id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2502.14178 | NeRF-3DTalker: Neural Radiance Field with 3D Prior Aided Audio
Disentanglement for Talking Head Synthesis | cs.GR cs.CV cs.MM cs.SD eess.AS | Talking head synthesis is to synthesize a lip-synchronized talking head video
using audio. Recently, the capability of NeRF to enhance the realism and
texture details of synthesized talking heads has attracted the attention of
researchers. However, most current NeRF methods based on audio are exclusively
concerned wi... |
2502.14180 | On the logical skills of large language models: evaluations using
arbitrarily complex first-order logic problems | cs.LG cs.CL | We present a method of generating first-order logic statements whose
complexity can be controlled along multiple dimensions. We use this method to
automatically create several datasets consisting of questions asking for the
truth or falsity of first-order logic statements in Zermelo-Fraenkel set
theory. While the res... |
2502.14182 | Multi-Faceted Studies on Data Poisoning can Advance LLM Development | cs.CR cs.LG | The lifecycle of large language models (LLMs) is far more complex than that
of traditional machine learning models, involving multiple training stages,
diverse data sources, and varied inference methods. While prior research on
data poisoning attacks has primarily focused on the safety vulnerabilities of
LLMs, these ... |
2502.14183 | Type 1 Diabetes Management using GLIMMER: Glucose Level Indicator Model
with Modified Error Rate | cs.LG cs.AI | Managing Type 1 Diabetes (T1D) demands constant vigilance as individuals
strive to regulate their blood glucose levels to avert the dangers of
dysglycemia (hyperglycemia or hypoglycemia). Despite the advent of
sophisticated technologies such as automated insulin delivery (AID) systems,
achieving optimal glycemic cont... |
2502.14184 | Bayesian SegNet for Semantic Segmentation with Improved Interpretation
of Microstructural Evolution During Irradiation of Materials | cs.CV cs.LG | Understanding the relationship between the evolution of microstructures of
irradiated LiAlO2 pellets and tritium diffusion, retention and release could
improve predictions of tritium-producing burnable absorber rod performance.
Given expert-labeled segmented images of irradiated and unirradiated pellets,
we trained D... |
2502.14185 | REFLEX Dataset: A Multimodal Dataset of Human Reactions to Robot
Failures and Explanations | cs.RO | This work presents REFLEX: Robotic Explanations to FaiLures and Human
EXpressions, a comprehensive multimodal dataset capturing human reactions to
robot failures and subsequent explanations in collaborative settings. It aims
to facilitate research into human-robot interaction dynamics, addressing the
need to study re... |
2502.14187 | Federated Fine-Tuning of Large Language Models: Kahneman-Tversky vs.
Direct Preference Optimization | cs.LG cs.CL | We evaluate Kahneman-Tversky Optimization (KTO) as a fine-tuning method for
large language models (LLMs) in federated learning (FL) settings, comparing it
against Direct Preference Optimization (DPO). Using Alpaca-7B as the base
model, we fine-tune on a realistic dataset under both methods and evaluate
performance us... |
2502.14189 | QUAD-LLM-MLTC: Large Language Models Ensemble Learning for Healthcare
Text Multi-Label Classification | cs.CL | The escalating volume of collected healthcare textual data presents a unique
challenge for automated Multi-Label Text Classification (MLTC), which is
primarily due to the scarcity of annotated texts for training and their nuanced
nature. Traditional machine learning models often fail to fully capture the
array of exp... |
2502.14190 | Stereo Image Coding for Machines with Joint Visual Feature Compression | cs.CV eess.IV | 2D image coding for machines (ICM) has achieved great success in coding
efficiency, while less effort has been devoted to stereo image fields. To
promote the efficiency of stereo image compression (SIC) and intelligent
analysis, the stereo image coding for machines (SICM) is formulated and
explored in this paper. Mor... |
2502.14191 | Multimodal RewardBench: Holistic Evaluation of Reward Models for Vision
Language Models | cs.CV cs.AI | Reward models play an essential role in training vision-language models
(VLMs) by assessing output quality to enable aligning with human preferences.
Despite their importance, the research community lacks comprehensive open
benchmarks for evaluating multimodal reward models in VLMs. To address this
gap, we introduce ... |
2502.14192 | NLP-AKG: Few-Shot Construction of NLP Academic Knowledge Graph Based on
LLM | cs.CL cs.DL | Large language models (LLMs) have been widely applied in question answering
over scientific research papers. To enhance the professionalism and accuracy of
responses, many studies employ external knowledge augmentation. However,
existing structures of external knowledge in scientific literature often focus
solely on ... |
2502.14195 | Bridging Text and Vision: A Multi-View Text-Vision Registration Approach
for Cross-Modal Place Recognition | cs.CV | Mobile robots necessitate advanced natural language understanding
capabilities to accurately identify locations and perform tasks such as package
delivery. However, traditional visual place recognition (VPR) methods rely
solely on single-view visual information and cannot interpret human language
descriptions. To ove... |
2502.14197 | Adaptive Sparsified Graph Learning Framework for Vessel Behavior
Anomalies | cs.LG cs.AI | Graph neural networks have emerged as a powerful tool for learning
spatiotemporal interactions. However, conventional approaches often rely on
predefined graphs, which may obscure the precise relationships being modeled.
Additionally, existing methods typically define nodes based on fixed spatial
locations, a strateg... |
2502.14198 | Antenna Position and Beamforming Optimization for Movable Antenna
Enabled ISAC: Optimal Solutions and Efficient Algorithms | cs.IT eess.SP math.IT | In this paper, we propose an integrated sensing and communication (ISAC)
system enabled by movable antennas (MAs), which can dynamically adjust antenna
positions to enhance both sensing and communication performance for future
wireless networks. To characterize the benefits of MA-enabled ISAC systems, we
first derive... |
2502.14200 | Causal Mean Field Multi-Agent Reinforcement Learning | cs.AI cs.MA | Scalability remains a challenge in multi-agent reinforcement learning and is
currently under active research. A framework named mean-field reinforcement
learning (MFRL) could alleviate the scalability problem by employing the Mean
Field Theory to turn a many-agent problem into a two-agent problem. However,
this frame... |
2502.14202 | Do LLMs Consider Security? An Empirical Study on Responses to
Programming Questions | cs.SE cs.AI cs.CL cs.LG | The widespread adoption of conversational LLMs for software development has
raised new security concerns regarding the safety of LLM-generated content. Our
motivational study outlines ChatGPT's potential in volunteering
context-specific information to the developers, promoting safe coding
practices. Motivated by this... |
2502.14204 | On-the-fly Preference Alignment via Principle-Guided Decoding | cs.CL cs.AI | With the rapidly expanding landscape of large language models, aligning model
generations with human values and preferences is becoming increasingly
important. Popular alignment methods, such as Reinforcement Learning from Human
Feedback, have shown significant success in guiding models with greater
control. However,... |
2502.14205 | Accurate Forgetting for Heterogeneous Federated Continual Learning | cs.LG cs.AI | Recent years have witnessed a burgeoning interest in federated learning (FL).
However, the contexts in which clients engage in sequential learning remain
under-explored. Bridging FL and continual learning (CL) gives rise to a
challenging practical problem: federated continual learning (FCL). Existing
research in FCL ... |
2502.14208 | A Non-Asymptotic Theory of Seminorm Lyapunov Stability: From
Deterministic to Stochastic Iterative Algorithms | cs.LG math.OC stat.ML | We study the problem of solving fixed-point equations for
seminorm-contractive operators and establish foundational results on the
non-asymptotic behavior of iterative algorithms in both deterministic and
stochastic settings. Specifically, in the deterministic setting, we prove a
fixed-point theorem for seminorm-cont... |
2502.14209 | Spatial and Frequency Domain Adaptive Fusion Network for Image
Deblurring | cs.CV | Image deblurring aims to reconstruct a latent sharp image from its
corresponding blurred one. Although existing methods have achieved good
performance, most of them operate exclusively in either the spatial domain or
the frequency domain, rarely exploring solutions that fuse both domains. In
this paper, we propose a ... |
2502.14210 | Sample Complexity of Linear Quadratic Regulator Without Initial
Stability | math.OC cs.LG cs.SY eess.SY | Inspired by REINFORCE, we introduce a novel receding-horizon algorithm for
the Linear Quadratic Regulator (LQR) problem with unknown parameters. Unlike
prior methods, our algorithm avoids reliance on two-point gradient estimates
while maintaining the same order of sample complexity. Furthermore, it
eliminates the res... |
2502.14211 | Transfer-Prompting: Enhancing Cross-Task Adaptation in Large Language
Models via Dual-Stage Prompts Optimization | cs.CL | Large language models (LLMs) face significant challenges when balancing
multiple high-level objectives, such as generating coherent, relevant, and
high-quality responses while maintaining efficient task adaptation across
diverse tasks. To address these challenges, we introduce Transfer-Prompting, a
novel two-stage fr... |
2502.14212 | Less is More: On the Importance of Data Quality for Unit Test Generation | cs.SE cs.IR | Unit testing is crucial for software development and maintenance. Effective
unit testing ensures and improves software quality, but writing unit tests is
time-consuming and labor-intensive. Recent studies have proposed deep learning
(DL) techniques or large language models (LLMs) to automate unit test
generation. The... |
2502.14214 | Asymmetric Co-Training for Source-Free Few-Shot Domain Adaptation | cs.LG cs.CV | Source-free unsupervised domain adaptation (SFUDA) has gained significant
attention as an alternative to traditional unsupervised domain adaptation
(UDA), which relies on the constant availability of labeled source data.
However, SFUDA approaches come with inherent limitations that are frequently
overlooked. These ch... |
2502.14215 | Towards Secure Program Partitioning for Smart Contracts with LLM's
In-Context Learning | cs.SE cs.AI | Smart contracts are highly susceptible to manipulation attacks due to the
leakage of sensitive information. Addressing manipulation vulnerabilities is
particularly challenging because they stem from inherent data confidentiality
issues rather than straightforward implementation bugs. To tackle this by
preventing sens... |
2502.14218 | Rethinking Spiking Neural Networks from an Ensemble Learning Perspective | cs.LG cs.AI | Spiking neural networks (SNNs) exhibit superior energy efficiency but suffer
from limited performance. In this paper, we consider SNNs as ensembles of
temporal subnetworks that share architectures and weights, and highlight a
crucial issue that affects their performance: excessive differences in initial
states (neuro... |
2502.14219 | Investigating the Impact of LLM Personality on Cognitive Bias
Manifestation in Automated Decision-Making Tasks | cs.AI | Large Language Models (LLMs) are increasingly used in decision-making, yet
their susceptibility to cognitive biases remains a pressing challenge. This
study explores how personality traits influence these biases and evaluates the
effectiveness of mitigation strategies across various model architectures. Our
findings ... |
2502.14221 | H3DE-Net: Efficient and Accurate 3D Landmark Detection in Medical
Imaging | cs.CV | 3D landmark detection is a critical task in medical image analysis, and
accurately detecting anatomical landmarks is essential for subsequent medical
imaging tasks. However, mainstream deep learning methods in this field struggle
to simultaneously capture fine-grained local features and model global spatial
relations... |
2502.14222 | Enhancing Pavement Sensor Data Acquisition for AI-Driven Transportation
Research | cs.DB cs.AI eess.SP | Effective strategies for sensor data management are essential for advancing
transportation research, especially in the current data-driven era, due to the
advent of novel applications in artificial intelligence. This paper presents
comprehensive guidelines for managing transportation sensor data, encompassing
both ar... |
2502.14226 | Designing Parameter and Compute Efficient Diffusion Transformers using
Distillation | cs.CV eess.IV | Diffusion Transformers (DiTs) with billions of model parameters form the
backbone of popular image and video generation models like DALL.E,
Stable-Diffusion and SORA. Though these models are necessary in many
low-latency applications like Augmented/Virtual Reality, they cannot be
deployed on resource-constrained Edge... |
2502.14227 | SleepGMUformer: A gated multimodal temporal neural network for sleep
staging | cs.LG cs.AI | Sleep staging is a key method for assessing sleep quality and diagnosing
sleep disorders. However, current deep learning methods face challenges: 1)
postfusion techniques ignore the varying contributions of different modalities;
2) unprocessed sleep data can interfere with frequency-domain information. To
tackle thes... |
2502.14231 | Real-Time Sampling-based Online Planning for Drone Interception | cs.RO cs.LG cs.SY eess.SY | This paper studies high-speed online planning in dynamic environments. The
problem requires finding time-optimal trajectories that conform to system
dynamics, meeting computational constraints for real-time adaptation, and
accounting for uncertainty from environmental changes. To address these
challenges, we propose ... |
2502.14234 | OBELiX: A Curated Dataset of Crystal Structures and Experimentally
Measured Ionic Conductivities for Lithium Solid-State Electrolytes | cond-mat.mtrl-sci cs.LG | Solid-state electrolyte batteries are expected to replace liquid electrolyte
lithium-ion batteries in the near future thanks to their higher theoretical
energy density and improved safety. However, their adoption is currently
hindered by their lower effective ionic conductivity, a quantity that governs
charge and dis... |
2502.14235 | OG-Gaussian: Occupancy Based Street Gaussians for Autonomous Driving | cs.CV cs.AI | Accurate and realistic 3D scene reconstruction enables the lifelike creation
of autonomous driving simulation environments. With advancements in 3D Gaussian
Splatting (3DGS), previous studies have applied it to reconstruct complex
dynamic driving scenes. These methods typically require expensive LiDAR sensors
and pre... |
2502.14238 | No Minima, No Collisions: Combining Modulation and Control Barrier
Function Strategies for Feasible Dynamical Collision Avoidance | cs.RO cs.SY eess.SY | As prominent real-time safety-critical reactive control techniques, Control
Barrier Function Quadratic Programs (CBF-QPs) work for control affine systems
in general but result in local minima in the generated trajectories and
consequently cannot ensure convergence to the goals. Contrarily, Modulation of
Dynamical Sys... |
2502.14242 | On the Contraction Analysis of Nonlinear System with Multiple
Equilibrium Points | eess.SY cs.SY | In this work, we leverage the 2-contraction theory, which extends the
capabilities of classical contraction theory, to develop a global stability
framework. Coupled with powerful geometric tools such as the Poincare index
theory, the 2-contraction theory enables us to analyze the stability of planar
nonlinear systems... |
2502.14245 | Mitigating Lost-in-Retrieval Problems in Retrieval Augmented Multi-Hop
Question Answering | cs.CL | In this paper, we identify a critical problem, "lost-in-retrieval", in
retrieval-augmented multi-hop question answering (QA): the key entities are
missed in LLMs' sub-question decomposition. "Lost-in-retrieval" significantly
degrades the retrieval performance, which disrupts the reasoning chain and
leads to the incor... |
2502.14247 | Pandora3D: A Comprehensive Framework for High-Quality 3D Shape and
Texture Generation | cs.GR cs.AI cs.CV | This report presents a comprehensive framework for generating high-quality 3D
shapes and textures from diverse input prompts, including single images,
multi-view images, and text descriptions. The framework consists of 3D shape
generation and texture generation. (1). The 3D shape generation pipeline
employs a Variati... |
2502.14251 | Bayesian Parameter Inference and Uncertainty Quantification for a
Computational Pulmonary Hemodynamics Model Using Gaussian Processes | stat.AP cs.CE physics.bio-ph | Patient-specific modeling is a valuable tool in cardiovascular disease
research, offering insights beyond what current clinical equipment can measure.
Given the limitations of available clinical data, models that incorporate
uncertainty can provide clinicians with better guidance for tailored
treatments. However, suc... |
2502.14252 | Towards efficient quantum algorithms for diffusion probability models | quant-ph cs.LG | A diffusion probabilistic model (DPM) is a generative model renowned for its
ability to produce high-quality outputs in tasks such as image and audio
generation. However, training DPMs on large, high-dimensional datasets such as
high-resolution images or audio incurs significant computational, energy, and
hardware co... |
2502.14254 | Mem2Ego: Empowering Vision-Language Models with Global-to-Ego Memory for
Long-Horizon Embodied Navigation | cs.RO cs.AI | Recent advancements in Large Language Models (LLMs) and Vision-Language
Models (VLMs) have made them powerful tools in embodied navigation, enabling
agents to leverage commonsense and spatial reasoning for efficient exploration
in unfamiliar environments. Existing LLM-based approaches convert global
memory, such as s... |
2502.14255 | Effects of Prompt Length on Domain-specific Tasks for Large Language
Models | cs.CL cs.AI cs.ET cs.LG | In recent years, Large Language Models have garnered significant attention
for their strong performance in various natural language tasks, such as machine
translation and question answering. These models demonstrate an impressive
ability to generalize across diverse tasks. However, their effectiveness in
tackling dom... |
2502.14258 | Does Time Have Its Place? Temporal Heads: Where Language Models Recall
Time-specific Information | cs.CL cs.AI | While the ability of language models to elicit facts has been widely
investigated, how they handle temporally changing facts remains underexplored.
We discover Temporal Heads, specific attention heads primarily responsible for
processing temporal knowledge through circuit analysis. We confirm that these
heads are pre... |
2502.14259 | LabTOP: A Unified Model for Lab Test Outcome Prediction on Electronic
Health Records | cs.LG | Lab tests are fundamental for diagnosing diseases and monitoring patient
conditions. However, frequent testing can be burdensome for patients, and test
results may not always be immediately available. To address these challenges,
we propose LabTOP, a unified model that predicts lab test outcomes by
leveraging a langu... |
2502.14260 | EyeBench: A Call for More Rigorous Evaluation of Retinal Image
Enhancement | eess.IV cs.AI cs.CV | Over the past decade, generative models have achieved significant success in
enhancement fundus images.However, the evaluation of these models still
presents a considerable challenge. A comprehensive evaluation benchmark for
fundus image enhancement is indispensable for three main reasons: 1) The
existing denoising m... |
2502.14264 | SPRIG: Stackelberg Perception-Reinforcement Learning with Internal Game
Dynamics | cs.AI | Deep reinforcement learning agents often face challenges to effectively
coordinate perception and decision-making components, particularly in
environments with high-dimensional sensory inputs where feature relevance
varies. This work introduces SPRIG (Stackelberg Perception-Reinforcement
learning with Internal Game d... |
2502.14267 | Money Recognition for the Visually Impaired: A Case Study on Sri Lankan
Banknotes | cs.CV | Currency note recognition is a critical accessibility need for blind
individuals, as identifying banknotes accurately can impact their independence
and security in financial transactions. Several traditional and technological
initiatives have been taken to date. Nevertheless, these approaches are less
user-friendly a... |
2502.14268 | MCQA-Eval: Efficient Confidence Evaluation in NLG with Gold-Standard
Correctness Labels | cs.CL cs.AI | Large Language Models (LLMs) require robust confidence estimation,
particularly in critical domains like healthcare and law where unreliable
outputs can lead to significant consequences. Despite much recent work in
confidence estimation, current evaluation frameworks rely on correctness
functions -- various heuristic... |
2502.14270 | Predicting Fetal Birthweight from High Dimensional Data using Advanced
Machine Learning | cs.LG | Birth weight serves as a fundamental indicator of neonatal health, closely
linked to both early medical interventions and long-term developmental risks.
Traditional predictive models, often constrained by limited feature selection
and incomplete datasets, struggle to achieve overlooking complex maternal and
fetal int... |
2502.14271 | PaperHelper: Knowledge-Based LLM QA Paper Reading Assistant | cs.CL | In the paper, we introduce a paper reading assistant, PaperHelper, a potent
tool designed to enhance the capabilities of researchers in efficiently
browsing and understanding scientific literature. Utilizing the
Retrieval-Augmented Generation (RAG) framework, PaperHelper effectively
minimizes hallucinations commonly ... |
2502.14272 | Capturing Nuanced Preferences: Preference-Aligned Distillation for Small
Language Models | cs.CL cs.AI | Aligning small language models (SLMs) with human values typically involves
distilling preference knowledge from large language models (LLMs). However,
existing distillation methods model preference knowledge in teacher LLMs by
comparing pairwise responses, overlooking the extent of difference between
responses. This ... |
2502.14273 | LLM-EvRep: Learning an LLM-Compatible Event Representation Using a
Self-Supervised Framework | cs.CV cs.AI cs.MM | Recent advancements in event-based recognition have demonstrated significant
promise, yet most existing approaches rely on extensive training, limiting
their adaptability for efficient processing of event-driven visual content.
Meanwhile, large language models (LLMs) have exhibited remarkable zero-shot
capabilities a... |
2502.14275 | Fact or Guesswork? Evaluating Large Language Model's Medical Knowledge
with Structured One-Hop Judgment | cs.CL cs.LG | Large language models (LLMs) have been widely adopted in various downstream
task domains. However, their ability to directly recall and apply factual
medical knowledge remains under-explored. Most existing medical QA benchmarks
assess complex reasoning or multi-hop inference, making it difficult to isolate
LLMs' inhe... |
2502.14276 | STeCa: Step-level Trajectory Calibration for LLM Agent Learning | cs.LG cs.AI cs.CL | Large language model (LLM)-based agents have shown promise in tackling
complex tasks by interacting dynamically with the environment. Existing work
primarily focuses on behavior cloning from expert demonstrations and preference
learning through exploratory trajectory sampling. However, these methods often
struggle in... |
2502.14279 | OrchardDepth: Precise Metric Depth Estimation of Orchard Scene from
Monocular Camera Images | cs.CV | Monocular depth estimation is a rudimentary task in robotic perception.
Recently, with the development of more accurate and robust neural network
models and different types of datasets, monocular depth estimation has
significantly improved performance and efficiency. However, most of the
research in this area focuses... |
2502.14280 | EpMAN: Episodic Memory AttentioN for Generalizing to Longer Contexts | cs.CL cs.AI | Recent advances in Large Language Models (LLMs) have yielded impressive
successes on many language tasks. However, efficient processing of long
contexts using LLMs remains a significant challenge. We introduce
\textbf{EpMAN} -- a method for processing long contexts in an \textit{episodic
memory} module while \textit{... |
2502.14281 | Correcting Noisy Multilabel Predictions: Modeling Label Noise through
Latent Space Shifts | cs.LG cs.AI | Noise in data appears to be inevitable in most real-world machine learning
applications and would cause severe overfitting problems. Not only can data
features contain noise, but labels are also prone to be noisy due to human
input. In this paper, rather than noisy label learning in multiclass
classifications, we ins... |
2502.14282 | PC-Agent: A Hierarchical Multi-Agent Collaboration Framework for Complex
Task Automation on PC | cs.CV | In the field of MLLM-based GUI agents, compared to smartphones, the PC
scenario not only features a more complex interactive environment, but also
involves more intricate intra- and inter-app workflows. To address these
issues, we propose a hierarchical agent framework named PC-Agent. Specifically,
from the perceptio... |
2502.14285 | Vulnerability of Text-to-Image Models to Prompt Template Stealing: A
Differential Evolution Approach | cs.CL | Prompt trading has emerged as a significant intellectual property concern in
recent years, where vendors entice users by showcasing sample images before
selling prompt templates that can generate similar images. This work
investigates a critical security vulnerability: attackers can steal prompt
templates using only ... |
2502.14289 | Drift: Decoding-time Personalized Alignments with Implicit User
Preferences | cs.CL | Personalized alignments for individual users have been a long-standing goal
in large language models (LLMs). We introduce Drift, a novel framework that
personalizes LLMs at decoding time with implicit user preferences. Traditional
Reinforcement Learning from Human Feedback (RLHF) requires thousands of
annotated examp... |
2502.14293 | Graph Anomaly Detection via Adaptive Test-time Representation Learning
across Out-of-Distribution Domains | cs.LG cs.AI cs.SI | Graph Anomaly Detection (GAD) has demonstrated great effectiveness in
identifying unusual patterns within graph-structured data. However, while
labeled anomalies are often scarce in emerging applications, existing
supervised GAD approaches are either ineffective or not applicable when moved
across graph domains due t... |
2502.14294 | DAG: Deep Adaptive and Generative $K$-Free Community Detection on
Attributed Graphs | cs.SI | Community detection on attributed graphs with rich semantic and topological
information offers great potential for real-world network analysis, especially
user matching in online games. Graph Neural Networks (GNNs) have recently
enabled Deep Graph Clustering (DGC) methods to learn cluster assignments from
semantic an... |
2502.14297 | An Evaluation of Sakana's AI Scientist for Autonomous Research: Wishful
Thinking or an Emerging Reality Towards 'Artificial General Research
Intelligence' (AGRI)? | cs.IR cs.AI cs.LG | A major step toward Artificial General Intelligence (AGI) and Super
Intelligence is AI's ability to autonomously conduct research - what we term
Artificial General Research Intelligence (AGRI). If machines could generate
hypotheses, conduct experiments, and write research papers without human
intervention, it would t... |
2502.14298 | Generalization Certificates for Adversarially Robust Bayesian Linear
Regression | cs.LG stat.ML | Adversarial robustness of machine learning models is critical to ensuring
reliable performance under data perturbations. Recent progress has been on
point estimators, and this paper considers distributional predictors. First,
using the link between exponential families and Bregman divergences, we
formulate an adversa... |
2502.14301 | SEA-HELM: Southeast Asian Holistic Evaluation of Language Models | cs.CL cs.AI | With the rapid emergence of novel capabilities in Large Language Models
(LLMs), the need for rigorous multilingual and multicultural benchmarks that
are integrated has become more pronounced. Though existing LLM benchmarks are
capable of evaluating specific capabilities of LLMs in English as well as in
various mid- t... |
2502.14302 | MedHallu: A Comprehensive Benchmark for Detecting Medical Hallucinations
in Large Language Models | cs.CL cs.AI cs.LG | Advancements in Large Language Models (LLMs) and their increasing use in
medical question-answering necessitate rigorous evaluation of their
reliability. A critical challenge lies in hallucination, where models generate
plausible yet factually incorrect outputs. In the medical domain, this poses
serious risks to pati... |
2502.14305 | Efficient AI in Practice: Training and Deployment of Efficient LLMs for
Industry Applications | cs.IR cs.LG | Large language models (LLMs) have demonstrated remarkable performance across
a wide range of industrial applications, from search and recommendations to
generative tasks. Although scaling laws indicate that larger models generally
yield better generalization and performance, their substantial computational
requiremen... |
2502.14307 | {\mu}RL: Discovering Transient Execution Vulnerabilities Using
Reinforcement Learning | cs.CR cs.AR cs.LG | We propose using reinforcement learning to address the challenges of
discovering microarchitectural vulnerabilities, such as Spectre and Meltdown,
which exploit subtle interactions in modern processors. Traditional methods
like random fuzzing fail to efficiently explore the vast instruction space and
often miss vulne... |
2502.14309 | On Theoretical Limits of Learning with Label Differential Privacy | cs.LG cs.IT math.IT | Label differential privacy (DP) is designed for learning problems involving
private labels and public features. While various methods have been proposed
for learning under label DP, the theoretical limits remain largely unexplored.
In this paper, we investigate the fundamental limits of learning with label DP
in both... |
2502.14311 | The Impact and Feasibility of Self-Confidence Shaping for AI-Assisted
Decision-Making | cs.HC cs.CL cs.CY | In AI-assisted decision-making, it is crucial but challenging for humans to
appropriately rely on AI, especially in high-stakes domains such as finance and
healthcare. This paper addresses this problem from a human-centered perspective
by presenting an intervention for self-confidence shaping, designed to
calibrate s... |
2502.14314 | ODVerse33: Is the New YOLO Version Always Better? A Multi Domain
benchmark from YOLO v5 to v11 | cs.CV | You Look Only Once (YOLO) models have been widely used for building real-time
object detectors across various domains. With the increasing frequency of new
YOLO versions being released, key questions arise. Are the newer versions
always better than their previous versions? What are the core innovations in
each YOLO v... |
2502.14315 | Unveiling Cultural Blind Spots: Analyzing the Limitations of mLLMs in
Procedural Text Comprehension | cs.CL | Despite the impressive performance of multilingual large language models
(mLLMs) in various natural language processing tasks, their ability to
understand procedural texts, particularly those with culture-specific content,
remains largely unexplored. Texts describing cultural procedures, including
rituals, traditiona... |
2502.14316 | Textured 3D Regenerative Morphing with 3D Diffusion Prior | cs.CV cs.AI | Textured 3D morphing creates smooth and plausible interpolation sequences
between two 3D objects, focusing on transitions in both shape and texture. This
is important for creative applications like visual effects in filmmaking.
Previous methods rely on establishing point-to-point correspondences and
determining smoot... |
2502.14317 | ParallelComp: Parallel Long-Context Compressor for Length Extrapolation | cs.CL | Efficiently handling long contexts is crucial for large language models
(LLMs). While rotary position embeddings (RoPEs) enhance length generalization,
effective length extrapolation remains challenging and often requires costly
fine-tuning. In contrast, recent training-free approaches suffer from the
attention sink ... |
2502.14318 | Line Goes Up? Inherent Limitations of Benchmarks for Evaluating Large
Language Models | cs.CL cs.AI cs.LG | Large language models (LLMs) regularly demonstrate new and impressive
performance on a wide range of language, knowledge, and reasoning benchmarks.
Such rapid progress has led many commentators to argue that LLM general
cognitive capabilities have likewise rapidly improved, with the implication
that such models are b... |
2502.14321 | Beyond Self-Talk: A Communication-Centric Survey of LLM-Based
Multi-Agent Systems | cs.MA cs.CL | Large Language Models (LLMs) have recently demonstrated remarkable
capabilities in reasoning, planning, and decision-making. Building upon these
strengths, researchers have begun incorporating LLMs into multi-agent systems
(MAS), where agents collaborate or compete through natural language
interactions to tackle task... |
2502.14327 | ChemHTS: Hierarchical Tool Stacking for Enhancing Chemical Agents | cs.CE | Large Language Models (LLMs) have demonstrated remarkable potential in
scientific research, particularly in chemistry-related tasks such as molecular
design, reaction prediction, and property estimation. While tool-augmented LLMs
have been introduced to enhance reasoning and computation in these domains,
existing app... |
2502.14332 | A Collaborative Jade Recognition System for Mobile Devices Based on
Lightweight and Large Models | cs.CV cs.IR | With the widespread adoption and development of mobile devices, vision-based
recognition applications have become a hot topic in research. Jade, as an
important cultural heritage and artistic item, has significant applications in
fields such as jewelry identification and cultural relic preservation. However,
existing... |
2502.14333 | A Survey on Feedback-based Multi-step Reasoning for Large Language
Models on Mathematics | cs.CL cs.AI | Recent progress in large language models (LLM) found chain-of-thought
prompting strategies to improve the reasoning ability of LLMs by encouraging
problem solving through multiple steps. Therefore, subsequent research aimed to
integrate the multi-step reasoning process into the LLM itself through process
rewards as f... |
2502.14334 | Purest Quantum State Identification | quant-ph cs.AI | Precise identification of quantum states under noise constraints is essential
for quantum information processing. In this study, we generalize the classical
best arm identification problem to quantum domains, designing methods for
identifying the purest one within $K$ unknown $n$-qubit quantum states using
$N$ sample... |
2502.14335 | Information Types in Product Reviews | cs.CL | Information in text is communicated in a way that supports a goal for its
reader. Product reviews, for example, contain opinions, tips, product
descriptions, and many other types of information that provide both direct
insights, as well as unexpected signals for downstream applications. We devise
a typology of 24 com... |
2502.14338 | English Please: Evaluating Machine Translation for Multilingual Bug
Reports | cs.CL cs.SE | Accurate translation of bug reports is critical for efficient collaboration
in global software development. In this study, we conduct the first
comprehensive evaluation of machine translation (MT) performance on bug
reports, analyzing the capabilities of DeepL, AWS Translate, and ChatGPT using
data from the Visual St... |
2502.14340 | Earlier Tokens Contribute More: Learning Direct Preference Optimization
From Temporal Decay Perspective | cs.CL | Direct Preference Optimization (DPO) has gained attention as an efficient
alternative to reinforcement learning from human feedback (RLHF) for aligning
large language models (LLMs) with human preferences. Despite its advantages,
DPO suffers from a length bias, generating responses longer than those from the
reference... |
2502.14344 | Towards Accurate Binary Spiking Neural Networks: Learning with Adaptive
Gradient Modulation Mechanism | cs.CV | Binary Spiking Neural Networks (BSNNs) inherit the eventdriven paradigm of
SNNs, while also adopting the reduced storage burden of binarization
techniques. These distinct advantages grant BSNNs lightweight and
energy-efficient characteristics, rendering them ideal for deployment on
resource-constrained edge devices. ... |
2502.14345 | FlowAgent: Achieving Compliance and Flexibility for Workflow Agents | cs.AI | The integration of workflows with large language models (LLMs) enables
LLM-based agents to execute predefined procedures, enhancing automation in
real-world applications. Traditional rule-based methods tend to limit the
inherent flexibility of LLMs, as their predefined execution paths restrict the
models' action spac... |
2502.14350 | Optimize Cardinality Estimation Model Pretraining by Simplifying the
Training Datasets | cs.DB cs.LG | The cardinality estimation is a key aspect of query optimization research,
and its performance has significantly improved with the integration of machine
learning. To overcome the "cold start" problem or the lack of model
transferability in learned cardinality estimators, some pre-training
cardinality estimation mode... |
2502.14351 | SegAnyPET: Universal Promptable Segmentation from Positron Emission
Tomography Images | cs.CV | Positron Emission Tomography (PET) imaging plays a crucial role in modern
medical diagnostics by revealing the metabolic processes within a patient's
body, which is essential for quantification of therapy response and monitoring
treatment progress. However, the segmentation of PET images presents unique
challenges du... |
2502.14352 | SR-LLM: Rethinking the Structured Representation in Large Language Model | cs.CL | Structured representations, exemplified by Abstract Meaning Representation
(AMR), have long been pivotal in computational linguistics. However, their role
remains ambiguous in the Large Language Models (LLMs) era. Initial attempts to
integrate structured representation into LLMs via a zero-shot setting yielded
inferi... |
2502.14353 | Eliminating Majority Illusions | cs.CC cs.SI | An opinion illusion refers to a phenomenon in social networks where agents
may witness distributions of opinions among their neighbours that do not
accurately reflect the true distribution of opinions in the population as a
whole. A specific case of this occurs when there are only two possible choices,
such as whethe... |
2502.14354 | Self-Improvement Towards Pareto Optimality: Mitigating Preference
Conflicts in Multi-Objective Alignment | cs.LG cs.CL | Multi-Objective Alignment (MOA) aims to align LLMs' responses with multiple
human preference objectives, with Direct Preference Optimization (DPO) emerging
as a prominent approach. However, we find that DPO-based MOA approaches suffer
from widespread preference conflicts in the data, where different objectives
favor ... |
2502.14355 | Triply Laplacian Scale Mixture Modeling for Seismic Data Noise
Suppression | cs.CV | Sparsity-based tensor recovery methods have shown great potential in
suppressing seismic data noise. These methods exploit tensor sparsity measures
capturing the low-dimensional structures inherent in seismic data tensors to
remove noise by applying sparsity constraints through soft-thresholding or
hard-thresholding ... |
2502.14356 | Full-Step-DPO: Self-Supervised Preference Optimization with Step-wise
Rewards for Mathematical Reasoning | cs.CL | Direct Preference Optimization (DPO) often struggles with long-chain
mathematical reasoning. Existing approaches, such as Step-DPO, typically
improve this by focusing on the first erroneous step in the reasoning chain.
However, they overlook all other steps and rely heavily on humans or GPT-4 to
identify erroneous st... |
2502.14358 | An exposition of recent list-size bounds of FRS Codes | cs.CC cs.IT math.CO math.IT | In the last year, there have been some remarkable improvements in the
combinatorial list-size bounds of Folded Reed Solomon codes and multiplicity
codes. Starting from the work on Kopparty, Ron-Zewi, Saraf and Wootters (SIAM
J. Comput. 2023) (and subsequent simplifications due to Tamo (IEEE Trans.
Inform. Theory 2024... |
2502.14359 | Triangulating LLM Progress through Benchmarks, Games, and Cognitive
Tests | cs.CL | We examine three evaluation paradigms: large question-answering benchmarks
(e.g., MMLU and BBH), interactive games (e.g., Signalling Games or Taboo), and
cognitive tests (e.g., for working memory or theory of mind). First, we
investigate which of the former two-benchmarks or games-is most effective at
discriminating ... |
2502.14360 | Weed Detection using Convolutional Neural Network | cs.CV | In this paper we use convolutional neural networks (CNNs) for weed detection
in agricultural land. We specifically investigate the application of two CNN
layer types, Conv2d and dilated Conv2d, for weed detection in crop fields. The
suggested method extracts features from the input photos using pre-trained
models, wh... |
2502.14361 | Retrieval-Augmented Process Reward Model for Generalizable Mathematical
Reasoning | cs.AI cs.IR | While large language models (LLMs) have significantly advanced mathematical
reasoning, Process Reward Models (PRMs) have been developed to evaluate the
logical validity of reasoning steps. However, PRMs still struggle with
out-of-distribution (OOD) challenges. This paper identifies key OOD issues,
including step OOD,... |
2502.14363 | Topology-Aware Wavelet Mamba for Airway Structure Segmentation in
Postoperative Recurrent Nasopharyngeal Carcinoma CT Scans | eess.IV cs.CV | Nasopharyngeal carcinoma (NPC) patients often undergo radiotherapy and
chemotherapy, which can lead to postoperative complications such as limited
mouth opening and joint stiffness, particularly in recurrent cases that require
re-surgery. These complications can affect airway function, making accurate
postoperative a... |
2502.14365 | Is Q-learning an Ill-posed Problem? | cs.LG cs.AI | This paper investigates the instability of Q-learning in continuous
environments, a challenge frequently encountered by practitioners.
Traditionally, this instability is attributed to bootstrapping and regression
model errors. Using a representative reinforcement learning benchmark, we
systematically examine the effe... |
2502.14366 | Entropy-UID: A Method for Optimizing Information Density | cs.CL cs.AI | Balanced and efficient information flow is essential for optimizing language
generation models. In this work, we propose Entropy-UID, a new token selection
method that balances entropy and Uniform Information Density (UID) principles
for enhanced efficiency of text generation. Our approach adaptively adjusts
token se... |
2502.14370 | PPO-MI: Efficient Black-Box Model Inversion via Proximal Policy
Optimization | cs.LG cs.CV | Model inversion attacks pose a significant privacy risk by attempting to
reconstruct private training data from trained models. Most of the existing
methods either depend on gradient estimation or require white-box access to
model parameters, which limits their applicability in practical scenarios. In
this paper, we ... |
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