article stringlengths 507 295k | abstract stringlengths 417 1.92k | category listlengths 1 6 |
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# 1 Introduction
Worldwide, many bridges are exposed to high traffic loads, extreme weather events, sea salt, and de-icing chemicals, leading to defects. At the same time, most industrialized countries face a growing stock of old infrastructure [2, 3]. To determine rehabilitation measurements and immediate actions, su... | Adequate bridge inspection is increasingly challenging in many countries due
to growing ailing stocks, compounded with a lack of staff and financial
resources. Automating the key task of visual bridge inspection, classification
of defects and building components on pixel level, improves efficiency,
increases accuracy a... | [
"cs.CV"
] |
# 1. Introduction
In the healthcare domain, the decisions made by AI systems can impact the well-being or life of people [1]. In this context, it has been proposed that providing explanations about AI models or single predictions could potentially increase clinicians’ appropriate trust [2–5] and ultimately boost adopt... | Despite promising developments in Explainable Artificial Intelligence, the
practical value of XAI methods remains under-explored and insufficiently
validated in real-world settings. Robust and context-aware evaluation is
essential, not only to produce understandable explanations but also to ensure
their trustworthiness... | [
"cs.HC",
"cs.AI",
"cs.LG"
] |
# 1 Introduction
EHRs store richly structured, longitudinal data spanning diagnoses, laboratory results, procedures, medications, and outcomes—resources that are critical for predictive modeling and clinical decision support [8, 11]. However, regulations such as the U.S. HIPAA Privacy Rule and the EU GDPR impose stric... | Electronic health records (EHRs) contain richly structured, longitudinal data
essential for predictive modeling, yet stringent privacy regulations (e.g.,
HIPAA, GDPR) often restrict access to individual-level records. We introduce
Query, Don't Train (QDT): a structured-data foundation-model interface enabling
tabular i... | [
"cs.DB"
] |
# 1 Introduction
Formal methods offer robust mathematical guarantees for system reliability [Huth and Ryan, 2004], but their widespread adoption is impeded by high expertise and labor demands, traditionally limiting their application to safety-critical domains where failures have catastrophic consequences [Clarke et a... | Large language models (LLMs) show remarkable promise for democratizing
automated reasoning by generating formal specifications. However, a fundamental
tension exists: LLMs are probabilistic, while formal verification demands
deterministic guarantees. This paper addresses this epistemological gap by
comprehensively inve... | [
"cs.CL",
"cs.AI",
"cs.LO",
"cs.SE"
] |
# I. INTRODUCTION
P dOaitnat rcelporuedss nhtatvieonbeicnocmoemapuftoerungdratpiohincasl a3nDd gceompeuttriecr vision, with applications in various domains, including archaeology [1], augmented reality [2], autonomous driving [3], robotic navigation [4], [5]. Building on this, 3D visual grounding, which aims to locali... | 3D visual grounding (3DVG) is a critical task in scene understanding that
aims to identify objects in 3D scenes based on text descriptions. However,
existing methods rely on separately pre-trained vision and text encoders,
resulting in a significant gap between the two modalities in terms of spatial
geometry and semant... | [
"cs.CV"
] |
# 1 Introduction
Foundation models have shown remarkable success in diverse domains such as natural language [Raffel et al., 2023, Paaß and Giesselbach, 2023, Touvron et al., 2023], computer vision [Dosovitskiy et al., 2021, Radford et al., 2021, Wang et al., 2022, Bao et al., 2022], and audio processing [Chen et al.,... | Graph machine learning architectures are typically tailored to specific tasks
on specific datasets, which hinders their broader applicability. This has led
to a new quest in graph machine learning: how to build graph foundation models
capable of generalizing across arbitrary graphs and features? In this work, we
presen... | [
"cs.LG",
"cs.SI",
"stat.ML"
] |
# A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning
Asbjørn Munk1,2,+,\*, Stefano Cerri3,1,2,14+,\*, Jakob Ambsdorf1,2, Julia Machnio1,2, Sebastian Nørgaard Llambias1,2, Vardan Nersesjan3,12, Christian Hedeager Krag10,11, Peirong Liu4,5,6,9, Pablo Rocamora Garc´ıa1,2,... | We present FOMO60K, a large-scale, heterogeneous dataset of 60,529 brain
Magnetic Resonance Imaging (MRI) scans from 13,900 sessions and 11,187
subjects, aggregated from 16 publicly available sources. The dataset includes
both clinical- and research-grade images, multiple MRI sequences, and a wide
range of anatomical a... | [
"eess.IV",
"cs.CV"
] |
introduction of new emission caps. The caps for diesel passenger cars concerning pollutants such as carbon monoxide $( C O )$ , hydrocarbons and nitrogen oxides $( H C + N O _ { X } )$ , and particulate matter $( P M )$ are detailed in Table 15. For each emission group, the caps for these three pollutants are summed to... | High-resolution energy consumption and emissions datasets are essential for
localized policy-making, resource optimization, and climate action planning.
They enable municipalities to monitor mitigation strategies and foster
engagement among governments, businesses, and communities. However, smaller
municipalities often... | [
"cs.DB",
"E",
"E.m"
] |
# 1. Introduction
Distribution matching (DM) is a versatile domaininvariant representation learning technique that has been applied to tasks such as fair classification, domain adaptation, and domain translation. Non-parametric DM methods struggle with scalability and adversarial DM approaches suffer from instability ... | Distribution matching (DM) is a versatile domain-invariant representation
learning technique that has been applied to tasks such as fair classification,
domain adaptation, and domain translation. Non-parametric DM methods struggle
with scalability and adversarial DM approaches suffer from instability and mode
collapse.... | [
"cs.LG",
"cs.CY"
] |
# 1 Introduction
Methane, the principal constituent of natural gas, is a potent greenhouse gas with a global warming potential over 80 times greater than that of carbon dioxide over a 20-year period [29]. Despite its relatively short atmospheric lifetime compared to carbon dioxide, methane is highly effective at absor... | Real-time identification and quantification of greenhouse-gas emissions under
transient atmospheric conditions is a critical challenge in environmental
monitoring. We introduce a spatio-temporal inversion framework that embeds a
deep-learning surrogate of computational fluid dynamics (CFD) within a
sequential Monte Car... | [
"cs.LG",
"stat.AP",
"stat.ML"
] |
# 1 Introduction
Story points (SP) serve as the primary metric in Agile methodologies to measure the size, complexity, and effort required for each user story1. Agile teams typically use subjective methods such as planning poker to estimate these points, but this process often exhibits inconsistency and variable accur... | This research explores the application of Multimodal Generative AI to enhance
story point estimation in Agile software development. By integrating text,
image, and categorical data using advanced models like BERT, CNN, and XGBoost,
our approach surpasses the limitations of traditional single-modal estimation
methods. T... | [
"cs.SE",
"cs.AI",
"68T07, 68T45",
"I.2.6; I.2.10; D.2.9; H.2.8"
] |
# 1 Introduction
The progress of culture and technology is reflected in language, which adapts to incorporate novel meanings into existing words or by entirely changing their semantics. Such changes exhibit systematic regularities with respect to word frequency and polysemy (Bréal, 1904; Ullman, 1962), and can be dete... | Measuring how semantics of words change over time improves our understanding
of how cultures and perspectives change. Diachronic word embeddings help us
quantify this shift, although previous studies leveraged substantial temporally
annotated corpora. In this work, we use a corpus of 9.5 million Croatian news
articles ... | [
"cs.CL"
] |
# Introduction
Generative artificial intelligence models, particularly large language models (LLMs), have demonstrated remarkable capabilities, rapidly integrating into various aspects of technology and daily life. As these systems gain more influence over decisions, recommendations, and content creation, ensuring the... | Controlling the generation of large language models (LLMs) remains a central
challenge to ensure their safe and reliable deployment. While prompt
engineering and finetuning are common approaches, recent work has explored
latent steering, a lightweight technique that alters LLM internal activations
to guide generation. ... | [
"cs.CL",
"cs.AI",
"cs.LG"
] |
# 1 Introduction
Osteoradionecrosis of the jaw (ORN) is a debilitating complication that arises following radiation therapy for head and neck malignancies [1]. It involves the necrosis of previously irradiated bone, culminating in chronic, non-healing wounds that substantially increase the risk of infection and other ... | Advances in treatment technology now allow for the use of customizable
3D-printed hydrogel wound dressings for patients with osteoradionecrosis (ORN)
of the jaw (ONJ). Meanwhile, deep learning has enabled precise segmentation of
3D medical images using tools like nnUNet.
However, the scarcity of labeled data in ONJ i... | [
"eess.IV",
"cs.AI",
"cs.CV"
] |
# 1 Introduction
Evolutionary computation is a powerful method for black-box optimization problems. Evolutionary computation has been applied to numerous domains, including optimization of weather radar networks [39], radar system design [18], and precipitation nowcasting when combined with machine learning techniques... | Differential Evolution (DE) is a widely used evolutionary algorithm for
black-box optimization problems. However, in modern DE implementations, a major
challenge lies in the limited population diversity caused by the fixed
population size enforced by the generational replacement. Population size is a
critical control p... | [
"cs.NE",
"cs.AI",
"G.1.6; I.2.8"
] |
# 1 Introduction
Large language models (LLMs) are increasingly integrated into decision-support systems across high-stakes domains such as hiring, healthcare, and loan approvals [1–3]. In these contexts, ensuring fairness and transparency is not just an ethical imperative but often a legal requirement, as recent regul... | Large language models (LLMs) are increasingly used in decision-support
systems across high-stakes domains such as hiring and university admissions,
where decisions often involve selecting among competing alternatives. While
prior work has noted positional order biases in LLM-driven comparisons, these
biases have not be... | [
"cs.AI"
] |
# 1. Introduction
Subset selection, also known as coreset selection (Zheng et al., 2023; Wan et al., 2024b), has become an effective approach to improve model training efficiency by identifying a small, representative subset of training data without significantly compromising model performance. This task is particular... | One-shot subset selection serves as an effective tool to reduce deep learning
training costs by identifying an informative data subset based on the
information extracted by an information extractor (IE). Traditional IEs,
typically pre-trained on the target dataset, are inherently dataset-dependent.
Foundation models (F... | [
"cs.CV",
"cs.LG"
] |
# 1. Introduction
A key objective in exploratory analysis is to minimize data-to-analysis time while enabling real-time interactions and efficient analytical computations on very large data files. In many cases, the development of approximate and incremental techniques is essential for addressing the aforementioned ch... | Minimizing data-to-analysis time while enabling real-time interaction and
efficient analytical computations on large datasets are fundamental objectives
of contemporary exploratory systems. Although some of the recent adaptive
indexing and on-the-fly processing approaches address most of these needs,
there are cases, w... | [
"cs.DB",
"97R50, 68P05, 68P15",
"H.3.1; H.2.4; E.1"
] |
# 1. Introduction
Accurate segmentation in medical imaging is crucial for a variety of clinical applications, from computer-aided diagnostics to treatment planning (Yang and Yu, 2021). In the context of Multiple Sclerosis (MS) research, the segmentation of hyperintensity areas, identifiable on head MRI scans, are indi... | Accurate segmentation of white matter hyperintensities (WMH) is crucial for
clinical decision-making, particularly in the context of multiple sclerosis.
However, domain shifts, such as variations in MRI machine types or acquisition
parameters, pose significant challenges to model calibration and uncertainty
estimation.... | [
"eess.IV",
"cs.CV"
] |
# I. INTRODUCTION
Large Language Models (LLMs) have emerged as a fundamental tool in modern software development [1]–[3], demonstrating exceptional language understanding and generation capabilities. Their application has shown remarkable potential across various software engineering tasks [4], [5], particularly in co... | Trustworthy evaluation methods for code snippets play a crucial role in
neural code generation. Traditional methods, which either rely on reference
solutions or require executable test cases, have inherent limitation in
flexibility and scalability. The recent LLM-as-Judge methodology offers a
promising alternative by d... | [
"cs.SE",
"cs.AI"
] |
# 1 Introduction
Search-based software engineering (SBSE) has been a prominent field for nearly a quarter-century—approaching its silver jubilee—since it was first introduced by Harman and Jones [17] in 2001. Over the years, it has rapidly evolved to address emerging and complex software engineering problems. It has b... | Search-based software engineering (SBSE), at the intersection of artificial
intelligence (AI) and software engineering, has been an active area of research
for about 25 years. It has been applied to solve numerous problems across the
entire software engineering lifecycle and has demonstrated its versatility in
multiple... | [
"cs.SE",
"cs.AI"
] |
# 1 Introduction
Urban traffic congestion has become one of the most pressing challenges in modern cities, leading to increased travel times, environmental pollution, and economic losses. As urban populations continue to grow, efficient traffic management systems are essential for maintaining the functionality of urba... | Efficient traffic signal control (TSC) is essential for mitigating urban
congestion, yet existing reinforcement learning (RL) methods face challenges in
scaling to large networks while maintaining global coordination. Centralized RL
suffers from scalability issues, while decentralized approaches often lack
unified obje... | [
"cs.LG",
"cs.AI"
] |
# 1 Introduction
Transformers [Vaswani et al., 2017] are expressive set encoders, which when paired with positional encodings, can serve as sequence encoders. The attention mechanism in a transformer block allows us to model the long and short term dependencies in a sequence in an input-dependent manner instead of rel... | Various forms of sparse attention have been explored to mitigate the
quadratic computational and memory cost of the attention mechanism in
transformers. We study sparse transformers not through a lens of efficiency but
rather in terms of learnability and generalization. Empirically studying a
range of attention mechani... | [
"cs.LG"
] |
# 1 INTRODUCTION
The growing demand to automate software development tasks has led to the emergence of automated techniques for generating software artifacts, such as code snippets [2, 35], code changes [13, 43], and summarization [30]. However, evaluating the correctness of those generated artifacts remains a challen... | Large Language Models (LLMs) and other automated techniques have been
increasingly used to support software developers by generating software
artifacts such as code snippets, patches, and comments. However, accurately
assessing the correctness of these generated artifacts remains a significant
challenge. On one hand, h... | [
"cs.SE",
"cs.AI",
"cs.CL"
] |
# GitHub Proxy Server: A tool for supporting massive data collection on GitHub
Hudson Silva Borges Universidade Federal de Mato Grosso do Sul - UFMS Campo Grande - MS - Brasil hudson.borges@ufms.br
Marco Tulio Valente Universidade Federal de Minas Gerais - UFMG Belo Horizonte - MG - Brasil mtov@dcc.ufmg.br
# RESUMO
... | GitHub is the most popular social coding platform and widely used by
developers and organizations to host their open-source projects around the
world. Besides that, the platform has a web API that allow developers collect
information from public repositories hosted on it. However, collecting massive
amount of data from... | [
"cs.SE"
] |
# 1 Introduction
Spiking neural networks (SNNs) [1] and in particular recurrent SNNs (RSNNs) constitute the basis of energy-efficient computation in the brain [2] and in neuromorphic hardware [3]. While RSNNs can be implemented efficiently in neuromorphic systems, training of these models with powerful gradient-based ... | Recurrent spiking neural networks (RSNNs) can be implemented very efficiently
in neuromorphic systems. Nevertheless, training of these models with powerful
gradient-based learning algorithms is mostly performed on standard digital
hardware using Backpropagation through time (BPTT). However, BPTT has
substantial limitat... | [
"cs.NE",
"cs.AI",
"cs.LG"
] |
# 1 INTRODUCTION
As human society deepens its reliance on information systems and information technology, the need to develop information systems in an efficient, rigorous, and dependable way, is vital. Conceptual modeling has long been recognized as a valuable foundation from which to develop information systems beca... | Conceptual modeling is an important part of information systems development
and use that involves identifying and representing relevant aspects of reality.
Although the past decades have experienced continuous digitalization of
services and products that impact business and society, conceptual modeling
efforts are stil... | [
"cs.HC",
"cs.DB"
] |
# 1 Introduction
Semantic technologies, and in particular knowledge graphs (KGs), have been utilised in a variety of applications over time, including search engines, data integration, enterprise settings and machine learning. Numerous methods were proposed to assist their life-cycle and exploitation [12] leading to t... | The SPARQL query language is the standard method to access knowledge graphs
(KGs). However, formulating SPARQL queries is a significant challenge for
non-expert users, and remains time-consuming for the experienced ones. Best
practices recommend to document KGs with competency questions and example
queries to contextua... | [
"cs.DB",
"cs.AI",
"cs.IR"
] |
# I. INTRODUCTION
Technology is not just a reflection of societal needs, it actively shapes behaviors, interactions, and the way we engage with the world. As software increasingly drives user interactions and automates processes, acknowledging its socioeconomic and environmental impacts becomes crucial. Analyzing thes... | In the international software engineering research community, the premier
conference (ICSE) features since a decade a special track on the role of SE In
Society (or SEIS track). In this work, we want to use the articles published in
this track as a proxy or example of the research in this field, in terms of
covered top... | [
"cs.SE"
] |
# 1 Introduction
Logs are textual records generated during software execution to capture runtime events, states, and contextual information [82]. A typical log statement consists of three components: a verbosity level, logging variables, and logging texts [16, 31]. In particular, as the example shown below, the loggin... | Developers use logging statements to create logs that document system
behavior and aid in software maintenance. As such, high-quality logging is
essential for effective maintenance; however, manual logging often leads to
errors and inconsistency. Recent methods emphasize using large language models
(LLMs) for automated... | [
"cs.SE"
] |
# 1. Introduction
Learning to Optimize (L2O) is a promising new approach in applying learning-based methods to tackle optimization problems. In particular, L2O concentrates on problems with well-defined objective functions and constraints [7]. Thus, black-box optimization strategies, such as Bayesian Optimization [24]... | Learning to optimize (L2O) is an emerging technique to solve mathematical
optimization problems with learning-based methods. Although with great success
in many real-world scenarios such as wireless communications, computer
networks, and electronic design, existing L2O works lack theoretical
demonstration of their perf... | [
"cs.LG",
"math.OC"
] |
# 1 Introduction
Due to their intrinsic hierarchical nature, material properties depend on the coupling of various domains, among others, materials chemistry, defect engineering, microstructure physics, and mechanical engineering. This often requires multiscale simulation approaches to adequately model materials with ... | Numerous Workflow Management Systems (WfMS) have been developed in the field
of computational materials science with different workflow formats, hindering
interoperability and reproducibility of workflows in the field. To address this
challenge, we introduce here the Python Workflow Definition (PWD) as a workflow
excha... | [
"cs.SE",
"cond-mat.mtrl-sci"
] |
# 1 Introduction
Instruction tuning has emerged as a powerful paradigm to improve the performance and alignment of large language models (LLMs) by fine-tuning them on instruction-response pairs [2, 11, 32, 51, 52]. Recent studies indicate that data quality, rather than quantity alone, is crucial for substantial perfor... | Instruction tuning has emerged as a critical paradigm for improving the
capabilities and alignment of large language models (LLMs). However, existing
iterative model-aware data selection methods incur significant computational
overhead, as they rely on repeatedly performing full-dataset model inference to
estimate samp... | [
"cs.LG",
"cs.AI",
"cs.DB"
] |
# 1 Introduction
In recent years, Vision-Language Models (VLMs) [1, 2, 3, 4, 5] have achieved remarkable progress in visual-linguistic understanding, demonstrating strong performance and generalization capabilities. Building on this success, there is growing interest in extending VLMs to end-to-end robotic control by ... | Vision-Language-Action (VLA) models have recently made significant advance in
multi-task, end-to-end robotic control, due to the strong generalization
capabilities of Vision-Language Models (VLMs). A fundamental challenge in
developing such models is effectively aligning the vision-language space with
the robotic actio... | [
"cs.RO",
"cs.AI",
"cs.CV"
] |
# 1 Introduction
Inverse problems arise in many domains where observed data is the result of a noisy and potentially lossy transformation of an underlying signal we wish to recover. More formally, the degradation process is modeled as follows:
$$
\begin{array} { r } { \boldsymbol { y } = \boldsymbol { \mathcal { A } ... | This work addresses image restoration tasks through the lens of inverse
problems using unpaired datasets. In contrast to traditional approaches --
which typically assume full knowledge of the forward model or access to paired
degraded and ground-truth images -- the proposed method operates under minimal
assumptions and... | [
"cs.CV",
"cs.LG",
"eess.IV"
] |
# 1 Introduction
Robotic manipulation tasks—such as grasping, placing, and assembling objects—are notoriously difficult to program explicitly due to their high complexity and variability. Recently, imitation learning $( I L )$ offers a promising alternative, allowing robots to acquire manipulation skills by mimicking ... | Diffusion Policy (DP) enables robots to learn complex behaviors by imitating
expert demonstrations through action diffusion. However, in practical
applications, hardware limitations often degrade data quality, while real-time
constraints restrict model inference to instantaneous state and scene
observations. These limi... | [
"cs.CV",
"cs.RO"
] |
# 1 Introduction
Long sequence capability offers the ultimate unlock for a wide range of AI applications from RAG, multi-turn conversation, long document summarization, multi-modality support, and many more. This is evident from the continuous increase in the max sequence length supported by popular Open Source LLMs, ... | Long sequences are critical for applications like RAG, long document
summarization, multi-modality, etc., and modern LLMs, like Llama 4 Scout,
support max sequence length of up to 10 million tokens. However, outside of
enterprise labs, long sequence training is challenging for the AI community
with limited system suppo... | [
"cs.LG"
] |
# 1 Introduction
The rapid advancement of large language models (LLMs) has significantly improved performance across various downstream tasks and made realtime human-computer interaction an essential part of daily life, offering substantial convenience to users (Achiam et al., 2023; Touvron et al., 2023a,b; Chiang et ... | Large language models (LLMs) exhibit remarkable reasoning capabilities across
diverse downstream tasks. However, their autoregressive nature leads to
substantial inference latency, posing challenges for real-time applications.
Speculative sampling mitigates this issue by introducing a drafting phase
followed by a paral... | [
"cs.CL",
"cs.AI"
] |
# 1 Introduction
The escalating global challenges of water scarcity, climate change, and their profound impacts on ecosystems and human societies underscore the critical importance of understanding and forecasting surface water dynamics [34, 40]. Effective water resource management for agriculture, energy, and consump... | Forecasting surface water dynamics is crucial for water resource management
and climate change adaptation. However, the field lacks comprehensive datasets
and standardized benchmarks. In this paper, we introduce HydroChronos, a
large-scale, multi-modal spatiotemporal dataset for surface water dynamics
forecasting desig... | [
"cs.CV"
] |
# 1 Introduction
Large Language Models (LLMs) have advanced at a remarkable pace within recent years, driven primarily by larger models and bigger training datasets [12, 28]. As a result, training and fine-tuning LLMs has become prohibitively expensive, with all but the biggest players unable to implement full paramet... | Parameter-Efficient Fine-Tuning (PEFT) methods have become crucial for
rapidly adapting large language models (LLMs) to downstream tasks.
Prefix-Tuning, an early and effective PEFT technique, demonstrated the ability
to achieve performance comparable to full fine-tuning with significantly
reduced computational and memo... | [
"cs.CL",
"cs.AI"
] |
# I. INTRODUCTION
Code review is a crucial software development practice that enhances code quality, facilitates knowledge sharing, and detects defects [11], [31]. Formal inspections, a longstanding form of code review [18], [21], require practitioners to examine and modify code changes before they are merged into pro... | Context: Code reviews are crucial for software quality. Recent AI advances
have allowed large language models (LLMs) to review and fix code; now, there
are tools that perform these reviews. However, their reliability and accuracy
have not yet been systematically evaluated. Objective: This study compares
different LLMs'... | [
"cs.SE",
"cs.AI"
] |
# 1 Introduction
Zero-Shot Stance Detection (ZSSD) aims to identify the stance expressed in text toward targets absent during training, a task increasingly vital for analyzing polarized discourse on social media where new topics continually emerge and labeled data are often scarce (Allaway and Mckeown, 2020; Liang et ... | Zero-shot stance detection (ZSSD) aims to identify the stance of text toward
previously unseen targets, a setting where conventional supervised models often
fail due to reliance on labeled data and shallow lexical cues. Inspired by
human cognitive reasoning, we propose the Cognitive Inductive Reasoning
Framework (CIRF)... | [
"cs.CL",
"I.2.7, I.2.6"
] |
# 1 Introduction
A GUI agent is an intelligent system capable of autonomously interacting with graphical user interfaces by perceiving visual elements, understanding task objectives, and executing corresponding actions [15]. The development of GUI agents holds significant promise for automating GUI operations, reducin... | The development of high-quality datasets is crucial for benchmarking and
advancing research in Graphical User Interface (GUI) agents. Despite their
importance, existing datasets are often constructed under idealized conditions,
overlooking the diverse anomalies frequently encountered in real-world
deployments. To addre... | [
"cs.AI"
] |
# 1 Introduction
Large language models (LLMs) [65, 63, 60, 5] have demonstrated remarkable generalization capabilities [51, 67, 72, 71] across a wide range of tasks [52, 23], but their inference cost [14, 55] grows rapidly with scale, hindering practical deployment and efficiency. Mixture-of-Experts (MoE) [8, 3, 36] a... | Mixture-of-Experts (MoE) models enable efficient scaling of large language
models (LLMs) by activating only a subset of experts per input. However, we
observe that the commonly used auxiliary load balancing loss often leads to
expert overlap and overly uniform routing, which hinders expert specialization
and degrades o... | [
"cs.CL",
"cs.SE",
"68T07",
"I.2.7"
] |
# 1 Introduction
Understanding temporal relations is a challenging yet underexplored area in natural language processing (Ning et al., 2020; Chen et al., 2021; Zhou et al., 2019). This challenge persists despite the prevalence of Large Language Models (LLMs) (Chan et al., 2023; Fang et al., 2023), whose training proce... | Accurately understanding temporal relations between events is a critical
building block of diverse tasks, such as temporal reading comprehension (TRC)
and relation extraction (TRE). For example in TRC, we need to understand the
temporal semantic differences between the following two questions that are
lexically near-id... | [
"cs.CL"
] |
# 1 Introduction
LLM unlearning, the targeted removal of specific, undesirable knowledge from trained models [1–4], has emerged as a critical tool for enhancing the privacy, safety, and security of generative models. In privacy contexts, it enables the erasure of personal identifiers and copyrighted material from mode... | Machine unlearning (MU) for large language models (LLMs), commonly referred
to as LLM unlearning, seeks to remove specific undesirable data or knowledge
from a trained model, while maintaining its performance on standard tasks.
While unlearning plays a vital role in protecting data privacy, enforcing
copyright, and mit... | [
"cs.LG"
] |
# I. INTRODUCTION
B (EAFDOSRsE) the dr spalfoeytymeantdofrelAiautboilimtya mdusDt bviengr gSoyrsotuesmlys validated, a process traditionally requiring billions of miles on-road driving by Automated Vehicles (AVs) [1]. However, conventional mileage-based on-road testing has been deemed impractical due to its high costs... | The safety and reliability of Automated Driving Systems (ADSs) must be
validated prior to large-scale deployment. Among existing validation
approaches, scenario-based testing has been regarded as a promising method to
improve testing efficiency and reduce associated costs. Recently, the emergence
of Large Language Mode... | [
"cs.SE"
] |
# I. INTRODUCTION
We propose JITScope, a system that visualizes the evolution of Just-in-Time (JIT) compiler’s Intermediate Representation (IR) using a backend-driven, phase-aware, graph-based visualization. This paper outlines this visualization framework’s architecture, design decisions, challenges, and future direc... | The complexity of modern Just-In-Time (JIT) compiler optimization poses
significant challenges for developers seeking to understand and debug
intermediate representation (IR) behavior. This work introduces JITScope, an
interactive visualization framework that illustrates how IR nodes and
instructions evolve across comp... | [
"cs.SE",
"D.3.4; D.2.2; I.3.8"
] |
# I. INTRODUCTION
The publication of the Bitcoin white paper in 2008 and the subsequent launch of the Bitcoin blockchain in 2009 sparked significant interest and research into blockchain technology. This emerging technology has garnered widespread attention from businesses, researchers, and the software industry due t... | This paper addresses the challenge of creating smart contracts for
applications represented using Business Process Management and Notation (BPMN)
models. In our prior work we presented a methodology that automates the
generation of smart contracts from BPMN models. This approach abstracts the
BPMN flow control, making ... | [
"cs.SE",
"cs.CR"
] |
# 1 Introduction
Machine Translation (MT) is one of the few NLP technologies that has been widely available online for decades. As both translation quality and internet access have improved (Gaspari and Hutchins, 2007), MT has gained a large and diverse user base. Millions of people use it to communicate across langua... | Machine Translation (MT) tools are widely used today, often in contexts where
professional translators are not present. Despite progress in MT technology, a
gap persists between system development and real-world usage, particularly for
non-expert users who may struggle to assess translation reliability. This paper
advo... | [
"cs.CL",
"cs.AI"
] |
# 1. Introduction
Large reasoning models (LRMs), such as OpenAI o1 (OpenAI, 2024a) and DeepSeek-R1 (DeepSeek-AI et al., 2025), have demonstrated remarkable success by extending the length of reasoning through large-scale reinforcement learning (RL). In recent months, both the open-source community and commercial organ... | We introduce MiniMax-M1, the world's first open-weight, large-scale
hybrid-attention reasoning model. MiniMax-M1 is powered by a hybrid
Mixture-of-Experts (MoE) architecture combined with a lightning attention
mechanism. The model is developed based on our previous MiniMax-Text-01 model,
which contains a total of 456 b... | [
"cs.CL",
"cs.LG"
] |
# I. INTRODUCTION
”Data is the new gold” [1] – In the context of artificial intelligence (AI), data serves as the essential fuel driving the performance and innovation of AI systems. High-quality data enables models to learn complex patterns, identify subtle relationships, and make predictions that guide decision-maki... | Large language models (LLMs) can be trained or fine-tuned on data obtained
without the owner's consent. Verifying whether a specific LLM was trained on
particular data instances or an entire dataset is extremely challenging.
Dataset watermarking addresses this by embedding identifiable modifications in
training data to... | [
"cs.CL",
"cs.CR"
] |
# 1 Introduction
This paper focuses on finite two-player zero-sum games (TPZSGs), where two players interact in an adversarial setting with strictly opposing objectives, as first analyzed in [31, 32]. These games play a foundational role in game theory and online learning, capturing adversarial interactions in various... | We study a two-player zero-sum game (TPZSG) in which the row player aims to
maximize their payoff against an adversarial column player, under an unknown
payoff matrix estimated through bandit feedback. We propose and analyze two
algorithms: ETC-TPZSG, which directly applies ETC to the TPZSG setting and
ETC-TPZSG-AE, wh... | [
"cs.LG",
"cs.GT"
] |
# 1 INTRODUCTION
High-dimensional embedding vectors generated by deep learning models are becoming an important form of data representation for complex, unstructured data such as images [38, 44], audios [9], and texts [32, 50]. The models convert input data to vectors in an embedding space and capture the data semanti... | Modern deep learning models capture the semantics of complex data by
transforming them into high-dimensional embedding vectors. Emerging
applications, such as retrieval-augmented generation, use approximate nearest
neighbor (ANN) search in the embedding vector space to find similar data.
Existing vector databases provi... | [
"cs.DB",
"cs.LG"
] |
# 1 INTRODUCTION
Recently, diffusion LLMs have become a widely discussed topic in Natural Language Processing research (Nie et al., 2025; Ye et al., 2025). They are regarded as a potential solution to key limitations of traditional auto-regressive LLMs, including the reversal curse (Berglund et al., 2023), complex rea... | Large Language Diffusion Models, or diffusion LLMs, have emerged as a
significant focus in NLP research, with substantial effort directed toward
understanding their scalability and downstream task performance. However, their
long-context capabilities remain unexplored, lacking systematic analysis or
methods for context... | [
"cs.CL"
] |
# 1 Introduction
Recent advances in process mining have improved the ability to capture and analyze complex organizational workflows through event logs. However, this progress has led to an increasing abundance of process models, often overlapping in scope or providing divergent insights for different stakeholders (e.... | Process mining is increasingly adopted in modern organizations, producing
numerous process models that, while valuable, can lead to model overload and
decision-making complexity. This paper explores a multi-criteria
decision-making (MCDM) approach to evaluate and prioritize process models by
incorporating both quantita... | [
"cs.CY",
"cs.DB"
] |
# 1 Introduction
In recent years, large language models (LLMs) have been evolving rapidly, demonstrating high performance across various tasks (OpenAI, 2023; Anthropic, 2024; Google, 2024) and exerting significant influence. In addition to the high-performing proprietary models, there have been active efforts to devel... | As large language models (LLMs) continue to advance, reliable evaluation
methods are essential particularly for open-ended, instruction-following tasks.
LLM-as-a-Judge enables automatic evaluation using LLMs as evaluators, but its
reliability remains uncertain. In this work, we analyze key factors affecting
its trustwo... | [
"cs.CL"
] |
# 1 Introduction
In industrial inspection and maintenance, accurate simulation and interaction with complex environments is essential to ensure safety, efficiency, and precision. However, traditional 3D modeling methods face significant limitations in hazardous or confined spaces due to the impracticality of deploying... | To address the challenges of 3D modeling and structural simulation in
industrial environment, such as the difficulty of equipment deployment, and the
difficulty of balancing accuracy and real-time performance, this paper proposes
an integrated workflow, which integrates high-fidelity 3D reconstruction based
on monocula... | [
"cs.CV"
] |
# 1 Introduction
Large language models (LLMs) are expected to perform well on many different tasks. Therefore, training data is a heterogeneous mix, where instances can vary greatly in terms of format, contents, tasks, and languages, e.g. code generation [Lozhkov et al., 2024; Manh et al., 2023; Kocetkov et al., 2022;... | One of the most profound challenges of modern machine learning is performing
well on the long-tail of rare and underrepresented features. Large
general-purpose models are trained for many tasks, but work best on
high-frequency use cases. After training, it is hard to adapt a model to
perform well on specific use cases ... | [
"cs.CL",
"cs.LG"
] |
# 1 Introduction
One of the most popular methods of conducting linguistic research has consisted of handcrafting paradigmatic utterances followed by gathering native speakers’ judgements. Yet, it is questionable how much these constructed utterances reflect realworld language use. As a result, plenty of debate has ari... | For linguists, embedded clauses have been of special interest because of
their intricate distribution of syntactic and semantic features. Yet, current
research relies on schematically created language examples to investigate these
constructions, missing out on statistical information and naturally-occurring
examples th... | [
"cs.CL"
] |
# I. INTRODUCTION
empowerment-based skill discovery [20] and pure exploration methods [21]. Empowerment-based methods aim to maximize the Mutual Information (MI) between states and skills, and the MI term can be estimated by different variational estimators [22]. These methods have shown effectiveness in learning disc... | Unsupervised Reinforcement Learning (RL) aims to discover diverse behaviors
that can accelerate the learning of downstream tasks. Previous methods
typically focus on entropy-based exploration or empowerment-driven skill
learning. However, entropy-based exploration struggles in large-scale state
spaces (e.g., images), a... | [
"cs.LG"
] |
# I. INTRODUCTION
“Vibe coding” refers to a novel, emergent mode of software development in which the human programmer (1a) operates less as a direct implementer of code and more as a high-level coordinator who collaborates with LLMs through iterative prompting and strategic direction [1]. Coined by Andrej Karpathy An... | This review presents a comprehensive analysis of two emerging paradigms in
AI-assisted software development: vibe coding and agentic coding. While both
leverage large language models (LLMs), they differ fundamentally in autonomy,
architectural design, and the role of the developer. Vibe coding emphasizes
intuitive, hum... | [
"cs.SE",
"cs.AI",
"cs.CL"
] |
# 1 Introduction
Knowledge Graphs [7] (KGs) have become a foundational technology for integrating and querying heterogeneous data across domains such as climate science, cultural heritage, and life sciences. A core strength of KGs lies in their ability to make data explicit, interoperable, and semantically rich, align... | Knowledge Graphs (KGs) are increasingly adopted as a foundational technology
for integrating heterogeneous data in domains such as climate science, cultural
heritage, and the life sciences. Declarative mapping languages like R2RML and
RML have played a central role in enabling scalable and reusable KG
construction, off... | [
"cs.DB",
"cs.AI"
] |
# SCISSOR: Mitigating Semantic Bias through Cluster-Aware Siamese Networks for Robust Classification
Shuo Yang 1 Bardh Prenkaj 1 Gjergji Kasneci 1
# Abstract
Shortcut learning undermines model generalization to out-of-distribution data. While the literature attributes shortcuts to biases in superficial features, we ... | Shortcut learning undermines model generalization to out-of-distribution
data. While the literature attributes shortcuts to biases in superficial
features, we show that imbalances in the semantic distribution of sample
embeddings induce spurious semantic correlations, compromising model
robustness. To address this issu... | [
"cs.LG"
] |
# 1 Introduction
The security of the software supply chain has emerged as a critical concern in today’s interconnected and rapidly evolving digital landscape. The complexity of the software supply chain, combined with the growing number of stakeholders involved in the software ecosystem, has significantly increased th... | The escalating complexity of modern software development environments has
heightened concerns around supply chain security. However, existing frameworks
often fall short in translating abstract security principles into concrete,
actionable practices. This paper introduces the Software Security Mapping
Framework, a stru... | [
"cs.SE"
] |
# I. INTRODUCTION
Phishing is a well-known attack technique dating back to at least the 1990s [1]. As the use of the internet has continued to grow, so have the assets accessible online. In today’s digital world, most businesses and organizations are connected to the internet, resulting in a substantial volume of emai... | Phishing attacks remain a significant threat to modern cybersecurity, as they
successfully deceive both humans and the defense mechanisms intended to protect
them. Traditional detection systems primarily focus on email metadata that
users cannot see in their inboxes. Additionally, these systems struggle with
phishing e... | [
"cs.CR",
"cs.AI"
] |
# 1 Introduction
Transformer architectures have emerged as the backbone of modern deep learning, powering state-of-the-art advancements across diverse fields such as natural language processing [32], computer vision [2], reinforcement learning [26] and beyond [6, 21]. However, their self-attention mechanism, while eff... | Transformer models face scalability challenges in causal language modeling
(CLM) due to inefficient memory allocation for growing key-value (KV) caches,
which strains compute and storage resources. Existing methods like Grouped
Query Attention (GQA) and token-level KV optimization improve efficiency but
rely on rigid r... | [
"cs.CL",
"cs.LG"
] |
# 1 INTRODUCTION
Retrieval Augmented Generation (RAG) has emerged as a key technique for enhancing LLM performance in question answering (QA) by incorporating external knowledge [21, 34]. The LLM prompt is enriched with retrieved information to mitigate issues related to unknown or sparse knowledge within the model it... | Retrieval-Augmented Generation (RAG) enriches Large Language Models (LLMs) by
combining their internal, parametric knowledge with external, non-parametric
sources, with the goal of improving factual correctness and minimizing
hallucinations. The LiveRAG 2025 challenge explores RAG solutions to maximize
accuracy on Data... | [
"cs.IR",
"cs.AI",
"cs.LG"
] |
# 1 INTRODUCTION
Advances in deep learning have made it possible to embed data as vectors in high-dimensional vector spaces so that the distance between vectors captures various notions of semantic similarity. This opens up a new interface for users as well as AI models/agents to interact with large information stores... | Vector indexing enables semantic search over diverse corpora and has become an important interface to databases for both users and AI agents. Efficient vector search requires deep optimizations in database systems. This has motivated a new class of specialized vector databases that optimize for vector search quality an... | [
"cs.DB",
"cs.IR"
] |
# I. INTRODUCTION
Since the publishing of the famous IBM manifesto on autonomic computing by Kephart and Chess [1] almost two decades ago, the interest in the self-\* properties of the systems in software engineering has increased rapidly. Some of the most broadly spread and often found self-\* properties in the liter... | In the last two decades, the popularity of self-adaptive systems in the field of software and systems engineering has drastically increased. However, despite the extensive work on self-adaptive systems, the literature still lacks a common agreement on the definition of these systems. To this day, the notion of self-ada... | [
"cs.SE"
] |
# 1 Introduction
The concept of independence is appealing to many fields. In databases, it describes when a relation is the cross product for some of its projections (Paredaens 1980). Indeed, the cross product is one of the most fundamental operations, important to designing and querying databases (Elmasri and Navathe... | We initiate an investigation how the fundamental concept of independence can be represented effectively in the presence of incomplete information. The concepts of possible and certain independence are proposed, and first results regarding the axiomatisability and computational complexity of implication problems associa... | [
"cs.DB"
] |
# 1. Introduction
Cone-beam computed tomography (CBCT) has become an essential imaging modality in modern radiation therapy. Mounted on linear accelerator and C-arm gantries, flat-panel CBCT systems provide in-room volumetric imaging that enables sub-millimeter patient setup verification, adaptive replanning based on ... | Cone-beam CT (CBCT) is widely used in clinical radiotherapy for image-guided treatment, improving setup accuracy, adaptive planning, and motion management. However, slow gantry rotation limits performance by introducing motion artifacts, blurring, and increased dose. This work aims to develop a clinically feasible meth... | [
"cs.CV"
] |
# 1 Introduction
Vector-based similarity search is a core problem with broad applications in machine learning, data mining, and information retrieval. It involves retrieving data points in high-dimensional space that are most similar to a given query vector based on a specific similarity measure. This task is central ... | In this paper, we study the angle testing problem in high-dimensional Euclidean spaces and propose two projection-based probabilistic kernel functions, one designed for angle comparison and the other for angle thresholding. Unlike existing approaches that rely on random projection vectors drawn from Gaussian distributi... | [
"cs.LG",
"cs.AI",
"cs.CV",
"cs.DB",
"cs.DS"
] |
# 1. Introduction
As the area of distributed optimization grows — owing to recent applications in federated learning (McMahan et al., 2017) and large-scale distributed deep learning (Verbraeken et al., 2020) — the gap between theory and practice has grown proportionally. Local Stochastic Gradient Descent (SGD) and its... | Existing analysis of Local (Stochastic) Gradient Descent for heterogeneous objectives requires stepsizes $η\leq 1/K$ where $K$ is the communication interval, which ensures monotonic decrease of the objective. In contrast, we analyze Local Gradient Descent for logistic regression with separable, heterogeneous data using... | [
"cs.LG"
] |
introduction. Their widespread adoption is often credited to their dramatically improved trainability: residual networks train faster, more stably, and achieve higher accuracy than their feedforward counterparts. While numerous techniques, ranging from improved initialization to advanced learning rate schedules, have b... | Residual connections remain ubiquitous in modern neural network architectures nearly a decade after their introduction. Their widespread adoption is often credited to their dramatically improved trainability: residual networks train faster, more stably, and achieve higher accuracy than their feedforward counterparts. W... | [
"cs.LG",
"cs.AI"
] |
# 1 Introduction
Recent advancements in machine learning have produced foundation models. These models are notable for their capacity to generalize across diverse tasks and datasets, extending beyond the confines of training data [70, 149, 157]. Their task and data agnostic characteristic [12] distinguishes them from ... | Current research on tabular foundation models often overlooks the complexities of large-scale, real-world data by treating tables as isolated entities and assuming information completeness, thereby neglecting the vital operational context. To address this, we introduce the concept of Semantically Linked Tables (SLT), r... | [
"cs.LG",
"cs.AI",
"cs.DB"
] |
# Rigor in AI: Doing Rigorous AI Work Requires a Broader, Responsible AI-Informed Conception of Rigor
Alexandra Olteanu\* Microsoft Research
Su Lin Blodgett Microsoft Research
Agathe Balayn Microsoft Research
Angelina Wang Stanford University
Fernando Diaz Carnegie Mellon University
Flavio du Pin Calmon Harvard U... | In AI research and practice, rigor remains largely understood in terms of methodological rigor -- such as whether mathematical, statistical, or computational methods are correctly applied. We argue that this narrow conception of rigor has contributed to the concerns raised by the responsible AI community, including ove... | [
"cs.CY",
"cs.AI",
"cs.LG"
] |
# 1 Introduction
Scientific breakthroughs play a foundational role in advancing human knowledge [45], driving technological innovation, and improving societal well-being [3]. However, the traditional paradigm of natural science research remains slow and labor-intensive [46], where countless experiments must be perform... | Scientific embodied agents play a crucial role in modern laboratories by automating complex experimental workflows. Compared to typical household environments, laboratory settings impose significantly higher demands on perception of physical-chemical transformations and long-horizon planning, making them an ideal testb... | [
"cs.RO",
"cs.SE"
] |
# 1 Introduction
Word embedding (WE) is an advancement in the natural language processing (NLP) area that makes computers better understand text-based content. As a type of word representation, it is considered one remarkable breakthrough of deep learning in solving challenging NLP problems[1]. With WE models, words a... | Word embedding (WE) techniques are advanced textual semantic representation models oriented from the natural language processing (NLP) area. Inspired by their effectiveness in facilitating various NLP tasks, more and more researchers attempt to adopt these WE models for their software engineering (SE) tasks, of which s... | [
"cs.SE"
] |
# 1 Introduction
In the domain of language acquisition tools, a key capability is the measurement of the linguistic difficulty of text. Traditionally, this has been used to assess a language learner’s ability by evaluating their writing (Arnold et al., 2018; Ballier et al., 2019; Kerz et al., 2021). With the advent of... | There is an unmet need to evaluate the language difficulty of short, conversational passages of text, particularly for training and filtering Large Language Models (LLMs). We introduce Ace-CEFR, a dataset of English conversational text passages expert-annotated with their corresponding level of text difficulty. We expe... | [
"cs.CL",
"cs.AI"
] |
Introduction
Designing and implementing robust test automation frameworks has emerged as a critical factor in ensuring the reliability and quality of software applications in today’s fastpaced development environment. This work focuses on leveraging the capabilities of Cucumber-BDD integrated with Java to create a fra... | Modern software development demands rapid, reliable testing methods to maintain high quality in increasingly complex systems. This paper details a comprehensive approach to designing and implementing robust test automation frameworks by leveraging Cucumber BDD with Java. By utilizing Cucumber BDD natural language synta... | [
"cs.SE"
] |
# 1. Introduction
Games play a pivotal role in the field of AI, offering unique challenges to the research community and serving as fertile ground for the development of novel AI algorithms. Board games, such as Go [1] and chess [2], provide ideal settings for perfect-information scenarios, where all agents are fully ... | People need to internalize the skills of AI agents to improve their own capabilities. Our paper focuses on Mahjong, a multiplayer game involving imperfect information and requiring effective long-term decision-making amidst randomness and hidden information. Through the efforts of AI researchers, several impressive Mah... | [
"cs.AI"
] |
# 1 Introduction
A foundational insight in linguistics research is that applying minimal changes to a sentence can render it entirely acceptable or unacceptable to native speakers (Chomsky, 1965). Minimal pairs, as illustrated in Example (1), are a widely used diagnostic tool in linguistics.
We contribute to this gro... | We introduce TurBLiMP, the first Turkish benchmark of linguistic minimal pairs, designed to evaluate the linguistic abilities of monolingual and multilingual language models (LMs). Covering 16 linguistic phenomena with 1000 minimal pairs each, TurBLiMP fills an important gap in linguistic evaluation resources for Turki... | [
"cs.CL"
] |
# 1 Introduction
Since the introduction of the Transformer architecture [36], language modeling has undergone a paradigm shift, enabling the development of models with unprecedented scale and performance. However, the resulting Large Language Models (LLMs), often comprising hundreds of billions of parameters, pose sig... | Large Language Models (LLMs) are central to many contemporary AI applications, yet their extensive parameter counts pose significant challenges for deployment in memory- and compute-constrained environments. Recent works in eXplainable AI (XAI), particularly on attribution methods, suggest that interpretability can als... | [
"cs.LG",
"cs.AI",
"cs.CL"
] |
# 1 Introduction
Serverless NoSQL databases have emerged as pivotal technologies in cloud-native environments, supporting large-scale, highly available applications. These systems offer elastic and flexible data storage solutions without the need for infrastructure management, effectively meeting the demands of modern... | Multi-tenant architectures enhance the elasticity and resource utilization of NoSQL databases by allowing multiple tenants to co-locate and share resources. However, in large-scale cloud environments, the diverse and dynamic nature of workloads poses significant challenges for multi-tenant NoSQL databases. Based on our... | [
"cs.DB"
] |
# 1 Introduction
The advent of large-scale foundation models (FMs), such as large language models (LLMs), is reshaping the software development process. By leveraging training on source code repositories and textual artifacts from the software development process, these models can support software makers in various ta... | Foundation Models (FMs) have shown remarkable capabilities in various natural language tasks. However, their ability to accurately capture stakeholder requirements remains a significant challenge for using FMs for software development. This paper introduces a novel approach that leverages an FM-powered multi-agent syst... | [
"cs.SE",
"cs.AI"
] |
# 1. Introduction
Reinforcement learning (RL) is a general computational framework for building agents that learn to maximize a scalar reward from their experience. RL agents sense their environment and produce actions at every single timestep, yet effective reward maximization in complex environments requires reasoni... | Developing agents capable of exploring, planning and learning in complex open-ended environments is a grand challenge in artificial intelligence (AI). Hierarchical reinforcement learning (HRL) offers a promising solution to this challenge by discovering and exploiting the temporal structure within a stream of experienc... | [
"cs.AI"
] |
# 1 INTRODUCTION
Neighborhood environments influence human well-being and have become a focus of research in urban studies, public health, and family social science [12]. Assessing neighborhood environmental features is important for understanding how neighborhood contexts affect outcomes like adolescent development [... | Traditionally, neighborhood studies have employed interviews, surveys, and manual image annotation guided by detailed protocols to identify environmental characteristics, including physical disorder, decay, street safety, and sociocultural symbols, and to examine their impact on developmental and health outcomes. While... | [
"cs.HC",
"cs.AI"
] |
# 1 Introduction
Human interactions shape relationships through shared understandings, influenced not just by explicit words but by emotional and pragmatic nuances that convey implicit meanings. The ability to interpret beyond the literal meaning of language, known as pragmatics, is essential for social cognition, int... | Pragmatics, the ability to infer meaning beyond literal interpretation, is crucial for social cognition and communication. While LLMs have been benchmarked for their pragmatic understanding, improving their performance remains underexplored. Existing methods rely on annotated labels but overlook the reasoning process h... | [
"cs.CL",
"cs.AI"
] |
# 1 Introduction
The rapid progress in LLM capabilities—specifically their ability to follow instructions and maintain large contexts—has made them a natural choice in many applications. Natural Language to SQL (NL2SQL) is a long-standing and important task in many businesscritical scenarios, requiring a deep understa... | NL2SQL approaches have greatly benefited from the impressive capabilities of large language models (LLMs). In particular, bootstrapping an NL2SQL system for a specific domain can be as simple as instructing an LLM with sufficient contextual information, such as schema details and translation demonstrations. However, bu... | [
"cs.CL",
"cs.DB"
] |
# 1 Introduction
Agricultural systems face increasing pressure to meet the growing demand for food while maintaining ecological balance and mitigating climate impacts [54]. Rural landscapes not only serve as the backbone of food production but also play a pivotal role in sequestering carbon, regulating water cycles, a... | Effective management of agricultural landscapes is critical for meeting global biodiversity targets, but efforts are hampered by the absence of detailed, large-scale ecological maps. To address this, we introduce Farmscapes, the first large-scale (covering most of England), high-resolution (25cm) map of rural landscape... | [
"cs.CV",
"cs.LG"
] |
# 1 Introduction
As the demand for larger and more capable neural networks continues to grow [Kaplan et al., 2020, Brown et al., 2020], the need for architectures that can scale efficiently—without incurring prohibitive computational costs—has become increasingly important. This is especially true in the context of la... | Sparse Mixture of Experts (MoE) models offer a scalable and efficient architecture for training large neural networks by activating only a subset of parameters ("experts") for each input. A learned router computes a distribution over these experts, and assigns input tokens to a small subset. However, without auxiliary ... | [
"cs.LG"
] |
# 1 Introduction
Understanding road topology is essential for safe and effective autonomous driving, as it provides vehicles with crucial spatial and contextual information for navigation. A comprehensive topology model requires reasoning over lane-to-lane (L2L) and lane-to-traffic-element (L2T) relationships, enablin... | Accurate road topology reasoning is critical for autonomous driving, enabling effective navigation and adherence to traffic regulations. Central to this task are lane perception and topology reasoning. However, existing methods typically focus on either lane detection or Lane-to-Lane (L2L) topology reasoning, often \te... | [
"cs.CV"
] |
# 1 Introduction
Modern engineered systems are becoming more complex as they incorporate a greater number of diverse and autonomous components. This growing complexity is widely considered as one of the defining factors of modern systems engineering practices [33]. A notable artifact of growing system complexity is th... | Modern systems exhibit unprecedented complexity due to their increased scale, interconnectedness, and the heterogeneity of their digital and physical components. In response to scaling challenges, the system-of-systems (SoS) paradigm proposes flexible aggregations of subsystems into a larger whole, while maintaining th... | [
"cs.ET",
"cs.SE"
] |
# 1 INTRODUCTION
Graph neural networks (GNNs) have demonstrated promising performances in graph analytical tasks such as classification. Given a graph $G$ (a network representation of a real-world dataset), a GNN $\mathcal { M }$ aims to learn the node representations of $G$ that can be converted to proper results for... | This paper proposes a novel approach to generate subgraph explanations for graph neural networks GNNs that simultaneously optimize multiple measures for explainability. Existing GNN explanation methods often compute subgraphs (called ``explanatory subgraphs'') that optimize a pre-defined, single explainability measure,... | [
"cs.LG",
"cs.DB"
] |
# 1 Introduction
Measurement enables scientific progress. In computer science and machine learning, this requires the creation of efficient benchmarks that provide a stable foundation for evaluation, ensuring that observed performance scores reflect genuine capabilities for real-world tasks.
Table Union Search (TUS) ... | Recent table representation learning and data discovery methods tackle table union search (TUS) within data lakes, which involves identifying tables that can be unioned with a given query table to enrich its content. These methods are commonly evaluated using benchmarks that aim to assess semantic understanding in real... | [
"cs.IR",
"cs.AI",
"cs.CL",
"cs.DB",
"cs.LG"
] |
# 1 Introduction
Visual Information Extraction (VIE) (Wan et al., 2024; Kuang et al., 2023; Hong et al., 2022; Kim et al., 2022) aims to generate structured information, such as JSON, from unstructured document images. This capability is crucial for various medical applications such as report interpretation (Li et al.... | Visual Information Extraction (VIE) converts unstructured document images into structured formats like JSON, critical for medical applications such as report analysis and online consultations. Traditional methods rely on OCR and language models, while end-to-end multimodal models offer direct JSON generation. However, ... | [
"cs.CL"
] |
# 1 Introduction
There has been significant recent interest in formalisms for reasoning over temporal data [Artale et al., 2017]. Since its introduction by Brandt et al. [2017; 2018], the DatalogMTL language, which extends Datalog [Abiteboul et al., 1995] with operators from metric temporal logic (MTL) [Koymans, 1990]... | In this paper, we explore the issue of inconsistency handling in DatalogMTL, an extension of Datalog with metric temporal operators. Since facts are associated with time intervals, there are different manners to restore consistency when they contradict the rules, such as removing facts or modifying their time intervals... | [
"cs.LO",
"cs.AI",
"cs.DB"
] |
# 1. Introduction
Object detection is indispensable for accurately identifying and localizing objects of interest in aerial imagery [5]. It plays a crucial role in various applications, such as environmental monitoring, urban planning, and rescue operations [1, 25, 35]. Most existing aerial detectors primarily focus o... | In recent years, language-guided open-world aerial object detection has gained significant attention due to its better alignment with real-world application needs. However, due to limited datasets, most existing language-guided methods primarily focus on vocabulary, which fails to meet the demands of more fine-grained ... | [
"cs.CV",
"cs.DB"
] |
# 1. Introduction
Conversational speech recognition (Conv-ASR), which aims to transcribe natural spoken language accurately, remains a significant challenge in the speech processing area [1, 2]. Unlike isolated speech segments, conversational speech typically involves spontaneous, unstructured language, occasional spe... | This paper introduces the integration of language-specific bi-directional context into a speech large language model (SLLM) to improve multilingual continuous conversational automatic speech recognition (ASR). We propose a character-level contextual masking strategy during training, which randomly removes portions of t... | [
"cs.CL",
"eess.AS"
] |
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