article stringlengths 507 295k | abstract stringlengths 417 1.92k | category listlengths 1 6 |
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# 1 Introduction
Real-world enterprise data, such as financial transactions, supply chain data, e-commerce records, product catalogs, customer interactions, and electronic health records, are predominantly stored in relational databases [8]. These databases typically consist of multiple tables, each dedicated to diffe... | Relational Deep Learning (RDL) is a promising approach for building
state-of-the-art predictive models on multi-table relational data by
representing it as a heterogeneous temporal graph. However, commonly used Graph
Neural Network models suffer from fundamental limitations in capturing complex
structural patterns and ... | [
"cs.LG",
"cs.AI",
"cs.DB"
] |
# 1 Introduction
The intensive use of chatbots based on Large Language Models (LLMs) has been associated with the promotion of superficial learning habits and a decline in critical thinking skills in their users, particularly students (Gerlich, 2025; Schei et al., 2024). Motivated by this fact, rather than relying on ... | The widespread adoption of chat interfaces based on Large Language Models
(LLMs) raises concerns about promoting superficial learning and undermining the
development of critical thinking skills. Instead of relying on LLMs purely for
retrieving factual information, this work explores their potential to foster
deeper rea... | [
"cs.CL",
"cs.HC"
] |
# 1 Introduction
Event-based cameras [4] are being actively explored in traffic monitoring applications due to their unique ability to capture fast-moving objects with low latency, high temporal resolution, and energy efficiency [22]. Unlike conventional frame-based cameras that capture scenes at fixed intervals, even... | Event cameras are gaining traction in traffic monitoring applications due to
their low latency, high temporal resolution, and energy efficiency, which makes
them well-suited for real-time object detection at traffic intersections.
However, the development of robust event-based detection models is hindered by
the limite... | [
"cs.CV"
] |
# 1 Introduction
Generative AI techniques have been proposed for various aspects of coding for tasks ranging from coding assistants [1] to optimisation [2] and vulnerability detection [3] for which promising results are being heeded. Indeed, for many cases traditional types of code verification (be it at compile/devel... | Large Language Models (LLMs) are being used more and more for various coding
tasks, including to help coders identify bugs and are a promising avenue to
support coders in various tasks including vulnerability detection --
particularly given the flexibility of such generative AI models and tools. Yet
for many tasks it m... | [
"cs.SE",
"cs.AI",
"cs.ET",
"cs.LG"
] |
# I. INTRODUCTION
ously visited locations in unmanned systems, serves as a cornerstone for achieving long-term autonomy in robotics and self-driving platforms [1]. This capability is crucial for autonomous mobile robots to achieve precise and robust positioning in unknown environments [2]. Place recognition also has a... | LiDAR-based place recognition serves as a crucial enabler for long-term
autonomy in robotics and autonomous driving systems. Yet, prevailing
methodologies relying on handcrafted feature extraction face dual challenges:
(1) Inconsistent point cloud density, induced by ego-motion dynamics and
environmental disturbances d... | [
"cs.CV"
] |
# 1 Introduction
# 2 Comparative Analysis of Functional Competences and Failure
# Modes 6
2.1 Dimension 1: Semantic Coherence and Verification Challenges . 8
2.1.1 Semantic incoherence in GenAI 10
2.2 Dimension 2: Security Robustness and Risk Profiles 12
2.3 Dimension 3: Epistemic limits 14
2.3.1 Context Integration... | With the rise of generative AI (GenAI), Large Language Models are
increasingly employed for code generation, becoming active co-authors alongside
human programmers. Focusing specifically on this application domain, this paper
articulates distinct ``Architectures of Error'' to ground an epistemic
distinction between hum... | [
"cs.AI",
"cs.CL",
"cs.CY",
"cs.SE"
] |
# I. INTRODUCTION
Promising progress has been made in autonomous driving (AD) in recent years; however, some challenging problems in AD have yet to be solved, especially under dynamic, multimodal environments, such as contextual understanding and interpretability [1]. Commonly adopted AD architectures, whether modular... | We introduce DriveAgent, a novel multi-agent autonomous driving framework
that leverages large language model (LLM) reasoning combined with multimodal
sensor fusion to enhance situational understanding and decision-making.
DriveAgent uniquely integrates diverse sensor modalities-including camera,
LiDAR, GPS, and IMU-wi... | [
"cs.RO",
"cs.DB"
] |
# I. INTRODUCTION
Reinforcement Learning (RL) has achieved great success in solving decision-making tasks, such as gaming AI [1], [2], [3], autonomous driving [4], [5], [6], robotic manipulation [7], [8], [9], etc. The reward function plays a pivotal role in policy learning of RL. As the complexity of the task increas... | Preference-based Reinforcement Learning (PbRL) methods provide a solution to
avoid reward engineering by learning reward models based on human preferences.
However, poor feedback- and sample- efficiency still remain the problems that
hinder the application of PbRL. In this paper, we present a novel efficient
query sele... | [
"cs.RO",
"cs.AI"
] |
# 1. Introduction
While the Semantic Web and ontology engineering are still fundamental as common languages to exchange data (mainly related to the ‘Interoperable’ of the FAIR principles [1]), we have seen some different recent trends in networked and shared knowledge, reflecting the difficulties that practitioners ... | Linked Data and labelled property graphs (LPG) are two data management
approaches with complementary strengths and weaknesses, making their
integration beneficial for sharing datasets and supporting software ecosystems.
In this paper, we introduce rdf2pg, an extensible framework for mapping RDF
data to semantically equ... | [
"cs.DB",
"cs.AI"
] |
# I. INTRODUCTION
# A. Motivation
O tRioGnAspNaIcZeINcaGnrpersovuirdce sainneaffimciuelntitdriemseonusricoenamlacnlagsseimfiecanmechanism for users or application systems to efficiently operate a large set of resources from different dimensions. [1]. The Resource Space Model is a normalized space that classifies reso... | Organizing resources in a multidimensional classification space is an
approach to efficiently managing and querying large-scale resources. This paper
defines an aggregation query on subspace defined by a range on the partial
order on coordinate tree at each dimension, where each point contains resources
aggregated alon... | [
"cs.DB",
"cs.AI"
] |
# 1. Introduction
Koopman operator theory provides a framework for nonlinear dynamical system analysis and timeseries forecasting by mapping dynamics to a space of real-valued measurement functions, enabling a linear operator representation. Despite the advantage of linearity, the operator is generally infinite-dimens... | Koopman operator theory provides a framework for nonlinear dynamical system
analysis and time-series forecasting by mapping dynamics to a space of
real-valued measurement functions, enabling a linear operator representation.
Despite the advantage of linearity, the operator is generally
infinite-dimensional. Therefore, ... | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
# 1 Introduction
Recent years have witnessed a shift from specialized models to large foundation models capable of performing a plethora of tasks, particularly in language [Touvron et al., 2023, OpenAI, 2023, Bai et al., 2023, Qwen et al., 2025, DeepSeek-AI et al., 2025]. This paradigm shift has led to the development... | Low-Rank Adaptation (LoRA) has emerged as a widely adopted
parameter-efficient fine-tuning (PEFT) technique for foundation models. Recent
work has highlighted an inherent asymmetry in the initialization of LoRA's
low-rank factors, which has been present since its inception and was presumably
derived experimentally. Thi... | [
"stat.ML",
"cs.AI",
"cs.LG",
"cs.NE",
"math.ST",
"stat.TH"
] |
# I. INTRODUCTION
Go’s rising popularity in cloud, infrastructure, and blockchain applications raises security concerns due to its unique runtime and concurrency features (goroutines, channels). Reports indicate over $6 6 \%$ of Go modules contain vulnerabilities [4], highlighting the critical need for robust security... | The widespread adoption of the Go programming language in infrastructure
backends and blockchain projects has heightened the need for improved security
measures. Established techniques such as unit testing, static analysis, and
program fuzzing provide foundational protection mechanisms. Although symbolic
execution tool... | [
"cs.SE",
"cs.CR"
] |
# 1 Introduction
Industry and academia are increasingly using large language models (LLMs) to solve problems which require semantic understanding. These problems range from unstructured document processing [6, 11], to multi-modal question answering [34, 36, 43], to semantic search and ranking [37]. In order to achieve... | LLMs enable an exciting new class of data processing applications over large
collections of unstructured documents. Several new programming frameworks have
enabled developers to build these applications by composing them out of
semantic operators: a declarative set of AI-powered data transformations with
natural langua... | [
"cs.DB",
"cs.AI",
"H.2.4; I.2.5"
] |
# 1 Introduction
Generative AI tools (GenAI), as exemplified by a range of generative pretrained transformers (GPTs) that began entering the market in November, 2022 [70], have been broadly heralded as transformational tools for productivity and efficiency [121, 75]creativity [108], and the alleviation of tedium [90].... | Generative AI tools have become more prevalent in engineering workflows,
particularly through chatbots and code assistants. As the perceived accuracy of
these tools improves, questions arise about whether and how those who work in
high-precision domains might maintain vigilance for errors, and what other
aspects of usi... | [
"cs.HC",
"cs.AI"
] |
# 1 Introduction
A healthcare conversational system is a dialoguebased framework specifically developed for the medical domain. Its primary purpose is to interact with patients, systematically collect supplementary symptom information, facilitate preliminary diagnostic processes, and provide automated recommendations ... | With the advancement of large language models, many dialogue systems are now
capable of providing reasonable and informative responses to patients' medical
conditions. However, when patients consult their doctor, they may experience
negative emotions due to the severity and urgency of their situation. If the
model can ... | [
"cs.CL",
"cs.AI"
] |
# I. INTRODUCTION
Nowadays, the application domains of Unmanned Aerial Vehicles (UAVs) have rapidly expanded, penetrating extensively into critical fields such as military, civilian, and commercial sectors [1]. Leveraging their flexibility and adaptability, UAVs have demonstrated significant advantages in diverse task... | This paper presents VisLanding, a monocular 3D perception-based framework for
safe UAV (Unmanned Aerial Vehicle) landing. Addressing the core challenge of
autonomous UAV landing in complex and unknown environments, this study
innovatively leverages the depth-normal synergy prediction capabilities of the
Metric3D V2 mod... | [
"cs.CV",
"cs.RO"
] |
# 1. Introduction
Foundation models have revolutionized domains such as natural language (Brown, 2020; Touvron et al., 2023), vision (Wang et al., 2023; Yuan et al., 2021), tabular data (Zhang et al., 2023b; Yang et al., 2024), and graphs (Liu et al., 2023; Tang et al., 2024), offering scalable and generalized framewo... | We introduce Griffin, the first foundation model attemptation designed
specifically for Relational Databases (RDBs). Unlike previous smaller models
focused on single RDB tasks, Griffin unifies the data encoder and task decoder
to handle diverse tasks. Additionally, we enhance the architecture by
incorporating a cross-a... | [
"cs.LG",
"cs.AI",
"cs.DB"
] |
# 1 Introduction
Generalization is a fundamental concept across various branches of science. In the realm of machine learning, the notion of model generalizability serves as a measure of how effectively a model, learned from a limited set of training samples, can extend its performance to unseen data during testing. W... | Describing real-world entities can vary across different sources, posing a
challenge when integrating or exchanging data. We study the problem of
joinability under syntactic transformations, where two columns are not
equi-joinable but can become equi-joinable after some transformations.
Discovering those transformation... | [
"cs.DB"
] |
# 1 INTRODUCTION
Modern database systems increasingly support both relational and graph queries, as reflected in the emerging SQL/PGQ standard. SQL/PGQ is part of the broader GQL initiative [8], which has been under development since 2019 by both academia and industry contributors under the ISO auspices. GQL consists ... | SQL/PGQ is a new standard that integrates graph querying into relational
systems, allowing users to freely switch between graph patterns and SQL. Our
experiments show performance gaps between these models, as queries written in
both formalisms can exhibit varying performance depending on the formalism
used, suggesting ... | [
"cs.DB"
] |
# 1 Introduction
Incorporating non-parametric knowledge into large language models (LLMs) through additional retrieval modules has emerged as a promising approach to enhance both accuracy and the timeliness of information (Borgeaud et al., 2022; Izacard et al., 2023). This issue has led to the rapid development of var... | This paper presents a novel approach for unified retrieval-augmented
generation (RAG) systems using the recent emerging large language model (LLM)
agent concept. Specifically, Agent LLM, which utilizes LLM as fundamental
controllers, has become a promising approach to enable the interpretability of
RAG tasks, especiall... | [
"cs.CL",
"cs.AI",
"cs.DB",
"cs.IR"
] |
# 1 Introduction
The rapid development of autonomous driving technology has placed increasingly higher demands on the interpretability of decision-making systems. Achieving a "white-box" autonomous driving decision model, where the internal logic of the decision-making process is transparent and comprehensible to huma... | How to construct an interpretable autonomous driving decision-making system
has become a focal point in academic research. In this study, we propose a
novel approach that leverages large language models (LLMs) to generate
executable, rule-based decision systems to address this challenge.
Specifically, harnessing the st... | [
"cs.AI"
] |
# Introduction
Using a more compact CNF encoding can make the entire difference between a combinatorial problem being solvable (even in many CPU years) and it being intractable (Subercaseaux and Heule, 2023; Heule and Scheucher, 2024; Wesley, 2024; Heule and Szeider, 2015; Schidler and Szeider, 2024; Qian et al., 2025... | We show how several graph problems (e.g., vertex-cover, independent-set,
$k$-coloring) can be encoded into CNF using only $O(|V|^2 / \lg |V|)$ many
clauses, as opposed to the $\Omega(|V|^2)$ constraints used by standard
encodings. This somewhat surprising result is a simple consequence of a result
of Erd\H{o}s, Chung, ... | [
"cs.LO",
"cs.AI",
"cs.DS"
] |
# 1. Introduction
Most NoSQL systems follow a “schema-on-read” approach, allowing data to be stored without a predefined schema. While this schemaless1 nature grants developers the flexibility to handle frequent changes in data structures — common in modern applications — it also introduces a key challenge: building d... | In this paper, we present a static code analysis strategy to extract logical
schemas from NoSQL applications. Our solution is based on a model-driven
reverse engineering process composed of a chain of platform-independent model
transformations. The extracted schema conforms to the \uschema{} unified
metamodel, which ca... | [
"cs.DB"
] |
# 1. Introduction
In statistical learning theory, the probably approximately correct (PAC) framework (Valiant, 1984) is central to understanding binary classification learnability. A key result shows that PAC learnability is fully determined by the VC dimension (Vapnik and Chervonenkis, 1974; Blumer et al., 1989), ele... | We study the task of bandit learning, also known as best-arm identification,
under the assumption that the true reward function f belongs to a known, but
arbitrary, function class F. We seek a general theory of bandit learnability,
akin to the PAC framework for classification. Our investigation is guided by
the followi... | [
"cs.LG",
"stat.ML"
] |
# I. INTRODUCTION
𝐱 = (10!, 11!) 1 P# = XYXY P\$ = XXYY P! = XYYX (10)(11) (10)(11) (10)(11) & 比江 √& 1101 1011 11102 𝑣" = 1101! 𝑣" = 1011! 𝑣" = 1110!
A aSpmacuel-tif-ildlinmg csuirovnea dSaFtaC, foir $\mathbf { x }$ otrto i na -wdiaymetno omnapl value, say $v$ that can be represented by a mapping function $T : ... | Space-filling curves (SFC, for short) have been widely applied to index
multi-dimensional data, which first maps the data to one dimension, and then a
one-dimensional indexing method, e.g., the B-tree indexes the mapped data.
Existing SFCs adopt a single mapping scheme for the whole data space. However,
a single mappin... | [
"cs.DB"
] |
# I. INTRODUCTION
Yangliuqing woodblock prints, esteemed as a significant facet of China's intangible cultural heritage (Qian, 2023), are renowned for their intricate textures, vibrant colors, and centuries-old craftsmanship (Liu, 2012). Originating during the Ming Dynasty (1368–1644) (Zhang, B., & Romainoor, N. H. 20... | Yangliuqing woodblock prints, a cornerstone of China's intangible cultural
heritage, are celebrated for their intricate designs and vibrant colors.
However, preserving these traditional art forms while fostering innovation
presents significant challenges. This study explores the DeepSeek + MidJourney
approach to genera... | [
"cs.GR",
"cs.CL",
"cs.CY"
] |
# I. INTRODUCTION
The rapid evolution of quantum software frameworks [16] presents a unique challenge for developers maintaining code across versions [10]. This issue is particularly evident in Qiskit, one of the most widely adopted platforms for quantum programming. The recent release of version 2.0 introduced substa... | As quantum software frameworks evolve, developers face increasing challenges
in maintaining compatibility with rapidly changing APIs. In this work, we
present a novel methodology for refactoring Qiskit code using large language
models (LLMs). We begin by extracting a taxonomy of migration scenarios from
the different s... | [
"cs.SE",
"cs.AI",
"cs.ET"
] |
# 1 Introduction
Many real-world applications require large language models to integrate scattered information and infer logical answers to novel questions. For instance, an AI assistant supporting human resource specialists in determining an employee’s tax rate must combine information about the employee’s marital st... | Merging or routing low-rank adapters (LoRAs) has emerged as a popular
solution for enhancing large language models, particularly when data access is
restricted by regulatory or domain-specific constraints. This position paper
argues that the research community should shift its focus from developing new
merging or routi... | [
"cs.CL",
"cs.AI"
] |
# 1 Introduction
Modern database management systems (DBMS) are universal solutions for management of large amount of information, nonetheless, the functionality provided by DBMS in some cases is excessive, resulting in technical requirements that exceed the capabilities of existing hardware platforms.
But with the gr... | The article addresses the problem of storing data in extreme environmental
conditions with limited computing resources and memory. There is a requirement
to create portable, fault-tolerant, modular database management systems (DBMS)
that are optimized for use in embedded systems. Existing databases, such as
LittleDB, L... | [
"cs.DB",
"68P15",
"H.2.4"
] |
# 1 INTRODUCTION
Analyzing human mobility and geolocation data offers a wealth of applications that touch nearly every aspect of modern life [22, 23]. To truly understand human mobility, it is essential to uncover the underlying relationships between places and the activities they attract, as these connections shape w... | Capturing human mobility is essential for modeling how people interact with
and move through physical spaces, reflecting social behavior, access to
resources, and dynamic spatial patterns. To support scalable and transferable
analysis across diverse geographies and contexts, there is a need for a
generalizable foundati... | [
"cs.AI"
] |
# 1 Introduction
Filling out paperwork is a pervasive and tedious task. Although some paper forms have been replaced by fillable rich-text PDFs, many are only available as pure images either in their original format or as scanned physical documents. These forms represent the most challenging task because agents can on... | Completing paperwork is a challenging and time-consuming problem. Form
filling is especially challenging in the pure-image domain without access to
OCR, typeset PDF text, or a DOM. For computer agents, it requires multiple
abilities, including multi-modal understanding, information retrieval, and
tool-use. We present a... | [
"cs.AI"
] |
# 1 Introduction
Some criticisms of the current deep learning (DL) paradigm rightly note that current best models and methods are overfit to the popular datasets [1]. The limitations of testing on large datasets have become apparent. For example, performance on ImageNet has exceeded saturation, where models are progre... | The Abstraction and Reasoning Corpus (ARC-AGI) presents a formidable
challenge for AI systems. Despite the typically low performance on ARC, the
deep learning paradigm remains the most effective known strategy for generating
skillful (state-of-the-art) neural networks (NN) across varied modalities and
tasks in vision, ... | [
"cs.AI",
"cs.LG"
] |
# 1. Introduction
Globally detecting long-lasting changes to the Earth’s surface is critical for informing decisions around tackling looming environmental, climate, and conservation challenges [4, 7, 10, 18]. Monitoring deforestation helps to understand where and why forest loss is happening; tracking urban expansion ... | In the face of pressing environmental issues in the 21st century, monitoring
surface changes on Earth is more important than ever. Large-scale remote
sensing, such as satellite imagery, is an important tool for this task.
However, using supervised methods to detect changes is difficult because of the
lack of satellite ... | [
"cs.CV"
] |
# 1 Introduction
In recent years, the advent of Generative Artificial Intelligence (AI) has accelerated the process of developing new software. However, there are studies [20] showing that users who use AI assistants tend to introduce more bugs and vulnerabilities into their code, compared to those who write code on t... | Interactive Theorem Proving was repeatedly shown to be fruitful combined with
Generative Artificial Intelligence. This paper assesses multiple approaches to
Rocq generation and illuminates potential avenues for improvement. We highlight
the importance of thorough premise selection for generating Rocq proofs and
propose... | [
"cs.LG",
"cs.AI",
"cs.LO",
"cs.SE"
] |
# 1 Introduction
Text-to-image (T2I) latent diffusion models (LDMs) have significantly advanced the field of image generation (59; 66), showcasing remarkable fidelity and enhanced creative control in image editing (7; 10; 15; 28; 31). However, the efficacy of image editing is not uniform, as modifications affecting at... | Adapting text-to-image (T2I) latent diffusion models for video editing has
shown strong visual fidelity and controllability, but challenges remain in
maintaining causal relationships in video content. Edits affecting causally
dependent attributes risk generating unrealistic or misleading outcomes if
these relationships... | [
"cs.CV",
"cs.AI"
] |
# 1 Introduction
Large Language Models (LLMs) have dramatically transformed the landscape of information access, enabling systems that transcend traditional ranked retrieval and instead generate comprehensive, report-style answers to complex, multi-faceted queries. These deep research systems, exemplified by recent co... | The emergence of Large Language Models (LLMs) has transformed information
access, with current LLMs also powering deep research systems that can generate
comprehensive report-style answers, through planned iterative search,
retrieval, and reasoning. Still, current deep research systems lack the
geo-temporal capabilitie... | [
"cs.CL",
"cs.IR"
] |
# 1 Introduction
Detecting human activities from still images is a challenging problem in computer vision, largely due to the subtle and complex variations inherent in human behaviour. In this work, we address this task using a subset of MSCOCO 2017 validation split introduced by Lin et al. (2015), each labeled as wal... | Recognising human activity in a single photo enables indexing, safety and
assistive applications, yet lacks motion cues. Using 285 MSCOCO images labelled
as walking, running, sitting, and standing, scratch CNNs scored 41% accuracy.
Fine-tuning multimodal CLIP raised this to 76%, demonstrating that contrastive
vision-la... | [
"cs.CV",
"cs.CL"
] |
# 1. Introduction
Reward models are a fundamental concept in reinforcement learning and define what an agent optimizes for. For large language models (LLMs), fine-tuning with reward models is a common post-training step to align the model outputs with desired behaviors and objectives. A widely adopted approach is to l... | In aligning large language models (LLMs), reward models have played an
important role, but are standardly trained as discriminative models and rely
only on labeled human preference data. In this paper, we explore methods that
train reward models using both unlabeled and labeled data. Building on the
generative models i... | [
"cs.CL",
"cs.AI"
] |
# I. INTRODUCTION
Ego-motion estimation is critical for autonomous navigation [1], using either proprioceptive sensors (e.g., odometers, IMUs) or exteroceptive sensors (e.g., cameras, LiDAR, radar). While proprioceptive sensors offer reliable short-term odometry, they accumulate drift without external correction. In G... | We present a method for estimating ego-velocity in autonomous navigation by
integrating high-resolution imaging radar with an inertial measurement unit.
The proposed approach addresses the limitations of traditional radar-based
ego-motion estimation techniques by employing a neural network to process
complex-valued raw... | [
"cs.RO",
"cs.AI",
"eess.SP"
] |
# 1 Introduction
Anonymization is widely regarded as a crucial tool for protecting privacy in an era of big data processing. Theoretically, it serves as a means to mitigate risks associated with the misuse of personal data by ensuring that individuals can no longer be identified. In practice, however, anonymization re... | Anonymization is a foundational principle of data privacy regulation, yet its
practical application remains riddled with ambiguity and inconsistency. This
paper introduces the concept of anonymity-washing -- the misrepresentation of
the anonymity level of ``sanitized'' personal data -- as a critical privacy
concern. Wh... | [
"cs.CR",
"cs.DB"
] |
# 1 Introduction
In software development, AI-assisted code generators have become vital to increase productivity, maintain consistency, enforce standards, and refine existing codebases. Industry leaders are increasingly adopting LLMs to automate the process of code generation, testing, and project document writing. Si... | As the quality of code generated by Large Language Models (LLMs) improves,
their adoption in the software industry for automated code generation continues
to grow. Researchers primarily focus on enhancing the functional correctness of
the generated code while commonly overlooking its energy efficiency and
environmental... | [
"cs.SE",
"cs.AI"
] |
# 1 Introduction
Brain disorders such as Alzheimer’s disease together with brain tumors are substantial global health issues. Millions of patients are impacted, and medical practitioners face challenges in diagnosing them [2]. Studies have demonstrated that, depending on the patient’s age and the brain region involved... | Accurate diagnosis of brain disorders such as Alzheimer's disease and brain
tumors remains a critical challenge in medical imaging. Conventional methods
based on manual MRI analysis are often inefficient and error-prone. To address
this, we propose DGG-XNet, a hybrid deep learning model integrating VGG16 and
DenseNet12... | [
"cs.CV"
] |
# I. INTRODUCTION
The rapid digitalization of healthcare has led to the proliferation of electronic health records (EHRs), offering unprecedented opportunities for data-driven medical research and clinical decision-making [19], [41]. However, leveraging this data at scale remains challenging due to stringent privacy r... | The rise of electronic health records (EHRs) has unlocked new opportunities
for medical research, but privacy regulations and data heterogeneity remain key
barriers to large-scale machine learning. Federated learning (FL) enables
collaborative modeling without sharing raw data, yet faces challenges in
harmonizing diver... | [
"cs.LG",
"cs.SE"
] |
# 1 INTRODUCTION
With the massive volume of video data in real-world applications, analyzing video content has become increasingly crucial across various domains [2, 7–9, 11, 13, 14]. A common task in video analytics involves identifying a specific short video segment within a longer video. For instance, consider a su... | Current video analytics approaches face a fundamental trade-off between
flexibility and efficiency. End-to-end Vision Language Models (VLMs) often
struggle with long-context processing and incur high computational costs, while
neural-symbolic methods depend heavily on manual labeling and rigid rule
design. In this pape... | [
"cs.DB",
"cs.AI",
"cs.CV",
"cs.IR",
"cs.MM"
] |
# 1 Introduction 9
1.1 Inference-Time Policy Steering 11
1.2 Task and Motion Imitation 12
1.3 Outline 13
# Inference-Time Policy Steering with Diffusion 15
# 2.1 Introduction 15
2.2 Method 17
2.2.1 Specification of User Intent 17
2.2.2 Policy Steering 18
2.3 Experiments 22
2.3.1 Maze2D - Continuous Motion Alignment... | Imitation learning has driven the development of generalist policies capable
of autonomously solving multiple tasks. However, when a pretrained policy makes
errors during deployment, there are limited mechanisms for users to correct its
behavior. While collecting additional data for finetuning can address such
issues, ... | [
"cs.RO",
"cs.AI",
"cs.HC",
"cs.LG"
] |
# 1 Introduction
Latent-space monitors have emerged as a promising approach for detecting harmful behaviour in large language models (LLMs) at runtime. Unlike traditional black-box techniques, latent-space monitors leverage the internal representations of the model, potentially enabling more accurate and robust detect... | Latent-space monitors aim to detect undesirable behaviours in large language
models by leveraging internal model representations rather than relying solely
on black-box outputs. These methods have shown promise in identifying
behaviours such as deception and unsafe completions, but a critical open
question remains: can... | [
"cs.LG"
] |
# 1 Introduction
Recent large language models (LLMs) have demonstrated impressive performance in generation tasks. However, these models often struggle to attribute their outputs accurately through proper citations to source material, particularly in attributed generation tasks such as question answering (QA) (Bohnet ... | Recent large language models (LLMs) achieve impressive performance in
source-conditioned text generation but often fail to correctly provide
fine-grained attributions for their outputs, undermining verifiability and
trust. Moreover, existing attribution methods do not explain how and why models
leverage the provided so... | [
"cs.CL",
"cs.AI"
] |
1 Introduction and Objective .
# 2 Relevant Fundamentals and Related Work .
2.1 Relevant Fundamentals . 2
2.1.1 Traditional Supervised Learning 2
2.1.2 FSL. 2
2.1.3 Time Series Classification . 5
2.2 Related Work 7
# 3 Use Case Description and Data Understanding . 9
3.1 Data Source. 9
3.2 Data Understanding 9
3.3 U... | Few-shot learning (FSL) has shown promise in vision but remains largely
unexplored for \emph{industrial} time-series data, where annotating every new
defect is prohibitively expensive. We present a systematic FSL study on
screw-fastening process monitoring, using a 2\,300-sample multivariate torque
dataset that covers ... | [
"cs.LG",
"cs.AI"
] |
# 1. Introduction
Privacy relevant laws that have come into effect in the last 6 years include predominantly the EU General Data Protection Regulation (GDPR) [1] and the California Consumer Privacy Act of 2018 (CCPA) [2]. California Privacy Rights Act (CPRA) [3] is a recent amendment to CCPA, whereas other countries a... | Free and open source software has gained a lot of momentum in the industry
and the research community. The latest advances in privacy legislation,
including the EU General Data Protection Regulation (GDPR) and the California
Consumer Privacy Act (CCPA), have forced the community to pay special attention
to users' data ... | [
"cs.SE"
] |
# 1 Introduction
The iterative process of coding, involving frequent edits and insertions (Bavarian et al., 2022; Fried et al., 2023), establishes Fill-in-the-Middle (FIM) code generation a prevalent task in code completion. Models tackling this must generate the missing code segment conditioned on both the preceding ... | Post-processing is crucial for the automatic evaluation of LLMs in
fill-in-the-middle (FIM) code generation due to the frequent presence of
extraneous code in raw outputs. This extraneous generation suggests a lack of
awareness regarding output boundaries, requiring truncation for effective
evaluation. The determinatio... | [
"cs.SE",
"cs.CL"
] |
# 1 Introduction
The integration of Machine Learning (ML) into various domains has revolutionised fields ranging from manufacturing to finance, offering unprecedented capabilities to learn from data and make accurate predictions. In safety- and ethic-critical domains such as healthcare, the ability to not only accurat... | In domains where transparency and trustworthiness are crucial, such as
healthcare, rule-based systems are widely used and often preferred over
black-box models for decision support systems due to their inherent
interpretability. However, as rule-based models grow complex, discerning
crucial features, understanding thei... | [
"cs.LG",
"cs.AI"
] |
# 1 Introduction
Bloom filters and other bit vector filters are used widely in database management systems to perform early data reduction [4–7, 16]. Bloom filters provide an efficient way to probabilistically remove rows early, reducing the number of rows participating in further processing and improving query perfor... | Bloom filters are used in query processing to perform early data reduction
and improve query performance. The optimal query plan may be different when
Bloom filters are used, indicating the need for Bloom filter-aware query
optimization. To date, Bloom filter-aware query optimization has only been
incorporated in a top... | [
"cs.DB"
] |
# I. INTRODUCTION
Large Language Models (LLMs) have demonstrated strong biomedical question-answering (QA) capabilities [1]. However, LLMs can produce factual inaccuracies, lack specific domain knowledge, and lack verifiability [2]. A major concern is hallucination, where LLMs generate factually incorrect responses du... | Biomedical question-answering (QA) systems require effective retrieval and
generation components to ensure accuracy, efficiency, and scalability. This
study systematically examines a Retrieval-Augmented Generation (RAG) system for
biomedical QA, evaluating retrieval strategies and response time trade-offs. We
first ass... | [
"cs.IR",
"cs.AI",
"cs.DB",
"cs.LG"
] |
# 1 Introduction
Although large language models (LLMs) have demonstrated remarkable performance across a wide range of general tasks (Jiang et al., 2023; Chowdhery et al., 2023; Jian et al., 2023; Touvron et al., 2023b), they still fall short in certain tasks or domains, such as reasoning (Tong et al., 2024; Srivastav... | Parameter-efficient fine-tuning (PEFT) methods, particularly Low-Rank
Adaptation (LoRA), offer an efficient way to adapt large language models with
reduced computational costs. However, their performance is limited by the small
number of trainable parameters. Recent work combines LoRA with the
Mixture-of-Experts (MoE),... | [
"cs.CL"
] |
# 1. Introduction
The current literature on temporal data analysis is divided into two main branches: one, mainly considering numerical fluctuations in different dimensions as Multivariate Time Series (MTS) [16,26,19,32], and the other focusing on a characterization as a linear sequence of discrete events, be they dur... | This paper offers a hybrid explainable temporal data processing pipeline,
DataFul Explainable MultivariatE coRrelatIonal Temporal Artificial inTElligence
(EMeriTAte+DF), bridging numerical-driven temporal data classification with an
event-based one through verified artificial intelligence principles, enabling
human-exp... | [
"cs.DB",
"cs.AI"
] |
# Curation and Analysis of MIMICEL – An Event Log for MIMIC-IV Emergency Department
Jia Wei1,∗, Chun Ouyang1, Bemali Wickramanayake1, Zhipeng ${ \mathsf { H } } { \mathsf { e } } ^ { 1 }$ , Keshara Perera2, and Catarina Moreira3
1School of Information Systems, Queensland University of Technology, Brisbane, 4000, Aust... | The global issue of overcrowding in emergency departments (ED) necessitates
the analysis of patient flow through ED to enhance efficiency and alleviate
overcrowding. However, traditional analytical methods are time-consuming and
costly. The healthcare industry is embracing process mining tools to analyse
healthcare pro... | [
"cs.DB"
] |
# 1 Introduction
# CCS Concepts
• Software and its engineering $$ Automatic programming;
Software maintenance tools; Software evolution.
# Keywords
Automated Software Engineering, Agentic Systems, AI for Software Development
Large language models (LLMs) have shown promise in coding, reasoning, and problem solving.... | The growth of Large Language Model (LLM) technology has raised expectations
for automated coding. However, software engineering is more than coding and is
concerned with activities including maintenance and evolution of a project. In
this context, the concept of LLM agents has gained traction, which utilize LLMs
as rea... | [
"cs.SE",
"cs.AI"
] |
# 1 Introduction
With the technological advancements of the last couple of decades, machine learning (ML) and artificial intelligence (AI) play an important part in automated decisionmaking pipelines [1–3]. Even though these tools are generally created by optimising with respect to their accuracy and performance, ther... | Fairness has been identified as an important aspect of Machine Learning and
Artificial Intelligence solutions for decision making. Recent literature offers
a variety of approaches for debiasing, however many of them fall short when the
data collection is imbalanced. In this paper, we focus on a particular case,
fairnes... | [
"cs.LG",
"cs.CY"
] |
# 1. Introduction
Machine learning (ML) systems are typically designed under the assumption that the training and test sets are sampled from the same statistical distribution. However, this often does not hold in practice. For example, during deployment, test data may include previously unseen classes. In such cases, ... | We present a theory for the construction of out-of-distribution (OOD)
detection features for neural networks. We introduce random features for OOD
through a novel information-theoretic loss functional consisting of two terms,
the first based on the KL divergence separates resulting in-distribution (ID)
and OOD feature ... | [
"cs.LG"
] |
# 1 Introduction
Leading reasoning models on math, science, and coding benchmarks learn to utilize chain-of-thought via reinforcement learning with verifiable rewards (RLVR) [1, 2, 3, 4]. These models are optimized to maximize verifiable rewards by comparing predicted final answers to ground truth. Models trained with... | We study the process through which reasoning models trained with
reinforcement learning on verifiable rewards (RLVR) can learn to solve new
problems. We find that RLVR drives performance through two main means: (1) by
compressing pass@$k$ into pass@1 and (2) via "capability gain" in which models
learn to solve new prob... | [
"cs.LG",
"cs.AI",
"cs.CL"
] |
# 1. Introduction
Large language models (LLMs) (Achiam et al., 2023; Abdin et al., 2024; Yang et al., 2024) have demonstrated impressive capabilities across diverse tasks such as mathematics (Zhang et al., 2024b;a; Yue et al., 2024), coding (Nam et al., 2024; Chew et al., 2023; Kim et al., 2024a), and reasoning (Hao e... | Large language models (LLMs) have shown impressive capabilities across tasks
such as mathematics, coding, and reasoning, yet their learning ability, which
is crucial for adapting to dynamic environments and acquiring new knowledge,
remains underexplored. In this work, we address this gap by introducing a
framework insp... | [
"cs.CL",
"cs.AI"
] |
# 1 Introduction
DevOps practitioners rely on configuration management tools like Ansible for IT automation tasks. In order to complete these tasks, practitioners use scripts, which are referred to as automation scripts (Parnin et al., 2017). While these scripts save time and manage thousands of servers (ansible, 2022... | LLMs show promise in code generation, yet their effectiveness for IT
automation tasks, particularly for tools like Ansible, remains understudied.
Existing benchmarks rely primarily on synthetic tasks that fail to capture the
needs of practitioners who use IT automation tools, such as Ansible. We present
ITAB (IT Automa... | [
"cs.CL",
"cs.SE"
] |
# I. INTRODUCTION
Visual content plays an increasingly important role in our current digital ecosystem. With the proliferation of smartphones, tablets, and other digital devices, the consumption of video content has surged across a wide range of applications, including live streaming, digital broadcasting, video confe... | This paper presents a general-purpose video super-resolution (VSR) method,
dubbed VSR-HE, specifically designed to enhance the perceptual quality of
compressed content. Targeting scenarios characterized by heavy compression, the
method upscales low-resolution videos by a ratio of four, from 180p to 720p or
from 270p to... | [
"eess.IV",
"cs.CV"
] |
# 1 Introduction
Large Language Models (LLMs) and agent frameworks are catalyzing a profound transformation in software engineering [63, 38, 51, 25, 28, 19, 65], significantly improving the functional correctness of their code generation and starting to rival human engineers in certain tasks [7, 58, 23]. However, this... | Large Language Models (LLMs) generate functionally correct solutions but
often fall short in code efficiency, a critical bottleneck for real-world
deployment. In this paper, we introduce a novel test-time iterative
optimization framework to address this, employing a closed-loop system where
LLMs iteratively refine code... | [
"cs.SE",
"cs.AI"
] |
# Introduction
The development of large language models tailored to the field of Traditional Chinese Medicine (TCM) [1,2] has emerged as a significant research direction. Given the unique and intricate nature of the TCM knowledge system, the construction of intelligent tools specifically designed for this domain can s... | Traditional Chinese Medicine (TCM), as an effective alternative medicine, has
been receiving increasing attention. In recent years, the rapid development of
large language models (LLMs) tailored for TCM has underscored the need for an
objective and comprehensive evaluation framework to assess their performance on
real-... | [
"cs.CL",
"cs.DB"
] |
# 1 Introduction
Code-switching (CSW)—the act of alternating between two or more languages within a single discourse (Das et al., 2023; Zhang et al., 2023; Ochieng et al., 2024)—is a common phenomenon in multilingual communities (Bullock and Toribio, 2009; Parekh et al., 2020; Do˘gruöz et al., 2021), and increasingly ... | Code-switching (CSW) is the act of alternating between two or more languages
within a single discourse. This phenomenon is widespread in multilingual
communities, and increasingly prevalent in online content, where users
naturally mix languages in everyday communication. As a result, Large Language
Models (LLMs), now c... | [
"cs.CL"
] |
# 1 Introduction
Code obfuscation involves the transformation of a program into a form intentionally more difficult to understand, targeting both human analysts and automated analysis tools [20]. While the functionality remains identical, the syntax and structure are deliberately altered to obscure the code’s true pur... | Large language models (LLMs) have shown promise in software engineering, yet
their effectiveness for binary analysis remains unexplored. We present the
first comprehensive evaluation of commercial LLMs for assembly code
deobfuscation. Testing seven state-of-the-art models against four obfuscation
scenarios (bogus contr... | [
"cs.SE",
"cs.AI",
"cs.CR"
] |
# 1 INTRODUCTION
The emergence of reasoning capabilities in Large Language Models (LLMs) has marked a major leap forward, particularly in tasks involving mathematics and programming (Guo et al., 2025; Jaech et al., 2024; Zeng et al., 2024; Yang et al., 2025; Kavukcuoglu, Koray, 2025). To enable such reasoning, LLMs ar... | Recent advances in Large Language Models (LLMs) have showcased impressive
reasoning abilities in structured tasks like mathematics and programming,
largely driven by Reinforcement Learning with Verifiable Rewards (RLVR), which
uses outcome-based signals that are scalable, effective, and robust against
reward hacking. H... | [
"cs.CL",
"cs.AI",
"cs.LG"
] |
# 1 Introduction
Modern data-intensive applications continuously generate diverse types of data stored in data lakes [16], encompassing structured data (e.g., tables, graphs), semi-structured data (e.g., JSON, HTML), and unstructured data (e.g., text, images, video). This diversity introduces a critical challenge of d... | The variety of data in data lakes presents significant challenges for data
analytics, as data scientists must simultaneously analyze multi-modal data,
including structured, semi-structured, and unstructured data. While Large
Language Models (LLMs) have demonstrated promising capabilities, they still
remain inadequate f... | [
"cs.DB",
"cs.AI"
] |
# 1 INTRODUCTION
NL2SQL translates natural language questions into SQL queries, making data analysis accessible to non-technical users and serving as a foundation for intelligent data applications like smart dashboards and visualizations. For instance, a business owner can simply ask, “What were last month’s total sal... | NL2SQL (natural language to SQL) translates natural language questions into
SQL queries, thereby making structured data accessible to non-technical users,
serving as the foundation for intelligent data applications. State-of-the-art
NL2SQL techniques typically perform translation by retrieving database-specific
informa... | [
"cs.DB",
"cs.CL"
] |
# 1. Introduction
When browsing through our camera albums, we often find ourselves wishing to view the videos we’ve captured from different camera poses. For instance, seeing footage originally shot from the side as if it were filmed from the front, or transforming a moving shot into one that appears as if taken from ... | We introduce Vid-CamEdit, a novel framework for video camera trajectory
editing, enabling the re-synthesis of monocular videos along user-defined
camera paths. This task is challenging due to its ill-posed nature and the
limited multi-view video data for training. Traditional reconstruction methods
struggle with extrem... | [
"cs.CV"
] |
# 1 Introduction
Graph neural networks (GNNs), and specifically message-passing neural networks [16, 17], have become a dominant approach for representation learning on graph-structured data [31, 37]. Since the expressiveness of standard GNNs within the message-passing framework is limited by their inability to distin... | We propose and study Hierarchical Ego Graph Neural Networks (HEGNNs), an
expressive extension of graph neural networks (GNNs) with hierarchical node
individualization, inspired by the Individualization-Refinement paradigm for
graph isomorphism testing. HEGNNs generalize subgraph-GNNs and form a hierarchy
of increasingl... | [
"cs.LG",
"cs.AI",
"cs.LO",
"I.2.6; F.2.0"
] |
# 1 Introduction
Transformer-based LLMs with long-context capabilities have significantly enhanced real-world applications, including long-document analysis and personalized conversational agents [1, 19, 46]. However, increasing context lengths substantially raises both memory consumption for KV caching and computatio... | Transformer-based large language models (LLMs) cache context as key-value
(KV) pairs during inference. As context length grows, KV cache sizes expand,
leading to substantial memory overhead and increased attention latency. This
paper introduces KVzip, a query-agnostic KV cache eviction method enabling
effective reuse o... | [
"cs.DB",
"cs.LG"
] |
# 1 Introduction
Since the advent of large language models (LLMs), there has been ongoing debate about the utility of symbolic representations such as Abstract Meaning Representations (AMRs; Banarescu et al., 2013) in (LLM-based) pipelines and existing NLP tasks. While some studies report limited or negative impact of... | Natural Language Inference (NLI) relies heavily on adequately parsing the
semantic content of the premise and hypothesis. In this work, we investigate
whether adding semantic information in the form of an Abstract Meaning
Representation (AMR) helps pretrained language models better generalize in NLI.
Our experiments in... | [
"cs.CL"
] |
# 1 Introduction
Preference optimization (PO) methods such as DPO (Rafailov et al., 2024) have shown success in improving LLMs’ performance in various tasks (Dubois et al., 2024). These methods usually involve a contrastive learning objective that encourages LLMs to generate a preferred response $y ^ { + }$ with highe... | Recent research has attempted to associate preference optimization (PO)
performance with the underlying preference datasets. In this work, our
observation is that the differences between the preferred response $y^+$ and
dispreferred response $y^-$ influence what LLMs can learn, which may not match
the desirable differe... | [
"cs.CL"
] |
# I. INTRODUCTION
Camera and LiDAR are two of the most popular sensors applied in autonomous driving. The camera captures colorful images with dense semantic context, while the LiDAR measures distances of sparse points with intensity that reflect the rough outline of the ambient scene. Their data fusion compensates th... | Cameras and LiDAR are essential sensors for autonomous vehicles. The fusion
of camera and LiDAR data addresses the limitations of individual sensors but
relies on precise extrinsic calibration. Recently, numerous end-to-end
calibration methods have been proposed; however, most predict extrinsic
parameters in a single s... | [
"cs.CV"
] |
# 1 INTRODUCTION
Video data is pervasive in today’s world, playing a critical role in various applications such as autonomous machines [23, 29, 32], education [28], healthcare [82], security and surveillance [51, 92], e-commerce [118, 120], and many others [56, 67]. Recently, VideoLanguage Models (VideoLMs) have demon... | Recently, Video-Language Models (VideoLMs) have demonstrated remarkable
capabilities, offering significant potential for flexible and powerful video
query systems. These models typically rely on Vision Transformers (ViTs), which
process video frames individually to extract visual embeddings. However,
generating embeddi... | [
"cs.DC",
"cs.CV"
] |
# I. INTRODUCTION
Mosquito-borne diseases continue to be a leading cause of illness and death worldwide, particularly affecting lowand middle-income countries. According to the World Health Organization (WHO), approximately 700 million people are affected by mosquito-borne illnesses every year, resulting in over one m... | Mosquito-borne diseases pose a major global health risk, requiring early
detection and proactive control of breeding sites to prevent outbreaks. In this
paper, we present VisText-Mosquito, a multimodal dataset that integrates visual
and textual data to support automated detection, segmentation, and reasoning
for mosqui... | [
"cs.CV",
"cs.CL"
] |
1 INTRODUCTION
The penetration of software‑based systems has transformed the ways in which almost every indus‑ try operates. From controlling nuclear power stations to maneuvering spacecraft, complex software systems are used to interface with many critical systems. It is essential to ensure that these soft‑ ware syst... | Software systems have grown as an indispensable commodity used across various
industries, and almost all essential services depend on them for effective
operation. The software is no longer an independent or stand-alone piece of
code written by a developer but rather a collection of packages designed by
multiple develo... | [
"cs.SE",
"cs.CR"
] |
# I. INTRODUCTION
LLMs have become a key tool for automated software engineering. They are being used in a variety of contexts, with increasing autonomy [1]. Importantly, they are commonly used for code generation tasks, and their outputs are being integrated into software [2]. These decisions are being made with expe... | This paper investigates the ability of large language models (LLMs) to
recognise and solve tasks which have been obfuscated beyond recognition.
Focusing on competitive programming and benchmark tasks (LeetCode and MATH), we
compare performance across multiple models and obfuscation methods, such as
noise and redaction.... | [
"cs.LG",
"cs.SE"
] |
# 1 Introduction
Deploying Deep Learning (DL) models in real-time applications is challenging due to their high computational demands, particularly on edge devices such as smartphones and IoT systems (Szegedy et al., 2017; Deng et al., 2020; Krishnamoorthi, 2018). Traditional DL models, while effective, often exceed t... | Model compression is critical for deploying deep learning models on
resource-constrained devices. We introduce a novel method enhancing knowledge
distillation with integrated gradients (IG) as a data augmentation strategy.
Our approach overlays IG maps onto input images during training, providing
student models with de... | [
"cs.CV",
"cs.AI",
"cs.LG",
"68T05, 68T07",
"I.2.6; I.4.2; I.4.9"
] |
# 1 Introduction
Along with the digitalization of healthcare and significant advancements in radiology, various multimedia data like videos, images and texts stored in the Picture Archiving and Communication Systems (PACS) of hospitals are increasing faster than ever. When facing difficult cases in clinical routines, ... | Performance evaluation for Content-Based Image Retrieval (CBIR) remains a
crucial but unsolved problem today especially in the medical domain. Various
evaluation metrics have been discussed in the literature to solve this problem.
Most of the existing metrics (e.g., precision, recall) are adapted from
classification ta... | [
"cs.CV"
] |
# 1 Introduction
The rise of open-weight foundation models, such as CLIP [42, 22], T5 [43] and the more recent Gemma [56], Llama [16] and DeepSeek [9], has caused a paradigm shift in the field of machine learning. Instead of training a model from scratch as was previously the norm, it is now increasingly common for pr... | Modern deep learning is increasingly characterized by the use of open-weight
foundation models that can be fine-tuned on specialized datasets. This has led
to a proliferation of expert models and adapters, often shared via platforms
like HuggingFace and AdapterHub. To leverage these resources, numerous model
upcycling ... | [
"cs.LG",
"cs.AI"
] |
# 1 Introduction
Solving partial differential equations (PDEs) underpins a vast array of phenomena in engineering and the physical sciences, from fluid flow and heat transfer to fracture mechanics and structural deformation. Traditional numerical methods offer rigorous error bounds and adaptable frameworks, but they o... | We present a novel graph-informed transformer operator (GITO) architecture
for learning complex partial differential equation systems defined on irregular
geometries and non-uniform meshes. GITO consists of two main modules: a hybrid
graph transformer (HGT) and a transformer neural operator (TNO). HGT leverages
a graph... | [
"cs.LG"
] |
# I. INTRODUCTION
Cloud-native applications are engineered to fully exploit modern cloud computing environments by adhering to scalability, elasticity, resilience, and continuous delivery principles. Built as collections of loosely coupled microservices, these applications are typically containerized and orchestrated ... | Modern distributed applications increasingly rely on cloud-native platforms
to abstract the complexity of deployment and scalability. As the de facto
orchestration standard, Kubernetes enables this abstraction, but its
declarative configuration model makes the architectural understanding
difficult. Developers, operator... | [
"cs.SE",
"cs.DC"
] |
# 1 Introduction
Most research in Data Science focuses on technical resources, overlooking project organization and management. Many Data Science projects fail or fall short of delivering expected value, with 82% of teams lacking a process model or methodology [1].
Cross Industry Standard Process for Data Mining (CRI... | This study explores the integration of eXtreme Programming (XP) and the
Cross-Industry Standard Process for Data Mining (CRISP-DM) in agile Data
Science projects. We conducted a case study at the e-commerce company Elo7 to
answer the research question: How can the agility of the XP method be
integrated with CRISP-DM in... | [
"cs.SE",
"cs.AI",
"cs.LG"
] |
# 1. INTRODUCTION
Let $( \mathsf { X } , \mathcal { X } )$ and $( \mathsf { Y } , \mathsf { y } )$ be measurable spaces, and let $( x _ { 1 } , y _ { 1 } ) , \ldots , ( x _ { n } , y _ { n } ) \in \mathsf X \times \mathsf Y$ represent a training data set drawn from random elements $( X _ { 1 } , Y _ { 1 } ) , \ldots ,... | Most existing literature on supervised machine learning assumes that the
training dataset is drawn from an i.i.d. sample. However, many real-world
problems exhibit temporal dependence and strong correlations between the
marginal distributions of the data-generating process, suggesting that the
i.i.d. assumption is ofte... | [
"stat.ML",
"cs.LG",
"math.PR",
"68W40, 68T10, 60J05"
] |
# 1. Introduction
A key challenge in multi-modal human activity understanding tasks, such as human activity recognition (HAR), human pose estimation (HPE), retrieval, or person reidentification (RE-ID) “in the wild” is obtaining paired sensor data for each individual in a multi-person scene (e.g., IMU with human poses... | Despite LiDAR (Light Detection and Ranging) being an effective
privacy-preserving alternative to RGB cameras to perceive human activities, it
remains largely underexplored in the context of multi-modal contrastive
pre-training for human activity understanding (e.g., human activity recognition
(HAR), retrieval, or perso... | [
"cs.CV"
] |
# 1 Introduction
LLM-based agentic AI systems combine multi-step reasoning with external tools and memory to solve open-ended tasks such as code generation, web navigation, planning, and transactional services like booking and ordering [Acharya et al., 2025]. By doing so, they extend to complex, real-world problems be... | Agentic AI systems, which build on Large Language Models (LLMs) and interact
with tools and memory, have rapidly advanced in capability and scope. Yet,
since LLMs have been shown to struggle in multilingual settings, typically
resulting in lower performance and reduced safety, agentic systems risk
inheriting these limi... | [
"cs.DB",
"cs.CL",
"cs.CR"
] |
# 1 Introduction
In recent years, the integration of toolchains [1, 2, 3, 4] and iterative reasoning [5, 6, 7, 8] has significantly enhanced large language models (LLMs) in code-related tasks [9, 10, 11]. These advancements have enabled LLMs to proficiently complete code snippets [12, 13], debug errors [14], and even ... | The ultimate goal of code agents is to solve complex tasks autonomously.
Although large language models (LLMs) have made substantial progress in code
generation, real-world tasks typically demand full-fledged code repositories
rather than simple scripts. Building such repositories from scratch remains a
major challenge... | [
"cs.SE",
"cs.AI"
] |
# 1. Introduction
Zeroth-Order Optimization (ZOO) provides powerful tools for optimizing functions where explicit gradients are unavailable or expensive to compute. However, the underlying mechanisms of popular ZOO methods, particularly those employing randomized finite differences, and their connection to other optim... | Zeroth-Order Optimization (ZOO) provides powerful tools for optimizing
functions where explicit gradients are unavailable or expensive to compute.
However, the underlying mechanisms of popular ZOO methods, particularly those
employing randomized finite differences, and their connection to other
optimization paradigms l... | [
"cs.LG"
] |
# I. INTRODUCTION
The safe (i.e., state-constrained) domain of attraction (DOA) of a given dynamical system is the set of state values from which trajectories are guaranteed to converge to an equilibrium point of interest under the system’s dynamics, while satisfying specified state constraints. Such a set provides a ... | Analysis of nonlinear autonomous systems typically involves estimating
domains of attraction, which have been a topic of extensive research interest
for decades. Despite that, accurately estimating domains of attraction for
nonlinear systems remains a challenging task, where existing methods are
conservative or limited... | [
"eess.SY",
"cs.AI",
"cs.SY"
] |
# 1 Introduction
Graph Neural Networks (GNNs) have emerged as a robust machine learning paradigm to learn expressive representations of graph-structured data through message passing, exhibiting remarkable performance across various AI applications, such as molecular interactions[Huang et al., 2020]. However, most exis... | Federated graph learning is a widely recognized technique that promotes
collaborative training of graph neural networks (GNNs) by multi-client
graphs.However, existing approaches heavily rely on the communication of model
parameters or gradients for federated optimization and fail to adequately
address the data heterog... | [
"cs.LG",
"cs.AI",
"cs.DB",
"cs.SI"
] |
# 1 INTRODUCTION
Relying on natural language in software requirements often leads to ambiguity. In addition, requirements which are not expressed in a formal mathematical notation cannot be guaranteed through formal verification techniques as required to meet standards e.g. [9, 11, 70] in safety critical software. Exp... | This draft is a working document, having a summary of nighty-four (94) papers
with additional sections on Traceability of Software Requirements (Section 4),
Formal Methods and Its Tools (Section 5), Unifying Theories of Programming
(UTP) and Theory of Institutions (Section 6). Please refer to abstract of
[7,8]. Key dif... | [
"cs.SE",
"cs.AI",
"D.2.1; D.2.4; D.2.10; F.4.1; F.4.3"
] |
# Introduction to Food Processing
As concern grows over the health impacts of processed foods1–3, researchers, policymakers, and consumers alike are asking a critical question: What does it really mean for a food to be processed? The answer is far from simple. While we increasingly rely on epidemiological data to draw... | This chapter explores the evolution, classification, and health implications
of food processing, while emphasizing the transformative role of machine
learning, artificial intelligence (AI), and data science in advancing food
informatics. It begins with a historical overview and a critical review of
traditional classifi... | [
"cs.CL",
"cs.AI",
"cs.CY",
"cs.DB",
"cs.LG"
] |
# 1 Introduction
Large Language Models (LLMs) have demonstrated remarkable success across a wide range of natural language processing (NLP) tasks (Brown et al. (2020), Chowdhery et al. (2023), Touvron et al. (2023a), Ouyang et al. (2022)), including question answering (Kamalloo et al., 2023), summarization (Liu et al.... | Large Language Models (LLMs) excel at many NLP tasks, but struggle with
multi-hop reasoning and factual consistency, limiting their effectiveness on
knowledge-intensive tasks like complex question answering (QA). Linking
Knowledge Graphs (KG) and LLMs has shown promising results, but LLMs generally
lack the ability to ... | [
"cs.CL",
"cs.IR",
"cs.LG"
] |
# 1. Introduction
In recent years, speech synthesis technologies have achieved remarkable progress, enabling the generation of increasingly more natural and convincing synthetic voices. While these advancements in text-to-speech (TTS) and voice conversion (VC) systems demonstrate the potential of conversational AI app... | Recent advancements in speech synthesis technologies have led to increasingly
advanced spoofing attacks, posing significant challenges for automatic speaker
verification systems. While systems based on self-supervised learning (SSL)
models, particularly the XLSR-Conformer model, have demonstrated remarkable
performance... | [
"cs.SD",
"cs.CL",
"eess.AS"
] |
# 1 Introduction
Despite significant advancements in language models (LMs), challenges persist in effectively handling the tail data, such as accommodating the needs of unseen language groups and addressing social biases (Gallegos et al., 2024; Guerreiro et al., 2023). This gap underscores the importance of research e... | In this study, we investigate the potential of language models (LMs) in
aiding patients experiencing anomia, a difficulty identifying the names of
items. Identifying the intended target item from patient's circumlocution
involves the two challenges of term failure and error: (1) The terms relevant
to identifying the it... | [
"cs.CL"
] |
# 1 Introduction
Theory of Mind (ToM), the capability to infer others’ mental states such as beliefs, desires, and intentions, is substantial for narrative comprehension (Premack and Woodruff, 1978; Apperly, 2010), where understanding charaters’ motivations and
Book name: King Lear, Character: King Lear, Plot: King L... | A compelling portrayal of characters is essential to the success of narrative
writing. For readers, appreciating a character's traits requires the ability to
infer their evolving beliefs, desires, and intentions over the course of a
complex storyline, a cognitive skill known as Theory-of-Mind (ToM). Performing
ToM reas... | [
"cs.CL"
] |
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