id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2502.06860 | AutoSketch: VLM-assisted Style-Aware Vector Sketch Completion | [
"cs.CV",
"cs.GR"
] | The ability to automatically complete a partial sketch that depicts a complex scene, e.g., "a woman chatting with a man in the park", is very useful. However, existing sketch generation methods create sketches from scratch; they do not complete a partial sketch in the style of the original. To address this challenge, w... | {
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2502.06861 | Design Considerations in Offline Preference-based RL | [
"cs.LG",
"cs.AI"
] | Offline algorithms for Reinforcement Learning from Human Preferences (RLHF), which use only a fixed dataset of sampled responses given an input, and preference feedback among these responses, have gained increasing prominence in the literature on aligning language models. In this paper, we study how the different desig... | {
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2502.06862 | Poincar\'e Inequality for Local Log-Polyak-Lojasiewicz Measures :
Non-asymptotic Analysis in Low-temperature Regime | [
"cs.LG",
"math.CA",
"math.FA",
"math.PR",
"stat.ML"
] | Potential functions in highly pertinent applications, such as deep learning in over-parameterized regime, are empirically observed to admit non-isolated minima. To understand the convergence behavior of stochastic dynamics in such landscapes, we propose to study the class of \logPLmeasure\ measures $\mu_\epsilon \propt... | {
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2502.06863 | BF-GAN: Development of an AI-driven Bubbly Flow Image Generation Model
Using Generative Adversarial Networks | [
"cs.CV",
"cs.AI"
] | A generative AI architecture called bubbly flow generative adversarial networks (BF-GAN) is developed, designed to generate realistic and high-quality bubbly flow images through physically conditioned inputs, jg and jf. Initially, 52 sets of bubbly flow experiments under varying conditions are conducted to collect 140,... | {
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2502.06864 | Knowledge Graph-Guided Retrieval Augmented Generation | [
"cs.CL",
"cs.AI"
] | Retrieval-augmented generation (RAG) has emerged as a promising technology for addressing hallucination issues in the responses generated by large language models (LLMs). Existing studies on RAG primarily focus on applying semantic-based approaches to retrieve isolated relevant chunks, which ignore their intrinsic rela... | {
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2502.06865 | Deep Ritz method with Fourier feature mapping: A deep learning approach
for solving variational models of microstructure | [
"cs.LG"
] | This paper presents a novel approach that combines the Deep Ritz Method (DRM) with Fourier feature mapping to solve minimization problems comprised of multi-well, non-convex energy potentials. These problems present computational challenges as they lack a global minimum. Through an investigation of three benchmark prob... | {
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2502.06866 | Global Ease of Living Index: a machine learning framework for
longitudinal analysis of major economies | [
"cs.LG",
"cs.AI",
"econ.EM",
"stat.AP",
"stat.ML"
] | The drastic changes in the global economy, geopolitical conditions, and disruptions such as the COVID-19 pandemic have impacted the cost of living and quality of life. It is important to understand the long-term nature of the cost of living and quality of life in major economies. A transparent and comprehensive living ... | {
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2502.06867 | Forbidden Science: Dual-Use AI Challenge Benchmark and Scientific
Refusal Tests | [
"cs.CL",
"cs.AI"
] | The development of robust safety benchmarks for large language models requires open, reproducible datasets that can measure both appropriate refusal of harmful content and potential over-restriction of legitimate scientific discourse. We present an open-source dataset and testing framework for evaluating LLM safety mec... | {
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2502.06868 | Related Knowledge Perturbation Matters: Rethinking Multiple Pieces of
Knowledge Editing in Same-Subject | [
"cs.CL",
"cs.AI"
] | Knowledge editing has become a promising approach for efficiently and precisely updating knowledge embedded in large language models (LLMs). In this work, we focus on Same-Subject Editing, which involves modifying multiple attributes of a single entity to ensure comprehensive and consistent updates to entity-centric kn... | {
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2502.06869 | A Survey on Explainable Deep Reinforcement Learning | [
"cs.LG",
"cs.AI"
] | Deep Reinforcement Learning (DRL) has achieved remarkable success in sequential decision-making tasks across diverse domains, yet its reliance on black-box neural architectures hinders interpretability, trust, and deployment in high-stakes applications. Explainable Deep Reinforcement Learning (XRL) addresses these chal... | {
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2502.06870 | Bridging Traffic State and Trajectory for Dynamic Road Network and
Trajectory Representation Learning | [
"cs.LG",
"cs.AI"
] | Effective urban traffic management is vital for sustainable city development, relying on intelligent systems with machine learning tasks such as traffic flow prediction and travel time estimation. Traditional approaches usually focus on static road network and trajectory representation learning, and overlook the dynami... | {
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2502.06871 | FlavorDiffusion: Predicting Food Pairings and Chemical Interactions
Using Diffusion Models | [
"cs.LG",
"cs.AI"
] | The study of food pairing has evolved beyond subjective expertise with the advent of machine learning. This paper presents FlavorDiffusion, a novel framework leveraging diffusion models to predict food-chemical interactions and ingredient pairings without relying on chromatography. By integrating graph-based embeddings... | {
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2502.06872 | Towards Trustworthy Retrieval Augmented Generation for Large Language
Models: A Survey | [
"cs.CL",
"cs.AI"
] | Retrieval-Augmented Generation (RAG) is an advanced technique designed to address the challenges of Artificial Intelligence-Generated Content (AIGC). By integrating context retrieval into content generation, RAG provides reliable and up-to-date external knowledge, reduces hallucinations, and ensures relevant context ac... | {
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2502.06873 | Multimodal Cognitive Reframing Therapy via Multi-hop Psychotherapeutic
Reasoning | [
"cs.CL",
"cs.AI"
] | Previous research has revealed the potential of large language models (LLMs) to support cognitive reframing therapy; however, their focus was primarily on text-based methods, often overlooking the importance of non-verbal evidence crucial in real-life therapy. To alleviate this gap, we extend the textual cognitive refr... | {
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2502.06874 | Group Reasoning Emission Estimation Networks | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Accurate greenhouse gas (GHG) emission reporting is critical for governments, businesses, and investors. However, adoption remains limited particularly among small and medium enterprises due to high implementation costs, fragmented emission factor databases, and a lack of robust sector classification methods. To addres... | {
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2502.06875 | Beyond Vision: How Large Language Models Interpret Facial Expressions
from Valence-Arousal Values | [
"cs.CV",
"cs.AI",
"cs.CL"
] | Large Language Models primarily operate through text-based inputs and outputs, yet human emotion is communicated through both verbal and non-verbal cues, including facial expressions. While Vision-Language Models analyze facial expressions from images, they are resource-intensive and may depend more on linguistic prior... | {
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2502.06876 | Mix Data or Merge Models? Balancing the Helpfulness, Honesty, and
Harmlessness of Large Language Model via Model Merging | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Achieving balanced alignment of large language models (LLMs) in terms of Helpfulness, Honesty, and Harmlessness (3H optimization) constitutes a cornerstone of responsible AI, with existing methods like data mixture strategies facing limitations including reliance on expert knowledge and conflicting optimization signals... | {
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2502.06877 | WirelessGPT: A Generative Pre-trained Multi-task Learning Framework for
Wireless Communication | [
"cs.LG"
] | This paper introduces WirelessGPT, a pioneering foundation model specifically designed for multi-task learning in wireless communication and sensing. Specifically, WirelessGPT leverages large-scale wireless channel datasets for unsupervised pretraining and extracting universal channel representations, which captures co... | {
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2502.06878 | Deep Learning Meets Oversampling: A Learning Framework to Handle
Imbalanced Classification | [
"cs.LG"
] | Despite extensive research spanning several decades, class imbalance is still considered a profound difficulty for both machine learning and deep learning models. While data oversampling is the foremost technique to address this issue, traditional sampling techniques are often decoupled from the training phase of the p... | {
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2502.06879 | CluStRE: Streaming Graph Clustering with Multi-Stage Refinement | [
"cs.LG",
"cs.DB"
] | We present CluStRE, a novel streaming graph clustering algorithm that balances computational efficiency with high-quality clustering using multi-stage refinement. Unlike traditional in-memory clustering approaches, CluStRE processes graphs in a streaming setting, significantly reducing memory overhead while leveraging ... | {
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2502.06882 | Multi-Agent Simulator Drives Language Models for Legal Intensive
Interaction | [
"cs.CL",
"cs.AI"
] | Large Language Models (LLMs) have significantly advanced legal intelligence, but the scarcity of scenario data impedes the progress toward interactive legal scenarios. This paper introduces a Multi-agent Legal Simulation Driver (MASER) to scalably generate synthetic data by simulating interactive legal scenarios. Lever... | {
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2502.06884 | Learning Conformal Abstention Policies for Adaptive Risk Management in
Large Language and Vision-Language Models | [
"cs.LG",
"cs.AI"
] | Large Language and Vision-Language Models (LLMs/VLMs) are increasingly used in safety-critical applications, yet their opaque decision-making complicates risk assessment and reliability. Uncertainty quantification (UQ) helps assess prediction confidence and enables abstention when uncertainty is high. Conformal predict... | {
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2502.06885 | Topological derivative approach for deep neural network architecture
adaptation | [
"cs.LG",
"cs.AI"
] | This work presents a novel algorithm for progressively adapting neural network architecture along the depth. In particular, we attempt to address the following questions in a mathematically principled way: i) Where to add a new capacity (layer) during the training process? ii) How to initialize the new capacity? At the... | {
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2502.06887 | Gradient Based Method for the Fusion of Lattice Quantizers | [
"cs.LG",
"cs.AI"
] | In practical applications, lattice quantizers leverage discrete lattice points to approximate arbitrary points in the lattice. An effective lattice quantizer significantly enhances both the accuracy and efficiency of these approximations. In the context of high-dimensional lattice quantization, previous work proposed u... | {
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2502.06888 | Klotski: Efficient Mixture-of-Expert Inference via Expert-Aware
Multi-Batch Pipeline | [
"cs.LG",
"cs.AI"
] | Mixture of Experts (MoE), with its distinctive sparse structure, enables the scaling of language models up to trillions of parameters without significantly increasing computational costs. However, the substantial parameter size presents a challenge for inference, as the expansion in GPU memory cannot keep pace with the... | {
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2502.06889 | Secure Visual Data Processing via Federated Learning | [
"cs.CV"
] | As the demand for privacy in visual data management grows, safeguarding sensitive information has become a critical challenge. This paper addresses the need for privacy-preserving solutions in large-scale visual data processing by leveraging federated learning. Although there have been developments in this field, previ... | {
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2502.06890 | LLMs for Drug-Drug Interaction Prediction: A Comprehensive Comparison | [
"cs.LG",
"cs.AI",
"q-bio.QM"
] | The increasing volume of drug combinations in modern therapeutic regimens needs reliable methods for predicting drug-drug interactions (DDIs). While Large Language Models (LLMs) have revolutionized various domains, their potential in pharmaceutical research, particularly in DDI prediction, remains largely unexplored. T... | {
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2502.06891 | ScaffoldGPT: A Scaffold-based Large Language Model for Drug Improvement | [
"q-bio.BM",
"cs.CL",
"cs.LG"
] | Drug optimization has become increasingly crucial in light of fast-mutating virus strains and drug-resistant cancer cells. Nevertheless, it remains challenging as it necessitates retaining the beneficial properties of the original drug while simultaneously enhancing desired attributes beyond its scope. In this work, we... | {
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2502.06892 | Certifying Language Model Robustness with Fuzzed Randomized Smoothing:
An Efficient Defense Against Backdoor Attacks | [
"cs.LG",
"cs.AI"
] | The widespread deployment of pre-trained language models (PLMs) has exposed them to textual backdoor attacks, particularly those planted during the pre-training stage. These attacks pose significant risks to high-reliability applications, as they can stealthily affect multiple downstream tasks. While certifying robustn... | {
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2502.06893 | A New Hybrid Intelligent Approach for Multimodal Detection of Suspected
Disinformation on TikTok | [
"cs.CV",
"cs.CL",
"cs.MM",
"cs.SC"
] | In the context of the rapid dissemination of multimedia content, identifying disinformation on social media platforms such as TikTok represents a significant challenge. This study introduces a hybrid framework that combines the computational power of deep learning with the interpretability of fuzzy logic to detect susp... | {
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2502.06894 | AI-Driven HSI: Multimodality, Fusion, Challenges, and the Deep Learning
Revolution | [
"cs.CV",
"cs.AI"
] | Hyperspectral imaging (HSI) captures spatial and spectral data, enabling analysis of features invisible to conventional systems. The technology is vital in fields such as weather monitoring, food quality control, counterfeit detection, healthcare diagnostics, and extending into defense, agriculture, and industrial auto... | {
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2502.06895 | A Comprehensive Review of U-Net and Its Variants: Advances and
Applications in Medical Image Segmentation | [
"eess.IV",
"cs.CV"
] | Medical images often exhibit low and blurred contrast between lesions and surrounding tissues, with considerable variation in lesion edges and shapes even within the same disease, leading to significant challenges in segmentation. Therefore, precise segmentation of lesions has become an essential prerequisite for patie... | {
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2502.06897 | PyPotteryInk: One-Step Diffusion Model for Sketch to Publication-ready
Archaeological Drawings | [
"cs.GR",
"cs.AI",
"cs.CV"
] | Archaeological pottery documentation traditionally requires a time-consuming manual process of converting pencil sketches into publication-ready inked drawings. I present PyPotteryInk, an open-source automated pipeline that transforms archaeological pottery sketches into standardised publication-ready drawings using a ... | {
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2502.06898 | Large Language Models for In-File Vulnerability Localization Can Be
"Lost in the End" | [
"cs.SE",
"cs.AI"
] | Recent advancements in artificial intelligence have enabled processing of larger inputs, leading everyday software developers to increasingly rely on chat-based large language models (LLMs) like GPT-3.5 and GPT-4 to detect vulnerabilities across entire files, not just within functions. This new development practice req... | {
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2502.06899 | A Sociotechnical Approach for Knowledge Management (KM) | [
"cs.DB",
"cs.AI"
] | This article presents a sociotechnical framework for KM. This sociotechnical vision of KM allows: (1) to remove KM from a commercial concern; (2) to divide the different KM technologies; and (3) to question the paradigms associated with the social and technical components of KM. It is precisely this last point that thi... | {
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2502.06900 | Polynomial Regret Concentration of UCB for Non-Deterministic State
Transitions | [
"cs.LG",
"cs.DM"
] | Monte Carlo Tree Search (MCTS) has proven effective in solving decision-making problems in perfect information settings. However, its application to stochastic and imperfect information domains remains limited. This paper extends the theoretical framework of MCTS to stochastic domains by addressing non-deterministic st... | {
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2502.06901 | Enabling Autoregressive Models to Fill In Masked Tokens | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Historically, LLMs have been trained using either autoregressive (AR) or masked language modeling (MLM) objectives, with AR models gaining dominance in recent years. However, AR models are inherently incapable of masked infilling, which is the ability to predict masked tokens between past and future context. In contras... | {
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2502.06902 | Emergence of Episodic Memory in Transformers: Characterizing Changes in
Temporal Structure of Attention Scores During Training | [
"cs.LG",
"cs.AI",
"cs.CL"
] | We investigate in-context temporal biases in attention heads and transformer outputs. Using cognitive science methodologies, we analyze attention scores and outputs of the GPT-2 models of varying sizes. Across attention heads, we observe effects characteristic of human episodic memory, including temporal contiguity, pr... | {
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2502.06905 | Lightweight Dataset Pruning without Full Training via Example Difficulty
and Prediction Uncertainty | [
"cs.LG",
"cs.AI"
] | Recent advances in deep learning rely heavily on massive datasets, leading to substantial storage and training costs. Dataset pruning aims to alleviate this demand by discarding redundant examples. However, many existing methods require training a model with a full dataset over a large number of epochs before being abl... | {
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2502.06906 | Learning-based estimation of cattle weight gain and its influencing
factors | [
"cs.LG",
"cs.AI"
] | Many cattle farmers still depend on manual methods to measure the live weight gain of cattle at set intervals, which is time consuming, labour intensive, and stressful for both the animals and handlers. A remote and autonomous monitoring system using machine learning (ML) or deep learning (DL) can provide a more effici... | {
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2502.06907 | Can ChatGPT Diagnose Alzheimer's Disease? | [
"cs.LG",
"cs.AI"
] | Can ChatGPT diagnose Alzheimer's Disease (AD)? AD is a devastating neurodegenerative condition that affects approximately 1 in 9 individuals aged 65 and older, profoundly impairing memory and cognitive function. This paper utilises 9300 electronic health records (EHRs) with data from Magnetic Resonance Imaging (MRI) an... | {
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2502.06909 | Satisfaction-Aware Incentive Scheme for Federated Learning in Industrial
Metaverse: DRL-Based Stackbelberg Game Approach | [
"cs.LG",
"cs.AI",
"cs.GT"
] | Industrial Metaverse leverages the Industrial Internet of Things (IIoT) to integrate data from diverse devices, employing federated learning and meta-computing to train models in a distributed manner while ensuring data privacy. Achieving an immersive experience for industrial Metaverse necessitates maintaining a balan... | {
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2502.06910 | TimeKAN: KAN-based Frequency Decomposition Learning Architecture for
Long-term Time Series Forecasting | [
"cs.LG",
"cs.AI"
] | Real-world time series often have multiple frequency components that are intertwined with each other, making accurate time series forecasting challenging. Decomposing the mixed frequency components into multiple single frequency components is a natural choice. However, the information density of patterns varies across ... | {
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2502.06911 | Foundation Models for Anomaly Detection: Vision and Challenges | [
"cs.LG",
"cs.AI"
] | As data continues to grow in volume and complexity across domains such as finance, manufacturing, and healthcare, effective anomaly detection is essential for identifying irregular patterns that may signal critical issues. Recently, foundation models (FMs) have emerged as a powerful tool for advancing anomaly detection... | {
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2502.06913 | A Simple yet Effective DDG Predictor is An Unsupervised Antibody
Optimizer and Explainer | [
"q-bio.QM",
"cs.AI",
"cs.LG"
] | The proteins that exist today have been optimized over billions of years of natural evolution, during which nature creates random mutations and selects them. The discovery of functionally promising mutations is challenged by the limited evolutionary accessible regions, i.e., only a small region on the fitness landscape... | {
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2502.06914 | UniZyme: A Unified Protein Cleavage Site Predictor Enhanced with Enzyme
Active-Site Knowledge | [
"q-bio.QM",
"cs.AI",
"cs.LG"
] | Enzyme-catalyzed protein cleavage is essential for many biological functions. Accurate prediction of cleavage sites can facilitate various applications such as drug development, enzyme design, and a deeper understanding of biological mechanisms. However, most existing models are restricted to an individual enzyme, whic... | {
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2502.06915 | Analytic Personalized Federated Meta-Learning | [
"cs.DC",
"cs.LG"
] | Analytic federated learning (AFL) which updates model weights only once by using closed-form least-square (LS) solutions can reduce abundant training time in gradient-free federated learning (FL). The current AFL framework cannot support deep neural network (DNN) training, which hinders its implementation on complex ma... | {
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2502.06916 | Hyper Compressed Fine-Tuning of Large Foundation Models with Quantum
Inspired Adapters | [
"cs.LG",
"cs.AI",
"eess.SP",
"quant-ph"
] | Fine-tuning pre-trained large foundation models for specific tasks has become increasingly challenging due to the computational and storage demands associated with full parameter updates. Parameter-Efficient Fine-Tuning (PEFT) methods address this issue by updating only a small subset of model parameters using adapter ... | {
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2502.06917 | Krum Federated Chain (KFC): Using blockchain to defend against
adversarial attacks in Federated Learning | [
"cs.LG",
"cs.AI"
] | Federated Learning presents a nascent approach to machine learning, enabling collaborative model training across decentralized devices while safeguarding data privacy. However, its distributed nature renders it susceptible to adversarial attacks. Integrating blockchain technology with Federated Learning offers a promis... | {
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2502.06918 | Leveraging GPT-4o Efficiency for Detecting Rework Anomaly in Business
Processes | [
"cs.LG",
"cs.AI"
] | This paper investigates the effectiveness of GPT-4o-2024-08-06, one of the Large Language Models (LLM) from OpenAI, in detecting business process anomalies, with a focus on rework anomalies. In our study, we developed a GPT-4o-based tool capable of transforming event logs into a structured format and identifying rework... | {
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} |
2502.06919 | Select before Act: Spatially Decoupled Action Repetition for Continuous
Control | [
"cs.LG",
"cs.AI",
"cs.RO"
] | Reinforcement Learning (RL) has achieved remarkable success in various continuous control tasks, such as robot manipulation and locomotion. Different to mainstream RL which makes decisions at individual steps, recent studies have incorporated action repetition into RL, achieving enhanced action persistence with improve... | {
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2502.06920 | Direct Estimation of Pediatric Heart Rate Variability from BOLD-fMRI: A
Machine Learning Approach Using Dynamic Connectivity | [
"eess.IV",
"cs.AI",
"cs.LG"
] | In many pediatric fMRI studies, cardiac signals are often missing or of poor quality. A tool to extract Heart Rate Variation (HRV) waveforms directly from fMRI data, without the need for peripheral recording devices, would be highly beneficial. We developed a machine learning framework to accurately reconstruct HRV for... | {
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2502.06921 | GraNNite: Enabling High-Performance Execution of Graph Neural Networks
on Resource-Constrained Neural Processing Units | [
"cs.LG",
"cs.AI",
"cs.AR"
] | Graph Neural Networks (GNNs) are vital for learning from graph-structured data, enabling applications in network analysis, recommendation systems, and speech analytics. Deploying them on edge devices like client PCs and laptops enhances real-time processing, privacy, and cloud independence. GNNs aid Retrieval-Augmented... | {
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2502.06922 | Synthetic Audio Helps for Cognitive State Tasks | [
"cs.SD",
"cs.AI",
"cs.CL",
"cs.LG"
] | The NLP community has broadly focused on text-only approaches of cognitive state tasks, but audio can provide vital missing cues through prosody. We posit that text-to-speech models learn to track aspects of cognitive state in order to produce naturalistic audio, and that the signal audio models implicitly identify is ... | {
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2502.06923 | Do Attention Heads Compete or Cooperate during Counting? | [
"cs.LG",
"cs.AI"
] | We present an in-depth mechanistic interpretability analysis of training small transformers on an elementary task, counting, which is a crucial deductive step in many algorithms. In particular, we investigate the collaboration/competition among the attention heads: we ask whether the attention heads behave as a pseudo-... | {
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2502.06924 | XAMBA: Enabling Efficient State Space Models on Resource-Constrained
Neural Processing Units | [
"cs.LG",
"cs.AI"
] | State-Space Models (SSMs) have emerged as efficient alternatives to transformers for sequential data tasks, offering linear or near-linear scalability with sequence length, making them ideal for long-sequence applications in NLP, vision, and edge AI, including real-time transcription, translation, and contextual search... | {
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2502.06925 | Occam's model: Selecting simpler representations for better
transferability estimation | [
"cs.LG",
"cs.AI"
] | Fine-tuning models that have been pre-trained on large datasets has become a cornerstone of modern machine learning workflows. With the widespread availability of online model repositories, such as Hugging Face, it is now easier than ever to fine-tune pre-trained models for specific tasks. This raises a critical questi... | {
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2502.06927 | Neighborhood-Order Learning Graph Attention Network for Fake News
Detection | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Fake news detection is a significant challenge in the digital age, which has become increasingly important with the proliferation of social media and online communication networks. Graph Neural Networks (GNN)-based methods have shown high potential in analyzing graph-structured data for this problem. However, a major l... | {
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2502.06939 | Generalizable automated ischaemic stroke lesion segmentation with vision
transformers | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Ischaemic stroke, a leading cause of death and disability, critically relies on neuroimaging for characterising the anatomical pattern of injury. Diffusion-weighted imaging (DWI) provides the highest expressivity in ischemic stroke but poses substantial challenges for automated lesion segmentation: susceptibility artef... | {
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2502.06957 | GAS: Generative Avatar Synthesis from a Single Image | [
"cs.CV"
] | We introduce a generalizable and unified framework to synthesize view-consistent and temporally coherent avatars from a single image, addressing the challenging problem of single-image avatar generation. While recent methods employ diffusion models conditioned on human templates like depth or normal maps, they often st... | {
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2502.06963 | Task Offloading in Vehicular Edge Computing using Deep Reinforcement
Learning: A Survey | [
"cs.LG",
"cs.AI",
"cs.DC",
"cs.MA"
] | The increasing demand for Intelligent Transportation Systems (ITS) has introduced significant challenges in managing the complex, computation-intensive tasks generated by modern vehicles while offloading tasks to external computing infrastructures such as edge computing (EC), nearby vehicular , and UAVs has become infl... | {
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2502.06967 | Downlink and Uplink ISAC in Continuous-Aperture Array (CAPA) Systems | [
"cs.IT",
"eess.SP",
"math.IT"
] | A continuous-aperture array (CAPA)-based integrated sensing and communications (ISAC) framework is proposed for both downlink and uplink scenarios. Within this framework, continuous operator-based signal models are employed to describe the sensing and communication processes. The performance of communication and sensin... | {
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2502.06970 | Model Diffusion for Certifiable Few-shot Transfer Learning | [
"cs.LG",
"stat.ML"
] | In modern large-scale deep learning, a prevalent and effective workflow for solving low-data problems is adapting powerful pre-trained foundation models (FMs) to new tasks via parameter-efficient fine-tuning (PEFT). However, while empirically effective, the resulting solutions lack generalisation guarantees to certify ... | {
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2502.06971 | User-Preference Meets Pareto-Optimality: Multi-Objective Bayesian
Optimization with Local Gradient Search | [
"cs.LG"
] | Incorporating user preferences into multi-objective Bayesian optimization (MOBO) allows for personalization of the optimization procedure. Preferences are often abstracted in the form of an unknown utility function, estimated through pairwise comparisons of potential outcomes. However, utility-driven MOBO methods can y... | {
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2502.06973 | Indoor Light and Heat Estimation from a Single Panorama | [
"cs.CV"
] | This paper presents a novel application for directly estimating indoor light and heat maps from captured indoor-outdoor High Dynamic Range (HDR) panoramas. In our image-based rendering method, the indoor panorama is used to estimate the 3D room layout, while the corresponding outdoor panorama serves as an environment m... | {
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2502.06975 | Position: Episodic Memory is the Missing Piece for Long-Term LLM Agents | [
"cs.AI"
] | As Large Language Models (LLMs) evolve from text-completion tools into fully fledged agents operating in dynamic environments, they must address the challenge of continually learning and retaining long-term knowledge. Many biological systems solve these challenges with episodic memory, which supports single-shot learni... | {
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2502.06976 | Who is Helping Whom? Analyzing Inter-dependencies to Evaluate
Cooperation in Human-AI Teaming | [
"cs.MA",
"cs.AI"
] | The long-standing research challenges of Human-AI Teaming(HAT) and Zero-shot Cooperation(ZSC) have been tackled by applying multi-agent reinforcement learning(MARL) to train an agent by optimizing the environment reward function and evaluating their performance through task performance metrics such as task reward. Howe... | {
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2502.06978 | Dual Conic Proxy for Semidefinite Relaxation of AC Optimal Power Flow | [
"math.OC",
"cs.LG"
] | The nonlinear, non-convex AC Optimal Power Flow (AC-OPF) problem is fundamental for power systems operations. The intrinsic complexity of AC-OPF has fueled a growing interest in the development of optimization proxies for the problem, i.e., machine learning models that predict high-quality, close-to-optimal solutions. ... | {
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2502.06982 | Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale
Google TPU Systems with ML Productivity Goodput | [
"cs.LG"
] | Recent years have seen the emergence of machine learning (ML) workloads deployed in warehouse-scale computing (WSC) settings, also known as ML fleets. As the computational demands placed on ML fleets have increased due to the rise of large models and growing demand for ML applications, it has become increasingly critic... | {
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2502.06987 | Universal Vessel Segmentation for Multi-Modality Retinal Images | [
"eess.IV",
"cs.CV"
] | We identify two major limitations in the existing studies on retinal vessel segmentation: (1) Most existing works are restricted to one modality, i.e, the Color Fundus (CF). However, multi-modality retinal images are used every day in the study of retina and retinal diseases, and the study of vessel segmentation on the... | {
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2502.06988 | A Compiler for Operations on Relations with Bag Semantics | [
"cs.PL",
"cs.DB"
] | We describe an abstract loop-based intermediate representation that can express fused implementations of relational algebra expressions on sets and bags (multisets). The loops are abstracted away from physical data structures thus making it easier to generate, reason about, and perform optimization like fusion on. The ... | {
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2502.06990 | Investigating the Zone of Proximal Development of Language Models for
In-Context Learning | [
"cs.CL"
] | In this paper, we introduce a learning analytics framework to analyze the in-context learning (ICL) behavior of large language models (LLMs) through the lens of the Zone of Proximal Development (ZPD), an established theory in educational psychology. ZPD delineates the space between what a learner is capable of doing un... | {
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2502.06994 | SyncMind: Measuring Agent Out-of-Sync Recovery in Collaborative Software
Engineering | [
"cs.SE",
"cs.AI",
"cs.CL"
] | Software engineering (SE) is increasingly collaborative, with developers working together on shared complex codebases. Effective collaboration in shared environments requires participants -- whether humans or AI agents -- to stay on the same page as their environment evolves. When a collaborator's understanding diverge... | {
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2502.06995 | Epistemic Uncertainty in Conformal Scores: A Unified Approach | [
"stat.ML",
"cs.LG"
] | Conformal prediction methods create prediction bands with distribution-free guarantees but do not explicitly capture epistemic uncertainty, which can lead to overconfident predictions in data-sparse regions. Although recent conformal scores have been developed to address this limitation, they are typically designed for... | {
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2502.06996 | A view on learning robust goal-conditioned value functions: Interplay
between RL and MPC | [
"eess.SY",
"cs.SY"
] | Reinforcement learning (RL) and model predictive control (MPC) offer a wealth of distinct approaches for automatic decision-making. Given the impact both fields have had independently across numerous domains, there is growing interest in combining the general-purpose learning capability of RL with the safety and robust... | {
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2502.06997 | Conditional diffusion model with spatial attention and latent embedding
for medical image segmentation | [
"eess.IV",
"cs.CV"
] | Diffusion models have been used extensively for high quality image and video generation tasks. In this paper, we propose a novel conditional diffusion model with spatial attention and latent embedding (cDAL) for medical image segmentation. In cDAL, a convolutional neural network (CNN) based discriminator is used at eve... | {
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2502.06999 | Outsourced diffusion sampling: Efficient posterior inference in latent
spaces of generative models | [
"cs.LG"
] | Any well-behaved generative model over a variable $\mathbf{x}$ can be expressed as a deterministic transformation of an exogenous ('outsourced') Gaussian noise variable $\mathbf{z}$: $\mathbf{x}=f_\theta(\mathbf{z})$. In such a model (e.g., a VAE, GAN, or continuous-time flow-based model), sampling of the target variab... | {
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2502.07001 | From Image to Video: An Empirical Study of Diffusion Representations | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Diffusion models have revolutionized generative modeling, enabling unprecedented realism in image and video synthesis. This success has sparked interest in leveraging their representations for visual understanding tasks. While recent works have explored this potential for image generation, the visual understanding capa... | {
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2502.07003 | AstroLoc: Robust Space to Ground Image Localizer | [
"cs.CV"
] | Astronauts take thousands of photos of Earth per day from the International Space Station, which, once localized on Earth's surface, are used for a multitude of tasks, ranging from climate change research to disaster management. The localization process, which has been performed manually for decades, has recently been ... | {
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2502.07004 | Demystifying Singular Defects in Large Language Models | [
"cs.CL"
] | Large transformer models are known to produce high-norm tokens. In vision transformers (ViTs), such tokens have been mathematically modeled through the singular vectors of the linear approximations of layers. However, in large language models (LLMs), the underlying causes of high-norm tokens remain largely unexplored, ... | {
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2502.07005 | Geometry-aware RL for Manipulation of Varying Shapes and Deformable
Objects | [
"cs.LG",
"cs.RO"
] | Manipulating objects with varying geometries and deformable objects is a major challenge in robotics. Tasks such as insertion with different objects or cloth hanging require precise control and effective modelling of complex dynamics. In this work, we frame this problem through the lens of a heterogeneous graph that co... | {
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2502.07007 | Grounding Creativity in Physics: A Brief Survey of Physical Priors in
AIGC | [
"cs.CV"
] | Recent advancements in AI-generated content have significantly improved the realism of 3D and 4D generation. However, most existing methods prioritize appearance consistency while neglecting underlying physical principles, leading to artifacts such as unrealistic deformations, unstable dynamics, and implausible objects... | {
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2502.07008 | Early Operative Difficulty Assessment in Laparoscopic Cholecystectomy
via Snapshot-Centric Video Analysis | [
"cs.CV"
] | Purpose: Laparoscopic cholecystectomy (LC) operative difficulty (LCOD) is highly variable and influences outcomes. Despite extensive LC studies in surgical workflow analysis, limited efforts explore LCOD using intraoperative video data. Early recognition of LCOD could allow prompt review by expert surgeons, enhance ope... | {
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2502.07011 | DROP: Poison Dilution via Knowledge Distillation for Federated Learning | [
"cs.LG",
"cs.CR",
"cs.DC"
] | Federated Learning is vulnerable to adversarial manipulation, where malicious clients can inject poisoned updates to influence the global model's behavior. While existing defense mechanisms have made notable progress, they fail to protect against adversaries that aim to induce targeted backdoors under different learnin... | {
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2502.07015 | Data Warehouse Design for Multiple Source Forest Inventory Management
and Image Processing | [
"cs.DB"
] | This research developed a prototype data warehouse to integrate multi-source forestry data for long-term monitoring, management, and sustainability. The data warehouse is intended to accommodate all types of imagery from various platforms, LiDAR point clouds, survey records, and paper documents, with the capability to ... | {
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2502.07016 | Confidence Intervals for Evaluation of Data Mining | [
"stat.ML",
"cs.LG"
] | In data mining, when binary prediction rules are used to predict a binary outcome, many performance measures are used in a vast array of literature for the purposes of evaluation and comparison. Some examples include classification accuracy, precision, recall, F measures, and Jaccard index. Typically, these performance... | {
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2502.07017 | Finding Words Associated with DIF: Predicting Differential Item
Functioning using LLMs and Explainable AI | [
"cs.CL",
"cs.AI"
] | We fine-tuned and compared several encoder-based Transformer large language models (LLM) to predict differential item functioning (DIF) from the item text. We then applied explainable artificial intelligence (XAI) methods to these models to identify specific words associated with DIF. The data included 42,180 items des... | {
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2502.07021 | Federated Sinkhorn | [
"cs.DC",
"cs.LG"
] | In this work we investigate the potential of solving the discrete Optimal Transport (OT) problem with entropy regularization in a federated learning setting. Recall that the celebrated Sinkhorn algorithm transforms the classical OT linear program into strongly convex constrained optimization, facilitating first order m... | {
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2502.07022 | AIMS.au: A Dataset for the Analysis of Modern Slavery Countermeasures in
Corporate Statements | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Despite over a decade of legislative efforts to address modern slavery in the supply chains of large corporations, the effectiveness of government oversight remains hampered by the challenge of scrutinizing thousands of statements annually. While Large Language Models (LLMs) can be considered a well established solutio... | {
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2502.07025 | Detecting Neurodegenerative Diseases using Frame-Level Handwriting
Embeddings | [
"cs.LG",
"cs.CV"
] | In this study, we explored the use of spectrograms to represent handwriting signals for assessing neurodegenerative diseases, including 42 healthy controls (CTL), 35 subjects with Parkinson's Disease (PD), 21 with Alzheimer's Disease (AD), and 15 with Parkinson's Disease Mimics (PDM). We applied CNN and CNN-BLSTM model... | {
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} |
2502.07026 | Machine Learning for Everyone: Simplifying Healthcare Analytics with
BigQuery ML | [
"cs.LG",
"cs.AI"
] | Machine learning (ML) is transforming healthcare by enabling predictive analytics, personalized treatments, and improved patient outcomes. However, traditional ML workflows require specialized skills, infrastructure, and resources, limiting accessibility for many healthcare professionals. This paper explores how Google... | {
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} |
2502.07027 | Representational Alignment with Chemical Induced Fit for Molecular
Relational Learning | [
"cs.LG",
"cs.AI"
] | Molecular Relational Learning (MRL) is widely applied in natural sciences to predict relationships between molecular pairs by extracting structural features. The representational similarity between substructure pairs determines the functional compatibility of molecular binding sites. Nevertheless, aligning substructure... | {
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} |
2502.07029 | Leveraging Allophony in Self-Supervised Speech Models for Atypical
Pronunciation Assessment | [
"cs.CL",
"cs.AI",
"cs.LG",
"eess.AS"
] | Allophony refers to the variation in the phonetic realization of a phoneme based on its phonetic environment. Modeling allophones is crucial for atypical pronunciation assessment, which involves distinguishing atypical from typical pronunciations. However, recent phoneme classifier-based approaches often simplify this ... | {
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} |
2502.07030 | PrismAvatar: Real-time animated 3D neural head avatars on edge devices | [
"cs.CV",
"cs.GR",
"cs.LG"
] | We present PrismAvatar: a 3D head avatar model which is designed specifically to enable real-time animation and rendering on resource-constrained edge devices, while still enjoying the benefits of neural volumetric rendering at training time. By integrating a rigged prism lattice with a 3D morphable head model, we use ... | {
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} |
2502.07036 | Automated Consistency Analysis of LLMs | [
"cs.CR",
"cs.AI",
"cs.LG"
] | Generative AI (Gen AI) with large language models (LLMs) are being widely adopted across the industry, academia and government. Cybersecurity is one of the key sectors where LLMs can be and/or are already being used. There are a number of problems that inhibit the adoption of trustworthy Gen AI and LLMs in cybersecurit... | {
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} |
2502.07039 | Boosting of Classification Models with Human-in-the-Loop Computational
Visual Knowledge Discovery | [
"cs.LG",
"cs.HC"
] | High-risk artificial intelligence and machine learning classification tasks, such as healthcare diagnosis, require accurate and interpretable prediction models. However, classifier algorithms typically sacrifice individual case-accuracy for overall model accuracy, limiting analysis of class overlap areas regardless of ... | {
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} |
2502.07042 | Building networks of shared research interests by embedding words into a
representation space | [
"cs.SI"
] | Departments within a university are not only administrative units, but also an effort to gather investigators around common fields of academic study. A pervasive challenge is connecting members with shared research interests both within and between departments. Here I describe a workflow that adapts methods from natura... | {
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} |
2502.07045 | Scalable and Ethical Insider Threat Detection through Data Synthesis and
Analysis by LLMs | [
"cs.CR",
"cs.AI",
"cs.CL",
"cs.CY"
] | Insider threats wield an outsized influence on organizations, disproportionate to their small numbers. This is due to the internal access insiders have to systems, information, and infrastructure. %One example of this influence is where anonymous respondents submit web-based job search site reviews, an insider threat r... | {
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} |
2502.07046 | SnipGen: A Mining Repository Framework for Evaluating LLMs for Code | [
"cs.SE",
"cs.AI",
"cs.LG"
] | Language Models (LLMs), such as transformer-based neural networks trained on billions of parameters, have become increasingly prevalent in software engineering (SE). These models, trained on extensive datasets that include code repositories, exhibit remarkable capabilities for SE tasks. However, evaluating their effect... | {
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} |
2502.07049 | LLMs in Software Security: A Survey of Vulnerability Detection
Techniques and Insights | [
"cs.CR",
"cs.AI"
] | Large Language Models (LLMs) are emerging as transformative tools for software vulnerability detection, addressing critical challenges in the security domain. Traditional methods, such as static and dynamic analysis, often falter due to inefficiencies, high false positive rates, and the growing complexity of modern sof... | {
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} |
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