id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2502.02863 | OceanChat: The Effect of Virtual Conversational AI Agents on Sustainable
Attitude and Behavior Change | [
"cs.HC",
"cs.AI"
] | Marine ecosystems face unprecedented threats from climate change and plastic pollution, yet traditional environmental education often struggles to translate awareness into sustained behavioral change. This paper presents OceanChat, an interactive system leveraging large language models to create conversational AI agent... | {
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2502.02866 | A Systematic Approach for Assessing Large Language Models' Test Case
Generation Capability | [
"cs.SE",
"cs.AI"
] | Software testing ensures the quality and reliability of software products, but manual test case creation is labor-intensive. With the rise of large language models (LLMs), there is growing interest in unit test creation with LLMs. However, effective assessment of LLM-generated test cases is limited by the lack of stand... | {
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2502.02867 | Domain-Invariant Per-Frame Feature Extraction for Cross-Domain Imitation
Learning with Visual Observations | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Imitation learning (IL) enables agents to mimic expert behavior without reward signals but faces challenges in cross-domain scenarios with high-dimensional, noisy, and incomplete visual observations. To address this, we propose Domain-Invariant Per-Frame Feature Extraction for Imitation Learning (DIFF-IL), a novel IL m... | {
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2502.02869 | OmniRL: In-Context Reinforcement Learning by Large-Scale Meta-Training
in Randomized Worlds | [
"cs.LG",
"cs.AI"
] | We introduce OmniRL, a highly generalizable in-context reinforcement learning (ICRL) model that is meta-trained on hundreds of thousands of diverse tasks. These tasks are procedurally generated by randomizing state transitions and rewards within Markov Decision Processes. To facilitate this extensive meta-training, we ... | {
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2502.02870 | Uncertainty Quantification with the Empirical Neural Tangent Kernel | [
"stat.ML",
"cs.LG"
] | While neural networks have demonstrated impressive performance across various tasks, accurately quantifying uncertainty in their predictions is essential to ensure their trustworthiness and enable widespread adoption in critical systems. Several Bayesian uncertainty quantification (UQ) methods exist that are either che... | {
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2502.02871 | Position: Multimodal Large Language Models Can Significantly Advance
Scientific Reasoning | [
"cs.CL",
"cs.AI"
] | Scientific reasoning, the process through which humans apply logic, evidence, and critical thinking to explore and interpret scientific phenomena, is essential in advancing knowledge reasoning across diverse fields. However, despite significant progress, current scientific reasoning models still struggle with generaliz... | {
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2502.02872 | Achieving Operational Universality through a Turing Complete Chemputer | [
"cs.CL"
] | The most fundamental abstraction underlying all modern computers is the Turing Machine, that is if any modern computer can simulate a Turing Machine, an equivalence which is called Turing completeness, it is theoretically possible to achieve any task that can be algorithmically described by executing a series of discre... | {
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2502.02874 | Vertical Federated Learning for Failure-Cause Identification in
Disaggregated Microwave Networks | [
"cs.NI",
"cs.AI",
"cs.DC",
"cs.LG"
] | Machine Learning (ML) has proven to be a promising solution to provide novel scalable and efficient fault management solutions in modern 5G-and-beyond communication networks. In the context of microwave networks, ML-based solutions have received significant attention. However, current solutions can only be applied to m... | {
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2502.02875 | Heterogeneous Value Decomposition Policy Fusion for Multi-Agent
Cooperation | [
"cs.MA"
] | Value decomposition (VD) has become one of the most prominent solutions in cooperative multi-agent reinforcement learning. Most existing methods generally explore how to factorize the joint value and minimize the discrepancies between agent observations and characteristics of environmental states. However, direct decom... | {
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2502.02883 | SensorChat: Answering Qualitative and Quantitative Questions during
Long-Term Multimodal Sensor Interactions | [
"cs.AI",
"cs.HC"
] | Natural language interaction with sensing systems is crucial for enabling all users to comprehend sensor data and its impact on their everyday lives. However, existing systems, which typically operate in a Question Answering (QA) manner, are significantly limited in terms of the duration and complexity of sensor data t... | {
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2502.02885 | Expertized Caption Auto-Enhancement for Video-Text Retrieval | [
"cs.CV",
"cs.AI",
"cs.LG"
] | The burgeoning field of video-text retrieval has witnessed significant advancements with the advent of deep learning. However, the challenge of matching text and video persists due to inadequate textual descriptions of videos. The substantial information gap between the two modalities hinders a comprehensive understand... | {
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2502.02887 | Variations on the Expectation Due to Changes in the Probability Measure | [
"cs.IT",
"cs.LG",
"math.IT",
"math.PR",
"math.ST",
"stat.TH"
] | Closed-form expressions are presented for the variation of the expectation of a given function due to changes in the probability measure used for the expectation. They unveil interesting connections with Gibbs probability measures, the mutual information, and the lautum information. | {
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2502.02891 | INST-Sculpt: Interactive Stroke-based Neural SDF Sculpting | [
"cs.GR",
"cs.CV"
] | Recent advances in implicit neural representations have made them a popular choice for modeling 3D geometry, achieving impressive results in tasks such as shape representation, reconstruction, and learning priors. However, directly editing these representations poses challenges due to the complex relationship between m... | {
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2502.02893 | Lowering the Barrier of Machine Learning: Achieving Zero Manual Labeling
in Review Classification Using LLMs | [
"cs.CL"
] | With the internet's evolution, consumers increasingly rely on online reviews for service or product choices, necessitating that businesses analyze extensive customer feedback to enhance their offerings. While machine learning-based sentiment classification shows promise in this realm, its technical complexity often bar... | {
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2502.02895 | Enhancing Quantum-ready QUBO-based Suppression for Object Detection with
Appearance and Confidence Features | [
"cs.CV"
] | Quadratic Unconstrained Binary Optimization (QUBO)-based suppression in object detection is known to have superiority to conventional Non-Maximum Suppression (NMS), especially for crowded scenes where NMS possibly suppresses the (partially-) occluded true positives with low confidence scores. Whereas existing QUBO form... | {
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2502.02896 | A Benchmark for the Detection of Metalinguistic Disagreements between
LLMs and Knowledge Graphs | [
"cs.CL",
"cs.AI"
] | Evaluating large language models (LLMs) for tasks like fact extraction in support of knowledge graph construction frequently involves computing accuracy metrics using a ground truth benchmark based on a knowledge graph (KG). These evaluations assume that errors represent factual disagreements. However, human discourse ... | {
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2502.02901 | Policy Abstraction and Nash Refinement in Tree-Exploiting PSRO | [
"cs.GT",
"cs.AI"
] | Policy Space Response Oracles (PSRO) interleaves empirical game-theoretic analysis with deep reinforcement learning (DRL) to solve games too complex for traditional analytic methods. Tree-exploiting PSRO (TE-PSRO) is a variant of this approach that iteratively builds a coarsened empirical game model in extensive form u... | {
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2502.02903 | What is in a name? Mitigating Name Bias in Text Embeddings via
Anonymization | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Text-embedding models often exhibit biases arising from the data on which they are trained. In this paper, we examine a hitherto unexplored bias in text-embeddings: bias arising from the presence of $\textit{names}$ such as persons, locations, organizations etc. in the text. Our study shows how the presence of $\textit... | {
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2502.02904 | ScholaWrite: A Dataset of End-to-End Scholarly Writing Process | [
"cs.HC",
"cs.CL",
"q-bio.NC"
] | Writing is a cognitively demanding task involving continuous decision-making, heavy use of working memory, and frequent switching between multiple activities. Scholarly writing is particularly complex as it requires authors to coordinate many pieces of multiform knowledge. To fully understand writers' cognitive thought... | {
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2502.02905 | AI-driven materials design: a mini-review | [
"cond-mat.mtrl-sci",
"cs.LG"
] | Materials design is an important component of modern science and technology, yet traditional approaches rely heavily on trial-and-error and can be inefficient. Computational techniques, enhanced by modern artificial intelligence (AI), have greatly accelerated the design of new materials. Among these approaches, inverse... | {
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2502.02907 | PoleStack: Robust Pole Estimation of Irregular Objects from Silhouette
Stacking | [
"cs.CV"
] | We present an algorithm to estimate the rotation pole of a principal-axis rotator using silhouette images collected from multiple camera poses. First, a set of images is stacked to form a single silhouette-stack image, where the object's rotation introduces reflective symmetry about the imaged pole direction. We estima... | {
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2502.02908 | COSMosFL: Ensemble of Small Language Models for Fault Localisation | [
"cs.SE",
"cs.LG"
] | LLMs are rapidly being adopted to build powerful tools and agents for software engineering, but most of them rely heavily on extremely large closed-source models. This, in turn, can hinder wider adoption due to security issues as well as financial cost and environmental impact. Recently, a number of open source Small L... | {
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2502.02909 | SPARC: Subspace-Aware Prompt Adaptation for Robust Continual Learning in
LLMs | [
"cs.LG",
"cs.AI",
"cs.CL"
] | We propose SPARC, a lightweight continual learning framework for large language models (LLMs) that enables efficient task adaptation through prompt tuning in a lower-dimensional space. By leveraging principal component analysis (PCA), we identify a compact subspace of the training data. Optimizing prompts in this lower... | {
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2502.02910 | DANDI: Diffusion as Normative Distribution for Deep Neural Network Input | [
"cs.SE",
"cs.LG"
] | Surprise Adequacy (SA) has been widely studied as a test adequacy metric that can effectively guide software engineers towards inputs that are more likely to reveal unexpected behaviour of Deep Neural Networks (DNNs). Intuitively, SA is an out-of-distribution metric that quantifies the dissimilarity between the given i... | {
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2502.02912 | MobiCLR: Mobility Time Series Contrastive Learning for Urban Region
Representations | [
"cs.LG",
"cs.AI"
] | Recently, learning effective representations of urban regions has gained significant attention as a key approach to understanding urban dynamics and advancing smarter cities. Existing approaches have demonstrated the potential of leveraging mobility data to generate latent representations, providing valuable insights i... | {
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2502.02913 | Real-Time Privacy Risk Measurement with Privacy Tokens for Gradient
Leakage | [
"cs.LG",
"cs.CR"
] | The widespread deployment of deep learning models in privacy-sensitive domains has amplified concerns regarding privacy risks, particularly those stemming from gradient leakage during training. Current privacy assessments primarily rely on post-training attack simulations. However, these methods are inherently reactive... | {
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2502.02917 | Interactive Symbolic Regression through Offline Reinforcement Learning:
A Co-Design Framework | [
"cs.LG",
"cs.AI",
"cs.SC"
] | Symbolic Regression (SR) holds great potential for uncovering underlying mathematical and physical relationships from observed data. However, the vast combinatorial space of possible expressions poses significant challenges for both online search methods and pre-trained transformer models. Additionally, current state-o... | {
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2502.02919 | Maximizing the Position Embedding for Vision Transformers with Global
Average Pooling | [
"cs.CV",
"cs.LG"
] | In vision transformers, position embedding (PE) plays a crucial role in capturing the order of tokens. However, in vision transformer structures, there is a limitation in the expressiveness of PE due to the structure where position embedding is simply added to the token embedding. A layer-wise method that delivers PE t... | {
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2502.02920 | Adaptive Budget Optimization for Multichannel Advertising Using
Combinatorial Bandits | [
"cs.LG",
"cs.AI"
] | Effective budget allocation is crucial for optimizing the performance of digital advertising campaigns. However, the development of practical budget allocation algorithms remain limited, primarily due to the lack of public datasets and comprehensive simulation environments capable of verifying the intricacies of real-w... | {
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2502.02921 | Robust Reward Alignment via Hypothesis Space Batch Cutting | [
"cs.LG"
] | Reward design for reinforcement learning and optimal control agents is challenging. Preference-based alignment addresses this by enabling agents to learn rewards from ranked trajectory pairs provided by humans. However, existing methods often struggle from poor robustness to unknown false human preferences. In this wor... | {
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2502.02922 | Elucidating the Preconditioning in Consistency Distillation | [
"cs.LG",
"cs.CV"
] | Consistency distillation is a prevalent way for accelerating diffusion models adopted in consistency (trajectory) models, in which a student model is trained to traverse backward on the probability flow (PF) ordinary differential equation (ODE) trajectory determined by the teacher model. Preconditioning is a vital tech... | {
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2502.02924 | TopoCL: Topological Contrastive Learning for Time Series | [
"cs.LG",
"cs.AI"
] | Universal time series representation learning is challenging but valuable in real-world applications such as classification, anomaly detection, and forecasting. Recently, contrastive learning (CL) has been actively explored to tackle time series representation. However, a key challenge is that the data augmentation pro... | {
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2502.02925 | Data denoising with self consistency, variance maximization, and the
Kantorovich dominance | [
"stat.ME",
"cs.LG",
"math.PR",
"math.ST",
"stat.TH"
] | We introduce a new framework for data denoising, partially inspired by martingale optimal transport. For a given noisy distribution (the data), our approach involves finding the closest distribution to it among all distributions which 1) have a particular prescribed structure (expressed by requiring they lie in a parti... | {
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2502.02928 | Large Language Model Guided Self-Debugging Code Generation | [
"cs.SE",
"cs.AI"
] | Automated code generation is gaining significant importance in intelligent computer programming and system deployment. However, current approaches often face challenges in computational efficiency and lack robust mechanisms for code parsing and error correction. In this work, we propose a novel framework, PyCapsule, wi... | {
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2502.02932 | Dominance Regions of Pursuit-evasion Games in Non-anticipative
Information Patterns | [
"math.OC",
"cs.GT",
"cs.SY",
"eess.SY"
] | The evader's dominance region is an important concept and the foundation of geometric methods for pursuit-evasion games. This article mainly reveals the relevant properties of the evader's dominance region, especially in non-anticipative information patterns. We can use these properties to research pursuit-evasion game... | {
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2502.02934 | Gait-Net-augmented Implicit Kino-dynamic MPC for Dynamic
Variable-frequency Humanoid Locomotion over Discrete Terrains | [
"cs.RO",
"cs.SY",
"eess.SY"
] | Current optimization-based control techniques for humanoid locomotion struggle to adapt step duration and placement simultaneously in dynamic walking gaits due to their reliance on fixed-time discretization, which limits responsiveness to terrain conditions and results in suboptimal performance in challenging environme... | {
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2502.02936 | Every Angle Is Worth A Second Glance: Mining Kinematic Skeletal
Structures from Multi-view Joint Cloud | [
"cs.CV"
] | Multi-person motion capture over sparse angular observations is a challenging problem under interference from both self- and mutual-occlusions. Existing works produce accurate 2D joint detection, however, when these are triangulated and lifted into 3D, available solutions all struggle in selecting the most accurate can... | {
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2502.02938 | LLaVAC: Fine-tuning LLaVA as a Multimodal Sentiment Classifier | [
"cs.CL"
] | We present LLaVAC, a method for constructing a classifier for multimodal sentiment analysis. This method leverages fine-tuning of the Large Language and Vision Assistant (LLaVA) to predict sentiment labels across both image and text modalities. Our approach involves designing a structured prompt that incorporates both ... | {
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2502.02941 | Fast T2T: Optimization Consistency Speeds Up Diffusion-Based
Training-to-Testing Solving for Combinatorial Optimization | [
"cs.LG"
] | Diffusion models have recently advanced Combinatorial Optimization (CO) as a powerful backbone for neural solvers. However, their iterative sampling process requiring denoising across multiple noise levels incurs substantial overhead. We propose to learn direct mappings from different noise levels to the optimal soluti... | {
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2502.02943 | Behavioral Homophily in Social Media via Inverse Reinforcement Learning:
A Reddit Case Study | [
"cs.SI",
"cs.LG"
] | Online communities play a critical role in shaping societal discourse and influencing collective behavior in the real world. The tendency for people to connect with others who share similar characteristics and views, known as homophily, plays a key role in the formation of echo chambers which further amplify polarizati... | {
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2502.02945 | LLM-KT: Aligning Large Language Models with Knowledge Tracing using a
Plug-and-Play Instruction | [
"cs.CL",
"cs.AI"
] | The knowledge tracing (KT) problem is an extremely important topic in personalized education, which aims to predict whether students can correctly answer the next question based on their past question-answer records. Prior work on this task mainly focused on learning the sequence of behaviors based on the IDs or textua... | {
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2502.02951 | VQA-Levels: A Hierarchical Approach for Classifying Questions in VQA | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Designing datasets for Visual Question Answering (VQA) is a difficult and complex task that requires NLP for parsing and computer vision for analysing the relevant aspects of the image for answering the question asked. Several benchmark datasets have been developed by researchers but there are many issues with using th... | {
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2502.02954 | Direct Distributional Optimization for Provable Alignment of Diffusion
Models | [
"cs.LG"
] | We introduce a novel alignment method for diffusion models from distribution optimization perspectives while providing rigorous convergence guarantees. We first formulate the problem as a generic regularized loss minimization over probability distributions and directly optimize the distribution using the Dual Averaging... | {
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2502.02955 | ReachAgent: Enhancing Mobile Agent via Page Reaching and Operation | [
"cs.CL",
"cs.AI"
] | Recently, mobile AI agents have gained increasing attention. Given a task, mobile AI agents can interact with mobile devices in multiple steps and finally form a GUI flow that solves the task. However, existing agents tend to focus on most task-relevant elements at each step, leading to local optimal solutions and igno... | {
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2502.02957 | Control Search Rankings, Control the World: What is a Good Search
Engine? | [
"cs.IR",
"cs.CY"
] | This paper examines the ethical question, 'What is a good search engine?' Since search engines are gatekeepers of global online information, it is vital they do their job ethically well. While the Internet is now several decades old, the topic remains under-explored from interdisciplinary perspectives. This paper prese... | {
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2502.02958 | Position: Editing Large Language Models Poses Serious Safety Risks | [
"cs.CL"
] | Large Language Models (LLMs) contain large amounts of facts about the world. These facts can become outdated over time, which has led to the development of knowledge editing methods (KEs) that can change specific facts in LLMs with limited side effects. This position paper argues that editing LLMs poses serious safety ... | {
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2502.02963 | (Neural-Symbolic) Machine Learning for Inconsistency Measurement | [
"cs.AI"
] | We present machine-learning-based approaches for determining the \emph{degree} of inconsistency -- which is a numerical value -- for propositional logic knowledge bases. Specifically, we present regression- and neural-based models that learn to predict the values that the inconsistency measures $\incmi$ and $\incat$ wo... | {
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2502.02966 | FACTER: Fairness-Aware Conformal Thresholding and Prompt Engineering for
Enabling Fair LLM-Based Recommender Systems | [
"cs.IR",
"cs.AI",
"cs.CY",
"cs.LG"
] | We propose FACTER, a fairness-aware framework for LLM-based recommendation systems that integrates conformal prediction with dynamic prompt engineering. By introducing an adaptive semantic variance threshold and a violation-triggered mechanism, FACTER automatically tightens fairness constraints whenever biased patterns... | {
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2502.02967 | Demonstrating a Control Framework for Physical Human-Robot Interaction
Toward Industrial Applications | [
"cs.RO",
"cs.SY",
"eess.SY"
] | Human-Robot Interaction (pHRI) is critical for implementing Industry 5.0 which focuses on human-centric approaches. However, few studies explore the practical alignment of pHRI to industrial grade performance. This paper introduces a versatile control framework designed to bridge this gap by incorporating the torque-ba... | {
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2502.02970 | Membership Inference Attack Should Move On to Distributional Statistics
for Distilled Generative Models | [
"cs.LG"
] | Membership inference attacks (MIAs) determine whether certain data instances were used to train a model by exploiting the differences in how the model responds to seen versus unseen instances. This capability makes MIAs important in assessing privacy leakage within modern generative AI systems. However, this paper reve... | {
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2502.02972 | Label Anything: An Interpretable, High-Fidelity and Prompt-Free
Annotator | [
"cs.RO",
"cs.LG"
] | Learning-based street scene semantic understanding in autonomous driving (AD) has advanced significantly recently, but the performance of the AD model is heavily dependent on the quantity and quality of the annotated training data. However, traditional manual labeling involves high cost to annotate the vast amount of r... | {
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2502.02975 | TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential
Dynamics | [
"cs.LG",
"cs.AI"
] | Future link prediction is a fundamental challenge in various real-world dynamic systems. To address this, numerous temporal graph neural networks (temporal GNNs) and benchmark datasets have been developed. However, these datasets often feature excessive repeated edges and lack complex sequential dynamics, a key charact... | {
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2502.02977 | Disentangling CLIP Features for Enhanced Localized Understanding | [
"cs.CV"
] | Vision-language models (VLMs) demonstrate impressive capabilities in coarse-grained tasks like image classification and retrieval. However, they struggle with fine-grained tasks that require localized understanding. To investigate this weakness, we comprehensively analyze CLIP features and identify an important issue: ... | {
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2502.02982 | FedMobileAgent: Training Mobile Agents Using Decentralized Self-Sourced
Data from Diverse Users | [
"cs.AI"
] | The advancement of mobile agents has opened new opportunities for automating tasks on mobile devices. Training these agents requires large-scale high-quality data, which is costly using human labor. Given the vast number of mobile phone users worldwide, if automated data collection from them is feasible, the resulting ... | {
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2502.02984 | Learning Efficient Flocking Control based on Gibbs Random Fields | [
"cs.RO",
"cs.LG",
"cs.SY",
"eess.SY"
] | Flocking control is essential for multi-robot systems in diverse applications, yet achieving efficient flocking in congested environments poses challenges regarding computation burdens, performance optimality, and motion safety. This paper addresses these challenges through a multi-agent reinforcement learning (MARL) f... | {
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2502.02988 | Training an LLM-as-a-Judge Model: Pipeline, Insights, and Practical
Lessons | [
"cs.CL",
"cs.AI",
"cs.LG"
] | The rapid advancement of large language models (LLMs) has opened new possibilities for their adoption as evaluative judges. This paper introduces Themis, a fine-tuned LLM judge that delivers sophisticated context-aware evaluations. We provide a comprehensive overview of the development pipeline for Themis, highlighting... | {
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2502.02996 | Building Bridges between Regression, Clustering, and Classification | [
"stat.ML",
"cs.LG"
] | Regression, the task of predicting a continuous scalar target y based on some features x is one of the most fundamental tasks in machine learning and statistics. It has been observed and theoretically analyzed that the classical approach, meansquared error minimization, can lead to suboptimal results when training neur... | {
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2502.02997 | Assessing Research Impact in Indian Conference Proceedings: Insights
from Collaboration and Citations | [
"cs.IR"
] | Conferences serve as a crucial avenue for scientific communication. However, the increase in conferences and the subsequent publication of proceedings have prompted inquiries regarding the research quality being showcased at such events. This investigation delves into the conference publications indexed by Springer's L... | {
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2502.02998 | Conformal Uncertainty Indicator for Continual Test-Time Adaptation | [
"cs.LG"
] | Continual Test-Time Adaptation (CTTA) aims to adapt models to sequentially changing domains during testing, relying on pseudo-labels for self-adaptation. However, incorrect pseudo-labels can accumulate, leading to performance degradation. To address this, we propose a Conformal Uncertainty Indicator (CUI) for CTTA, lev... | {
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2502.03004 | MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large
Language Models and Retrieval-Augmented Generation | [
"cs.CL",
"cs.AI"
] | Large Language Models (LLMs) have demonstrated impressive capabilities across natural language processing tasks. However, their application to specialized domains such as medicine and biology requires further optimization to ensure factual accuracy, reliability, and contextual depth. We introduce MedBioLM, a domain-ada... | {
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2502.03005 | Driver Assistance System Based on Multimodal Data Hazard Detection | [
"cs.CV",
"cs.LG"
] | Autonomous driving technology has advanced significantly, yet detecting driving anomalies remains a major challenge due to the long-tailed distribution of driving events. Existing methods primarily rely on single-modal road condition video data, which limits their ability to capture rare and unpredictable driving incid... | {
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2502.03006 | An Augmented Backward-Corrected Projector Splitting Integrator for
Dynamical Low-Rank Training | [
"math.NA",
"cs.LG",
"cs.NA"
] | Layer factorization has emerged as a widely used technique for training memory-efficient neural networks. However, layer factorization methods face several challenges, particularly a lack of robustness during the training process. To overcome this limitation, dynamical low-rank training methods have been developed, uti... | {
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2502.03009 | Scaling Laws for Upcycling Mixture-of-Experts Language Models | [
"cs.LG",
"cs.CL"
] | Pretraining large language models (LLMs) is resource-intensive, often requiring months of training time even with high-end GPU clusters. There are two approaches of mitigating such computational demands: reusing smaller models to train larger ones (upcycling), and training computationally efficient models like mixture-... | {
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2502.03014 | xai_evals : A Framework for Evaluating Post-Hoc Local Explanation
Methods | [
"cs.LG",
"cs.AI",
"cs.ET"
] | The growing complexity of machine learning and deep learning models has led to an increased reliance on opaque "black box" systems, making it difficult to understand the rationale behind predictions. This lack of transparency is particularly challenging in high-stakes applications where interpretability is as important... | {
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2502.03016 | An analysis of optimization problems involving ReLU neural networks | [
"math.OC",
"cs.LG"
] | Solving mixed-integer optimization problems with embedded neural networks with ReLU activation functions is challenging. Big-M coefficients that arise in relaxing binary decisions related to these functions grow exponentially with the number of layers. We survey and propose different approaches to analyze and improve t... | {
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2502.03020 | Higher-order shortest paths in hypergraphs | [
"physics.soc-ph",
"cs.SI"
] | One of the defining features of complex networks is the connectivity properties that we observe emerging from local interactions. Recently, hypergraphs have emerged as a versatile tool to model networks with non-dyadic, higher-order interactions. Nevertheless, the connectivity properties of real-world hypergraphs remai... | {
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2502.03023 | Parametric Scaling Law of Tuning Bias in Conformal Prediction | [
"cs.LG",
"math.ST",
"stat.ME",
"stat.TH"
] | Conformal prediction is a popular framework of uncertainty quantification that constructs prediction sets with coverage guarantees. To uphold the exchangeability assumption, many conformal prediction methods necessitate an additional holdout set for parameter tuning. Yet, the impact of violating this principle on cover... | {
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2502.03029 | On Zero-Initialized Attention: Optimal Prompt and Gating Factor
Estimation | [
"cs.LG"
] | The LLaMA-Adapter has recently emerged as an efficient fine-tuning technique for LLaMA models, leveraging zero-initialized attention to stabilize training and enhance performance. However, despite its empirical success, the theoretical foundations of zero-initialized attention remain largely unexplored. In this paper, ... | {
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2502.03032 | Analyze Feature Flow to Enhance Interpretation and Steering in Language
Models | [
"cs.LG",
"cs.CL"
] | We introduce a new approach to systematically map features discovered by sparse autoencoder across consecutive layers of large language models, extending earlier work that examined inter-layer feature links. By using a data-free cosine similarity technique, we trace how specific features persist, transform, or first ap... | {
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2502.03033 | Aggregate to Adapt: Node-Centric Aggregation for Multi-Source-Free Graph
Domain Adaptation | [
"cs.LG"
] | Unsupervised graph domain adaptation (UGDA) focuses on transferring knowledge from labeled source graph to unlabeled target graph under domain discrepancies. Most existing UGDA methods are designed to adapt information from a single source domain, which cannot effectively exploit the complementary knowledge from multip... | {
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2502.03034 | Knowledge Distillation from Large Language Models for Household Energy
Modeling | [
"cs.CL",
"cs.LG"
] | Machine learning (ML) is increasingly vital for smart-grid research, yet restricted access to realistic, diverse data - often due to privacy concerns - slows progress and fuels doubts within the energy sector about adopting ML-based strategies. We propose integrating Large Language Models (LLMs) in energy modeling to g... | {
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2502.03035 | UMC: Unified Resilient Controller for Legged Robots with Joint
Malfunctions | [
"cs.RO"
] | Adaptation to unpredictable damages is crucial for autonomous legged robots, yet existing methods based on multi-policy or meta-learning frameworks face challenges like limited generalization and complex maintenance. To address this issue, we first analyze and summarize eight types of damage scenarios, including sensor... | {
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2502.03036 | FuXi-$\alpha$: Scaling Recommendation Model with Feature Interaction
Enhanced Transformer | [
"cs.IR"
] | Inspired by scaling laws and large language models, research on large-scale recommendation models has gained significant attention. Recent advancements have shown that expanding sequential recommendation models to large-scale recommendation models can be an effective strategy. Current state-of-the-art sequential recomm... | {
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2502.03038 | The Cake that is Intelligence and Who Gets to Bake it: An AI Analogy and
its Implications for Participation | [
"cs.AI",
"cs.CY",
"cs.LG"
] | In a widely popular analogy by Turing Award Laureate Yann LeCun, machine intelligence has been compared to cake - where unsupervised learning forms the base, supervised learning adds the icing, and reinforcement learning is the cherry on top. We expand this 'cake that is intelligence' analogy from a simple structural m... | {
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2502.03041 | Large Language Models Are Universal Recommendation Learners | [
"cs.IR",
"cs.LG"
] | In real-world recommender systems, different tasks are typically addressed using supervised learning on task-specific datasets with carefully designed model architectures. We demonstrate that large language models (LLMs) can function as universal recommendation learners, capable of handling multiple tasks within a unif... | {
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2502.03044 | RepLoRA: Reparameterizing Low-Rank Adaptation via the Perspective of
Mixture of Experts | [
"cs.LG"
] | Low-rank adaptation (LoRA) has emerged as a powerful method for fine-tuning large-scale foundation models. Despite its popularity, the theoretical understanding of LoRA has remained limited. This paper presents a theoretical analysis of LoRA by examining its connection to the Mixture of Experts models. Under this frame... | {
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2502.03047 | Kozax: Flexible and Scalable Genetic Programming in JAX | [
"cs.NE",
"cs.AI"
] | Genetic programming is an optimization algorithm inspired by natural selection which automatically evolves the structure of computer programs. The resulting computer programs are interpretable and efficient compared to black-box models with fixed structure. The fitness evaluation in genetic programming suffers from hig... | {
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2502.03048 | The Ensemble Kalman Update is an Empirical Matheron Update | [
"cs.LG",
"stat.ML"
] | The Ensemble Kalman Filter (EnKF) is a widely used method for data assimilation in high-dimensional systems. In this paper, we show that the ensemble update step of the EnKF is equivalent to an empirical version of the Matheron update popular in the study of Gaussian process regression. While this connection is simple,... | {
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2502.03052 | Understanding and Enhancing the Transferability of Jailbreaking Attacks | [
"cs.LG",
"cs.CR"
] | Jailbreaking attacks can effectively manipulate open-source large language models (LLMs) to produce harmful responses. However, these attacks exhibit limited transferability, failing to disrupt proprietary LLMs consistently. To reliably identify vulnerabilities in proprietary LLMs, this work investigates the transferab... | {
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2502.03053 | DOLFIN -- Document-Level Financial test set for Machine Translation | [
"cs.CL"
] | Despite the strong research interest in document-level Machine Translation (MT), the test sets dedicated to this task are still scarce. The existing test sets mainly cover topics from the general domain and fall short on specialised domains, such as legal and financial. Also, in spite of their document-level aspect, th... | {
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2502.03057 | High-frequency near-eye ground truth for event-based eye tracking | [
"cs.CV"
] | Event-based eye tracking is a promising solution for efficient and low-power eye tracking in smart eyewear technologies. However, the novelty of event-based sensors has resulted in a limited number of available datasets, particularly those with eye-level annotations, crucial for algorithm validation and deep-learning t... | {
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2502.03061 | Optimal Best Arm Identification with Post-Action Context | [
"cs.LG"
] | We introduce the problem of best arm identification (BAI) with post-action context, a new BAI problem in a stochastic multi-armed bandit environment and the fixed-confidence setting. The problem addresses the scenarios in which the learner receives a $\textit{post-action context}$ in addition to the reward after playin... | {
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2502.03062 | Time Series Anomaly Detection in the Frequency Domain with Statistical
Reliability | [
"stat.ML",
"cs.LG"
] | Effective anomaly detection in complex systems requires identifying change points (CPs) in the frequency domain, as abnormalities often arise across multiple frequencies. This paper extends recent advancements in statistically significant CP detection, based on Selective Inference (SI), to the frequency domain. The pro... | {
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2502.03065 | Scientometric Analysis of the German IR Community within TREC & CLEF | [
"cs.IR",
"cs.DL"
] | Within this study, the influence of the German Information Retrieval community on the retrieval campaigns Text Retrieval Conference (TREC) and Conference and Labs of the Evaluation Forum (CLEF) between 2000 and 2022 was analyzed based on metadata provided by OpenAlex and further metadata extracted with the GROBID frame... | {
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2502.03067 | Optimizing Electric Vehicles Charging using Large Language Models and
Graph Neural Networks | [
"eess.SY",
"cs.LG",
"cs.SY"
] | Maintaining grid stability amid widespread electric vehicle (EV) adoption is vital for sustainable transportation. Traditional optimization methods and Reinforcement Learning (RL) approaches often struggle with the high dimensionality and dynamic nature of real-time EV charging, leading to sub-optimal solutions. To add... | {
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2502.03072 | RoboGrasp: A Universal Grasping Policy for Robust Robotic Control | [
"cs.RO",
"cs.CV"
] | Imitation learning and world models have shown significant promise in advancing generalizable robotic learning, with robotic grasping remaining a critical challenge for achieving precise manipulation. Existing methods often rely heavily on robot arm state data and RGB images, leading to overfitting to specific object s... | {
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2502.03078 | Automatic Prompt Optimization Techniques: Exploring the Potential for
Synthetic Data Generation | [
"cs.HC",
"cs.LG"
] | Artificial Intelligence (AI) advancement is heavily dependent on access to large-scale, high-quality training data. However, in specialized domains such as healthcare, data acquisition faces significant constraints due to privacy regulations, ethical considerations, and limited availability. While synthetic data genera... | {
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2502.03080 | IAO Prompting: Making Knowledge Flow Explicit in LLMs through Structured
Reasoning Templates | [
"cs.CL"
] | While Large Language Models (LLMs) demonstrate impressive reasoning capabilities, understanding and validating their knowledge utilization remains challenging. Chain-of-thought (CoT) prompting partially addresses this by revealing intermediate reasoning steps, but the knowledge flow and application remain implicit. We ... | {
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2502.03081 | Human-Aligned Image Models Improve Visual Decoding from the Brain | [
"cs.CV",
"cs.LG"
] | Decoding visual images from brain activity has significant potential for advancing brain-computer interaction and enhancing the understanding of human perception. Recent approaches align the representation spaces of images and brain activity to enable visual decoding. In this paper, we introduce the use of human-aligne... | {
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2502.03086 | Implementing Large Quantum Boltzmann Machines as Generative AI Models
for Dataset Balancing | [
"cs.ET",
"cs.AI",
"cs.LG",
"cs.NE",
"quant-ph"
] | This study explores the implementation of large Quantum Restricted Boltzmann Machines (QRBMs), a key advancement in Quantum Machine Learning (QML), as generative models on D-Wave's Pegasus quantum hardware to address dataset imbalance in Intrusion Detection Systems (IDS). By leveraging Pegasus's enhanced connectivity a... | {
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2502.03092 | E-3SFC: Communication-Efficient Federated Learning with Double-way
Features Synthesizing | [
"cs.LG",
"cs.AI",
"cs.DC"
] | The exponential growth in model sizes has significantly increased the communication burden in Federated Learning (FL). Existing methods to alleviate this burden by transmitting compressed gradients often face high compression errors, which slow down the model's convergence. To simultaneously achieve high compression ef... | {
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"cs.SY": 0
} |
2502.03095 | Reveal the Mystery of DPO: The Connection between DPO and RL Algorithms | [
"cs.LG"
] | With the rapid development of Large Language Models (LLMs), numerous Reinforcement Learning from Human Feedback (RLHF) algorithms have been introduced to improve model safety and alignment with human preferences. These algorithms can be divided into two main frameworks based on whether they require an explicit reward (... | {
"Other": 0,
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} |
2502.03100 | A Bayesian perspective on single-shot laser characterization | [
"physics.optics",
"cs.LG",
"physics.ins-det"
] | We introduce a Bayesian framework for measuring spatio-temporal couplings (STCs) in ultra-intense lasers that reconceptualizes what constitutes a 'single-shot' measurement. Moving beyond traditional distinctions between single- and multi-shot devices, our approach provides rigorous criteria for determining when measure... | {
"Other": 0,
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} |
2502.03102 | Structured Token Retention and Computational Memory Paths in Large
Language Models | [
"cs.CL"
] | Memory retention mechanisms play a central role in determining the efficiency of computational architectures designed for processing extended sequences. Conventional methods for token management often impose fixed retention thresholds or rely on uniform attention weight distributions, leading to inefficient memory util... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
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"cs.SD": 0,
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} |
2502.03103 | Edge Attention Module for Object Classification | [
"cs.CV",
"cs.LG"
] | A novel ``edge attention-based Convolutional Neural Network (CNN)'' is proposed in this research for object classification task. With the advent of advanced computing technology, CNN models have achieved to remarkable success, particularly in computer vision applications. Nevertheless, the efficacy of the conventional ... | {
"Other": 0,
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} |
2502.03104 | Bellman Error Centering | [
"cs.LG",
"cs.AI"
] | This paper revisits the recently proposed reward centering algorithms including simple reward centering (SRC) and value-based reward centering (VRC), and points out that SRC is indeed the reward centering, while VRC is essentially Bellman error centering (BEC). Based on BEC, we provide the centered fixpoint for tabular... | {
"Other": 0,
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} |
2502.03108 | Multi-objective methods in Federated Learning: A survey and taxonomy | [
"cs.LG",
"cs.DC"
] | The Federated Learning paradigm facilitates effective distributed machine learning in settings where training data is decentralized across multiple clients. As the popularity of the strategy grows, increasingly complex real-world problems emerge, many of which require balancing conflicting demands such as fairness, uti... | {
"Other": 1,
"cs.AI": 0,
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"cs.SY": 0
} |
2502.03111 | Policies and Evaluation for Online Meeting Summarization | [
"cs.CL",
"cs.AI",
"cs.LG"
] | With more and more meetings moving to a digital domain, meeting summarization has recently gained interest in both academic and commercial research. However, prior academic research focuses on meeting summarization as an offline task, performed after the meeting concludes. In this paper, we perform the first systematic... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
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} |
2502.03117 | Meta-Learning-Based People Counting and Localization Models Employing
CSI from Commodity WiFi NICs | [
"cs.IT",
"eess.SP",
"math.IT"
] | In this paper, we consider people counting and localization systems exploiting channel state information (CSI) measured from commodity WiFi network interface cards (NICs). While CSI has useful information of amplitude and phase to describe signal propagation in a measurement environment of interest, CSI measurement suf... | {
"Other": 0,
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} |
2502.03118 | Tell2Reg: Establishing spatial correspondence between images by the same
language prompts | [
"cs.CV",
"cs.AI",
"eess.IV"
] | Spatial correspondence can be represented by pairs of segmented regions, such that the image registration networks aim to segment corresponding regions rather than predicting displacement fields or transformation parameters. In this work, we show that such a corresponding region pair can be predicted by the same langua... | {
"Other": 0,
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"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
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