"cs.PL",
"cs.DB"
] |
# I. INTRODUCTION
Remote sensing semantic segmentation aims to classify each pixel in satellite or aerial imagery into specific land cover types, playing a vital role in natural resource monitoring, urban planning, and environmental conservation. Current mainstream methods for semantic segmentation in remote sensing i... | Remote sensing semantic segmentation is crucial for extracting detailed land surface information, enabling applications such as environmental monitoring, land use planning, and resource assessment. In recent years, advancements in artificial intelligence have spurred the development of automatic remote sensing semantic... | [
"cs.CV",
"cs.AI"
] |
# 1 Introduction
Automatic summarization is the task of using machines to summarize natural language text (Maynez et al., 2020; Ranjitha and Kallimani, 2017; Li et al., 2014). This task has developed across many areas in computational linguistics for more than six decades (Luhn, 1958). Recently Large Language Models (... | The Achilles heel of Large Language Models (LLMs) is hallucination, which has drastic consequences for the clinical domain. This is particularly important with regards to automatically generating discharge summaries (a lengthy medical document that summarizes a hospital in-patient visit). Automatically generating these... | [
"cs.CL"
] |
# 1 Introduction
In recent years, advancements in Large Language Models (LLMs) have greatly broadened the scope of downstream applications (Devlin et al., 2019; Zhou et al., 2022), creating an increasing need for continual learning (CL) on user-specific private data in sectors such as finance (Zhao et al., 2024), law ... | Existing work on mitigating catastrophic forgetting in large language model (LLM) fine-tuning has primarily focused on preserving specific data or tasks, while critically overlooking the degradation of essential capabilities instilled through safety alignment, particularly the model's ability to faithfully express igno... | [
"cs.AI"
] |
# 1. Introduction
The classic PAC (Probably Approximately Correct) theory (Vapnik and Chervonenkis, 1974; Valiant, 1984) focuses on understanding the best worst-case (uniform) learning rate by a learning algorithm over all data distributions. Due to its distribution-free nature, the PAC framework fails to capture the ... | The universal learning framework has been developed to obtain guarantees on the learning rates that hold for any fixed distribution, which can be much faster than the ones uniformly hold over all the distributions. Given that the Empirical Risk Minimization (ERM) principle being fundamental in the PAC theory and ubiqui... | [
"stat.ML",
"cs.LG"
] |
# 1 Introduction
Time series forecasting is an essential task across domains such as finance [4, 41, 14, 1], energy [60, 52, 47], retail [39, 54, 6, 44], and public health [26, 3, 24, 23]. Recent advances have enabled researchers to scale time series corpora [31, 32, 33, 15, 18, 48, 56] and train foundation models [40... | While multimodal data sources are increasingly available from real-world forecasting, most existing research remains on unimodal time series. In this work, we present MoTime, a suite of multimodal time series forecasting datasets that pair temporal signals with external modalities such as text, metadata, and images. Co... | [
"cs.LG",
"cs.CL",
"cs.DB",
"cs.IR"
] |
# 1 Introduction
In recent years, the field of reinforcement learning (RL) [4] has undergone an impactful evolution, moving beyond reactive policy optimization to also include models that exhibit larger generalization and adaptability [18].Reinforcement Learning (RL) trains agents to interact with an environment and l... | Elastic Decision Transformers (EDTs) have proved to be particularly successful in offline reinforcement learning, offering a flexible framework that unifies sequence modeling with decision-making under uncertainty. Recent research has shown that incorporating intrinsic motivation mechanisms into EDTs improves performan... | [
"cs.LG",
"cs.AI"
] |
# 1. Introduction
Decision trees are widely used for interpretable machine learning (Rudin et al., 2022). Their structure of discrete decisions has long been leveraged for difficult tasks such as handling missing data (Therneau et al., 1997) and measuring variable importance (Breiman, 1984). Recent advances in decisio... | Decision trees are widely used for interpretable machine learning due to their clearly structured reasoning process. However, this structure belies a challenge we refer to as predictive equivalence: a given tree's decision boundary can be represented by many different decision trees. The presence of models with identic... | [
"cs.LG"
] |
# 1 Introduction
In recent years, large language models (LLMs) have achieved significant progress in natural language processing tasks [1, 2, 3], however, growing concerns have emerged over their vulnerability to backdoor attacks [4, 5, 6]. Traditional backdoor attack methods rely on data poisoning [7, 8, 9], where th... | Large language models (LLMs) have shown strong performance across natural language tasks, but remain vulnerable to backdoor attacks. Recent model editing-based approaches enable efficient backdoor injection by directly modifying parameters to map specific triggers to attacker-desired responses. However, these methods o... | [
"cs.CL"
] |
# I. INTRODUCTION
The rise of automation in manufacturing has brought significant advancements to production processes. However, are current artificial intelligence (AI) research methodologies ready to create successful, productive, and profitable AI applications? Despite extensive research, the success of industrial ... | Are current artificial intelligence (AI) research methodologies ready to create successful, productive, and profitable AI applications? This work presents a case study on an industrial AI use case called false call reduction for automated optical inspection to demonstrate the shortcomings of current best practices. We ... | [
"cs.LG"
] |
# 1 Introduction
The past decade has witnessed great success and prosperity of graph neural networks (GNNs) in diverse data science and engineering scenarios, such as traffic network (Hu et al. 2019; Jiang and Luo 2022), abnormal detection (Tang et al. 2022, 2024a), relational databases (Cappuzzo, Papotti, and Thirumu... | Spectral Graph Neural Networks (SGNNs) have attracted significant attention due to their ability to approximate arbitrary filters. They typically rely on supervision from downstream tasks to adaptively learn appropriate filters. However, under label-scarce conditions, SGNNs may learn suboptimal filters, leading to degr... | [
"cs.LG"
] |
# 1 Introduction
Scaling large-language models (LLMs) to adapt to diverse downstream tasks is non-trivial in realworld applications [31]. However, the increasing size and complexity of these models pose significant challenges in terms of computational resources and training efficiency. To alleviate considerable comput... | Scaling Low-Rank Adaptation (LoRA)-based Mixture-of-Experts (MoE) facilitates large language models (LLMs) to efficiently adapt to diverse tasks. However, traditional gating mechanisms that route inputs to the best experts may fundamentally hinder LLMs' scalability, leading to poor generalization and underfitting issue... | [
"cs.LG",
"cs.SE"
] |
# 1 Introduction
Recent advancements in Large Language Models (LLMs) and Vision-Language Models (VLMs) have significantly enhanced their complex reasoning capability, demonstrating remarkable problemsolving proficiency (Wei et al., 2022; Lu et al.,
# Text-Based Reasoning Task
# Video-Based Reasoning Task
# Ques... | Large Language Models (LLMs) are experiencing rapid advancements in complex reasoning, exhibiting remarkable generalization in mathematics and programming. In contrast, while spatial intelligence is fundamental for Vision-Language Models (VLMs) in real-world interaction, the systematic evaluation of their complex reaso... | [
"cs.CV"
] |
# 1 Introduction
Large language models (LLMs) have emerged as a core technology in artificial intelligence, with extensive applications in chatbots, content generation, code synthesis, and other domains, significantly enhancing the efficiency and user experience of human-computer interaction [5; 29; 23; 11]. However, ... | Weight decay is a standard regularization technique for training large language models (LLMs). While it is common to assign a uniform decay rate to every layer, this approach overlooks the structural diversity of LLMs and the varying spectral properties across modules. In this paper, we introduce AlphaDecay, a simple y... | [
"cs.CL",
"cs.AI",
"cs.LG"
] |
# 1 Introduction
Writing code is mainly a matter of rewriting code: debugging, refactoring, optimizing, and other activities within the software engineering lifecycle. But poor rewrites incur technical debt, with such debt costing up to $\$ 2$ trillion annually [1]. This problem will likely worsen as language models b... | Maintainable and general software allows developers to build robust applications efficiently, yet achieving these qualities often requires refactoring specialized solutions into reusable components. This challenge becomes particularly relevant as code agents become increasingly accurate at solving isolated programming ... | [
"cs.SE",
"cs.AI"
] |
# 1 Introduction
Web is a vast and dynamic source of structured and semi-structured information. Much of this information, found in natural language descriptions, tables, and repetitive patterns, is amenable to extraction and analysis. Extracting structured data records—repeating content elements from web pages—has si... | Effective evaluation of web data record extraction methods is crucial, yet hampered by static, domain-specific benchmarks and opaque scoring practices. This makes fair comparison between traditional algorithmic techniques, which rely on structural heuristics, and Large Language Model (LLM)-based approaches, offering ze... | [
"cs.DB",
"cs.AI",
"cs.IR"
] |
# 1. Introduction
Recent advances in neural rendering have yielded impressive capabilities for novel-view synthesis from posed RGB images. Among these, 3D Gaussian Splatting (3DGS) [KKLD23] has garnered significant attention due to its remarkable ability to reconstruct radiance fields rapidly and render high-quality n... | Recent methods, such as 2D Gaussian Splatting and Gaussian Opacity Fields, have aimed to address the geometric inaccuracies of 3D Gaussian Splatting while retaining its superior rendering quality. However, these approaches still struggle to reconstruct smooth and reliable geometry, particularly in scenes with significa... | [
"cs.CV"
] |
# 1. Introduction
Theory of Mind (ToM) is a psychological term referring to infer mental states to self and others. This capability is fundamental to human social cognition and emotional understanding. In recent years, the rapid development of large models raised researchers’ consideration: can they interact with us i... | As large language models evolve, there is growing anticipation that they will emulate human-like Theory of Mind (ToM) to assist with routine tasks. However, existing methods for evaluating machine ToM focus primarily on unimodal models and largely treat these models as black boxes, lacking an interpretative exploration... | [
"cs.AI"
] |
# 1 Introduction
Medical machine learning (ML) models are trained on datasets containing diverse patient characteristics. However, when certain subgroups are overor underrepresented, models may show unequal performance, raising fairness concerns. Addressing such disparities requires evaluation across subgroups—ideally... | Machine learning (ML) models are increasingly used to support clinical decision-making. However, real-world medical datasets are often noisy, incomplete, and imbalanced, leading to performance disparities across patient subgroups. These differences raise fairness concerns, particularly when they reinforce existing disa... | [
"cs.LG",
"cs.CY"
] |
# 1 Introduction
Natural Language Processing (NLP) technologies have evolved language models into powerful tools, yet their impact on complex societal issues across disciplines remains unclear. Diverse expertise is crucial for evaluating their effectiveness beyond technical benchmarks. Current NLP agents, mostly power... | Despite advances in designing personas for Large Language Models (LLM), challenges remain in aligning them with human cognitive processes and representing diverse stakeholder perspectives. We introduce a Social Cognitive Theory (SCT) agent design framework for designing, evaluating, and implementing psychologically gro... | [
"cs.MA",
"cs.CY",
"cs.DB"
] |
# 1 Introduction
The association of textual mentions in a document to the entities they refer to in a Knowledge Graph (KG) is crucial for many Natural Language Processing (NLP) applications, such as question answering or information retrieval. This task is known as Entity Linking (EL), and it is a fundamental step in ... | Recent advances in Large Language Models (LLMs) have positioned them as a prominent solution for Natural Language Processing tasks. Notably, they can approach these problems in a zero or few-shot manner, thereby eliminating the need for training or fine-tuning task-specific models. However, LLMs face some challenges, i... | [
"cs.LG",
"cs.AI",
"cs.DB"
] |
# 1 Introduction
Knee osteoarthritis (OA) is a degenerative joint disease characterised by cartilage breakdown, bone remodelling, and joint inflammation [1]. It is a leading cause of disability in older adults, resulting in pain, stiffness, and reduced function. The Kellgren-Lawrence (KL) scale is commonly used to gra... | Medical imaging plays a crucial role in assessing knee osteoarthritis (OA) risk by enabling early detection and disease monitoring. Recent machine learning methods have improved risk estimation (i.e., predicting the likelihood of disease progression) and predictive modelling (i.e., the forecasting of future outcomes ba... | [
"cs.CV",
"cs.LG"
] |
# 1. Introduction
Large Multimodal Models (LMMs) substantially enhance Large Language Models (LLMs) by incorporating visual inputs [15, 19, 26, 27]. Yet, memory and computational overhead quickly escalate when processing multi-frame videos [19, 48]. While a single image may generate hundreds of tokens, dense or long v... | We propose an efficient framework to compress multiple video-frame features before feeding them into large multimodal models, thereby mitigating the severe token explosion arising from long or dense videos. Our design leverages a bidirectional state-space-based block equipped with a gated skip connection and a learnabl... | [
"cs.CV"
] |
# 1 INTRODUCTION
Elections in the United States are decentralized and conducted by the states. The Help America Vote Act of 2002 prompted all states to modernize their voting infrastructure and retire lever machines. States overwhelmingly adopted voter marked paper ballots that yield a “voter verifiable paper audit ta... | We show the security risk associated with using machine learning classifiers in United States election tabulators. The central classification task in election tabulation is deciding whether a mark does or does not appear on a bubble associated to an alternative in a contest on the ballot. Barretto et al. (E-Vote-ID 202... | [
"cs.CR",
"cs.CV",
"cs.LG"
] |
# 1 Introduction
# 1.1 Context
Historical cadastral records, widely distributed throughout Europe, serve as invaluable documents to reconstruct past urban and territorial information1. These records document property ownership, usage functions, and other essential elements for taxation, offering high confidence in th... | Cadastral data reveal key information about the historical organization of cities but are often non-standardized due to diverse formats and human annotations, complicating large-scale analysis. We explore as a case study Venice's urban history during the critical period from 1740 to 1808, capturing the transition follo... | [
"cs.SE",
"cs.AI"
] |
1 Introduction 4
1.1 Related Works 6
# 2 Preliminaries 6
# 3 Algorithm: Group Bias Adaptation 9
3.1 Algorithm Motivations 9
3.2 Implementation Details 11
# 4 Experiments on Qwen2.5-1.5B LLM 15
5 Identifiability of Features 20
5.1 Main Results on Identifiability of Features . 21
5.2 Dicussion on Feature Co-occurren... | We study the challenge of achieving theoretically grounded feature recovery using Sparse Autoencoders (SAEs) for the interpretation of Large Language Models. Existing SAE training algorithms often lack rigorous mathematical guarantees and suffer from practical limitations such as hyperparameter sensitivity and instabil... | [
"cs.LG",
"cs.AI",
"cs.IT",
"stat.ML"
] |
# 1 Introduction
Software quality has become a cornerstone of modern software engineering, particularly in the context of large-scale and continuously evolving codebases. As software systems grow in size and complexity, maintaining high standards of quality is essential to ensure reliability, ease of maintenance, and ... | Software quality is critical in modern software engineering, especially in large and evolving codebases. This study analyzes the evolution of software quality metrics in five successive versions of the open-source Java testing framework TestNG. Using the static analysis tool Understand, eleven key object-oriented metri... | [
"cs.SE",
"cs.CE"
] |
# 1 Introduction
The Linux kernel is a critical system which serves as the foundation for numerous operating systems, servers, and embedded systems. To date, the Linux kernel has undergone continuous evolution over several decades, involving thousands of developers and billions of active users [36]. Given the widespre... | The Linux kernel is a critical system, serving as the foundation for numerous systems. Bugs in the Linux kernel can cause serious consequences, affecting billions of users. Fault localization (FL), which aims at identifying the buggy code elements in software, plays an essential role in software quality assurance. Whil... | [
"cs.AI",
"cs.SE"
] |
# I. INTRODUCTION
Most commercial software depends on open-source software components [1]. Although this approach reduces development costs, it results in a software supply chain that exposes an organization to cybersecurity risks [2], [3]. Many prior works have examined software supply chain security failures and hav... | Many critical information technology and cyber-physical systems rely on a supply chain of open-source software projects. OSS project maintainers often integrate contributions from external actors. While maintainers can assess the correctness of a change request, assessing a change request's cybersecurity implications i... | [
"cs.CR",
"cs.SE"
] |
# 1 Introduction
In the field of artificial intelligence, the pursuit of true intelligence in large language models (LLMs) has prompted researchers to look to biology for inspiration [Gutiérrez et al., 2024, Wu et al., 2025]. Just as organisms gradually accumulate knowledge through experience over time, LLMs need to p... | Long-term memory (LTM) is essential for large language models (LLMs) to achieve autonomous intelligence in complex, evolving environments. Despite increasing efforts in memory-augmented and retrieval-based architectures, there remains a lack of standardized benchmarks to systematically evaluate LLMs' long-term memory a... | [
"cs.CL",
"cs.AI"
] |
# 1 INTRODUCTION
Generative AI (genAI) tools (e.g., ChatGPT [86], Copilot [82]) are becoming increasingly native to software development [34]. While these tools promise to enhance productivity [64] and are reshaping how developers code and innovate [90], their adoption remains entangled with AI hype [17], skepticism [... | Generative AI (genAI) tools are advertised as productivity aids. Yet, issues related to miscalibrated trust and usage friction continue to hinder their adoption. Additionally, AI can be exclusionary, failing to support diverse users adequately, further exacerbating these concerns. One such aspect of diversity is cognit... | [
"cs.HC",
"cs.SE"
] |
# 1. Introduction
Databases are an essential part of modern computer systems for data storage and knowledge management. They are typically accessed via query languages like like SQL (for relational databases) or Cypher (for graph databases) which allow database experts to store and query data for insight. However, rec... | Knowledge graphs represent complex data using nodes, relationships, and properties. Cypher, a powerful query language for graph databases, enables efficient modeling and querying. Recent advancements in large language models allow translation of natural language questions into Cypher queries - Text2Cypher. A common app... | [
"cs.DB",
"cs.LG"
] |
# 1 Introduction
Large language models (LLMs) have recently shown promise as autonomous agents capable of solving complex, multi-step tasks across a wide range of domains. These LLM agents interact with an environment through structured actions—such as mouse clicks, keystrokes, or code executions—and are prompted to c... | We introduce AgentSynth, a scalable and cost-efficient pipeline for automatically synthesizing high-quality tasks and trajectory datasets for generalist computer-use agents. Leveraging information asymmetry, AgentSynth constructs subtasks that are simple during generation but significantly more challenging when compose... | [
"cs.CL"
] |
# 1 Introduction
Language models are increasingly used in education to seek information (Suri et al., 2024), tutoring (Chevalier et al., 2024), and automated assessment (Tlili et al., 2023; Stahl et al., 2024). A critical aspect of their pedagogical utility is their potential to tailor responses to learners with varyi... | Language models today are widely used in education, yet their ability to tailor responses for learners with varied informational needs and knowledge backgrounds remains under-explored. To this end, we introduce ELI-Why, a benchmark of 13.4K "Why" questions to evaluate the pedagogical capabilities of language models. We... | [
"cs.CL",
"cs.HC"
] |
# 1 Introduction
Large Multimodal Models (LMMs) $\mathrm { [ A A A ^ { + } 2 4 }$ , $\mathrm { M G F ^ { + } } 2 4$ , $Z \mathrm { G } \mathrm { G } ^ { + } 2 4$ , $\mathrm { W B T ^ { + } } 2 4$ , $\mathrm { C W T ^ { + } } 2 4$ , $\mathrm { B C L } ^ { + } 2 5 ]$ have achieved remarkable performance across a wide ra... | Large multimodal Mixture-of-Experts (MoEs) effectively scale the model size to boost performance while maintaining fixed active parameters. However, previous works primarily utilized full-precision experts during sparse up-cycling. Despite they show superior performance on end tasks, the large amount of experts introdu... | [
"cs.CV",
"cs.LG"
] |
# 1 Introduction
Graph neural networks (GNNs) have demonstrated significant performance and representational capacity in solving problems over graph-structured data, fueling a great deal of applications in a variety of domains, ranging from biology to finance, from social media to neuroscience [59]. In particular, amo... | Counterfactual learning is emerging as an important paradigm, rooted in causality, which promises to alleviate common issues of graph neural networks (GNNs), such as fairness and interpretability. However, as in many real-world application domains where conducting randomized controlled trials is impractical, one has to... | [
"cs.LG",
"cs.DB"
] |
# 1 Introduction
As robotic technology advances, robots are increasingly capable of providing a variety of services in different contexts. A key challenge in robotics is understanding and responding to human requests that are not always clear-cut, especially in lifesupport situations [34]. This requires interpreting b... | When designing robots to assist in everyday human activities, it is crucial to enhance user requests with visual cues from their surroundings for improved intent understanding. This process is defined as a multimodal classification task. However, gathering a large-scale dataset encompassing both visual and linguistic e... | [
"cs.CL",
"cs.AI",
"cs.RO"
] |
# 1 Introduction
Semantic segmentation has made remarkable strides with the advent of deep learning [1]. However, it faces significant challenges, particularly in scenarios with limited labelled data [2] and domain shifts [3]. To reduce dependence on costly pixel-wise annotations, semi-supervised approaches aim to lev... | Semi-supervised semantic segmentation remains challenging under severe label scarcity and domain variability. Vision-only methods often struggle to generalize, resulting in pixel misclassification between similar classes, poor generalization and boundary localization. Vision-Language Models offer robust, domain-invaria... | [
"cs.CV",
"cs.AI"
] |
# 1. Introduction
Modelling complex language patterns and solving complex language tasks are two of the primary reasons that Large Language Models (LLMs) have attracted considerable attention in recent years. While the LLMs track thrives on increasing model sizes and tackling more difficult tasks, another track is con... | Small Language Models (SLMs, or on-device LMs) have significantly fewer parameters than Large Language Models (LLMs). They are typically deployed on low-end devices, like mobile phones and single-board computers. Unlike LLMs, which rely on increasing model size for better generalisation, SLMs designed for edge applicat... | [
"cs.CL",
"cs.LG",
"math.NA"
] |
# 1 Introduction
Language models reason and solve problems using language. What is the connection (and the integration) between their linguistic systems and their impressive reasoning abilities? To investigate this question, we run a suite of experiments to analyze the how language models solve puzzles about diverse l... | Across languages, numeral systems vary widely in how they construct and combine numbers. While humans consistently learn to navigate this diversity, large language models (LLMs) struggle with linguistic-mathematical puzzles involving cross-linguistic numeral systems, which humans can learn to solve successfully. We inv... | [
"cs.CL",
"cs.AI"
] |
# 1 Introduction
Nearest neighbor search (NNS) over the vector data has been widely used in various real-world applications, such as embedding-based retrieval (EBR) for search engines and recommendation systems [12, 27, 38, 59, 61, 65], retrieval-augmented generation (RAG) for large language models (LLMs) [31, 42, 43,... | Filtered approximate nearest neighbor search (FANNS), an extension of approximate nearest neighbor search (ANNS) that incorporates scalar filters, has been widely applied to constrained retrieval of vector data. Despite its growing importance, no dedicated survey on FANNS over the vector-scalar hybrid data currently ex... | [
"cs.DB"
] |
# Introduction
Across multiple cognitive domains, from perception to language and reasoning, dual-route processing appears as a key computational strategy of the human brain to address complex tasks (Marshall & Newcombe, 1973; Dell et al., 1997; Tversky & Kahneman, 1974; Mishkin et al., 1983; Hickok & Poeppel, 2007; E... | It takes several years for the developing brain of a baby to fully master word repetition-the task of hearing a word and repeating it aloud. Repeating a new word, such as from a new language, can be a challenging task also for adults. Additionally, brain damage, such as from a stroke, may lead to systematic speech erro... | [
"cs.CL",
"cs.AI"
] |
# 1. Introduction
Forecasting on subseasonal-to-seasonal (S2S) timescales, typically defined as 2 weeks to ∼2 months, is vital for public health, disaster preparedness, agriculture, and energy/water management (White et al. 2017). Despite the clear benefits of skillful predictions on these timescales, S2S forecasting ... | Subseasonal-to-seasonal forecasting is crucial for public health, disaster preparedness, and agriculture, and yet it remains a particularly challenging timescale to predict. We explore the use of an interpretable AI-informed model analog forecasting approach, previously employed on longer timescales, to improve S2S pre... | [
"physics.ao-ph",
"cs.LG"
] |
# 1. Introduction
Thanks to the capability of graphs in representing complex relationships, graph generation (Zhu et al., 2022; Liu et al., 2023a) has become an essential task in various fields such as protein design (Ingraham et al., 2019), drug discovery (Bilodeau et al., 2022), and social network analysis (Li et al... | Graph generation has emerged as a critical task in fields ranging from molecule design to drug discovery. Contemporary approaches, notably diffusion and flow-based models, have achieved solid graph generative performance through constructing a probability path that interpolates between a reference distribution and the ... | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
# 1. Introduction
Expert systems (ES), AI-based decision-making frameworks, are widely used in critical areas such as medical diagnosis, fraud detection, manufacturing, cyber security, and risk analysis, where decisions must be accurate for safety and reliability (Shu-Hsien Liao, 2005). A major challenge in ES for cri... | Expert systems often operate in domains characterized by class-imbalanced tabular data, where detecting rare but critical instances is essential for safety and reliability. While conventional approaches, such as cost-sensitive learning, oversampling, and graph neural networks, provide partial solutions, they suffer fro... | [
"cs.LG"
] |
# 1 Introduction
Cohesive subgraph mining is a fundamental problem in graph theory with numerous real-world applications [13, 24, 32, 39]. Unlike cliques, which require complete connectivity, cohesive subgraphs provide more flexibility by allowing the absence of certain edges. In real-world graphs, data is often noisy... | Cohesive subgraph mining is a fundamental problem in graph theory with numerous real-world applications, such as social network analysis and protein-protein interaction modeling. Among various cohesive subgraphs, the $γ$-quasi-clique is widely studied for its flexibility in requiring each vertex to connect to at least ... | [
"cs.SI",
"cs.DB"
] |
# 1. INTRODUCTION
In general, any unintentional distortion of signals in audio technology is undesirable. However, the human auditory system is not sensitive to all forms of distortion and this insensitivity can be leveraged in the engineering of human-facing audio systems. Here, we consider the case of phase distorti... | Phase distortion refers to the alteration of the phase relationships between frequencies in a signal, which can be perceptible. In this paper, we discuss a special case of phase distortion known as phase-intercept distortion, which is created by a frequency-independent phase shift. We hypothesize that, though this form... | [
"eess.SP",
"cs.LG",
"eess.AS"
] |
# 1 Introduction
As large language generators [1, 2] surpass average human performance, their interaction with humans becomes increasingly prevalent. However, as they tend to generate incorrect information, or so-called hallucinated information, community’s concerns on their mis-alignment issues have significantly inc... | Large language generative models increasingly interact with humans, while their falsified responses raise concerns. To address this hallucination effect, selectively abstaining from answering, called selective generation, provides an effective way for generators to control the hallucination when it is unsure of their a... | [
"cs.LG"
] |
# 1. Introduction
The field of Explainable AI (XAI) [1, 2, 3] focuses on making ML models more transparent to their users. It has given rise to a wide range of methods focusing on different data modalities and model types (e.g. [4, 5, 6, 7]), and can help to improve model performance [8, 9, 10]. XAI has also been used... | Visual counterfactual explainers (VCEs) are a straightforward and promising approach to enhancing the transparency of image classifiers. VCEs complement other types of explanations, such as feature attribution, by revealing the specific data transformations to which a machine learning model responds most strongly. In t... | [
"cs.LG",
"cs.CV"
] |
# I. INTRODUCTION
Virology laboratories play a critical role in infectious disease research, diagnostics, and response [1] [2]. However, these high-containment environments expose researchers to significant occupational risks, including accidental contamination, direct contact with hazardous biological agents, and rep... | The convergence of robotics and virtual reality (VR) has enabled safer and more efficient workflows in high-risk laboratory settings, particularly virology labs. As biohazard complexity increases, minimizing direct human exposure while maintaining precision becomes essential. We propose GAMORA (Gesture Articulated Meta... | [
"cs.RO",
"cs.AI",
"cs.CV"
] |
# 1 INTRODUCTION
Recent datasets with complex geometric structures have highlighted the limitations of traditional Euclidean spaces in capturing hierarchical or tree-like data found in domains like complex networks (Krioukov et al., 2010), natural language processing (Lo´pez et al., 2019; Zhu et al., 2020; Lo´pez & St... | Hyperbolic geometry has emerged as a powerful tool for modeling complex, structured data, particularly where hierarchical or tree-like relationships are present. By enabling embeddings with lower distortion, hyperbolic neural networks offer promising alternatives to Euclidean-based models for capturing intricate data s... | [
"cs.LG",
"cs.AI"
] |
# 1 Introduction
Function realisations and improvement in software-defined vehicles require continuous software updates; hence, rapid, efficient, and continuous software development is crucial to maintaining competitiveness and user satisfaction. LLMs can be seen as a potential element in the software development pipe... | Software engineers in various industrial domains are already using Large Language Models (LLMs) to accelerate the process of implementing parts of software systems. When considering its potential use for ADAS or AD systems in the automotive context, there is a need to systematically assess this new setup: LLMs entail a... | [
"cs.SE",
"cs.AI"
] |
# 1 Introduction
Recent advances in large language models (LLMs) have demonstrated impressive capabilities across complex reasoning and generation tasks. However, benchmark gains are often prioritized over practical deployment concerns, leading to systems that rely on repeated calls to expensive proprietary LLMs. For ... | We study cost-efficient collaboration between strong and weak language models for repository-level code generation, where the weak model handles simpler tasks at lower cost, and the most challenging tasks are delegated to the strong model. While many works propose architectures for this task, few analyze performance re... | [
"cs.AI",
"cs.SE"
] |
# 1 INTRODUCTION
Logical specifications, expressed through formalised formulas, enable precise system modelling and automated verification, supporting correctness and the detection of critical properties. The replication of reasoning-based experiments is essential for validating the generalisability of such methods in... | This study empirically validates automated logical specification methods for behavioural models, focusing on their robustness, scalability, and reproducibility. By the systematic reproduction and extension of prior results, we confirm key trends, while identifying performance irregularities that suggest the need for ad... | [
"cs.SE"
] |
# I. INTRODUCTION
The need to generate dynamic and realistic speech-driven talking heads has intensified, driven by emerging applications in domains such as digital assistants [1], [2], virtual reality [3], [4], and filmmaking [5]–[7]. These applications demand high visual fidelity and seamless integration of critical... | Achieving high synchronization in the synthesis of realistic, speech-driven talking head videos presents a significant challenge. A lifelike talking head requires synchronized coordination of subject identity, lip movements, facial expressions, and head poses. The absence of these synchronizations is a fundamental flaw... | [
"cs.CV"
] |
# 1. Introduction.
Recently, computer scientists, cognitive scientists and others have systematically tested Large Language Models (LLMs) as ChatGPT1 on a wide series of different reasoning skills, sometimes with truly impressive results. A partial list of examples includes logical reasoning (e.g. Liu et al. 2023, Ban... | We propose a test for abstract causal reasoning in AI, based on scholarship in the philosophy of causation, in particular on the neuron diagrams popularized by D. Lewis. We illustrate the test on advanced Large Language Models (ChatGPT, DeepSeek and Gemini). Remarkably, these chatbots are already capable of correctly i... | [
"cs.AI",
"cs.LG"
] |
# 1 Introduction
Structured comments in docstring format, containing detailed descriptions of functionality, parameters, return values, exceptions, and use cases, play a key role in maintaining the codebase: they not only speed up developers’ understanding of the code, but also allow them to automatically generate doc... | Effective generation of structured code comments requires robust quality metrics for dataset curation, yet existing approaches (SIDE, MIDQ, STASIS) suffer from limited code-comment analysis. We propose CIDRe, a language-agnostic reference-free quality criterion combining four synergistic aspects: (1) relevance (code-co... | [
"cs.SE",
"cs.AI",
"cs.CL",
"cs.LG"
] |
# 1. Introduction
Classifying complex objects has extensive applications in various industries from industrial inspection, and healthcare to security systems. Traditional classification methods predominantly rely on visual data, leveraging deep learning models trained on images and videos [1, 2]. While these approache... | This paper presents a novel non-invasive object classification approach using acoustic scattering, demonstrated through a case study on hair assessment. When an incident wave interacts with an object, it generates a scattered acoustic field encoding structural and material properties. By emitting acoustic stimuli and c... | [
"cs.SD",
"cs.CL",
"eess.AS"
] |
# 1. Introduction
The ability to extract meaningful patterns from visual observations and systematically predict future outcomes represents a cornerstone of cognitive intelligence, forming the foundation for what cognitive scientists term System 2 reasoning—deliberate, systematic thinking that enables complex planning... | The cornerstone of cognitive intelligence lies in extracting hidden patterns from observations and leveraging these principles to systematically predict future outcomes. However, current image tokenization methods demonstrate significant limitations in tasks requiring symbolic abstraction and logical reasoning capabili... | [
"cs.CV"
] |
# 1 Introduction to Unsupervised Node Representation Learning
Unsupervised graph representation learning (UGRL) is an important area in machine learning that focuses on transforming complex, high-dimensional, and often sparse graph data into compact, dense vector representations [23]. The main goal is to capture and s... | Graph Neural Networks (GNNs) became useful for learning on non-Euclidean data. However, their best performance depends on choosing the right model architecture and the training objective, also called the loss function. Researchers have studied these parts separately, but a large-scale evaluation has not looked at how G... | [
"cs.LG"
] |
# 1 Introduction
Large Language Models (LLMs) have emerged as powerful tools capable of understanding and generating human-like text. Despite these advances, factuality – the ability of LLMs to provide responses that are truthful and faithful to the real-world knowledge encountered during pre-training – remains a pers... | Factuality in Large Language Models (LLMs) is a persistent challenge. Current benchmarks often assess short factual answers, overlooking the critical ability to generate structured, multi-record tabular outputs from parametric knowledge. We demonstrate that this relational fact retrieval is substantially more difficult... | [
"cs.CL",
"cs.AI",
"cs.DB"
] |
# 1 Introduction
Real-world systems such as social networks and communication networks are often modeled as temporal graphs, where edges are associated with time intervals or discrete time points, indicating when they are active. In such graphs, certain individuals play a crucial role in driving rapid information diff... | Temporal Betweenness Centrality (TBC) measures how often a node appears on optimal temporal paths, reflecting its importance in temporal networks. However, exact computation is highly expensive, and real-world TBC distributions are extremely imbalanced. The severe imbalance leads learning-based models to overfit to zer... | [
"cs.LG",
"cs.AI"
] |
# 1 INTRODUCTION
“In Eudoxia, (...), a carpet is preserved in which you can observe the city’s true form. At first sight nothing seems to resemble Eudoxia less than the design of that carpet (...), but if you pause and examine it carefully, you become convinced that each place in the carpet corresponds to a place in t... | Due to the variety of its target use cases and the large API surface area to cover, a data lakehouse (DLH) is a natural candidate for a composable data system. Bauplan is a composable DLH built on "spare data parts" and a unified Function-as-a-Service (FaaS) runtime for SQL queries and Python pipelines. While FaaS simp... | [
"cs.DB",
"cs.DC"
] |
# 1. Introduction
The MLC-SLM challenge focuses on multilingual conversational speech recognition and speaker diarization tasks, encompassing 11 languages: English (en), French (fr), German (de), Italian (it), Portuguese (pt), Spanish (es), Japanese (jp), Korean (ko), Russian (ru), Thai (th), and Vietnamese (vi). The ... | This paper presents Seewo's systems for both tracks of the Multilingual Conversational Speech Language Model Challenge (MLC-SLM), addressing automatic speech recognition (ASR) and speaker diarization with ASR (SD-ASR). We introduce a multi-stage training pipeline that explicitly enhances reasoning and self-correction i... | [
"cs.CL",
"cs.AI",
"cs.SD",
"eess.AS"
] |
# 1. Introduction
Influencer marketing, a marketing strategy used by social media influencers that impacts their followers’ decision-making, has gained significant traction in recent years [1]. As digital advertising continues to revolve around social media platforms, influencers play a pivotal role in shaping consume... | Influencer marketing has become a crucial feature of digital marketing strategies. Despite its rapid growth and algorithmic relevance, the field of computational studies in influencer marketing remains fragmented, especially with limited systematic reviews covering the computational methodologies employed. This makes o... | [
"cs.CY",
"cs.CL"
] |
# 1 Introduction
Breast cancer is among the most prevalent and fatal malignancies affecting women worldwide, posing a major global health burden [1]. Its metastatic nature—commonly spreading to the bones, liver, lungs, and brain—contributes significantly to its incurability [2, 40]. Early detection can dramatically in... | Deep learning-based pathological image analysis presents unique challenges due to the practical constraints of network design. Most existing methods apply computer vision models directly to medical tasks, neglecting the distinct characteristics of pathological images. This mismatch often leads to computational ineffici... | [
"cs.CV"
] |
# 1 Introduction
Relational databases are foundational to modern data infrastructure, powering analytics, reporting, and decision-making across domains. Yet, querying these databases typically requires fluency in SQL—a barrier for many users. Text-to-SQL systems aim to democratize access by translating natural languag... | Text-to-SQL systems translate natural language questions into executable SQL queries, and recent progress with large language models (LLMs) has driven substantial improvements in this task. Schema linking remains a critical component in Text-to-SQL systems, reducing prompt size for models with narrow context windows an... | [
"cs.CL",
"cs.AI",
"cs.DB"
] |
# 1 Introduction
The modern hardware landscape is undergoing a fundamental transformation. As Moore’s Law slows and Dennard scaling ends (Dennard et al.,
1974; Connatser, 2023), the demand for energyefficient, high-performance architectures has accelerated, particularly with the rise of machine learning (ML) applicat... | The hardware ecosystem is rapidly evolving, with increasing interest in translating low-level programs across different instruction set architectures (ISAs) in a quick, flexible, and correct way to enhance the portability and longevity of existing code. A particularly challenging class of this transpilation problem is ... | [
"cs.CL",
"cs.AR",
"cs.LG",
"cs.PL",
"cs.SE"
] |
# 1. INTRODUCTION
Film grain, appreciated in video production for its natural look and creative expression, originates from the physical exposure and development of photographic film. Unlike film, digital sensors do not undergo such a process and are therefore grain-free. To add texture, warmth or evoke nostalgia, fil... | Film grain, once a by-product of analog film, is now present in most cinematographic content for aesthetic reasons. However, when such content is compressed at medium to low bitrates, film grain is lost due to its random nature. To preserve artistic intent while compressing efficiently, film grain is analyzed and model... | [
"cs.CV",
"eess.IV"
] |
# 1 Introduction
Digital survey platforms have become essential tools in participatory research with various different tools available across different domains [41]. Many provide survey logic capabilities, typically allowing researchers to define conditional branching or skipping questions based on respondent answers.... | We present the CASE framework, an open-source platform for adaptive, context-aware participatory research, and pandemic preparedness. CASE implements an event-driven architecture that enables dynamic survey workflows, allowing real-time adaptation based on participant responses, external data, temporal conditions, and ... | [
"cs.SE",
"cs.CY",
"cs.HC"
] |
# 1 Introduction
Interest in the synthesis of classical artificial general intelligence [9, 15] with emerging quantum information processing (QIP) [1, 21, 25, 34] technologies has given rise to questions regarding how the underlying physical substrate upon which intelligent systems are constructed influences their nat... | The prospect of AGI instantiated on quantum substrates motivates the development of mathematical frameworks that enable direct comparison of their operation in classical and quantum environments. To this end, we introduce a Hamiltonian formalism for describing classical and quantum AGI tasks as a means of contrasting t... | [
"quant-ph",
"cs.AI"
] |
# 1 Introduction
Documents are a fundamental form for the preservation and exchange of information, and an important source for humans to learn and acquire knowledge (Gu et al., 2021; Chia et al., 2024; Deng et al., 2024). Document question answering is a core task for automated understanding and retrieval of informat... | Document Visual Question Answering (DocVQA) is a practical yet challenging task, which is to ask questions based on documents while referring to multiple pages and different modalities of information, e.g, images and tables. To handle multi-modality, recent methods follow a similar Retrieval Augmented Generation (RAG) ... | [
"cs.CV",
"cs.AI"
] |
# 1 Introduction
Large language models (LLMs) have shown immense success across various tasks in natural processing and information retrieval. They have been successfully applied to reformulate queries [6, 22] and documents [16], rerank documents [13, 17] and products [5], as well as train embeddings for dense indices... | Large Language Models (LLMs) have demonstrated significant strides across various information retrieval tasks, particularly as rerankers, owing to their strong generalization and knowledge-transfer capabilities acquired from extensive pretraining. In parallel, the rise of LLM-based chat interfaces has raised user expec... | [
"cs.IR",
"cs.AI",
"cs.CL"
] |
# 1 Introduction
The energy of the electrons in molecules and materials serves as a glue between their atoms, determining the stability and properties of the chemical structure. Accurately computing the electron energy is therefore essential for predictive modeling across a broad spectrum of applications, including as... | Density Functional Theory (DFT) is the most widely used electronic structure method for predicting the properties of molecules and materials. Although DFT is, in principle, an exact reformulation of the Schrödinger equation, practical applications rely on approximations to the unknown exchange-correlation (XC) function... | [
"physics.chem-ph",
"cs.AI",
"cs.CE",
"cs.LG",
"physics.comp-ph"
] |
introduction to the dataset, establishing foundational benchmarks for future research. We envision this dataset as a valuable resource for advancing machine learning applications in neuro-oncology, supporting both academic research and clinical decision-support development. datasetlink: https://www.kaggle. com/datasets... | Accurate segmentation and classification of brain tumors from Magnetic Resonance Imaging (MRI) remain key challenges in medical image analysis, largely due to the lack of high-quality, balanced, and diverse datasets. In this work, we present a new curated MRI dataset designed specifically for brain tumor segmentation a... | [
"eess.IV",
"cs.CV"
] |
# 1 Introduction
Students in introductory programming classes are generally able to write working code. However, they may not have a full understanding or comprehension of how the code works. In the experience of Lehtinen et al., around a third of their class of 125 students had difficulties explaining their code [2].... | Students often do not fully understand the code they have written. This sometimes does not become evident until later in their education, which can mean it is harder to fix their incorrect knowledge or misunderstandings. In addition, being able to fully understand code is increasingly important in a world where student... | [
"cs.SE",
"cs.AI",
"cs.PL"
] |
# 1. Introduction
Recent advancements in reinforcement learning from human feedback have shown that utilizing fine-grained token-level reward models can substantially enhance the performance of Proximal Policy Optimization (PPO) in aligning large language models. However, it is challenging to leverage such token-level... | Recent advancements in reinforcement learning from human feedback have shown that utilizing fine-grained token-level reward models can substantially enhance the performance of Proximal Policy Optimization (PPO) in aligning large language models. However, it is challenging to leverage such token-level reward as guidance... | [
"cs.LG",
"cs.AI",
"cs.CL"
] |
# 1 Introduction
The proliferation of AI-driven applications such as retrievalaugmented generation (RAG) [6, 17, 22, 26], personalized recommendation [12, 32, 36], machine learning [7, 11] and multimodal search [34] has led to explosive growth in the deployment of vector databases—specialized systems that manage and q... | Vector search underpins modern AI applications by supporting approximate nearest neighbor (ANN) queries over high-dimensional embeddings in tasks like retrieval-augmented generation (RAG), recommendation systems, and multimodal search. Traditional ANN search indices (e.g., HNSW) are limited by memory constraints at lar... | [
"cs.DB"
] |
# 1 Introduction
In the rapidly developing field of Large Language Models (LLMs), it is difficult to keep up with the latest developments and put them into context of prior work. Several LLMs are released every month and some of them are advertised as "better" $^ { \prime \prime }$ , "faster" $^ { \prime \prime }$ , "... | Current Large Language Models (LLMs) can assist developing program code beside many other things, but can they support working with Knowledge Graphs (KGs) as well? Which LLM is offering the best capabilities in the field of Semantic Web and Knowledge Graph Engineering (KGE)? Is this possible to determine without checki... | [
"cs.AI",
"cs.CL",
"cs.DB"
] |
# 1 Introduction
Retrieval-Augmented Generation (RAG) [28, 30] enhances Large Language Models (LLMs) by leveraging external knowledge. However, despite the availability of numerous retrieval models, no single retriever consistently outperforms others across diverse queries and corpora [25, 40]. Inspired by distributed... | Retrieval-Augmented Generation (RAG) systems typically rely on a single fixed retriever, despite growing evidence that no single retriever performs optimally across all query types. In this paper, we explore a query routing approach that dynamically selects from a pool of retrievers based on the query, using both train... | [
"cs.CL",
"cs.IR"
] |
# 1 Introduction
Probabilistic filters. Probabilistic filters are space efficient data structures for fast set membership queries. They have many practical applications, such as for database systems [24], networks [14, 25], storage systems [21] and sequence analysis in computational biology [17, 20, 23]. A probabilist... | Cuckoo filters are space-efficient approximate set membership data structures with a controllable false positive rate (FPR) and zero false negatives, similar to Bloom filters. In contrast to Bloom filters, Cuckoo filters store multi-bit fingerprints of keys in a hash table using variants of Cuckoo hashing, allowing eac... | [
"cs.DS",
"cs.DB"
] |
# 1. Introduction
In recent years, topics such as machine learning and quantum computing have become widely recognized in society for the importance of computation in a broad sense. Naturally, these trends have also significantly impacted the field of condensed matter science. Innovative computational methods in conde... | The Institute for Solid State Physics (ISSP) at The University of Tokyo has been carrying out a software development project named ``the Project for Advancement of Software Usability in Materials Science (PASUMS)". Since the launch of PASUMS, various open-source software programs have been developed/advanced, including... | [
"cs.SE",
"cond-mat.mtrl-sci",
"physics.comp-ph",
"physics.ed-ph"
] |
# 1. Introduction
Addressing data imbalance in computer vision tasks remains a core challenge for improving model performance (Zhang et al., 2021b; Ma et al., 2025). Imbalances in the number of training samples across classes often lead to biases during the learning process, making it difficult for deep learning model... | Visual attribute imbalance is a common yet underexplored issue in image classification, significantly impacting model performance and generalization. In this work, we first define the first-level and second-level attributes of images and then introduce a CLIP-based framework to construct a visual attribute dictionary, ... | [
"cs.CV",
"cs.AI"
] |
# 1 Introduction
Recent advances in large language models (LLMs) (Yang et al., 2024; Touvron et al., 2023; Abdin et al., 2024; Raffel et al., 2023; Brown et al., 2020; Devlin et al., 2019) have led to impressive results in a wide array of natural language processing tasks. Building on these successes, researchers have... | Multimodal Large Language Model (MLLM) often suffer from hallucinations. They over-rely on partial cues and generate incorrect responses. Recently, methods like Visual Contrastive Decoding (VCD) and Instruction Contrastive Decoding (ICD) have been proposed to mitigate hallucinations by contrasting predictions from pert... | [
"cs.CV",
"cs.CL"
] |
# I. INTRODUCTION
Background. Large language models (LLMs) have been widely adopted for code generation to enhance developer productivity. However, their performance largely depends on prompt quality, which requires significant developer expertise [1].
To enhance LLM code generation usability, researchers developed C... | With the widespread application of large language models in code generation, recent studies demonstrate that employing additional Chain-of-Thought generation models can significantly enhance code generation performance by providing explicit reasoning steps. However, as external components, CoT models are particularly v... | [
"cs.SE"
] |
# I. INTRODUCTION
H sUecMoAndNahnadve vaeryeminacrkhabwlhe laebidlirtiyv tnog imnakceomuspeleox teravfefriyc environments. For example, in busy urban traffic, human drivers quickly interpret numerous cues, such as distances to surrounding vehicles, potential hazards on the road, and the intention of other road users. ... | Despite the recent advancements in artificial intelligence technologies have shown great potential in improving transport efficiency and safety, autonomous vehicles(AVs) still face great challenge of driving in time-varying traffic flow, especially in dense and interactive situations. Meanwhile, human have free wills a... | [
"cs.AI"
] |
# 1. Introduction
Deep learning has driven transformative advances in computer vision, enabling convolutional neural networks (CNNs) and their variants to achieve state-of-the-art performance across image classification, detection, and segmentation tasks. Yet despite their predictive power, modern neural models remain... | Deep neural networks often achieve high accuracy, but ensuring their reliability under adversarial and distributional shifts remains a pressing challenge. We propose TriGuard, a unified safety evaluation framework that combines (1) formal robustness verification, (2) attribution entropy to quantify saliency concentrati... | [
"cs.LG",
"cs.AI"
] |
# 1 Introduction
Each year, an estimated 2.5 billion people buy something online. You are likely one of those people. Most e-commerce stores now routinely display product reviews. These are typically short, instructive paragraphs of text that detail the observations and judgments of other consumers, pointing to the re... | Reading and evaluating product reviews is central to how most people decide what to buy and consume online. However, the recent emergence of Large Language Models and Generative Artificial Intelligence now means writing fraudulent or fake reviews is potentially easier than ever. Through three studies we demonstrate tha... | [
"cs.CL",
"cs.AI",
"econ.GN"
] |
# 1 Introduction
In recent years, there has been a lot of work on table analysis (Li et al., 2024b; Zhao et al., 2024). Specifically, an increasing number of studies discuss table question answering (TableQA) tasks. (Yang et al., 2024b; Sarkar and Lausen, 2023; Zhou et al., 2024; Nguyen et al., 2024; Li et al., 2024a)... | With the rapid advancement of Large Language Models (LLMs), there is an increasing need for challenging benchmarks to evaluate their capabilities in handling complex tabular data. However, existing benchmarks are either based on outdated data setups or focus solely on simple, flat table structures. In this paper, we in... | [
"cs.CL"
] |
# 1 Introduction
Historical research is grounded in the analysis of past events through primary source material, for which archives constitute a cornerstone in facilitating discovery, access, and interpretation [9]. One peculiarity of research on the Holocaust in particular is the wide dispersal of sources due to, amo... | Historical study of the Holocaust is commonly hampered by the dispersed and fragmented nature of important archival sources relating to this event. The EHRI project set out to mitigate this problem by building a trans-national network of archives, researchers, and digital practitioners, and one of its main outcomes was... | [
"cs.DL",
"cs.CY",
"cs.DB",
"cs.SE"
] |
# 1 Introduction
The Shapes Constraint Language [1] (SHACL) has become a standard for validating RDF data, providing developers with a powerful tool to ensure that data conforms to specific constraints. However, SHACL is inherently designed to operate on a single graph and does not "natively"1 support the validation o... | The Shapes Constraint Language (SHACL) provides a powerful mechanism for validating RDF data against shape constraints, but is inherently designed for single-graph validation. This limitation makes SHACL unsuitable for natively validating RDF datasets comprising multiple named graphs. To address this gap, developers mu... | [
"cs.DB"
] |
# 1. Introduction
Functional maps represent correspondences between shapes as a change of basis matrix $\mathrm { [ O B C S ^ { * } 1 } 2$ , $\mathrm { O C B ^ { * } 1 6 } ]$ . This point of view has a myriad of benefits, such as efficiency and generality. Since their inception, multiple aspects of the functional maps... | We propose a novel approach for refining a given correspondence map between two shapes. A correspondence map represented as a functional map, namely a change of basis matrix, can be additionally treated as a 2D image. With this perspective, we train an image diffusion model directly in the space of functional maps, ena... | [
"cs.CV",
"cs.LG"
] |
# 1 Introduction
State-of-the-art reinforcement learning (RL) algorithms, such as Proximal Policy Optimization (PPO) [1] and Soft Actor-Critic (SAC) [2], are typically built on the assumption that the environment can be modeled as a Markov Decision Process (MDP). This framework implicitly assumes that the agent observ... | In standard Reinforcement Learning (RL) settings, the interaction between the agent and the environment is typically modeled as a Markov Decision Process (MDP), which assumes that the agent observes the system state instantaneously, selects an action without delay, and executes it immediately. In real-world dynamic env... | [
"cs.LG",
"cs.AI",
"cs.RO"
] |
# 1. Introduction
Surgical phases recognition aims to identify the representation of high-level surgical stages depicted in surgical videos [21]. This capability holds potential applications for fruitful downstream tasks, such as automatic indexing of surgical video databases [45], real-time monitoring of surgical pro... | Online surgical phase recognition has drawn great attention most recently due to its potential downstream applications closely related to human life and health. Despite deep models have made significant advances in capturing the discriminative long-term dependency of surgical videos to achieve improved recognition, the... | [
"cs.CV"
] |
# 1. Introduction
Generative large language models, as one of the core technologies in the field of artificial intelligence, exhibit tremendous potential in natural language processing He, Gao and Chen (2023), content creation Touvron, Martin, Stone and et al. (2023), and data analysis Achiam, Adler, Agarwal and et al... | Training data has been proven to be one of the most critical components in training generative AI. However, obtaining high-quality data remains challenging, with data privacy issues presenting a significant hurdle. To address the need for high-quality data. Synthesize data has emerged as a mainstream solution, demonstr... | [
"cs.CL"
] |
# 1 INTRODUCTION
The increasing amount of data in the shape of high-dimensional vectors (e.g., embeddings from large language models) has resulted in the surge of a new type of vector-centric systems known as Vector Databases (VecDBs) [19,29,30]. VecDBs like Weaviate, Milvus, Qdrant, and ChromaDB offer storage, manage... | Vector databases have emerged as a new type of systems that support efficient querying of high-dimensional vectors. Many of these offer their database as a service in the cloud. However, the variety of available CPUs and the lack of vector search benchmarks across CPUs make it difficult for users to choose one. In this... | [
"cs.DB",
"cs.AI"
] |
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