article stringlengths 507 295k | abstract stringlengths 417 1.92k | category listlengths 1 6 |
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# 1 Introduction
Large language model (LLM) capabilities have transformed numerous domains, from creative writing to scientific research. A critical detail of LLM deployment is the sampling method: the algorithm that determines how tokens are sampled during generation. Sampling strategies directly impact the quality a... | Sampling from language models impacts the quality and diversity of outputs,
affecting both research and real-world applications. Recently, Nguyen et al.
2024's "Turning Up the Heat: Min-p Sampling for Creative and Coherent LLM
Outputs" introduced a new sampler called min-p, claiming it achieves superior
quality and div... | [
"cs.CL",
"cs.LG"
] |
# 1 Introduction
Time series forecasting has traditionally relied on historical patterns and temporal dependencies to predict future values. However, in complex real-world applications such as electrical consumption prediction, the incorporation of external factors has proven crucial for improving forecast accuracy [1... | Accurate electrical consumption forecasting is crucial for efficient energy
management and resource allocation. While traditional time series forecasting
relies on historical patterns and temporal dependencies, incorporating external
factors -- such as weather indicators -- has shown significant potential for
improving... | [
"cs.LG",
"cs.AI"
] |
# Introduction
Multiple Sclerosis (MS) is a chronic autoimmune disorder that affects approximately 2.8 million individuals worldwide, making it one of the most prevalent neurological diseases among young adults (Sadeghibakhi et al., 2022). It
Machine learning (ML), particularly convolutional neural networks (CNNs), h... | Background: Accurate lesion segmentation is critical for multiple sclerosis
(MS) diagnosis, yet current deep learning approaches face robustness
challenges.
Aim: This study improves MS lesion segmentation by combining data fusion and
deep learning techniques.
Materials and Methods: We suggested novel radiomic featu... | [
"eess.IV",
"cs.CV"
] |
# 1 Introduction
LLM-based agent systems have seen widespread adoption across diverse domains, such as medicine [32], programming [15, 51], robotics [35, 55], psychology [41], and general-purpose personal assistants [13, 5]. Driven by rapid advancements, agent systems are emerging as a new software paradigm, playing a... | LLM-based agent systems are emerging as a new software paradigm and have been
widely adopted across diverse domains such as medicine, robotics, and
programming. However, maintaining these systems requires substantial effort, as
they are inevitably prone to bugs and continually evolve to meet changing
external requireme... | [
"cs.AI",
"cs.SE"
] |
# 1 Introduction
Cloud-native timeseries monitoring systems such as Prometheus [1], VictoriaMetrics [7], and Grafana Mimir [19] are widely used as the cloud telemetry platform, where various metrics such as sensor readings [81], IP network traffic information [12, 24, 25, 29, 59], and cluster CPU and memory utilizatio... | Timeseries monitoring systems such as Prometheus play a crucial role in
gaining observability of the underlying system components. These systems
collect timeseries metrics from various system components and perform
monitoring queries over periodic window-based aggregations (i.e., rule
queries). However, despite wide ad... | [
"cs.DB",
"cs.NI"
] |
# 1 Introduction
Recent advances in computational capabilities have sparked a data-centric paradigm shift in deep learning. Moving beyond an exclusive reliance on architectural innovations, the AI community now prioritizes large-scale data utilization, as evidenced by the success of GPT-4 [1] in language processing an... | Recent advances in graph machine learning have shifted to data-centric
paradigms, driven by two emerging fields: (1) Federated graph learning (FGL)
enables multi-client collaboration but faces challenges from data and task
heterogeneity, limiting its practicality; (2) Graph foundation models (GFM)
offer strong domain g... | [
"cs.LG",
"cs.AI",
"cs.DB",
"cs.SI"
] |
# 1 Introduction
Neural View Synthesis (NVS) has emerged as a transformative technology in computer vision and graphics, enabling the generation of photorealistic images from arbitrary camera viewpoints given sparse input views. The field has witnessed remarkable progress with the introduction of Neural Radiance Field... | 3D Gaussian Splatting (3DGS) has emerged as a promising approach for novel
view synthesis, offering real-time rendering with high visual fidelity.
However, its substantial storage requirements present significant challenges
for practical applications. While recent state-of-the-art (SOTA) 3DGS methods
increasingly incor... | [
"cs.CV"
] |
# Introduction
Recent advancements in image generation (Cao et al. 2024; Zhang et al. 2023; Xu et al. 2024; Ruiz et al. 2023) and video generation (Bar-Tal et al. 2024; Chen et al. 2025; Kong et al. 2024) have demonstrated remarkable success, enabling a wide range of applications in computer graphics, cultural heritag... | Generating realistic and controllable human motions, particularly those
involving rich multi-character interactions, remains a significant challenge
due to data scarcity and the complexities of modeling inter-personal dynamics.
To address these limitations, we first introduce a new large-scale rich video
human motion 2... | [
"cs.CV"
] |
# 1 Introduction
Program repair, or automatic bug fixing, promises to generate corrective patches for faulty code [Monperrus, 2018]. Recent years have seen dramatic improvements in the quality and complexity of patches thanks to learning based program repair, with the most complex bugs being repaired by frontier LLMs ... | Automatic program repair seeks to generate correct code from buggy programs,
with most approaches searching the correct program in a discrete, symbolic
space of source code tokens. This symbolic search is fundamentally limited by
its inability to directly reason about program behavior. We introduce
Gradient-Based Progr... | [
"cs.PL",
"cs.LG",
"cs.SE"
] |
# 1. Introduction
Code-switching (CS), the natural and fluid alternation between two or more languages within a single conversation or utterance, is a pervasive linguistic phenomenon worldwide, particularly as multilingualism grows [1]. Despite its widespread nature, research on CS, especially in Automatic Speech Reco... | Code-switching (CS), common in multilingual settings, presents challenges for
ASR due to scarce and costly transcribed data caused by linguistic complexity.
This study investigates building CS-ASR using synthetic CS data. We propose a
phrase-level mixing method to generate synthetic CS data that mimics natural
patterns... | [
"cs.CL",
"cs.AI",
"cs.SD",
"eess.AS"
] |
# 1. INTRODUCTION
Notation-level music transcription is the process of converting musical audio or symbolic data into a written form. This task is both challenging and essential in the field of Music Information Retrieval (MIR) [1]. Automatic Music Transcription (AMT) seeks to address the limitations of manual transcr... | Music transcription plays a pivotal role in Music Information Retrieval
(MIR), particularly for stringed instruments like the guitar, where symbolic
music notations such as MIDI lack crucial playability information. This
contribution introduces the Fretting-Transformer, an encoderdecoder model that
utilizes a T5 transf... | [
"cs.SD",
"cs.CL",
"cs.MM",
"eess.AS"
] |
# 1 Introduction
The remarkable success of the OpenAI-O1 series models (OpenAI, 2024) and DeepSeek-R1 (DeepSeekAI, 2025) has demonstrated the substantial potential of large-scale reinforcement learning (RL) for complex reasoning tasks, attracting significant research attention. However, the detailed methodologies empl... | We present Ring-lite, a Mixture-of-Experts (MoE)-based large language model
optimized via reinforcement learning (RL) to achieve efficient and robust
reasoning capabilities. Built upon the publicly available Ling-lite model, a
16.8 billion parameter model with 2.75 billion activated parameters, our
approach matches the... | [
"cs.CL",
"cs.AI"
] |
# 1 Introduction
Efficient query processing is essential for the performance of modern database management systems, particularly as the complexity and size of data continue to grow. Join operations, which combine records from multiple tables, play a pivotal role in this process; however, traditional join algorithms of... | Join processing is a fundamental operation in database management systems;
however, traditional join algorithms often encounter efficiency challenges when
dealing with complex queries that produce intermediate results much larger than
the final query output. The emergence of worst-case optimal join (WCOJ)
algorithms re... | [
"cs.DB"
] |
# 1 Introduction
In modern healthcare systems, accurate and timely diagnosis stands as a critical component in patient management and treatment [18]. To form a diagnosis, clinicians often engage in clinical decisionmaking through a dynamic, iterative process, called differential diagnosis. This process involves formin... | Clinical decision-making is a dynamic, interactive, and cyclic process where
doctors have to repeatedly decide on which clinical action to perform and
consider newly uncovered information for diagnosis and treatment. Large
Language Models (LLMs) have the potential to support clinicians in this
process, however, most ap... | [
"cs.CL",
"cs.AI",
"cs.LG"
] |
# 1 Introduction
Plagiarism is a prevalent challenge in computer science education, facilitated by the ease of duplicating and modifying digital assignments [15, 42, 35]. Although students generally acknowledge plagiarism as academic misconduct, some will engage in it despite the threat of consequences [68]. Therefore... | Plagiarism in programming assignments is a persistent issue in computer
science education, increasingly complicated by the emergence of automated
obfuscation attacks. While software plagiarism detectors are widely used to
identify suspicious similarities at scale and are resilient to simple
obfuscation techniques, they... | [
"cs.SE"
] |
# 1. Introduction
This paper introduces ReaLchords, a generative model tailored for online adaptive musical accompaniment. Emulating the spontaneity of live music jamming, ReaLchords generates chord accompaniments in response to a stream of monophonic melody notes, adapting on-the-fly to the unfolding musical narrativ... | Jamming requires coordination, anticipation, and collaborative creativity
between musicians. Current generative models of music produce expressive output
but are not able to generate in an \emph{online} manner, meaning simultaneously
with other musicians (human or otherwise). We propose ReaLchords, an online
generative... | [
"cs.SD",
"cs.AI"
] |
# 1 Introduction
Tropical cyclones (TCs, also known as hurricanes or typhoons) have been the most damaging single form of weatherrelated natural disaster globally in terms of both loss of life and economic damage in recent decades (World Meteorological Organization, 2021). In the United States (U.S.), storm surge has ... | Storm surge is one of the deadliest hazards posed by tropical cyclones (TCs),
yet assessing its current and future risk is difficult due to the phenomenon's
rarity and physical complexity. Recent advances in artificial intelligence
applications to natural hazard modeling suggest a new avenue for addressing
this problem... | [
"physics.ao-ph",
"cs.LG"
] |
# 1 Introduction
Counterfactual generation is fundamental to causal reasoning [37, 40, 1], allowing us to explore hypothetical scenarios, such as How would this patient’s disease have progressed if treatment A had been administered instead of treatment B? The ability to answer such causal questions is important across... | Counterfactual image generation aims to simulate realistic visual outcomes
under specific causal interventions. Diffusion models have recently emerged as
a powerful tool for this task, combining DDIM inversion with conditional
generation via classifier-free guidance (CFG). However, standard CFG applies a
single global ... | [
"cs.CV",
"cs.AI"
] |
# 1 Introduction
Vision-language models (VLMs) have made notable progress in general-domain tasks, such as crop anomaly detection[1] and intelligent video surveillance[2]. In the medical and healthcare domain, researchers have recently adapted VLMs to support medical visual question answering (VQA), with promising res... | Recent advancements in vision-language systems have improved the accuracy of
Radiological Visual Question Answering (VQA) Models. However, some challenges
remain across each stage of model development: limited expert-labeled images
hinders data procurement at scale; the intricate and nuanced patterns of
radiological im... | [
"cs.CV",
"cs.AI"
] |
# 1 Introduction
With the burgeoning development of machine learning (ML) applications, there is an increasing use of sensitive data, including financial transactions, medical records, and personal digital footprints, for training purposes. Numerous studies [39, 42, 53] have highlighted serious privacy risks associate... | Membership inference attacks (MIAs) pose a significant threat to the privacy
of machine learning models and are widely used as tools for privacy assessment,
auditing, and machine unlearning. While prior MIA research has primarily
focused on performance metrics such as AUC, accuracy, and TPR@low FPR - either
by developi... | [
"cs.LG"
] |
# 1 Introduction
Robotic systems are increasingly expected to operate in dynamic, uncertain, and unstructured environments, making self-adaptation a crucial capability. Unlike traditional robots that follow pre-programmed behaviours, self-adaptive robots exploit artificial intelligence (AI) and data-driven techniques ... | Self-adaptive robotic systems are designed to operate autonomously in dynamic
and uncertain environments, requiring robust mechanisms to monitor, analyse,
and adapt their behaviour in real-time. Unlike traditional robotic software,
which follows predefined logic, self-adaptive robots leverage artificial
intelligence, m... | [
"cs.SE",
"cs.RO"
] |
# 1 INTRODUCTION
Relational operations such as filtering, join, and group-by are the crux of data science tasks such as data analysis [36, 98, 103], data cleaning [19, 23], and feature engineering [25, 31]. They are commonly performed in dataframes—a table-like data structure widely used in data science due to their l... | Mojo is an emerging programming language built on MLIR (Multi-Level
Intermediate Representation) and JIT compilation. It enables transparent
optimizations with respect to the underlying hardware (e.g., CPUs, GPUs), while
allowing users to express their logic using Python-like user-friendly syntax.
Mojo has been shown t... | [
"cs.DB"
] |
# 1. Introduction
Reconstructing high-quality, animatable 3D human avatars from casually captured images is a crucial task in computer graphics, with broad applications like virtual reality and telepresence. A practical solution should support rapid and robust reconstruction from minimal input—ideally using only one o... | Reconstructing an animatable 3D human from casually captured images of an
articulated subject without camera or human pose information is a practical yet
challenging task due to view misalignment, occlusions, and the absence of
structural priors. While optimization-based methods can produce high-fidelity
results from m... | [
"cs.CV"
] |
# I. Introduction
Temporarily static object detection has many applications. Depending on the application, temporarily static objects can be abandoned items such as luggage, illegally parked vehicles, removed objects from the scene, etc. A significant amount of research has been done on the detection of abandoned item... | In general, background subtraction-based methods are used to detect moving
objects in visual tracking applications. In this paper, we employed a
background subtraction-based scheme to detect the temporarily stationary
objects. We proposed two schemes for stationary object detection, and we
compare those in terms of det... | [
"cs.CV"
] |
# 1 Introduction
X-ray computed tomography (XCT) is a critical technique with many applications in medical and industrial imaging. In industrial XCT, reconstructing a 3D object requires solving a large inverse problem using many projections from different angles. The resulting reconstruction can be used for internal i... | Cone-beam X-ray computed tomography (XCT) is an essential imaging technique
for generating 3D reconstructions of internal structures, with applications
ranging from medical to industrial imaging. Producing high-quality
reconstructions typically requires many X-ray measurements; this process can be
slow and expensive, e... | [
"eess.IV",
"cs.CV"
] |
# 1 Introduction
SWE-Dev
14000 train + 500 test samples 17 Chatbot LLMs Input Task Generate Code Evaluation 10 Reasoning LLMs
Codebase Updated Codebase Testcase 10 Multi-Agent Systems
num2words/ num2words/lang_EN.py Convert ordinal to English: test_ordinal
Project Requirement Description cl.da.es.fs Ntuom_2yWeoar(ds_... | Large Language Models (LLMs) have shown strong capability in diverse software
engineering tasks, e.g. code completion, bug fixing, and document generation.
However, feature-driven development (FDD), a highly prevalent real-world task
that involves developing new functionalities for large, existing codebases,
remains un... | [
"cs.SE",
"cs.CL"
] |
# 1 INTRODUCTION
Life narratives are deeply unique and valuable [10, 35, 82], shaped through an interplay of personal memories that involve achievements, struggles, and moments of reflection. Capturing and expressing these narratives through an autobiography is a powerful way for people to preserve their legacy [5, 15... | Every individual carries a unique and personal life story shaped by their
memories and experiences. However, these memories are often scattered and
difficult to organize into a coherent narrative, a challenge that defines the
task of autobiography writing. Existing conversational writing assistants tend
to rely on gene... | [
"cs.HC",
"cs.AI",
"cs.MA"
] |
# I. INTRODUCTION
Automatic code generation aims to reduce manual coding and boost productivity [1], with LLMs like GPT-4 [2] making significant advancements. However, ensuring accuracy and correctness remains a challenge. Recently, several approaches have been proposed to enhance LLM-based code generation. These incl... | Automatic code generation has gained significant momentum with the advent of
Large Language Models (LLMs) such as GPT-4. Although many studies focus on
improving the effectiveness of LLMs for code generation, very limited work
tries to understand the generated code's characteristics and leverage that to
improve failed ... | [
"cs.SE",
"cs.AI"
] |
# 1 Introduction
Cloud architecture design requires integrating diverse services to fulfill requirements while optimizing system qualities including scalability, security, and cost-efficiency [1, 2]. A central challenge is refining ambiguous requirements into precise specifications [3], requiring architects to identif... | Cloud architecture design is a complex process requiring both technical
expertise and architectural knowledge to develop solutions from frequently
ambiguous requirements. We present CloudArchitectBuddy, a system-driven cloud
architecture design support application with two key mechanisms: (1) structured
state managemen... | [
"cs.SE",
"cs.HC"
] |
# 1 Introduction
Simulators have proven to be an indispensable tool for synthesizing policies for robot control. Sim-to-real techniques are now the de facto standard in creating robust and performant policies for real robots [1, 2, 3, 4, 5]. Predominantly, these simulators have been used as black-box functions where p... | Contact forces pose a major challenge for gradient-based optimization of
robot dynamics as they introduce jumps in the system's velocities.
Penalty-based simulators, such as MuJoCo, simplify gradient computation by
softening the contact forces. However, realistically simulating hard contacts
requires very stiff contact... | [
"cs.RO",
"cs.LG",
"cs.SY",
"eess.SY",
"I.2.9; I.2.6; I.6.4; G.1.6"
] |
# 1 Introduction
The problem of estimating the mean of a random variable from a finite sample of its i.i.d. copies is fundamental in statistics and machine learning. When the random variable has exponentially decaying tails, the sample mean exhibits optimal or near-optimal performance. In particular, for $\varepsilon ... | The Median of Means (MoM) is a mean estimator that has gained popularity in
the context of heavy-tailed data. In this work, we analyze its performance in
the task of simultaneously estimating the mean of each function in a class
$\mathcal{F}$ when the data distribution possesses only the first $p$ moments
for $p \in (1... | [
"stat.ML",
"cs.LG"
] |
# Introduction
In the past decade, public repositories (e.g., Gene Expression Omnibus (GEO) [1], ArrayExpress [2], European Genome-phenome Archive (EGA) [3], Accelerating
Medicins Partnership Parkinson’s Disease (AMP-PD) [4], Synapse [5]) have facilitated access to thousands of omics datasets accompanied by clinical ... | The growing volume of omics and clinical data generated for neurodegenerative
diseases (NDs) requires new approaches for their curation so they can be
ready-to-use in bioinformatics. NeuroEmbed is an approach for the engineering
of semantically accurate embedding spaces to represent cohorts and samples. The
NeuroEmbed ... | [
"cs.CL"
] |
# 1 Introduction
Today’s smart IoT devices, such as smart speakers, smart bulbs, and various smart display devices, are commonly connected to home routers or mesh network hubs via WiFi. Beyond their primary role in communication, the WiFi signals between these devices inherently capture rich information about the surr... | WiFi sensing has emerged as a compelling contactless modality for human
activity monitoring by capturing fine-grained variations in Channel State
Information (CSI). Its ability to operate continuously and non-intrusively
while preserving user privacy makes it particularly suitable for health
monitoring. However, existi... | [
"eess.SP",
"cs.AI",
"cs.DB"
] |
# 1. Introduction
Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language processing, fundamentally reshaping the landscape of artificial intelligence. A burgeoning area of research now focuses on extending these powerful models to the speech modality, leveraging their powerful seman... | This report details the NTU Speechlab system developed for the Interspeech
2025 Multilingual Conversational Speech and Language Model (MLC-SLM) Challenge
(Task I), where we achieved 5th place. We present comprehensive analyses of our
multilingual automatic speech recognition system, highlighting key advancements
in mod... | [
"cs.CL",
"eess.AS"
] |
# 1 Introduction
Literary translation is a complex task that goes beyond simple word-for-word conversion. It demands a deep understanding of cultural nuances and the preservation of the author’s unique voice through creative adaptation for a new audience. Unlike technical translation, which prioritizes precision and c... | Literary translation requires preserving cultural nuances and stylistic
elements, which traditional metrics like BLEU and METEOR fail to assess due to
their focus on lexical overlap. This oversight neglects the narrative
consistency and stylistic fidelity that are crucial for literary works. To
address this, we propose... | [
"cs.CL"
] |
2. Introduction2
As software engineering data are symbolic by nature, in this chapter, we will present fault localization using symbolic methods. Symbolic methods tend to lend themselves naturally to give explanations, and this is exactly what we are looking for in fault localization. Indeed, we prefer a system with t... | This chapter illustrates the basic concepts of fault localization using a
data mining technique. It utilizes the Trityp program to illustrate the general
method. Formal concept analysis and association rule are two well-known methods
for symbolic data mining. In their original inception, they both consider data
in the ... | [
"cs.SE",
"cs.AI"
] |
# 1 Introduction
Sequential resource allocation (SRA) involves distributing limited resources across locations over time, where an agent allocates resources at a sequence of demand nodes while satisfying upper and lower bound constraints. The objective is to allocate resources efficiently while adhering to these const... | Sequential Resource Allocation with situational constraints presents a
significant challenge in real-world applications, where resource demands and
priorities are context-dependent. This paper introduces a novel framework,
SCRL, to address this problem. We formalize situational constraints as logic
implications and dev... | [
"cs.AI"
] |
# 1 Introduction
Diffusion Models (DMs) [1–3] are a state-of-the-art class of generative model, achieving high quality, diverse sampling of complex data distributions. A particularly successful application is in conditional generation of image data, enabling rapid progress in class-conditioned image generation [4–6], ... | The success of diffusion models has driven interest in performing conditional
sampling via training-free guidance of the denoising process to solve image
restoration and other inverse problems. A popular class of methods, based on
Diffusion Posterior Sampling (DPS), attempts to approximate the intractable
posterior sco... | [
"stat.ML",
"cs.CV",
"cs.LG"
] |
# 1 INTRODUCTION
When photographing an object, we often want both a full overview and fine-grained material details. However, everyday cameras have fixed resolution, forcing a trade-off between coverage and detail. For example, product images typically show a low-detail full view alongside isolated close-ups, which li... | We present UltraZoom, a system for generating gigapixel-resolution images of
objects from casually captured inputs, such as handheld phone photos. Given a
full-shot image (global, low-detail) and one or more close-ups (local,
high-detail), UltraZoom upscales the full image to match the fine detail and
scale of the clos... | [
"cs.GR",
"cs.CV"
] |
# 1 Introduction
Li-ion batteries are widely deployed for transportation, grid stabilization, and power tools. These applications require specialized batteries, e.g., with long service life, or performance under extreme conditions. Enhancing the usable lifespan and power density of future batteries will greatly aid in... | Interdisciplinary collaboration in battery science is required for rapid
evaluation of better compositions and materials. However, diverging domain
vocabulary and non-compatible experimental results slow down cooperation. We
critically assess the current state-of-the-art and develop a structured data
management and int... | [
"cs.DB",
"physics.data-an"
] |
# 1. Introduction
Code review is essential for improving code quality and detecting defects (Fagan, 2002). Modern Code Review (MCR) is widely used in open-source (Rigby et al., 2008; 2014; Rigby & Bird, 2013) and industrial settings (Sadowski et al., 2018; Shan et al., 2022), typically involving: (A) code submission, ... | The complexity of code reviews has driven efforts to automate review
comments, but prior approaches oversimplify this task by treating it as
snippet-level code-to-text generation and relying on text similarity metrics
like BLEU for evaluation. These methods overlook repository context, real-world
merge request evaluati... | [
"cs.SE",
"cs.AI",
"cs.CL",
"cs.LG"
] |
# I. INTRODUCTION
Public procurement represents a major component of government expenditure, necessitating effective management to ensure fair allocation of public funds and economic stability. However, traditional data storage formats, often tabular or unstructured, limit transparency, accessibility, and analytical d... | Public procurement plays a critical role in government operations, ensuring
the efficient allocation of resources and fostering economic growth. However,
traditional procurement data is often stored in rigid, tabular formats,
limiting its analytical potential and hindering transparency. This research
presents a methodo... | [
"cs.DB",
"cs.LG"
] |
# 1. Introduction
This paper proposes a variational inference algorithm based on particle methods to sample from multimodal densitiesMore precisely, we consider the problem of approximating measure of interest $\pi$ , which will be assumed to take the form
$$
\pi ( d x ) = \frac { 1 } { Z } \rho ( x ) d x ,
$$
where... | We propose a novel particle-based variational inference method designed to
work with multimodal distributions. Our approach, referred to as Branched Stein
Variational Gradient Descent (BSVGD), extends the classical Stein Variational
Gradient Descent (SVGD) algorithm by incorporating a random branching mechanism
that en... | [
"cs.LG",
"stat.CO",
"62F15, 65C05, 65C35"
] |
# 1 INTRODUCTION
In applications that involve dynamic decision-making, such as ad placement and recommendation systems, exploration can be costly and risky. These constraints limit the use of online exploration of actions, thereby motivating the study of offline policy learning methods. Off-policy learning (OPL) addre... | Off-policy learning (OPL) in contextual bandits aims to learn a
decision-making policy that maximizes the target rewards by using only
historical interaction data collected under previously developed policies.
Unfortunately, when rewards are only partially observed, the effectiveness of
OPL degrades severely. Well-know... | [
"cs.LG",
"62L05, 68T05",
"I.2.6; G.3"
] |
# 1 Introduction
Recent successes in harnessing internet-scale data to train image and language foundation models [1, 2, 3, 4, 5, 6] have spurred an analogous push in robotics. In contrast with earlier methods that focused on achieving expert-level capabilities in narrow, controlled domains, recent efforts in robotics... | Action-labeled data for robotics is scarce and expensive, limiting the
generalization of learned policies. In contrast, vast amounts of action-free
video data are readily available, but translating these observations into
effective policies remains a challenge. We introduce AMPLIFY, a novel framework
that leverages lar... | [
"cs.RO",
"cs.CV",
"cs.LG"
] |
# I. INTRODUCTION
For verification through model-checking, the complexity of the formal models used is highly critical. A usual approach to address this is through abstraction, and we propose structural abstraction of the environment model. Technically, our approach approximates environment objects via a composition o... | Safety verification of robot applications is extremely challenging due to the
complexity of the environment that a robot typically operates in. Formal
verification with model-checking provides guarantees but it may often take too
long or even fail for complex models of the environment. A usual solution
approach is abst... | [
"cs.RO",
"cs.SE"
] |
# Introduction
Parameter-efficient adaptation/fine-tuning [64] plays a central role in the practical deployment of large-scale pre-training models, especially in multi-task learning (MTL) scenarios [73, 57, 11, 58, 40]. Take large language models (LLMs) [75] in natural language processing (NLP) tasks as an example. To... | Adapting large-scale foundation models in multi-task scenarios often suffers
from task conflict and oblivion. To mitigate such issues, we propose a novel
''model MoE-ization'' strategy that leads to a conflict- and oblivion-resistant
multi-task adaptation method. Given a weight matrix of a pre-trained model, our
method... | [
"cs.LG"
] |
# 1 INTRODUCTION
Cardinality estimation (CardEst), which estimates the result size of an SQL query on a relational database, is a fundamental component
Tongyu Liu
Renmin University of
China
ltyzzz@ruc.edu.cn
Kai Zeng Huawei Technologies kai.zeng@huawei.com
Tao Ye Huawei Technologies yetao1@huawei.com
Nan Tang HKUS... | Cardinality estimation is a fundamental component in database systems,
crucial for generating efficient execution plans. Despite advancements in
learning-based cardinality estimation, existing methods may struggle to
simultaneously optimize the key criteria: estimation accuracy, inference time,
and storage overhead, li... | [
"cs.DB",
"H.2.4; E.5"
] |
# 1 Introduction
As large language models continue to advance, the design of their evaluations becomes increasingly important, as it shapes the development priorities of the next generation of models and guides the broader trajectory toward artificial general intelligence (Chang et al., 2024). Current benchmarks
Part... | As evaluation designs of large language models may shape our trajectory
toward artificial general intelligence, comprehensive and forward-looking
assessment is essential. Existing benchmarks primarily assess static knowledge,
while intelligence also entails the ability to rapidly learn from experience.
To this end, we ... | [
"cs.CL"
] |
# I. INTRODUCTION
MAGE segmentation is a foundational component of visual perception in intelligent transportation systems (ITS), enabling autonomous vehicles to interpret complex driving environments with precision and reliability [1]. By delineating road lanes, detecting obstacles, segmenting pedestrians, and recogn... | The integration of Large Language Models (LLMs) with computer vision is
profoundly transforming perception tasks like image segmentation. For
intelligent transportation systems (ITS), where accurate scene understanding is
critical for safety and efficiency, this new paradigm offers unprecedented
capabilities. This surv... | [
"cs.CV",
"cs.AI"
] |
# 1 Introduction
Join cardinality estimation is a challenging problem in database query optimization [33, 23], especially when queries include filter conditions on the joining tables [52]. Traditional data synopses such as histograms and samples [9] are built over entire relations before querying. While this maximizes... | Sketches have shown high accuracy in multi-way join cardinality estimation, a
critical problem in cost-based query optimization. Accurately estimating the
cardinality of a join operation -- analogous to its computational cost --
allows the optimization of query execution costs in relational database
systems. However, a... | [
"cs.DB",
"cs.LG"
] |
# 1 Introduction
Deep reinforcement learning (RL) has led to remarkable successes in domains ranging from games to robotics, largely by representing policies as highly parametrized neural networks and optimizing them end-to-end [Lillicrap et al., 2019; Schulman et al., 2017]. However, neural policies often struggle to... | Algorithms for learning programmatic representations for sequential
decision-making problems are often evaluated on out-of-distribution (OOD)
problems, with the common conclusion that programmatic policies generalize
better than neural policies on OOD problems. In this position paper, we argue
that commonly used benchm... | [
"cs.LG"
] |
# 1. Introduction
Dense retrieval retrieves documents by evaluating their similarity scores with user queries (Mitra et al., 2018; Gao &
Callan, 2021; Zhao et al., 2024c). It underpins many systems, in particular, retrieval-augmented generation (RAG) frameworks (Karpukhin et al., 2020), where retrieval accuracy is pa... | Although Multi-Vector Retrieval (MVR) has achieved the state of the art on
many information retrieval (IR) tasks, its performance highly depends on how to
decompose queries into smaller pieces, say phrases or tokens. However,
optimizing query decomposition for MVR performance is not end-to-end
differentiable. Even wors... | [
"cs.IR",
"cs.DB"
] |
# 1 Introduction
Monocular 3D human pose estimation is a fundamental task in computer vision that aims to predict human body poses in 3D space from a single RGB image. It serves as a key enabling technology for a wide range of applications, including motion analysis[1], human-computer interaction[2], [3] and virtual/... | Existing monocular 3D pose estimation methods primarily rely on joint
positional features, while overlooking intrinsic directional and angular
correlations within the skeleton. As a result, they often produce implausible
poses under joint occlusions or rapid motion changes. To address these
challenges, we propose the P... | [
"cs.CV",
"cs.AI"
] |
# 1 Introduction
Quantifying the spatial distributions of elastic properties, specifically Young’s modulus and Poisson’s ratio is crucial in numerous applications, including biomedical imaging. Young’s modulus characterizes a material’s local resistance to elastic (reversible) axial deformation, while Poisson’s ratio ... | Accurately estimating spatially heterogeneous elasticity parameters,
particularly Young's modulus and Poisson's ratio, from noisy displacement
measurements remains significantly challenging in inverse elasticity problems.
Existing inverse estimation techniques are often limited by instability,
pronounced sensitivity to... | [
"cs.LG"
] |
# 1 Introduction
Large Language Models (LLMs) have demonstrated remarkable capabilities in automatic code generation, enabling developers to translate natural language descriptions into executable programs (Hong et al., 2023; Liu et al., 2024a). However, as coding tasks grow in complexity, relying on a single LLM inst... | Large Language Models (LLMs) have demonstrated effectiveness in code
generation tasks. To enable LLMs to address more complex coding challenges,
existing research has focused on crafting multi-agent systems with agentic
workflows, where complex coding tasks are decomposed into sub-tasks, assigned
to specialized agents.... | [
"cs.SE",
"cs.AI",
"cs.CL"
] |
# 1 Introduction
Large language models (LLMs) have transformed the capabilities of conversational AI, advancing chatbots and overcoming their previous limitations to facilitate more nuanced and contextually aware interactions [21]. In contrast to traditional rule-based chatbots with constrained response patterns, LLM-... | Despite significant advancements in conversational AI, large language model
(LLM)-powered chatbots often struggle with personalizing their responses
according to individual user characteristics, such as technical expertise,
learning style, and communication preferences. This lack of personalization is
particularly prob... | [
"cs.AI"
] |
# 1 Introduction
With its powerful multimodal perception and generalization capabilities, the Multimodal Large Language Model (MLLM) has become a universal technical paradigm for addressing diverse scenarios and has demonstrated strong generative capabilities in video understanding [31, 48, 29, 1]. However, when appli... | Multimodal Large Language Models (MLLMs) struggle with long videos due to
fixed context windows and weak long-term dependency modeling. Existing
Retrieval-Augmented Generation (RAG) methods for videos use static retrieval
strategies, leading to inefficiencies for simple queries and information loss
for complex tasks. T... | [
"cs.CV"
] |
# I. INTRODUCTION
Longer undergraduate programming projects like capstones, hackathons, or sprints are often characterized by team collaboration and intense student dedication. Frequently, the client is an external entity, such as a company representative, and in these scenarios, the student team is responsible for st... | This full paper in innovative practice provides an automated tool to
summarize individual code contributions in project-based courses with external
clients. Real industry projects offer valuable learning opportunities by
immersing students in authentic problems defined by external clients. However,
the open-ended and h... | [
"cs.SE",
"cs.CY"
] |
# 1 Introduction
Linear contextual bandits (LinCB) provide a simple yet powerful framework for sequential decision-making. At each decision epoch, an agent selects an action from a set of context vectors to maximize cumulative rewards, assumed to be linear functions of the chosen contexts. A special case is the multi-... | In linear contextual bandits, the objective is to select actions that
maximize cumulative rewards, modeled as a linear function with unknown
parameters. Although Thompson Sampling performs well empirically, it does not
achieve optimal regret bounds. This paper proposes a nearly minimax optimal
Thompson Sampling for lin... | [
"stat.ML",
"cs.LG"
] |
# 1 Introduction
When developing software, large efforts are spent on quality assurance [2], which is mainly performed by conducting a number of automated and manual tests on the software. Developers use such tests to determine the compliance of the software with domain requirements as well as to determine the quality... | One important step in software development is testing the finished product
with actual users. These tests aim, among other goals, at determining
unintuitive behavior of the software as it is presented to the end-user.
Moreover, they aim to determine inconsistencies in the user-facing interface.
They provide valuable fe... | [
"cs.SE"
] |
# 1 Introduction
Cyber-physical systems (CPS) are integrated hardware-software systems where computation and physical processes are deeply intertwined. Ensuring safety [7] in these systems, in particular for the safety-critical ones is of high importance, as failures can have critical consequences. One of the key stra... | Cyber-physical systems (CPSs) are complex systems that integrate physical,
computational, and communication subsystems. The heterogeneous nature of these
systems makes their safety assurance challenging. In this paper, we propose a
novel automated approach for guardrailing cyber-physical systems using
property-based te... | [
"cs.SE"
] |
# 1 INTRODUCTION
Group aggregation, represented in SQL via GROUP BY, is a fundamental operation in analytical query processing, especially decisionsupport workloads [22]. To ensure that database systems continue to scale well with new many-core architectures, it is critical to build highly concurrent group aggregation... | Efficiently computing group aggregations (i.e., GROUP BY) on modern many-core
architectures is critical for analytic database systems. Today's engines
predominately use a partitioned approach to group aggregation, in which an
incoming data stream is partitioned by key values so that every row for a
particular key is se... | [
"cs.DB"
] |
# 1 Introduction
High-dimensional nearest neighbor search is a basic building block in many areas, including image and video processing [16, 21], information retrieval [6, 46], and algorithm design [10, 23]. It is central to modern machine learning, underlying document and media search based on learned embeddings [9, ... | Nearest neighbor search is central in machine learning, information
retrieval, and databases. For high-dimensional datasets, graph-based methods
such as HNSW, DiskANN, and NSG have become popular thanks to their empirical
accuracy and efficiency. These methods construct a directed graph over the
dataset and perform bea... | [
"cs.IR",
"cs.DB",
"cs.DS",
"cs.LG"
] |
# 1. Introduction
Following the emergence of big data and the ever-increasing public availability of datasets, each with tens of thousands of data points, research within the deep learning domain is accelerating [1]. Consequently, there are two key factors that need to be addressed. Firstly, the process by which we pr... | In order to address the scalability challenge within Neural Architecture
Search (NAS), we speed up NAS training via dynamic hard example mining within a
curriculum learning framework. By utilizing an autoencoder that enforces an
image similarity embedding in latent space, we construct an efficient kd-tree
structure to ... | [
"cs.CV"
] |
# 1 Introduction
A query optimizer is a performance-critical component in every database system. It translates declarative user queries into efficient execution plans [3, 45]. There have been numerous efforts to learn query optimizers (LQOs)(e.g., [18, 33, 34, 60]) to reduce the reliance on manual tuning and expert in... | Query optimization is critical in relational databases. Recently, numerous
Learned Query Optimizers (LQOs) have been proposed, demonstrating superior
performance over traditional hand-crafted query optimizers after short training
periods. However, the opacity and instability of machine learning models have
limited thei... | [
"cs.DB"
] |
# 1 Introduction
Diffusion and flow-based generative models have revolutionized generative modeling [25, 70, 45, 62, 14, 8], but they rely on slow iterative sampling. This has led to the development of approaches to accelerate generation. Advanced, higher-order samplers [68, 50, 51, 12, 89, 34, 61] help, but cannot pr... | Diffusion- and flow-based models have emerged as state-of-the-art generative
modeling approaches, but they require many sampling steps. Consistency models
can distill these models into efficient one-step generators; however, unlike
flow- and diffusion-based methods, their performance inevitably degrades when
increasing... | [
"cs.CV",
"cs.LG"
] |
# 1 Introduction
Machine learning systems have become increasingly prevalent in decision-making across various domains, including healthcare, finance, and criminal justice. While these systems promise more efficient and data-driven decisions, they also raise significant concerns regarding fairness and equity. As machi... | We address the regression problem under the constraint of demographic parity,
a commonly used fairness definition. Recent studies have revealed fair minimax
optimal regression algorithms, the most accurate algorithms that adhere to the
fairness constraint. However, these analyses are tightly coupled with specific
data ... | [
"stat.ML",
"cs.LG"
] |
# 1 Introduction
Large language models (LLMs) have become transformative tools in numerous domains, fundamentally changing the way we approach complex tasks in almost all areas of life [37]. Among their countless applications, the integration of LLMs in customer support has been particularly impactful [20], allowing b... | Following recent advancements in large language models (LLMs), LLM-based
chatbots have transformed customer support by automating interactions and
providing consistent, scalable service. While LLM-based conversational
recommender systems (CRSs) have attracted attention for their ability to
enhance the quality of recomm... | [
"cs.AI",
"cs.IR"
] |
# 1. Introduction
Large language models (LLMs) have achieved tremendous success in text processing (OpenAI, 2024), offering new ways to interact with machines. This progress has motivated efforts to extend their capabilities to speech to enable more natural spoken interactions with machines. However, modeling speech p... | The success of large language models in text processing has inspired their
adaptation to speech modeling. However, since speech is continuous and complex,
it is often discretized for autoregressive modeling. Speech tokens derived from
self-supervised models (known as semantic tokens) typically focus on the
linguistic a... | [
"cs.CL",
"cs.AI",
"cs.LG",
"cs.SD",
"eess.AS"
] |
# 1. Introduction
Quick adaptation is essential for survival in nature so much so that it can be equated to intelligence (Sternberg, 2019). Despite this, there is no Artificial Intelligence (AI) system to this day whose adaptive abilities are considered anywhere close to those of humans and animals. Large Language Mod... | Adaptation is the holy grail of intelligence, but even the best AI models
(like GPT) lack the adaptivity of toddlers. So the question remains: how can
machines adapt quickly? Despite a lot of progress on model adaptation to
facilitate continual and federated learning, as well as model merging, editing,
unlearning, etc.... | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
# 1 INTRODUCTION
The relational model, proposed over 50 years ago, has been the foundation of most high-performance database systems to date. The object-relational model, i.e., the relational model with abstract data types and related functionality, has generally satisfied the needs of most enterprise applications wit... | Spurred by a number of recent trends, we make the case that the relational
database systems should urgently move beyond supporting the basic
object-relational model and instead embrace a more abstract data model,
specifically, the entity-relationship model. We argue that the current RDBMSs
don't inherently support suff... | [
"cs.DB"
] |
# 1 Introduction
Deploying robots in human-centric settings like households requires balancing robot autonomy with humans’ sense of agency [1, 2, 3, 4, 5, 6]. Full teleoperation offers users fine-grained control but imposes a high cognitive load, whereas fully autonomous robots act independently but often misalign the... | Assistive teleoperation, where control is shared between a human and a robot,
enables efficient and intuitive human-robot collaboration in diverse and
unstructured environments. A central challenge in real-world assistive
teleoperation is for the robot to infer a wide range of human intentions from
user control inputs ... | [
"cs.RO",
"cs.AI"
] |
# 1 Introduction
In the last decade, the rise of deep learning has introduced prominent breakthroughs and achievements in object detection (OD) Zou et al. [2023], where models are usually trained under a closed-world assumption: test-time categories are the same as the training ones. However, during deployment in the ... | State-of-the-art Object Detection (OD) methods predominantly operate under a
closed-world assumption, where test-time categories match those encountered
during training. However, detecting and localizing unknown objects is crucial
for safety-critical applications in domains such as autonomous driving and
medical imagin... | [
"cs.CV"
] |
# 1 Introduction
With the rapid development of large-scale software systems, effective fault localization (FL) methods have become crucial. Over the years, numerous FL approaches (e.g. [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]) have been proposed to identify faulty statements in programs. These approaches typically ... | Test cases are indispensable for conducting effective fault localization
(FL). However, test cases in practice are severely class imbalanced, i.e. the
number of failing test cases (i.e. minority class) is much less than that of
passing ones (i.e. majority class). The severe class imbalance between failing
and passing t... | [
"cs.SE"
] |
# 1 Introduction
The European Union Deforestation Regulation (EUDR), effective December 30, 2025, mandates companies to verify that their products do not originate from recently deforested land (European Commission, 2023). With deforestation contributing $1 5 \%$ to global $\mathrm { C O _ { 2 } }$ emissions (ETC, 202... | The European Union Deforestation Regulation (EUDR) requires companies to
prove their products do not contribute to deforestation, creating a critical
demand for precise, asset-level environmental impact data. Current databases
lack the necessary detail, relying heavily on broad financial metrics and
manual data collect... | [
"cs.DB",
"cs.AI",
"cs.IR",
"cs.LG"
] |
# 1 INTRODUCTION
NL2SQL (natural language to SQL) systems translate natural language questions into SQL queries, allowing users with no technical background to interact with databases and create tools like reports or visualizations. For example, a manager could ask, “How many new customers did we acquire this quarter?... | NL2SQL (natural language to SQL) systems translate natural language into SQL
queries, allowing users with no technical background to interact with databases
and create tools like reports or visualizations. While recent advancements in
large language models (LLMs) have significantly improved NL2SQL accuracy,
schema ambi... | [
"cs.DB",
"cs.CL"
] |
# 1 INTRODUCTION
Many problems in software engineering involve optimization, search, or analysis in large, complex spaces [16]. Examples include selecting prioritized test cases for regression testing [56], detecting code clones in large codebases [41], and predicting defect-prone modules using historical data [14]. T... | Quantum computing has demonstrated the potential to solve computationally
intensive problems more efficiently than classical methods. Many software
engineering tasks, such as test case selection, static analysis, code clone
detection, and defect prediction, involve complex optimization, search, or
classification, makin... | [
"cs.SE",
"quant-ph"
] |
# 1 Introduction
In recent years, we have witnessed rapid advancements in visual generation and its tremendous application potential. Diffusion models [43, 40, 6, 39, 22] have elevated the quality of visual generation to amazing levels while enabling versatile conditional control. Meanwhile, autoregressive approaches ... | In this work, we reveal the limitations of visual tokenizers and VAEs in
preserving fine-grained features, and propose a benchmark to evaluate
reconstruction performance for two challenging visual contents: text and face.
Visual tokenizers and VAEs have significantly advanced visual generation and
multimodal modeling b... | [
"cs.CV",
"cs.DB"
] |
# 1 Introduction
Language models are about uncovering patterns in a sequence so they can guess what comes next. Before any of that happens, we must decide what the pieces of that sequence—the tokens—actually are. That choice is usually frozen in advance by a tokeniser that chops raw text into discrete units long befor... | Tokenization imposes a fixed granularity on the input text, freezing how a
language model operates on data and how far in the future it predicts. Byte
Pair Encoding (BPE) and similar schemes split text once, build a static
vocabulary, and leave the model stuck with that choice. We relax this rigidity
by introducing an ... | [
"cs.CL",
"cs.AI"
] |
# ARTICLE INFORMATION
Article title
A Structured Bangla Dataset of Disease-Symptom Associations to Improve Diagnostic Accuracy Authors
Abdullah Al Shafi1, Rowzatul Zannat2, Abdul Muntakim2,\*, Mahmudul Hasan1
# Affiliations
1Institute of Information and Communication Technology, Khulna University of Engineering & ... | Disease-symptom datasets are significant and in demand for medical research,
disease diagnosis, clinical decision-making, and AI-driven health management
applications. These datasets help identify symptom patterns associated with
specific diseases, thus improving diagnostic accuracy and enabling early
detection. The da... | [
"cs.CL"
] |
# 1 Introduction
external tools (Schick et al., 2023) and employ sophisticated planning and reasoning strategies such as ReAct (Yao et al., 2023) or Reflexion (Shinn et al., 2023) to dynamically adjust in uncertain environments.
While the rapid scaling of Large Language Models (LLMs) has led to promising results acro... | Benchmarks for Software Engineering (SE) AI agents, most notably SWE-bench,
have catalyzed progress in programming capabilities of AI agents. However, they
overlook critical developer workflows such as Version Control System (VCS)
operations. To address this issue, we present GitGoodBench, a novel benchmark
for evaluat... | [
"cs.SE",
"cs.AI"
] |
# 1 INTRODUCTION
Database Management Systems (DBMSs) are large, complex, and fundamental software systems. Unsurprisingly, they are prone to bugs. Various approaches have been proposed to detect logic bugs in them using automated testing [13, 26–28, 30, 32]. They primarily tackle the so-called test-oracle problem by v... | Various automated testing approaches have been proposed for Database
Management Systems (DBMSs). Many such approaches generate pairs of equivalent
queries to identify bugs that cause DBMSs to compute incorrect results, and
have found hundreds of bugs in mature, widely used DBMSs. Most of these
approaches are based on m... | [
"cs.SE",
"cs.DB"
] |
# 1 Introduction
Large Language Models (LLMs) have shown intriguing promise in optimizing code efficiency beyond compiler techniques [1–9]. Evaluating the effectiveness of these LM-based code optimizations relies on performance-stressing tests. For example, an optimization from recursion to iteration in Fibonacci numb... | Large Language Models (LLMs) have been increasingly used to optimize code
efficiency. Evaluating their effectiveness and further suggesting optimization
opportunities often rely on high-quality tests to demonstrate the performance
bottlenecks presented in the program. However, existing approaches rely on a
limited set ... | [
"cs.SE",
"D.2.5"
] |
# 1. INTRODUCTION
Sampling is a musical technique that “incorporates portions of existing sound recordings into a newly collaged composition” [1]. The samples often undergo significant modification during this creative process: they may be pitch-shifted, time-stretched and heavily processed with audio effects (hencefo... | Automatic sample identification (ASID), the detection and identification of
portions of audio recordings that have been reused in new musical works, is an
essential but challenging task in the field of audio query-based retrieval.
While a related task, audio fingerprinting, has made significant progress in
accurately r... | [
"cs.SD",
"cs.AI",
"cs.IR",
"H.5.5; I.2.6"
] |
# 1 Introduction
Building upon autoregressive (AR) models, large language models (LLMs) [21, 20] have unified and dominated language tasks with promising intelligence in generality and versatility, demonstrating a promising path toward artificial general intelligence (AGI). Recently, MAR series methods [18, 10, 33] ha... | Masked-based autoregressive models have demonstrated promising image
generation capability in continuous space. However, their potential for video
generation remains under-explored. In this paper, we propose \textbf{VideoMAR},
a concise and efficient decoder-only autoregressive image-to-video model with
continuous toke... | [
"cs.CV",
"cs.AI"
] |
# 1. Introduction
The recent development and wider accessibility of large language models (LLMs) have spurred discussions about how these language models can be used in survey research. Potential applications span the entire survey lifecycle, including using LLMs for questionnaire design and pretesting (e.g., Götz et ... | The recent development and wider accessibility of LLMs have spurred
discussions about how they can be used in survey research, including
classifying open-ended survey responses. Due to their linguistic capacities, it
is possible that LLMs are an efficient alternative to time-consuming manual
coding and the pre-training... | [
"cs.CL",
"cs.AI",
"cs.CY"
] |
introduction of LITMUS-P therefore represents a necessary step toward evaluating alignment under linguistically natural, semantically invariant, and adversarial perturbations—a crucial requirement for building scalable and trustworthy AI systems.
# M.3 Quantifying Stochastic Drift via AQI
While large language models ... | Alignment is no longer a luxury, it is a necessity. As large language models
(LLMs) enter high-stakes domains like education, healthcare, governance, and
law, their behavior must reliably reflect human-aligned values and safety
constraints. Yet current evaluations rely heavily on behavioral proxies such as
refusal rate... | [
"cs.CL",
"cs.AI"
] |
# 1. Introduction
General-purpose code refers to a set of program instructions written in formal languages such as Python, $\mathrm { C } { + } { + }$ , or Java, and is widely applied across diverse tasks including data processing, network communication, and algorithm implementation [1,2]. Through programming, users t... | Geospatial code generation is emerging as a key direction in the integration
of artificial intelligence and geoscientific analysis. However, there remains a
lack of standardized tools for automatic evaluation in this domain. To address
this gap, we propose AutoGEEval, the first multimodal, unit-level automated
evaluati... | [
"cs.SE",
"cs.AI",
"cs.CG",
"cs.CL",
"cs.DB"
] |
# 1 Introduction
The Robot Operating System (ROS) [15] is an increasingly popular framework for developing robotic applications. Its second major version, denoted as ROS 2, was designed to fill the needs of industrial use cases, including support for real-time execution. ROS 2 supports writing applications in differen... | The increasing popularity of the Rust programming language in building
robotic applications using the Robot Operating System (ROS 2) raises questions
about its real-time execution capabilities, particularly when employing
asynchronous programming. Existing real-time scheduling and response-time
analysis techniques for ... | [
"cs.SE"
] |
# I. INTRODUCTION
Accurate breast density classification plays a critical role in assessing breast cancer risk. High breast density has been shown to both obscure tumor detection on mammograms and correlate with an elevated risk of developing breast cancer [10]. As a result, the precise evaluation of breast density is... | Mammographic breast density classification is essential for cancer risk
assessment but remains challenging due to subjective interpretation and
inter-observer variability. This study compares multimodal and CNN-based
methods for automated classification using the BI-RADS system, evaluating
BioMedCLIP and ConvNeXt acros... | [
"eess.IV",
"cs.LG"
] |
# 1 Introduction
Large language model (LLM) distillation has become a widely used technique to reduce inference cost while retaining most teacher performance. Early knowledge distillation (KD) methods align student and teacher output logits [1, 2]. Later work shows that matching hidden features [3, 4], attention patte... | While knowledge distillation has become a mature field for compressing large
language models (LLMs) into smaller ones by aligning their outputs or internal
representations, the distillation of LLM-based agents, which involve planning,
memory, and tool use, remains relatively underexplored. Existing agent
distillation m... | [
"cs.AI"
] |
# 1 Introduction
Large Language Models (LLMs) have become an indispensable tool in the knowledge worker’s arsenal, providing a treasure trove of information at one’s fingertips. Retrieval-Augmented Generation (RAG) (Lewis et al., 2020) further extends the capabilities of these LLMs by grounding generic dialog using in... | The data landscape is rich with structured data, often of high value to
organizations, driving important applications in data analysis and machine
learning. Recent progress in representation learning and generative models for
such data has led to the development of natural language interfaces to
structured data, includ... | [
"cs.IR",
"cs.AI",
"cs.CL",
"cs.DB"
] |
# 1 Introduction
About one in nine people $( 1 0 . 9 \% )$ age 65 and older in the U.S. have Alzheimer’s Disease and Related Dementias (AD/ADRD) [106]. In 2023, 11.5M caregivers of people living with AD/ADRD provided an estimated 18.4 billion hours, or nearly 31 hours per week, of unpaid help [105]. Caregiving for AD/... | Alzheimer's Disease and Related Dementias (AD/ADRD) are progressive
neurodegenerative conditions that impair memory, thought processes, and
functioning. Family caregivers of individuals with AD/ADRD face significant
mental health challenges due to long-term caregiving responsibilities. Yet,
current support systems ofte... | [
"cs.HC",
"cs.AI"
] |
# 1. Introduction
Research into portrait generation now lets us create realistic 3D images via machine learning from photograph data-sets, with use in visual effects, games, and virtual reality. However, the problem of how to control the generation process to meet desired face attributes remains open. Such attributes ... | We consider the problem of disentangling 3D from large vision-language
models, which we show on generative 3D portraits. This allows free-form text
control of appearance attributes like age, hair style, and glasses, and 3D
geometry control of face expression and camera pose. In this setting, we assume
we use a pre-trai... | [
"cs.CV"
] |
# 1. Introduction
With the continued development of Large Language Models (LLMs) [1], [2], [3], specialized versions tailored for the code domain [4], [5], [6], have demonstrated promising capabilities in code understanding and generation. Notable applications such as Amazon CodeWhisperer and GitHub Copilot have signi... | As Large Language Models (LLMs) evolve in understanding and generating code,
accurately evaluating their reliability in analyzing source code
vulnerabilities becomes increasingly vital. While studies have examined LLM
capabilities in tasks like vulnerability detection and repair, they often
overlook the importance of b... | [
"cs.SE",
"cs.CL"
] |
# 1 Introduction
With the rapid advancement of Large Language Models (LLMs), their capabilities in assisting with various coding tasks have significantly improved. Tools like GitHub Copilot [Microsoft, 2023, Services, 2023] and models such as OpenAI Codex [Chen et al., 2021a] have enhanced developer productivity by au... | Recent text-to-SQL models have achieved strong performance, but their
effectiveness remains largely confined to SQLite due to dataset limitations.
However, real-world applications require SQL generation across multiple
dialects with varying syntax and specialized features, which remains a
challenge for current models. ... | [
"cs.CL",
"cs.AI",
"cs.DB"
] |
# 1 Introduction
Effective perception is fundamental to robotic manipulation in unstructured 3D environments. Recent advances in vision-based methods [24, 38, 27, 66] have enabled robots to infer actions directly from visual observations by leveraging powerful foundation models [32, 58, 59, 11], which facilitates the ... | Accurate action inference is critical for vision-based robotic manipulation.
Existing approaches typically follow either a Vision-to-Action (V-A) paradigm,
predicting actions directly from visual inputs, or a Vision-to-3D-to-Action
(V-3D-A) paradigm, leveraging intermediate 3D representations. However, these
methods of... | [
"cs.RO",
"cs.CV"
] |
1. Introduction . 2
1.1. The Emergence of LLM-Based Agentic AI and Multi-Agent Systems.. 2
1.2. The Criticality of Inter-Agent Communication in Complex AI Workflows .. 3
1.3. Introducing the Model Context Protocol (MCP) as an Interoperability Standard .. 3
1.4. Scope and Contributions of this Review: Bridging Design Pa... | This survey investigates how classical software design patterns can enhance
the reliability and scalability of communication in Large Language Model
(LLM)-driven agentic AI systems, focusing particularly on the Model Context
Protocol (MCP). It examines the foundational architectures of LLM-based agents
and their evolut... | [
"cs.SE"
] |
# 1 Introduction
Large Language Models (LLMs) have demonstrated a growing ability to analyze intricate social contexts and provide novel insights into human behavior and moral decision-making (Forbes et al., 2020; Hendrycks et al., 2021; Jiang et al., 2021; Vida et al., 2023). Recent work shows that, when given carefu... | Large Language Models (LLMs) have shown impressive moral reasoning abilities.
Yet they often diverge when confronted with complex, multi-factor moral
dilemmas. To address these discrepancies, we propose a framework that
synthesizes multiple LLMs' moral judgments into a collectively formulated moral
judgment, realigning... | [
"cs.CL",
"cs.AI"
] |
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