id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2502.11312 | AI Generations: From AI 1.0 to AI 4.0 | [
"cs.AI"
] | This paper proposes that Artificial Intelligence (AI) progresses through several overlapping generations: AI 1.0 (Information AI), AI 2.0 (Agentic AI), AI 3.0 (Physical AI), and now a speculative AI 4.0 (Conscious AI). Each of these AI generations is driven by shifting priorities among algorithms, computing power, and ... | {
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2502.11323 | A statistical theory of overfitting for imbalanced classification | [
"math.ST",
"cs.LG",
"stat.ML",
"stat.TH"
] | Classification with imbalanced data is a common challenge in data analysis, where certain classes (minority classes) account for a small fraction of the training data compared with other classes (majority classes). Classical statistical theory based on large-sample asymptotics and finite-sample corrections is often ine... | {
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2502.11324 | Robust High-Dimensional Mean Estimation With Low Data Size, an Empirical
Study | [
"stat.ML",
"cs.LG"
] | Robust statistics aims to compute quantities to represent data where a fraction of it may be arbitrarily corrupted. The most essential statistic is the mean, and in recent years, there has been a flurry of theoretical advancement for efficiently estimating the mean in high dimensions on corrupted data. While several al... | {
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2502.11329 | Differentially private fine-tuned NF-Net to predict GI cancer type | [
"cs.CV"
] | Based on global genomic status, the cancer tumor is classified as Microsatellite Instable (MSI) and Microsatellite Stable (MSS). Immunotherapy is used to diagnose MSI, whereas radiation and chemotherapy are used for MSS. Therefore, it is significant to classify a gastro-intestinal (GI) cancer tumor into MSI vs. MSS to ... | {
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2502.11330 | System Message Generation for User Preferences using Open-Source Models | [
"cs.CL",
"cs.AI"
] | System messages play a crucial role in interactions with large language models (LLMs), often serving as prompts to initiate conversations. Through system messages, users can assign specific roles, perform intended tasks, incorporate background information, specify various output formats and communication styles. Despit... | {
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2502.11331 | Transfer Learning of CATE with Kernel Ridge Regression | [
"stat.ME",
"cs.LG",
"stat.ML"
] | The proliferation of data has sparked significant interest in leveraging findings from one study to estimate treatment effects in a different target population without direct outcome observations. However, the transfer learning process is frequently hindered by substantial covariate shift and limited overlap between (i... | {
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2502.11333 | Inverse Flow and Consistency Models | [
"cs.LG",
"cs.AI"
] | Inverse generation problems, such as denoising without ground truth observations, is a critical challenge in many scientific inquiries and real-world applications. While recent advances in generative models like diffusion models, conditional flow matching, and consistency models achieved impressive results by casting g... | {
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2502.11335 | Personalized Ranking on Cascading Behavior Graphs for Accurate
Multi-Behavior Recommendation | [
"cs.IR"
] | Multi-behavior recommendation predicts items a user may purchase by analyzing diverse behaviors like viewing, adding to a cart, and purchasing. Existing methods fall into two categories: representation learning and graph ranking. Representation learning generates user and item embeddings to capture latent interaction p... | {
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2502.11336 | ExaGPT: Example-Based Machine-Generated Text Detection for Human
Interpretability | [
"cs.CL"
] | Detecting texts generated by Large Language Models (LLMs) could cause grave mistakes due to incorrect decisions, such as undermining student's academic dignity. LLM text detection thus needs to ensure the interpretability of the decision, which can help users judge how reliably correct its prediction is. When humans ve... | {
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2502.11337 | A Comparison of Human and Machine Learning Errors in Face Recognition | [
"cs.HC",
"cs.CV",
"cs.CY"
] | Machine learning applications in high-stakes scenarios should always operate under human oversight. Developing an optimal combination of human and machine intelligence requires an understanding of their complementarities, particularly regarding the similarities and differences in the way they make mistakes. We perform ... | {
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2502.11338 | WRT-SAM: Foundation Model-Driven Segmentation for Generalized Weld
Radiographic Testing | [
"cs.CV"
] | Radiographic testing is a fundamental non-destructive evaluation technique for identifying weld defects and assessing quality in industrial applications due to its high-resolution imaging capabilities. Over the past decade, deep learning techniques have significantly advanced weld defect identification in radiographic ... | {
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2502.11340 | S2TX: Cross-Attention Multi-Scale State-Space Transformer for Time
Series Forecasting | [
"cs.LG"
] | Time series forecasting has recently achieved significant progress with multi-scale models to address the heterogeneity between long and short range patterns. Despite their state-of-the-art performance, we identify two potential areas for improvement. First, the variates of the multivariate time series are processed in... | {
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2502.11345 | Hierarchical Graph Topic Modeling with Topic Tree-based Transformer | [
"cs.CL"
] | Textual documents are commonly connected in a hierarchical graph structure where a central document links to others with an exponentially growing connectivity. Though Hyperbolic Graph Neural Networks (HGNNs) excel at capturing such graph hierarchy, they cannot model the rich textual semantics within documents. Moreover... | {
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2502.11346 | Power-Measurement-Based Channel Autocorrelation Estimation for
IRS-Assisted Wideband Communications | [
"cs.IT",
"math.IT"
] | Channel state information (CSI) is essential to the performance optimization of intelligent reflecting surface (IRS)-aided wireless communication systems. However, the passive and frequency-flat reflection of IRS, as well as the high-dimensional IRS-reflected channels, have posed practical challenges for efficient IRS ... | {
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2502.11349 | Biases in Edge Language Models: Detection, Analysis, and Mitigation | [
"cs.LG",
"cs.PF",
"stat.ML"
] | The integration of large language models (LLMs) on low-power edge devices such as Raspberry Pi, known as edge language models (ELMs), has introduced opportunities for more personalized, secure, and low-latency language intelligence that is accessible to all. However, the resource constraints inherent in edge devices an... | {
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2502.11352 | A Framework for Learning Scoring Rules in Autonomous Driving Planning
Systems | [
"cs.RO",
"cs.LG"
] | In autonomous driving systems, motion planning is commonly implemented as a two-stage process: first, a trajectory proposer generates multiple candidate trajectories, then a scoring mechanism selects the most suitable trajectory for execution. For this critical selection stage, rule-based scoring mechanisms are particu... | {
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2502.11355 | "Nuclear Deployed!": Analyzing Catastrophic Risks in Decision-making of
Autonomous LLM Agents | [
"cs.CL",
"cs.AI",
"cs.CR",
"cs.CY"
] | Large language models (LLMs) are evolving into autonomous decision-makers, raising concerns about catastrophic risks in high-stakes scenarios, particularly in Chemical, Biological, Radiological and Nuclear (CBRN) domains. Based on the insight that such risks can originate from trade-offs between the agent's Helpful, Ha... | {
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2502.11356 | SAIF: A Sparse Autoencoder Framework for Interpreting and Steering
Instruction Following of Language Models | [
"cs.LG",
"cs.AI",
"cs.CL"
] | The ability of large language models (LLMs) to follow instructions is crucial for their practical applications, yet the underlying mechanisms remain poorly understood. This paper presents a novel framework that leverages sparse autoencoders (SAE) to interpret how instruction following works in these models. We demonstr... | {
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2502.11357 | Explorer: Scaling Exploration-driven Web Trajectory Synthesis for
Multimodal Web Agents | [
"cs.AI",
"cs.HC"
] | Recent success in large multimodal models (LMMs) has sparked promising applications of agents capable of autonomously completing complex web tasks. While open-source LMM agents have made significant advances in offline evaluation benchmarks, their performance still falls substantially short of human-level capabilities ... | {
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2502.11358 | Mimicking the Familiar: Dynamic Command Generation for Information Theft
Attacks in LLM Tool-Learning System | [
"cs.AI",
"cs.CR"
] | Information theft attacks pose a significant risk to Large Language Model (LLM) tool-learning systems. Adversaries can inject malicious commands through compromised tools, manipulating LLMs to send sensitive information to these tools, which leads to potential privacy breaches. However, existing attack approaches are b... | {
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2502.11360 | GeoDANO: Geometric VLM with Domain Agnostic Vision Encoder | [
"cs.CV",
"cs.CL"
] | We introduce GeoDANO, a geometric vision-language model (VLM) with a domain-agnostic vision encoder, for solving plane geometry problems. Although VLMs have been employed for solving geometry problems, their ability to recognize geometric features remains insufficiently analyzed. To address this gap, we propose a bench... | {
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2502.11361 | VLDBench: Vision Language Models Disinformation Detection Benchmark | [
"cs.CL"
] | The rapid rise of AI-generated content has made detecting disinformation increasingly challenging. In particular, multimodal disinformation, i.e., online posts-articles that contain images and texts with fabricated information are specially designed to deceive. While existing AI safety benchmarks primarily address bias... | {
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2502.11362 | Teleportation With Null Space Gradient Projection for Optimization
Acceleration | [
"cs.LG"
] | Optimization techniques have become increasingly critical due to the ever-growing model complexity and data scale. In particular, teleportation has emerged as a promising approach, which accelerates convergence of gradient descent-based methods by navigating within the loss invariant level set to identify parameters wi... | {
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2502.11364 | Blessing of Multilinguality: A Systematic Analysis of Multilingual
In-Context Learning | [
"cs.CL"
] | While multilingual large language models generally perform adequately, and sometimes even rival English performance on high-resource languages (HRLs), they often significantly underperform on low-resource languages (LRLs). Among several prompting strategies aiming at bridging the gap, multilingual in-context learning (... | {
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2502.11367 | Sparse Autoencoder Features for Classifications and Transferability | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Sparse Autoencoders (SAEs) provide potentials for uncovering structured, human-interpretable representations in Large Language Models (LLMs), making them a crucial tool for transparent and controllable AI systems. We systematically analyze SAE for interpretable feature extraction from LLMs in safety-critical classifica... | {
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2502.11368 | LLMs can Perform Multi-Dimensional Analytic Writing Assessments: A Case
Study of L2 Graduate-Level Academic English Writing | [
"cs.CL",
"cs.AI"
] | The paper explores the performance of LLMs in the context of multi-dimensional analytic writing assessments, i.e. their ability to provide both scores and comments based on multiple assessment criteria. Using a corpus of literature reviews written by L2 graduate students and assessed by human experts against 9 analytic... | {
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2502.11369 | Physics-Informed Gaussian Process Classification for Constraint-Aware
Alloy Design | [
"cond-mat.mtrl-sci",
"cs.LG"
] | Alloy design can be framed as a constraint-satisfaction problem. Building on previous methodologies, we propose equipping Gaussian Process Classifiers (GPCs) with physics-informed prior mean functions to model the boundaries of feasible design spaces. Through three case studies, we highlight the utility of informative ... | {
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2502.11370 | HI-GVF: Shared Control based on Human-Influenced Guiding Vector Fields
for Human-multi-robot Cooperation | [
"cs.RO"
] | Human-multi-robot shared control leverages human decision-making and robotic autonomy to enhance human-robot collaboration. While widely studied, existing systems often adopt a leader-follower model, limiting robot autonomy to some extent. Besides, a human is required to directly participate in the motion control of ro... | {
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2502.11371 | RAG vs. GraphRAG: A Systematic Evaluation and Key Insights | [
"cs.IR"
] | Retrieval-Augmented Generation (RAG) enhances the performance of LLMs across various tasks by retrieving relevant information from external sources, particularly on text-based data. For structured data, such as knowledge graphs, GraphRAG has been widely used to retrieve relevant information. However, recent studies hav... | {
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2502.11372 | Weibull Processes in Network Degree Distributions | [
"cs.SI",
"physics.soc-ph"
] | This study examines degree distributions in two large collaboration networks: the Microsoft Academic Graph (1800-2020) and Internet Movie Database (1900-2020), comprising $2.72 \times 10^8$ and $1.88 \times 10^6$ nodes respectively. Statistical comparison using $\chi^2$ measures showed that Weibull distributions fit th... | {
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2502.11374 | Leave No One Behind: Enhancing Diversity While Maintaining Accuracy in
Social Recommendation | [
"cs.IR"
] | Social recommendation, a branch of algorithms that utilizes social connection information to construct recommender systems, has demonstrated its effectiveness in enhancing recommendation accuracy. However, apart from accuracy, the diversity of recommendations also plays a critical role in user engagement. Unfortunately... | {
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2502.11375 | Robot Deformable Object Manipulation via NMPC-generated Demonstrations
in Deep Reinforcement Learning | [
"cs.RO",
"cs.LG"
] | In this work, we conducted research on deformable object manipulation by robots based on demonstration-enhanced reinforcement learning (RL). To improve the learning efficiency of RL, we enhanced the utilization of demonstration data from multiple aspects and proposed the HGCR-DDPG algorithm. It uses a novel high-dimens... | {
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2502.11377 | PrivilegedDreamer: Explicit Imagination of Privileged Information for
Rapid Adaptation of Learned Policies | [
"cs.RO",
"cs.LG"
] | Numerous real-world control problems involve dynamics and objectives affected by unobservable hidden parameters, ranging from autonomous driving to robotic manipulation, which cause performance degradation during sim-to-real transfer. To represent these kinds of domains, we adopt hidden-parameter Markov decision proces... | {
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2502.11379 | CCJA: Context-Coherent Jailbreak Attack for Aligned Large Language
Models | [
"cs.CR",
"cs.AI",
"cs.CL"
] | Despite explicit alignment efforts for large language models (LLMs), they can still be exploited to trigger unintended behaviors, a phenomenon known as "jailbreaking." Current jailbreak attack methods mainly focus on discrete prompt manipulations targeting closed-source LLMs, relying on manually crafted prompt template... | {
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2502.11380 | Exploring the Small World of Word Embeddings: A Comparative Study on
Conceptual Spaces from LLMs of Different Scales | [
"cs.CL"
] | A conceptual space represents concepts as nodes and semantic relatedness as edges. Word embeddings, combined with a similarity metric, provide an effective approach to constructing such a space. Typically, embeddings are derived from traditional distributed models or encoder-only pretrained models, whose objectives dir... | {
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2502.11381 | Without Paired Labeled Data: An End-to-End Self-Supervised Paradigm for
UAV-View Geo-Localization | [
"cs.CV",
"cs.AI"
] | UAV-View Geo-Localization (UVGL) aims to ascertain the precise location of a UAV by retrieving the most similar GPS-tagged satellite image. However, existing methods predominantly rely on supervised learning paradigms that necessitate annotated paired data for training, which incurs substantial annotation costs and imp... | {
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2502.11382 | A Physics-Informed Blur Learning Framework for Imaging Systems | [
"cs.CV"
] | Accurate blur estimation is essential for high-performance imaging across various applications. Blur is typically represented by the point spread function (PSF). In this paper, we propose a physics-informed PSF learning framework for imaging systems, consisting of a simple calibration followed by a learning process. Ou... | {
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2502.11386 | Intelligent Mobile AI-Generated Content Services via Interactive Prompt
Engineering and Dynamic Service Provisioning | [
"cs.NI",
"cs.LG"
] | Due to massive computational demands of large generative models, AI-Generated Content (AIGC) can organize collaborative Mobile AIGC Service Providers (MASPs) at network edges to provide ubiquitous and customized content generation for resource-constrained users. However, such a paradigm faces two significant challenges... | {
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2502.11387 | RoleMRC: A Fine-Grained Composite Benchmark for Role-Playing and
Instruction-Following | [
"cs.CL"
] | Role-playing is important for Large Language Models (LLMs) to follow diverse instructions while maintaining role identity and the role's pre-defined ability limits. Existing role-playing datasets mostly contribute to controlling role style and knowledge boundaries, but overlook role-playing in instruction-following sce... | {
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2502.11390 | MARS: Mesh AutoRegressive Model for 3D Shape Detailization | [
"cs.CV"
] | State-of-the-art methods for mesh detailization predominantly utilize Generative Adversarial Networks (GANs) to generate detailed meshes from coarse ones. These methods typically learn a specific style code for each category or similar categories without enforcing geometry supervision across different Levels of Detail ... | {
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2502.11393 | HellaSwag-Pro: A Large-Scale Bilingual Benchmark for Evaluating the
Robustness of LLMs in Commonsense Reasoning | [
"cs.CL"
] | Large language models (LLMs) have shown remarkable capabilities in commonsense reasoning; however, some variations in questions can trigger incorrect responses. Do these models truly understand commonsense knowledge, or just memorize expression patterns? To investigate this question, we present the first extensive robu... | {
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2502.11394 | Oversmoothing as Loss of Sign: Towards Structural Balance in Graph
Neural Networks | [
"cs.LG"
] | Oversmoothing is a common issue in graph neural networks (GNNs), where node representations become excessively homogeneous as the number of layers increases, resulting in degraded performance. Various strategies have been proposed to combat oversmoothing in practice, yet they are based on different heuristics and lack ... | {
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2502.11396 | Maintenance of Structural Hole Spanners in Dynamic Networks | [
"cs.SI"
] | Structural Hole (SH) spanners are the set of users who bridge different groups of users and are vital in numerous applications. Despite their importance, existing work for identifying SH spanners focuses only on static networks. However, real-world networks are highly dynamic where the underlying structure of the netwo... | {
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2502.11400 | Revisiting Robust RAG: Do We Still Need Complex Robust Training in the
Era of Powerful LLMs? | [
"cs.CL"
] | Retrieval-augmented generation (RAG) systems often suffer from performance degradation when encountering noisy or irrelevant documents, driving researchers to develop sophisticated training strategies to enhance their robustness against such retrieval noise. However, as large language models (LLMs) continue to advance,... | {
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2502.11401 | Following the Autoregressive Nature of LLM Embeddings via Compression
and Alignment | [
"cs.CL"
] | A new trend uses LLMs as dense text encoders via contrastive learning. However, since LLM embeddings predict the probability distribution of the next token, they are inherently generative and distributive, conflicting with contrastive learning, which requires embeddings to capture full-text semantics and align via cosi... | {
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2502.11404 | ToolCoder: A Systematic Code-Empowered Tool Learning Framework for Large
Language Models | [
"cs.CL"
] | Tool learning has emerged as a crucial capability for large language models (LLMs) to solve complex real-world tasks through interaction with external tools. Existing approaches face significant challenges, including reliance on hand-crafted prompts, difficulty in multi-step planning, and lack of precise error diagnosi... | {
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2502.11405 | LayAlign: Enhancing Multilingual Reasoning in Large Language Models via
Layer-Wise Adaptive Fusion and Alignment Strategy | [
"cs.CL"
] | Despite being pretrained on multilingual corpora, large language models (LLMs) exhibit suboptimal performance on low-resource languages. Recent approaches have leveraged multilingual encoders alongside LLMs by introducing trainable parameters connecting the two models. However, these methods typically focus on the enco... | {
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2502.11408 | Precise GPS-Denied UAV Self-Positioning via Context-Enhanced Cross-View
Geo-Localization | [
"cs.CV"
] | Image retrieval has been employed as a robust complementary technique to address the challenge of Unmanned Aerial Vehicles (UAVs) self-positioning. However, most existing methods primarily focus on localizing objects captured by UAVs through complex part-based representations, often overlooking the unique challenges as... | {
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2502.11410 | Structure based SAT dataset for analysing GNN generalisation | [
"cs.LG"
] | Satisfiability (SAT) solvers based on techniques such as conflict driven clause learning (CDCL) have produced excellent performance on both synthetic and real world industrial problems. While these CDCL solvers only operate on a per-problem basis, graph neural network (GNN) based solvers bring new benefits to the field... | {
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2502.11411 | Detecting and Filtering Unsafe Training Data via Data Attribution | [
"cs.LG"
] | Large language models (LLMs) are vulnerable to unsafe training data that even small amounts of unsafe data can lead to harmful model behaviors. Detecting and filtering such unsafe training data is essential for trustworthy model development. Current state-of-the-art (SOTA) approaches typically rely on training moderati... | {
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2502.11413 | Statistical Query Hardness of Multiclass Linear Classification with
Random Classification Noise | [
"cs.LG",
"stat.ML"
] | We study the task of Multiclass Linear Classification (MLC) in the distribution-free PAC model with Random Classification Noise (RCN). Specifically, the learner is given a set of labeled examples $(x, y)$, where $x$ is drawn from an unknown distribution on $R^d$ and the labels are generated by a multiclass linear class... | {
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2502.11414 | Unbiased Learning to Rank with Query-Level Click Propensity Estimation:
Beyond Pointwise Observation and Relevance | [
"cs.IR"
] | Most existing unbiased learning-to-rank (ULTR) approaches are based on the user examination hypothesis, which assumes that users will click a result only if it is both relevant and observed (typically modeled by position). However, in real-world scenarios, users often click only one or two results after examining multi... | {
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2502.11417 | DiSCo: Device-Server Collaborative LLM-Based Text Streaming Services | [
"cs.LG",
"cs.DC"
] | The rapid rise of large language models (LLMs) in text streaming services has introduced significant cost and Quality of Experience (QoE) challenges in serving millions of daily requests, especially in meeting Time-To-First-Token (TTFT) and Time-Between-Token (TBT) requirements for real-time interactions. Our real-worl... | {
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2502.11418 | TimeCAP: Learning to Contextualize, Augment, and Predict Time Series
Events with Large Language Model Agents | [
"cs.AI",
"cs.LG"
] | Time series data is essential in various applications, including climate modeling, healthcare monitoring, and financial analytics. Understanding the contextual information associated with real-world time series data is often essential for accurate and reliable event predictions. In this paper, we introduce TimeCAP, a t... | {
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2502.11419 | InsBank: Evolving Instruction Subset for Ongoing Alignment | [
"cs.CL"
] | Large language models (LLMs) typically undergo instruction tuning to enhance alignment. Recent studies emphasize that quality and diversity of instruction data are more crucial than quantity, highlighting the need to select diverse, high-quality subsets to reduce training costs. However, how to evolve these selected su... | {
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2502.11420 | Training-Free Guidance Beyond Differentiability: Scalable Path Steering
with Tree Search in Diffusion and Flow Models | [
"cs.LG"
] | Training-free guidance enables controlled generation in diffusion and flow models, but most existing methods assume differentiable objectives and rely on gradients. This work focuses on training-free guidance addressing challenges from non-differentiable objectives and discrete data distributions. We propose an algorit... | {
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2502.11422 | Planning of Heuristics: Strategic Planning on Large Language Models with
Monte Carlo Tree Search for Automating Heuristic Optimization | [
"cs.AI"
] | Heuristics have achieved great success in solving combinatorial optimization problems (COPs). However, heuristics designed by humans require too much domain knowledge and testing time. Given the fact that Large Language Models (LLMs) possess strong capabilities to understand and generate content, and a knowledge base t... | {
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2502.11423 | Exploring Persona Sentiment Sensitivity in Personalized Dialogue
Generation | [
"cs.CL"
] | Personalized dialogue systems have advanced considerably with the integration of user-specific personas into large language models (LLMs). However, while LLMs can effectively generate personalized responses, the influence of persona sentiment on dialogue quality remains underexplored. In this work, we conduct a large-s... | {
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2502.11425 | Counterfactual-Consistency Prompting for Relative Temporal Understanding
in Large Language Models | [
"cs.CL",
"cs.AI"
] | Despite the advanced capabilities of large language models (LLMs), their temporal reasoning ability remains underdeveloped. Prior works have highlighted this limitation, particularly in maintaining temporal consistency when understanding events. For example, models often confuse mutually exclusive temporal relations li... | {
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2502.11426 | Verti-Bench: A General and Scalable Off-Road Mobility Benchmark for
Vertically Challenging Terrain | [
"cs.RO"
] | Recent advancement in off-road autonomy has shown promises in deploying autonomous mobile robots in outdoor off-road environments. Encouraging results have been reported from both simulated and real-world experiments. However, unlike evaluating off-road perception tasks on static datasets, benchmarking off-road mobilit... | {
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2502.11427 | Do we Really Need Visual Instructions? Towards Visual Instruction-Free
Fine-tuning for Large Vision-Language Models | [
"cs.CL",
"cs.CV"
] | Visual instruction tuning has become the predominant technology in eliciting the multimodal task-solving capabilities of large vision-language models (LVLMs). Despite the success, as visual instructions require images as the input, it would leave the gap in inheriting the task-solving capabilities from the backbone LLM... | {
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2502.11429 | What's in a Query: Polarity-Aware Distribution-Based Fair Ranking | [
"cs.LG",
"cs.CY"
] | Machine learning-driven rankings, where individuals (or items) are ranked in response to a query, mediate search exposure or attention in a variety of safety-critical settings. Thus, it is important to ensure that such rankings are fair. Under the goal of equal opportunity, attention allocated to an individual on a ran... | {
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2502.11431 | Any Information Is Just Worth One Single Screenshot: Unifying Search
With Visualized Information Retrieval | [
"cs.CL"
] | With the popularity of multimodal techniques, it receives growing interests to acquire useful information in visual forms. In this work, we formally define an emerging IR paradigm called \textit{Visualized Information Retrieval}, or \textbf{Vis-IR}, where multimodal information, such as texts, images, tables and charts... | {
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2502.11433 | FLAG-Trader: Fusion LLM-Agent with Gradient-based Reinforcement Learning
for Financial Trading | [
"cs.AI",
"cs.CE",
"q-fin.TR"
] | Large language models (LLMs) fine-tuned on multimodal financial data have demonstrated impressive reasoning capabilities in various financial tasks. However, they often struggle with multi-step, goal-oriented scenarios in interactive financial markets, such as trading, where complex agentic approaches are required to i... | {
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2502.11435 | SMART: Self-Aware Agent for Tool Overuse Mitigation | [
"cs.AI",
"cs.CL",
"cs.LG"
] | Current Large Language Model (LLM) agents demonstrate strong reasoning and tool use capabilities, but often lack self-awareness, failing to balance these approaches effectively. This imbalance leads to Tool Overuse, where models unnecessarily rely on external tools for tasks solvable with parametric knowledge, increasi... | {
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2502.11436 | ADO: Automatic Data Optimization for Inputs in LLM Prompts | [
"cs.LG"
] | This study explores a novel approach to enhance the performance of Large Language Models (LLMs) through the optimization of input data within prompts. While previous research has primarily focused on refining instruction components and augmenting input data with in-context examples, our work investigates the potential ... | {
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2502.11437 | Learning Dexterous Bimanual Catch Skills through Adversarial-Cooperative
Heterogeneous-Agent Reinforcement Learning | [
"cs.RO",
"cs.AI"
] | Robotic catching has traditionally focused on single-handed systems, which are limited in their ability to handle larger or more complex objects. In contrast, bimanual catching offers significant potential for improved dexterity and object handling but introduces new challenges in coordination and control. In this pape... | {
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2502.11438 | SAFE-SQL: Self-Augmented In-Context Learning with Fine-grained Example
Selection for Text-to-SQL | [
"cs.CL"
] | Text-to-SQL aims to convert natural language questions into executable SQL queries. While previous approaches, such as skeleton-masked selection, have demonstrated strong performance by retrieving similar training examples to guide large language models (LLMs), they struggle in real-world scenarios where such examples ... | {
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2502.11439 | An Efficient Row-Based Sparse Fine-Tuning | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Fine-tuning is an important step in adapting foundation models such as large language models to downstream tasks. To make this step more accessible to users with limited computational budgets, it is crucial to develop fine-tuning methods that are memory and computationally efficient. Sparse Fine-tuning (SFT) and Low-ra... | {
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2502.11440 | Medical Image Registration Meets Vision Foundation Model: Prototype
Learning and Contour Awareness | [
"cs.CV"
] | Medical image registration is a fundamental task in medical image analysis, aiming to establish spatial correspondences between paired images. However, existing unsupervised deformable registration methods rely solely on intensity-based similarity metrics, lacking explicit anatomical knowledge, which limits their accur... | {
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2502.11441 | Which Retain Set Matters for LLM Unlearning? A Case Study on Entity
Unlearning | [
"cs.CL"
] | Large language models (LLMs) risk retaining unauthorized or sensitive information from their training data, which raises privacy concerns. LLM unlearning seeks to mitigate these risks by selectively removing specified data while maintaining overall model performance. However, most existing work focus on methods to achi... | {
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2502.11442 | Multi-Turn Multi-Modal Question Clarification for Enhanced
Conversational Understanding | [
"cs.IR",
"cs.AI",
"cs.CL",
"cs.LG"
] | Conversational query clarification enables users to refine their search queries through interactive dialogue, improving search effectiveness. Traditional approaches rely on text-based clarifying questions, which often fail to capture complex user preferences, particularly those involving visual attributes. While recent... | {
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2502.11444 | Does RAG Really Perform Bad For Long-Context Processing? | [
"cs.CL"
] | The efficient processing of long context poses a serious challenge for large language models (LLMs). Recently, retrieval-augmented generation (RAG) has emerged as a promising strategy for this problem, as it enables LLMs to make selective use of the long context for efficient computation. However, existing RAG approach... | {
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2502.11447 | Does Editing Provide Evidence for Localization? | [
"cs.LG",
"cs.AI"
] | A basic aspiration for interpretability research in large language models is to "localize" semantically meaningful behaviors to particular components within the LLM. There are various heuristics for finding candidate locations within the LLM. Once a candidate localization is found, it can be assessed by editing the int... | {
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2502.11448 | AGrail: A Lifelong Agent Guardrail with Effective and Adaptive Safety
Detection | [
"cs.AI"
] | The rapid advancements in Large Language Models (LLMs) have enabled their deployment as autonomous agents for handling complex tasks in dynamic environments. These LLMs demonstrate strong problem-solving capabilities and adaptability to multifaceted scenarios. However, their use as agents also introduces significant ri... | {
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2502.11449 | Tractable General Equilibrium | [
"cs.GT",
"cs.CE",
"econ.TH"
] | We study Walrasian economies (or general equilibrium models) and their solution concept, the Walrasian equilibrium. A key challenge in this domain is identifying price-adjustment processes that converge to equilibrium. One such process, t\^atonnement, is an auction-like algorithm first proposed in 1874 by L\'eon Walras... | {
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2502.11450 | Fishing For Cheap And Efficient Pruners At Initialization | [
"cs.LG",
"cs.AI"
] | Pruning offers a promising solution to mitigate the associated costs and environmental impact of deploying large deep neural networks (DNNs). Traditional approaches rely on computationally expensive trained models or time-consuming iterative prune-retrain cycles, undermining their utility in resource-constrained settin... | {
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2502.11451 | From Personas to Talks: Revisiting the Impact of Personas on
LLM-Synthesized Emotional Support Conversations | [
"cs.CL"
] | The rapid advancement of Large Language Models (LLMs) has revolutionized the generation of emotional support conversations (ESC), offering scalable solutions with reduced costs and enhanced data privacy. This paper explores the role of personas in the creation of ESC by LLMs. Our research utilizes established psycholog... | {
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2502.11453 | Connector-S: A Survey of Connectors in Multi-modal Large Language Models | [
"cs.LG",
"cs.AI"
] | With the rapid advancements in multi-modal large language models (MLLMs), connectors play a pivotal role in bridging diverse modalities and enhancing model performance. However, the design and evolution of connectors have not been comprehensively analyzed, leaving gaps in understanding how these components function and... | {
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2502.11454 | UniCBE: An Uniformity-driven Comparing Based Evaluation Framework with
Unified Multi-Objective Optimization | [
"cs.CL"
] | Human preference plays a significant role in measuring large language models and guiding them to align with human values. Unfortunately, current comparing-based evaluation (CBE) methods typically focus on a single optimization objective, failing to effectively utilize scarce yet valuable preference signals. To address ... | {
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2502.11456 | Leveraging Labelled Data Knowledge: A Cooperative Rectification Learning
Network for Semi-supervised 3D Medical Image Segmentation | [
"cs.CV",
"cs.AI"
] | Semi-supervised 3D medical image segmentation aims to achieve accurate segmentation using few labelled data and numerous unlabelled data. The main challenge in the design of semi-supervised learning methods consists in the effective use of the unlabelled data for training. A promising solution consists of ensuring cons... | {
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2502.11457 | Aligning Sentence Simplification with ESL Learner's Proficiency for
Language Acquisition | [
"cs.CL",
"cs.AI"
] | Text simplification is crucial for improving accessibility and comprehension for English as a Second Language (ESL) learners. This study goes a step further and aims to facilitate ESL learners' language acquisition by simplification. Specifically, we propose simplifying complex sentences to appropriate levels for learn... | {
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2502.11458 | Towards Efficient Pre-training: Exploring FP4 Precision in Large
Language Models | [
"cs.LG",
"cs.AI"
] | The burgeoning computational demands for training large language models (LLMs) necessitate efficient methods, including quantized training, which leverages low-bit arithmetic operations to reduce costs. While FP8 precision has shown potential, leveraging FP4 remains challenging due to inherent quantization errors and l... | {
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2502.11459 | Towards Responsible and Fair Data Science: Resource Allocation for
Inclusive and Sustainable Analytics | [
"cs.DB"
] | This project addresses the challenges of responsible and fair resource allocation in data science (DS), focusing on DS queries evaluation. Current DS practices often overlook the broader socio-economic, environmental, and ethical implications, including data sovereignty, fairness, and inclusivity. By integrating a deco... | {
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2502.11460 | UnitCoder: Scalable Iterative Code Synthesis with Unit Test Guidance | [
"cs.CL",
"cs.SE"
] | Large Language Models (LLMs) have demonstrated remarkable capabilities in various tasks, yet code generation remains a major challenge. Current approaches for obtaining high-quality code data primarily focus on (i) collecting large-scale pre-training data and (ii) synthesizing instruction data through prompt engineerin... | {
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2502.11461 | Doppler Correspondence: Non-Iterative Scan Matching With Doppler
Velocity-Based Correspondence | [
"cs.RO"
] | Achieving successful scan matching is essential for LiDAR odometry. However, in challenging environments with adverse weather conditions or repetitive geometric patterns, LiDAR odometry performance is degraded due to incorrect scan matching. Recently, the emergence of frequency-modulated continuous wave 4D LiDAR and 4D... | {
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2502.11462 | LMFCA-Net: A Lightweight Model for Multi-Channel Speech Enhancement with
Efficient Narrow-Band and Cross-Band Attention | [
"eess.AS",
"cs.LG",
"cs.SD"
] | Deep learning based end-to-end multi-channel speech enhancement methods have achieved impressive performance by leveraging sub-band, cross-band, and spatial information. However, these methods often demand substantial computational resources, limiting their practicality on terminal devices. This paper presents a lightw... | {
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2502.11465 | All Models Are Miscalibrated, But Some Less So: Comparing Calibration
with Conditional Mean Operators | [
"stat.ML",
"cs.LG"
] | When working in a high-risk setting, having well calibrated probabilistic predictive models is a crucial requirement. However, estimators for calibration error are not always able to correctly distinguish which model is better calibrated. We propose the \emph{conditional kernel calibration error} (CKCE) which is based ... | {
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2502.11466 | GiFT: Gibbs Fine-Tuning for Code Generation | [
"cs.LG",
"cs.CL",
"cs.SE"
] | Training Large Language Models (LLMs) with synthetic data is a prevalent practice in code generation. A key approach is self-training, where LLMs are iteratively trained on self-generated correct code snippets. In this case, the self-generated codes are drawn from a conditional distribution, conditioned on a specific s... | {
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2502.11467 | Approximation of Permutation Invariant Polynomials by Transformers:
Efficient Construction in Column-Size | [
"cs.LG",
"math.FA"
] | Transformers are a type of neural network that have demonstrated remarkable performance across various domains, particularly in natural language processing tasks. Motivated by this success, research on the theoretical understanding of transformers has garnered significant attention. A notable example is the mathematica... | {
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2502.11468 | Semantically Robust Unsupervised Image Translation for Paired Remote
Sensing Images | [
"cs.CV"
] | Image translation for change detection or classification in bi-temporal remote sensing images is unique. Although it can acquire paired images, it is still unsupervised. Moreover, strict semantic preservation in translation is always needed instead of multimodal outputs. In response to these problems, this paper propos... | {
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} |
2502.11469 | If Attention Serves as a Cognitive Model of Human Memory Retrieval, What
is the Plausible Memory Representation? | [
"cs.CL"
] | Recent work in computational psycholinguistics has revealed intriguing parallels between attention mechanisms and human memory retrieval, focusing primarily on Transformer architectures that operate on token-level representations. However, computational psycholinguistic research has also established that syntactic stru... | {
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} |
2502.11470 | Optimized detection of cyber-attacks on IoT networks via hybrid deep
learning models | [
"cs.CR",
"cs.AI"
] | The rapid expansion of Internet of Things (IoT) devices has increased the risk of cyber-attacks, making effective detection essential for securing IoT networks. This work introduces a novel approach combining Self-Organizing Maps (SOMs), Deep Belief Networks (DBNs), and Autoencoders to detect known and previously unsee... | {
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} |
2502.11471 | GLTW: Joint Improved Graph Transformer and LLM via Three-Word Language
for Knowledge Graph Completion | [
"cs.CL",
"cs.IR"
] | Knowledge Graph Completion (KGC), which aims to infer missing or incomplete facts, is a crucial task for KGs. However, integrating the vital structural information of KGs into Large Language Models (LLMs) and outputting predictions deterministically remains challenging. To address this, we propose a new method called G... | {
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} |
2502.11476 | FastMCTS: A Simple Sampling Strategy for Data Synthesis | [
"cs.CL"
] | Synthetic high-quality multi-step reasoning data can significantly enhance the performance of large language models on various tasks. However, most existing methods rely on rejection sampling, which generates trajectories independently and suffers from inefficiency and imbalanced sampling across problems of varying dif... | {
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} |
2502.11477 | Learning to Sample Effective and Diverse Prompts for Text-to-Image
Generation | [
"cs.CV"
] | Recent advances in text-to-image diffusion models have achieved impressive image generation capabilities. However, it remains challenging to control the generation process with desired properties (e.g., aesthetic quality, user intention), which can be expressed as black-box reward functions. In this paper, we focus on ... | {
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} |
2502.11478 | TAPS: Throat and Acoustic Paired Speech Dataset for Deep Learning-Based
Speech Enhancement | [
"cs.SD",
"cs.LG",
"eess.AS"
] | In high-noise environments such as factories, subways, and busy streets, capturing clear speech is challenging due to background noise. Throat microphones provide a solution with their noise-suppressing properties, reducing the noise while recording speech. However, a significant limitation remains: high-frequency info... | {
"Other": 0,
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} |
2502.11480 | Enhancing Offline Model-Based RL via Active Model Selection: A Bayesian
Optimization Perspective | [
"cs.LG",
"stat.ML"
] | Offline model-based reinforcement learning (MBRL) serves as a competitive framework that can learn well-performing policies solely from pre-collected data with the help of learned dynamics models. To fully unleash the power of offline MBRL, model selection plays a pivotal role in determining the dynamics model utilized... | {
"Other": 0,
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} |
2502.11481 | Variable-frame CNNLSTM for Breast Nodule Classification using Ultrasound
Videos | [
"cs.CV",
"cs.AI"
] | The intersection of medical imaging and artificial intelligence has become an important research direction in intelligent medical treatment, particularly in the analysis of medical images using deep learning for clinical diagnosis. Despite the advances, existing keyframe classification methods lack extraction of time s... | {
"Other": 0,
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} |
2502.11482 | DATA: Decomposed Attention-based Task Adaptation for Rehearsal-Free
Continual Learning | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Continual learning (CL) is essential for Large Language Models (LLMs) to adapt to evolving real-world demands, yet they are susceptible to catastrophic forgetting (CF). While traditional CF solutions rely on expensive data rehearsal, recent rehearsal-free methods employ model-based and regularization-based strategies t... | {
"Other": 0,
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"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
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