id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2502.09591 | Censor Dependent Variational Inference | cs.LG stat.ML | This paper provides a comprehensive analysis of variational inference in
latent variable models for survival analysis, emphasizing the distinctive
challenges associated with applying variational methods to survival data. We
identify a critical weakness in the existing methodology, demonstrating how a
poorly designed ... |
2502.09592 | A Data-Driven Method for Microgrid System Identification: Physically
Consistent Sparse Identification of Nonlinear Dynamics | eess.SY cs.SY | Microgrids (MGs) play a crucial role in utilizing distributed energy
resources (DERs) like solar and wind power, enhancing the sustainability and
flexibility of modern power systems. However, the inherent variability in MG
topology, power flow, and DER operating modes poses significant challenges to
the accurate syst... |
2502.09596 | KIMAs: A Configurable Knowledge Integrated Multi-Agent System | cs.AI cs.MA | Knowledge-intensive conversations supported by large language models (LLMs)
have become one of the most popular and helpful applications that can assist
people in different aspects. Many current knowledge-intensive applications are
centered on retrieval-augmented generation (RAG) techniques. While many
open-source RA... |
2502.09597 | Do LLMs Recognize Your Preferences? Evaluating Personalized Preference
Following in LLMs | cs.LG cs.CL | Large Language Models (LLMs) are increasingly used as chatbots, yet their
ability to personalize responses to user preferences remains limited. We
introduce PrefEval, a benchmark for evaluating LLMs' ability to infer, memorize
and adhere to user preferences in a long-context conversational setting.
PrefEval comprises... |
2502.09598 | GAIA: A Global, Multi-modal, Multi-scale Vision-Language Dataset for
Remote Sensing Image Analysis | cs.CV | The continuous operation of Earth-orbiting satellites generates vast and
ever-growing archives of Remote Sensing (RS) images. Natural language presents
an intuitive interface for accessing, querying, and interpreting the data from
such archives. However, existing Vision-Language Models (VLMs) are
predominantly traine... |
2502.09601 | CoT-Valve: Length-Compressible Chain-of-Thought Tuning | cs.AI cs.CL | Chain-of-Thought significantly enhances a model's reasoning capability, but
it also comes with a considerable increase in inference costs due to long
chains. With the observation that the reasoning path can be easily compressed
under easy tasks but struggle on hard tasks, we explore the feasibility of
elastically con... |
2502.09604 | SelfCite: Self-Supervised Alignment for Context Attribution in Large
Language Models | cs.CL cs.AI cs.LG | We introduce SelfCite, a novel self-supervised approach that aligns LLMs to
generate high-quality, fine-grained, sentence-level citations for the
statements in their generated responses. Instead of only relying on costly and
labor-intensive annotations, SelfCite leverages a reward signal provided by the
LLM itself th... |
2502.09606 | Human-LLM Coevolution: Evidence from Academic Writing | cs.CL cs.AI cs.CY cs.DL cs.LG | With a statistical analysis of arXiv paper abstracts, we report a marked drop
in the frequency of several words previously identified as overused by ChatGPT,
such as "delve", starting soon after they were pointed out in early 2024. The
frequency of certain other words favored by ChatGPT, such as "significant", has
in... |
2502.09608 | Instance Segmentation of Scene Sketches Using Natural Image Priors | cs.CV cs.GR | Sketch segmentation involves grouping pixels within a sketch that belong to
the same object or instance. It serves as a valuable tool for sketch editing
tasks, such as moving, scaling, or removing specific components. While image
segmentation models have demonstrated remarkable capabilities in recent years,
sketches ... |
2502.09609 | Score-of-Mixture Training: Training One-Step Generative Models Made
Simple via Score Estimation of Mixture Distributions | cs.LG cs.AI stat.ML | We propose Score-of-Mixture Training (SMT), a novel framework for training
one-step generative models by minimizing a class of divergences called the
$\alpha$-skew Jensen-Shannon divergence. At its core, SMT estimates the score
of mixture distributions between real and fake samples across multiple noise
levels. Simil... |
2502.09611 | Designing a Conditional Prior Distribution for Flow-Based Generative
Models | cs.LG cs.CV | Flow-based generative models have recently shown impressive performance for
conditional generation tasks, such as text-to-image generation. However,
current methods transform a general unimodal noise distribution to a specific
mode of the target data distribution. As such, every point in the initial
source distributi... |
2502.09613 | Latent Radiance Fields with 3D-aware 2D Representations | cs.CV | Latent 3D reconstruction has shown great promise in empowering 3D semantic
understanding and 3D generation by distilling 2D features into the 3D space.
However, existing approaches struggle with the domain gap between 2D feature
space and 3D representations, resulting in degraded rendering performance. To
address thi... |
2502.09614 | DexTrack: Towards Generalizable Neural Tracking Control for Dexterous
Manipulation from Human References | cs.RO cs.AI cs.CV cs.LG | We address the challenge of developing a generalizable neural tracking
controller for dexterous manipulation from human references. This controller
aims to manage a dexterous robot hand to manipulate diverse objects for various
purposes defined by kinematic human-object interactions. Developing such a
controller is c... |
2502.09615 | RigAnything: Template-Free Autoregressive Rigging for Diverse 3D Assets | cs.CV | We present RigAnything, a novel autoregressive transformer-based model, which
makes 3D assets rig-ready by probabilistically generating joints, skeleton
topologies, and assigning skinning weights in a template-free manner. Unlike
most existing auto-rigging methods, which rely on predefined skeleton template
and are l... |
2502.09616 | Variational Rectified Flow Matching | cs.LG cs.CV | We study Variational Rectified Flow Matching, a framework that enhances
classic rectified flow matching by modeling multi-modal velocity vector-fields.
At inference time, classic rectified flow matching 'moves' samples from a
source distribution to the target distribution by solving an ordinary
differential equation ... |
2502.09617 | LIFe-GoM: Generalizable Human Rendering with Learned Iterative Feedback
Over Multi-Resolution Gaussians-on-Mesh | cs.CV | Generalizable rendering of an animatable human avatar from sparse inputs
relies on data priors and inductive biases extracted from training on large
data to avoid scene-specific optimization and to enable fast reconstruction.
This raises two main challenges: First, unlike iterative gradient-based
adjustment in scene-... |
2502.09619 | Can this Model Also Recognize Dogs? Zero-Shot Model Search from Weights | cs.LG cs.CV | With the increasing numbers of publicly available models, there are probably
pretrained, online models for most tasks users require. However, current model
search methods are rudimentary, essentially a text-based search in the
documentation, thus users cannot find the relevant models. This paper presents
ProbeLog, a ... |
2502.09620 | Exploring the Potential of Encoder-free Architectures in 3D LMMs | cs.CV cs.AI cs.CL | Encoder-free architectures have been preliminarily explored in the 2D visual
domain, yet it remains an open question whether they can be effectively applied
to 3D understanding scenarios. In this paper, we present the first
comprehensive investigation into the potential of encoder-free architectures to
overcome the c... |
2502.09621 | MME-CoT: Benchmarking Chain-of-Thought in Large Multimodal Models for
Reasoning Quality, Robustness, and Efficiency | cs.CV cs.AI cs.CL | Answering questions with Chain-of-Thought (CoT) has significantly enhanced
the reasoning capabilities of Large Language Models (LLMs), yet its impact on
Large Multimodal Models (LMMs) still lacks a systematic assessment and in-depth
investigation. In this paper, we introduce MME-CoT, a specialized benchmark
evaluatin... |
2502.09622 | Theoretical Benefit and Limitation of Diffusion Language Model | cs.LG cs.AI cs.CL stat.ML | Diffusion language models have emerged as a promising approach for text
generation. One would naturally expect this method to be an efficient
replacement for autoregressive models since multiple tokens can be sampled in
parallel during each diffusion step. However, its efficiency-accuracy trade-off
is not yet well un... |
2502.09623 | Embed Any NeRF: Graph Meta-Networks for Neural Tasks on Arbitrary NeRF
Architectures | cs.CV | Neural Radiance Fields (NeRFs) have emerged as a groundbreaking paradigm for
representing 3D objects and scenes by encoding shape and appearance information
into the weights of a neural network. Recent works have shown how such weights
can be used as input to frameworks processing them to solve deep learning
tasks. Y... |
2502.09624 | Efficient and Trustworthy Block Propagation for Blockchain-enabled
Mobile Embodied AI Networks: A Graph Resfusion Approach | cs.AI cs.CR | By synergistically integrating mobile networks and embodied artificial
intelligence (AI), Mobile Embodied AI Networks (MEANETs) represent an advanced
paradigm that facilitates autonomous, context-aware, and interactive behaviors
within dynamic environments. Nevertheless, the rapid development of MEANETs is
accompanie... |
2502.09625 | Transformer Based Time-Series Forecasting for Stock | q-fin.CP cs.LG | To the naked eye, stock prices are considered chaotic, dynamic, and
unpredictable. Indeed, it is one of the most difficult forecasting tasks that
hundreds of millions of retail traders and professional traders around the
world try to do every second even before the market opens. With recent advances
in the developmen... |
2502.09626 | On the Bias, Fairness, and Bias Mitigation for a Wearable-based Freezing
of Gait Detection in Parkinson's Disease | eess.SP cs.LG | Freezing of gait (FOG) is a debilitating feature of Parkinson's disease (PD),
which is a cause of injurious falls among PD patients. Recent advances in
wearable-based human activity recognition (HAR) technology have enabled the
detection of FOG subtypes across benchmark datasets. Since FOG manifestation is
heterogene... |
2502.09635 | CORRECT: Context- and Reference-Augmented Reasoning and Prompting for
Fact-Checking | cs.CL cs.AI | Fact-checking the truthfulness of claims usually requires reasoning over
multiple evidence sentences. Oftentimes, evidence sentences may not be always
self-contained, and may require additional contexts and references from
elsewhere to understand coreferential expressions, acronyms, and the scope of a
reported findin... |
2502.09636 | Reading between the Lines: Can LLMs Identify Cross-Cultural
Communication Gaps? | cs.CL cs.AI | In a rapidly globalizing and digital world, content such as book and product
reviews created by people from diverse cultures are read and consumed by others
from different corners of the world. In this paper, we investigate the extent
and patterns of gaps in understandability of book reviews due to the presence
of cu... |
2502.09637 | Meta-Cultural Competence: Climbing the Right Hill of Cultural Awareness | cs.CY cs.AI cs.CL | Numerous recent studies have shown that Large Language Models (LLMs) are
biased towards a Western and Anglo-centric worldview, which compromises their
usefulness in non-Western cultural settings. However, "culture" is a complex,
multifaceted topic, and its awareness, representation, and modeling in LLMs and
LLM-based... |
2502.09638 | Jailbreaking to Jailbreak | cs.CL cs.AI | Refusal training on Large Language Models (LLMs) prevents harmful outputs,
yet this defense remains vulnerable to both automated and human-crafted
jailbreaks. We present a novel LLM-as-red-teamer approach in which a human
jailbreaks a refusal-trained LLM to make it willing to jailbreak itself or
other LLMs. We refer ... |
2502.09640 | Online Social Support Detection in Spanish Social Media Texts | cs.CL cs.AI | The advent of social media has transformed communication, enabling
individuals to share their experiences, seek support, and participate in
diverse discussions. While extensive research has focused on identifying
harmful content like hate speech, the recognition and promotion of positive and
supportive interactions r... |
2502.09642 | Krutrim LLM: Multilingual Foundational Model for over a Billion People | cs.CL cs.AI | India is a diverse society with unique challenges in developing AI systems,
including linguistic diversity, oral traditions, data accessibility, and
scalability. Existing foundation models are primarily trained on English,
limiting their effectiveness for India's population. Indic languages comprise
only 1 percent of... |
2502.09644 | From Argumentation to Deliberation: Perspectivized Stance Vectors for
Fine-grained (Dis)agreement Analysis | cs.CL cs.AI cs.CY | Debating over conflicting issues is a necessary first step towards resolving
conflicts. However, intrinsic perspectives of an arguer are difficult to
overcome by persuasive argumentation skills. Proceeding from a debate to a
deliberative process, where we can identify actionable options for resolving a
conflict requi... |
2502.09645 | From No to Know: Taxonomy, Challenges, and Opportunities for Negation
Understanding in Multimodal Foundation Models | cs.CL cs.AI | Negation, a linguistic construct conveying absence, denial, or contradiction,
poses significant challenges for multilingual multimodal foundation models.
These models excel in tasks like machine translation, text-guided generation,
image captioning, audio interactions, and video processing but often struggle
to accur... |
2502.09646 | Language Shift or Maintenance? An Intergenerational Study of the Tibetan
Community in Saudi Arabia | cs.CL cs.CY | The present study provides the first-ever report on the language shift from
Tibetan to Arabic among descendants of Tibetan families who migrated from the
Tibet region to Saudi Arabia around 70 years ago. The aim of this study was to
determine whether three age groups had adopted different practices in terms of
mainta... |
2502.09647 | Unveiling Simplicities of Attention: Adaptive Long-Context Head
Identification | cs.CL cs.LG | The ability to process long contexts is crucial for many natural language
processing tasks, yet it remains a significant challenge. While substantial
progress has been made in enhancing the efficiency of attention mechanisms,
there is still a gap in understanding how attention heads function in
long-context settings.... |
2502.09648 | UKTA: Unified Korean Text Analyzer | cs.CL cs.AI | Evaluating writing quality is complex and time-consuming often delaying
feedback to learners. While automated writing evaluation tools are effective
for English, Korean automated writing evaluation tools face challenges due to
their inability to address multi-view analysis, error propagation, and
evaluation explainab... |
2502.09649 | Imit Diff: Semantics Guided Diffusion Transformer with Dual Resolution
Fusion for Imitation Learning | cs.AI cs.CV cs.LG cs.RO | Visuomotor imitation learning enables embodied agents to effectively acquire
manipulation skills from video demonstrations and robot proprioception.
However, as scene complexity and visual distractions increase, existing methods
that perform well in simple scenes tend to degrade in performance. To address
this challe... |
2502.09650 | Principled Data Selection for Alignment: The Hidden Risks of Difficult
Examples | cs.CL cs.AI cs.LG | The alignment of large language models (LLMs) often assumes that using more
clean data yields better outcomes, overlooking the match between model capacity
and example difficulty. Challenging this, we propose a new principle:
Preference data vary in difficulty, and overly difficult examples hinder
alignment, by excee... |
2502.09651 | AI-VERDE: A Gateway for Egalitarian Access to Large Language Model-Based
Resources For Educational Institutions | cs.CL cs.CY | We present AI-VERDE, a unified LLM-as-a-platform service designed to
facilitate seamless integration of commercial, cloud-hosted, and on-premise
open LLMs in academic settings. AI-VERDE streamlines access management for
instructional and research groups by providing features such as robust access
control, privacy-pre... |
2502.09652 | GraphCompNet: A Position-Aware Model for Predicting and Compensating
Shape Deviations in 3D Printing | cs.CV cs.LG | This paper introduces a data-driven algorithm for modeling and compensating
shape deviations in additive manufacturing (AM), addressing challenges in
geometric accuracy and batch production. While traditional methods, such as
analytical models and metrology, laid the groundwork for geometric precision,
they are often... |
2502.09653 | SASVi -- Segment Any Surgical Video | eess.IV cs.CV | Purpose: Foundation models, trained on multitudes of public datasets, often
require additional fine-tuning or re-prompting mechanisms to be applied to
visually distinct target domains such as surgical videos. Further, without
domain knowledge, they cannot model the specific semantics of the target
domain. Hence, when... |
2502.09654 | Heterogeneous Mixture of Experts for Remote Sensing Image
Super-Resolution | eess.IV cs.CV | Remote sensing image super-resolution (SR) aims to reconstruct
high-resolution remote sensing images from low-resolution inputs, thereby
addressing limitations imposed by sensors and imaging conditions. However, the
inherent characteristics of remote sensing images, including diverse ground
object types and complex d... |
2502.09655 | Bidirectional Diffusion Bridge Models | cs.CV cs.AI | Diffusion bridges have shown potential in paired image-to-image (I2I)
translation tasks. However, existing methods are limited by their
unidirectional nature, requiring separate models for forward and reverse
translations. This not only doubles the computational cost but also restricts
their practicality. In this wor... |
2502.09656 | Multi-Omics Fusion with Soft Labeling for Enhanced Prediction of Distant
Metastasis in Nasopharyngeal Carcinoma Patients after Radiotherapy | q-bio.QM cs.CV eess.IV | Omics fusion has emerged as a crucial preprocessing approach in the field of
medical image processing, providing significant assistance to several studies.
One of the challenges encountered in the integration of omics data is the
presence of unpredictability arising from disparities in data sources and
medical imagin... |
2502.09657 | Integrating Spatiotemporal Vision Transformer into Digital Twins for
High-Resolution Heat Stress Forecasting in Campus Environments | cs.CV | Extreme heat events exacerbated by climate change pose significant challenges
to urban resilience and planning. This study introduces a climate-responsive
digital twin framework integrating the Spatiotemporal Vision Transformer
(ST-ViT) model to enhance heat stress forecasting and decision-making. Using a
Texas campu... |
2502.09658 | Neuro-Conceptual Artificial Intelligence: Integrating OPM with Deep
Learning to Enhance Question Answering Quality | cs.CL cs.AI | Knowledge representation and reasoning are critical challenges in Artificial
Intelligence (AI), particularly in integrating neural and symbolic approaches
to achieve explainable and transparent AI systems. Traditional knowledge
representation methods often fall short of capturing complex processes and
state changes. ... |
2502.09659 | Cancer Vaccine Adjuvant Name Recognition from Biomedical Literature
using Large Language Models | cs.CL cs.AI cs.CY | Motivation: An adjuvant is a chemical incorporated into vaccines that
enhances their efficacy by improving the immune response. Identifying adjuvant
names from cancer vaccine studies is essential for furthering research and
enhancing immunotherapies. However, the manual curation from the constantly
expanding biomedic... |
2502.09660 | Towards Fine-grained Interactive Segmentation in Images and Videos | cs.CV eess.IV | The recent Segment Anything Models (SAMs) have emerged as foundational visual
models for general interactive segmentation. Despite demonstrating robust
generalization abilities, they still suffer performance degradations in
scenarios demanding accurate masks. Existing methods for high-precision
interactive segmentati... |
2502.09662 | Generalizable Cervical Cancer Screening via Large-scale Pretraining and
Test-Time Adaptation | q-bio.QM cs.CV eess.IV | Cervical cancer is a leading malignancy in female reproductive system. While
AI-assisted cytology offers a cost-effective and non-invasive screening
solution, current systems struggle with generalizability in complex clinical
scenarios. To address this issue, we introduced Smart-CCS, a generalizable
Cervical Cancer S... |
2502.09663 | DiffEx: Explaining a Classifier with Diffusion Models to Identify
Microscopic Cellular Variations | cs.CV cs.AI cs.LG q-bio.CB | In recent years, deep learning models have been extensively applied to
biological data across various modalities. Discriminative deep learning models
have excelled at classifying images into categories (e.g., healthy versus
diseased, treated versus untreated). However, these models are often perceived
as black boxes ... |
2502.09664 | Image Super-Resolution with Guarantees via Conformal Generative Models | cs.CV cs.LG stat.ML | The increasing use of generative ML foundation models for image
super-resolution calls for robust and interpretable uncertainty quantification
methods. We address this need by presenting a novel approach based on conformal
prediction techniques to create a "confidence mask" capable of reliably and
intuitively communi... |
2502.09665 | Revealing Subtle Phenotypes in Small Microscopy Datasets Using Latent
Diffusion Models | cs.CV | Identifying subtle phenotypic variations in cellular images is critical for
advancing biological research and accelerating drug discovery. These variations
are often masked by the inherent cellular heterogeneity, making it challenging
to distinguish differences between experimental conditions. Recent advancements
in ... |
2502.09667 | k-LLMmeans: Summaries as Centroids for Interpretable and Scalable
LLM-Based Text Clustering | cs.CL cs.LG stat.ML | We introduce k-LLMmeans, a novel modification of the k-means clustering
algorithm that utilizes LLMs to generate textual summaries as cluster
centroids, thereby capturing contextual and semantic nuances often lost when
relying on purely numerical means of document embeddings. This modification
preserves the propertie... |
2502.09669 | Meta-INR: Efficient Encoding of Volumetric Data via Meta-Learning
Implicit Neural Representation | cs.CV cs.AI cs.GR | Implicit neural representation (INR) has emerged as a promising solution for
encoding volumetric data, offering continuous representations and seamless
compatibility with the volume rendering pipeline. However, optimizing an INR
network from randomly initialized parameters for each new volume is
computationally ineff... |
2502.09670 | The Science of Evaluating Foundation Models | cs.CL cs.AI | The emergent phenomena of large foundation models have revolutionized natural
language processing. However, evaluating these models presents significant
challenges due to their size, capabilities, and deployment across diverse
applications. Existing literature often focuses on individual aspects, such as
benchmark pe... |
2502.09672 | IMM-MOT: A Novel 3D Multi-object Tracking Framework with Interacting
Multiple Model Filter | cs.CV cs.RO | 3D Multi-Object Tracking (MOT) provides the trajectories of surrounding
objects, assisting robots or vehicles in smarter path planning and obstacle
avoidance. Existing 3D MOT methods based on the Tracking-by-Detection framework
typically use a single motion model to track an object throughout its entire
tracking proc... |
2502.09673 | Are Smarter LLMs Safer? Exploring Safety-Reasoning Trade-offs in
Prompting and Fine-Tuning | cs.CL cs.AI | Large Language Models (LLMs) have demonstrated remarkable success across
various NLP benchmarks. However, excelling in complex tasks that require
nuanced reasoning and precise decision-making demands more than raw language
proficiency--LLMs must reason, i.e., think logically, draw from past
experiences, and synthesiz... |
2502.09674 | The Hidden Dimensions of LLM Alignment: A Multi-Dimensional Safety
Analysis | cs.CL cs.AI | Large Language Models' safety-aligned behaviors, such as refusing harmful
queries, can be represented by linear directions in activation space. Previous
research modeled safety behavior with a single direction, limiting mechanistic
understanding to an isolated safety feature. In this work, we discover that
safety-ali... |
2502.09675 | Multi-level Conflict-Aware Network for Multi-modal Sentiment Analysis | cs.CL cs.AI cs.LG | Multimodal Sentiment Analysis (MSA) aims to recognize human emotions by
exploiting textual, acoustic, and visual modalities, and thus how to make full
use of the interactions between different modalities is a central challenge of
MSA. Interaction contains alignment and conflict aspects. Current works mainly
emphasize... |
2502.09680 | Object-Centric Latent Action Learning | cs.CV cs.AI | Leveraging vast amounts of internet video data for Embodied AI is currently
bottle-necked by the lack of action annotations and the presence of
action-correlated distractors. We propose a novel object-centric latent action
learning approach, based on VideoSaur and LAPO, that employs self-supervised
decomposition of s... |
2502.09682 | Lifespan tree of brain anatomy: diagnostic values for motor and
cognitive neurodegenerative diseases | eess.IV cs.LG | The differential diagnosis of neurodegenerative diseases, characterized by
overlapping symptoms, may be challenging. Brain imaging coupled with artificial
intelligence has been previously proposed for diagnostic support, but most of
these methods have been trained to discriminate only isolated diseases from
controls.... |
2502.09683 | Channel Dependence, Limited Lookback Windows, and the Simplicity of
Datasets: How Biased is Time Series Forecasting? | cs.LG | Time-series forecasting research has converged to a small set of datasets and
a standardized collection of evaluation scenarios. Such a standardization is to
a specific extent needed for comparable research. However, the underlying
assumption is, that the considered setting is a representative for the problem
as a wh... |
2502.09685 | A Novel Hybrid Approach to Contraceptive Demand Forecasting: Integrating
Point Predictions with Probabilistic Distributions | cs.LG stat.AP stat.ME | Accurate demand forecasting is vital for ensuring reliable access to
contraceptive products, supporting key processes like procurement, inventory,
and distribution. However, forecasting contraceptive demand in developing
countries presents challenges, including incomplete data, poor data quality,
and the need to acco... |
2502.09686 | Leveraging Machine Learning and Deep Learning Techniques for Improved
Pathological Staging of Prostate Cancer | cs.LG | Prostate cancer (Pca) continues to be a leading cause of cancer-related
mortality in men, and the limitations in precision of traditional diagnostic
methods such as the Digital Rectal Exam (DRE), Prostate-Specific Antigen (PSA)
testing, and biopsies underscore the critical importance of accurate staging
detection in ... |
2502.09687 | Mind What You Ask For: Emotional and Rational Faces of Persuasion by
Large Language Models | cs.CL cs.AI cs.HC | Be careful what you ask for, you just might get it. This saying fits with the
way large language models (LLMs) are trained, which, instead of being rewarded
for correctness, are increasingly rewarded for pleasing the recipient. So, they
are increasingly effective at persuading us that their answers are valuable.
But ... |
2502.09688 | Towards Virtual Clinical Trials of Radiology AI with Conditional
Generative Modeling | cs.CV cs.AI cs.LG | Artificial intelligence (AI) is poised to transform healthcare by enabling
personalized and efficient care through data-driven insights. Although
radiology is at the forefront of AI adoption, in practice, the potential of AI
models is often overshadowed by severe failures to generalize: AI models can
have performance... |
2502.09689 | Large Language Models and Provenance Metadata for Determining the
Relevance of Images and Videos in News Stories | cs.CL cs.CV cs.CY | The most effective misinformation campaigns are multimodal, often combining
text with images and videos taken out of context -- or fabricating them
entirely -- to support a given narrative. Contemporary methods for detecting
misinformation, whether in deepfakes or text articles, often miss the interplay
between multi... |
2502.09690 | Trust at Your Own Peril: A Mixed Methods Exploration of the Ability of
Large Language Models to Generate Expert-Like Systems Engineering Artifacts
and a Characterization of Failure Modes | cs.CL cs.AI | Multi-purpose Large Language Models (LLMs), a subset of generative Artificial
Intelligence (AI), have recently made significant progress. While expectations
for LLMs to assist systems engineering (SE) tasks are paramount; the
interdisciplinary and complex nature of systems, along with the need to
synthesize deep-doma... |
2502.09692 | NeuralCFD: Deep Learning on High-Fidelity Automotive Aerodynamics
Simulations | cs.LG cs.AI | Recent advancements in neural operator learning are paving the way for
transformative innovations in fields such as automotive aerodynamics. However,
key challenges must be overcome before neural network-based simulation
surrogates can be implemented at an industry scale. First, surrogates must
become scalable to lar... |
2502.09695 | Power System Electromagnetic Transient Stability: an Analysis Based on
Convergent Hamiltonian | eess.SY cs.SY | Transient stability is crucial to the reliable operation of power systems.
Existing theories rely on the simplified electromechanical models, substituting
the detailed electromagnetic dynamics of inductor and capacitor with their
impedance representations. However, this simplification is inadequate for the
growing pe... |
2502.09696 | ZeroBench: An Impossible Visual Benchmark for Contemporary Large
Multimodal Models | cs.CV | Large Multimodal Models (LMMs) exhibit major shortfalls when interpreting
images and, by some measures, have poorer spatial cognition than small children
or animals. Despite this, they attain high scores on many popular visual
benchmarks, with headroom rapidly eroded by an ongoing surge of model progress.
To address ... |
2502.09704 | Iterative quantum optimisation with a warm-started quantum state | quant-ph cond-mat.dis-nn cs.LG math.OC physics.comp-ph | We provide a method to prepare a warm-started quantum state from measurements
with an iterative framework to enhance the quantum approximate optimisation
algorithm (QAOA). The numerical simulations show the method can effectively
address the "stuck issue" of the standard QAOA using a single-string
warm-started initia... |
2502.09715 | Evaluating GPT's Capability in Identifying Stages of Cognitive
Impairment from Electronic Health Data | cs.LG cs.AI cs.CL | Identifying cognitive impairment within electronic health records (EHRs) is
crucial not only for timely diagnoses but also for facilitating research.
Information about cognitive impairment often exists within unstructured
clinician notes in EHRs, but manual chart reviews are both time-consuming and
error-prone. To ad... |
2502.09717 | Carbon- and Precedence-Aware Scheduling for Data Processing Clusters | cs.DC cs.CY cs.SY eess.SY | As large-scale data processing workloads continue to grow, their carbon
footprint raises concerns. Prior research on carbon-aware schedulers has
focused on shifting computation to align with availability of low-carbon
energy, but these approaches assume that each task can be executed
independently. In contrast, data ... |
2502.09720 | NestQuant: Nested Lattice Quantization for Matrix Products and LLMs | cs.LG cs.AI cs.IT math.IT | Post-training quantization (PTQ) has emerged as a critical technique for
efficient deployment of large language models (LLMs). This work proposes
NestQuant, a novel PTQ scheme for weights and activations that is based on
self-similar nested lattices. Recent work have mathematically shown such
quantizers to be informa... |
2502.09723 | Making Them a Malicious Database: Exploiting Query Code to Jailbreak
Aligned Large Language Models | cs.CR cs.AI cs.CL | Recent advances in large language models (LLMs) have demonstrated remarkable
potential in the field of natural language processing. Unfortunately, LLMs face
significant security and ethical risks. Although techniques such as safety
alignment are developed for defense, prior researches reveal the possibility of
bypass... |
2502.09724 | Navigating the Social Welfare Frontier: Portfolios for Multi-objective
Reinforcement Learning | cs.LG | In many real-world applications of reinforcement learning (RL), deployed
policies have varied impacts on different stakeholders, creating challenges in
reaching consensus on how to effectively aggregate their preferences.
Generalized $p$-means form a widely used class of social welfare functions for
this purpose, wit... |
2502.09728 | Perch like a bird: bio-inspired optimal maneuvers and nonlinear control
for Flapping-Wing Unmanned Aerial Vehicles | eess.SY cs.RO cs.SY math.OC | This research endeavors to design the perching maneuver and control in
ornithopter robots. By analyzing the dynamic interplay between the robot's
flight dynamics, feedback loops, and the environmental constraints, we aim to
advance our understanding of the perching maneuver, drawing parallels to
biological systems. I... |
2502.09731 | A CNN Approach to Automated Detection and Classification of Brain Tumors | cs.CV cs.AI | Brain tumors require an assessment to ensure timely diagnosis and effective
patient treatment. Morphological factors such as size, location, texture, and
variable appearance complicate tumor inspection. Medical imaging presents
challenges, including noise and incomplete images. This research article
presents a method... |
2502.09741 | FoNE: Precise Single-Token Number Embeddings via Fourier Features | cs.CL cs.LG | Large Language Models (LLMs) typically represent numbers using multiple
tokens, which requires the model to aggregate these tokens to interpret
numerical values. This fragmentation makes both training and inference less
efficient and adversely affects the model's performance on number-related
tasks. Inspired by the o... |
2502.09743 | Partial Colexifications Improve Concept Embeddings | cs.CL | While the embedding of words has revolutionized the field of Natural Language
Processing, the embedding of concepts has received much less attention so far.
A dense and meaningful representation of concepts, however, could prove useful
for several tasks in computational linguistics, especially those involving
cross-l... |
2502.09744 | Fine-Tuning Foundation Models with Federated Learning for Privacy
Preserving Medical Time Series Forecasting | cs.LG cs.CR | Federated Learning (FL) provides a decentralized machine learning approach,
where multiple devices or servers collaboratively train a model without sharing
their raw data, thus enabling data privacy. This approach has gained
significant interest in academia and industry due to its privacy-preserving
properties, which... |
2502.09747 | The Widespread Adoption of Large Language Model-Assisted Writing Across
Society | cs.CL | The recent advances in large language models (LLMs) attracted significant
public and policymaker interest in its adoption patterns. In this paper, we
systematically analyze LLM-assisted writing across four domains-consumer
complaints, corporate communications, job postings, and international
organization press releas... |
2502.09748 | Contracting Strategies for Electrolyzers to Secure Grid Connection: The
Dutch Case | math.OC cs.SY eess.SY | In response to increasing grid congestion in the Netherlands, non-firm
connection and transport agreements (CTAs) and capacity restriction contracts
(CRCs) have been introduced, allowing consumer curtailment in exchange for grid
tariff discounts or per-MW compensations. This study examines the interaction
between an ... |
2502.09749 | Vote-Tree-Planner: Optimizing Execution Order in LLM-based Task Planning
Pipeline via Voting | cs.RO cs.AI | Integrating large language models (LLMs) into closed-loop robotic task
planning has become increasingly popular within embodied artificial
intelligence. Previous efforts mainly focused on leveraging the strong
reasoning abilities of LLMs to enhance task planning performance while often
overlooking task planning effic... |
2502.09755 | Enhancing Jailbreak Attacks via Compliance-Refusal-Based Initialization | cs.CR cs.LG | Jailbreak attacks aim to exploit large language models (LLMs) and pose a
significant threat to their proper conduct; they seek to bypass models'
safeguards and often provoke transgressive behaviors. However, existing
automatic jailbreak attacks require extensive computational resources and are
prone to converge on su... |
2502.09757 | The AI-Therapist Duo: Exploring the Potential of Human-AI Collaboration
in Personalized Art Therapy for PICS Intervention | cs.HC cs.AI | Post-intensive care syndrome (PICS) is a multifaceted condition that arises
from prolonged stays in an intensive care unit (ICU). While preventing PICS
among ICU patients is becoming increasingly important, interventions remain
limited. Building on evidence supporting the effectiveness of art exposure in
addressing t... |
2502.09762 | Adaptive Teaming in Multi-Drone Pursuit: Simulation, Training, and
Deployment | cs.RO cs.AI | Adaptive teaming, the ability to collaborate with unseen teammates without
prior coordination, remains an underexplored challenge in multi-robot
collaboration. This paper focuses on adaptive teaming in multi-drone
cooperative pursuit, a critical task with real-world applications such as
border surveillance, search-an... |
2502.09765 | Differential Adjusted Parity for Learning Fair Representations | cs.LG cs.AI | The development of fair and unbiased machine learning models remains an
ongoing objective for researchers in the field of artificial intelligence. We
introduce the Differential Adjusted Parity (DAP) loss to produce unbiased
informative representations. It utilises a differentiable variant of the
adjusted parity metri... |
2502.09767 | Non-Markovian Discrete Diffusion with Causal Language Models | cs.LG cs.AI cs.CL | Discrete diffusion models have emerged as a flexible and controllable
paradigm for structured sequence modeling, yet they still lag behind causal
language models in expressiveness. To bridge the gap between two paradigms, we
introduce CaDDi, a causal discrete diffusion model that unifies sequential and
temporal model... |
2502.09768 | Complex Network Modelling with Power-law Activating Patterns and Its
Evolutionary Dynamics | cs.SI physics.soc-ph | Complex network theory provides a unifying framework for the study of
structured dynamic systems. The current literature emphasizes a widely reported
phenomenon of intermittent interaction among network vertices. In this paper,
we introduce a complex network model that considers the stochastic switching of
individual... |
2502.09775 | CellFlow: Simulating Cellular Morphology Changes via Flow Matching | q-bio.QM cs.CV cs.LG q-bio.BM q-bio.CB | Building a virtual cell capable of accurately simulating cellular behaviors
in silico has long been a dream in computational biology. We introduce
CellFlow, an image-generative model that simulates cellular morphology changes
induced by chemical and genetic perturbations using flow matching. Unlike prior
methods, Cel... |
2502.09777 | On the existence of EFX allocations in multigraphs | cs.GT cs.AI | We study the problem of "fairly" dividing indivisible goods to several agents
that have valuation set functions over the sets of goods. As fair we consider
the allocations that are envy-free up to any good (EFX), i.e., no agent envies
any proper subset of the goods given to any other agent. The existence or not
of EF... |
2502.09778 | Prompt and circumstance: A word-by-word LLM prompting approach to
interlinear glossing for low-resource languages | cs.CL | Partly automated creation of interlinear glossed text (IGT) has the potential
to assist in linguistic documentation. We argue that LLMs can make this process
more accessible to linguists because of their capacity to follow
natural-language instructions. We investigate the effectiveness of a
retrieval-based LLM prompt... |
2502.09779 | Automated Muscle and Fat Segmentation in Computed Tomography for
Comprehensive Body Composition Analysis | eess.IV cs.CV | Body composition assessment using CT images can potentially be used for a
number of clinical applications, including the prognostication of
cardiovascular outcomes, evaluation of metabolic health, monitoring of disease
progression, assessment of nutritional status, prediction of treatment response
in oncology, and ri... |
2502.09780 | Incentivize without Bonus: Provably Efficient Model-based Online
Multi-agent RL for Markov Games | cs.LG cs.AI cs.GT math.OC | Multi-agent reinforcement learning (MARL) lies at the heart of a plethora of
applications involving the interaction of a group of agents in a shared unknown
environment. A prominent framework for studying MARL is Markov games, with the
goal of finding various notions of equilibria in a sample-efficient manner,
such a... |
2502.09781 | Medical Applications of Graph Convolutional Networks Using Electronic
Health Records: A Survey | cs.LG | Graph Convolutional Networks (GCNs) have emerged as a promising approach to
machine learning on Electronic Health Records (EHRs). By constructing a graph
representation of patient data and performing convolutions on neighborhoods of
nodes, GCNs can capture complex relationships and extract meaningful insights
to supp... |
2502.09782 | Improving Acoustic Side-Channel Attacks on Keyboards Using Transformers
and Large Language Models | cs.LG cs.AI cs.CL eess.AS | The increasing prevalence of microphones in everyday devices and the growing
reliance on online services have amplified the risk of acoustic side-channel
attacks (ASCAs) targeting keyboards. This study explores deep learning
techniques, specifically vision transformers (VTs) and large language models
(LLMs), to enhan... |
2502.09787 | TableTalk: Scaffolding Spreadsheet Development with a Language Agent | cs.SE cs.AI cs.HC | Despite its ubiquity in the workforce, spreadsheet programming remains
challenging as programmers need both spreadsheet-specific knowledge (e.g., APIs
to write formulas) and problem-solving skills to create complex spreadsheets.
Large language models (LLMs) can help automate aspects of this process, and
recent advanc... |
2502.09790 | ExoMiner++ on TESS with Transfer Learning from Kepler: Transit
Classification and Vetting Catalog for 2-min Data | astro-ph.EP astro-ph.IM cs.LG | We present ExoMiner++, an enhanced deep learning model that builds on the
success of ExoMiner to improve transit signal classification in 2-minute TESS
data. ExoMiner++ incorporates additional diagnostic inputs, including
periodogram, flux trend, difference image, unfolded flux, and spacecraft
attitude control data, ... |
2502.09791 | Atom identification in bilayer moire materials with Gomb-Net | cond-mat.mtrl-sci cs.CV | Moire patterns in van der Waals bilayer materials complicate the analysis of
atomic-resolution images, hindering the atomic-scale insight typically
attainable with scanning transmission electron microscopy. Here, we report a
method to detect the positions and identity of atoms in each of the individual
layers that co... |
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