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train · 541k rows

id stringlengths 9 16	title stringlengths 4 278	categories listlengths 1 13	abstract stringlengths 3 4.08k	filtered_category_membership dict
2502.07237	DrugImproverGPT: A Large Language Model for Drug Optimization with Fine-Tuning via Structured Policy Optimization	[ "cs.LG", "cs.CL", "q-bio.BM", "stat.ML" ]	Finetuning a Large Language Model (LLM) is crucial for generating results towards specific objectives. This research delves into the realm of drug optimization and introduce a novel reinforcement learning algorithm to finetune a drug optimization LLM-based generative model, enhancing the original drug across target obj...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07238	Diffusion Suction Grasping with Large-Scale Parcel Dataset	[ "cs.CV", "cs.AI" ]	While recent advances in object suction grasping have shown remarkable progress, significant challenges persist particularly in cluttered and complex parcel handling scenarios. Two fundamental limitations hinder current approaches: (1) the lack of a comprehensive suction grasp dataset tailored for parcel manipulation t...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07239	Contextual Gesture: Co-Speech Gesture Video Generation through Context-aware Gesture Representation	[ "cs.CV", "cs.AI" ]	Co-speech gesture generation is crucial for creating lifelike avatars and enhancing human-computer interactions by synchronizing gestures with speech. Despite recent advancements, existing methods struggle with accurately identifying the rhythmic or semantic triggers from audio for generating contextualized gesture pat...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07242	Nonlinear Reed-Solomon codes and nonlinear skew quasi-cyclic codes	[ "cs.IT", "math.IT", "math.RA" ]	This article begins with an exploration of nonlinear codes ($\mathbb{F}_q$-linear subspaces of $\mathbb{F}_{q^m}^n$) which are generalizations of the familiar Reed-Solomon codes. This then leads to a wider exploration of nonlinear analogues of the skew quasi-cyclic codes of index $\ell$ first explored in 2010 by Abualr...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 1, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07243	Vevo: Controllable Zero-Shot Voice Imitation with Self-Supervised Disentanglement	[ "cs.SD", "cs.AI" ]	The imitation of voice, targeted on specific speech attributes such as timbre and speaking style, is crucial in speech generation. However, existing methods rely heavily on annotated data, and struggle with effectively disentangling timbre and style, leading to challenges in achieving controllable generation, especiall...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 1, "cs.SI": 0, "cs.SY": 0 }
2502.07244	Linear Transformers as VAR Models: Aligning Autoregressive Attention Mechanisms with Autoregressive Forecasting	[ "cs.LG", "cs.AI", "stat.ML" ]	Autoregressive attention-based time series forecasting (TSF) has drawn increasing interest, with mechanisms like linear attention sometimes outperforming vanilla attention. However, deeper Transformer architectures frequently misalign with autoregressive objectives, obscuring the underlying VAR structure embedded withi...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07246	Robust Indoor Localization in Dynamic Environments: A Multi-source Unsupervised Domain Adaptation Framework	[ "cs.CV", "physics.pop-ph" ]	Fingerprint localization has gained significant attention due to its cost-effective deployment, low complexity, and high efficacy. However, traditional methods, while effective for static data, often struggle in dynamic environments where data distributions and feature spaces evolve-a common occurrence in real-world sc...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07250	NARCE: A Mamba-Based Neural Algorithmic Reasoner Framework for Online Complex Event Detection	[ "cs.LG", "cs.AI" ]	Current machine learning models excel in short-span perception tasks but struggle to derive high-level insights from long-term observation, a capability central to understanding complex events (CEs). CEs, defined as sequences of short-term atomic events (AEs) governed by spatiotemporal rules, are challenging to detect ...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07254	Fairness in Multi-Agent AI: A Unified Framework for Ethical and Equitable Autonomous Systems	[ "cs.MA", "cs.AI", "cs.CY" ]	Ensuring fairness in decentralized multi-agent systems presents significant challenges due to emergent biases, systemic inefficiencies, and conflicting agent incentives. This paper provides a comprehensive survey of fairness in multi-agent AI, introducing a novel framework where fairness is treated as a dynamic, emerge...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 1, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 1, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07255	Beyond Confidence: Adaptive Abstention in Dual-Threshold Conformal Prediction for Autonomous System Perception	[ "cs.RO", "cs.LG" ]	Safety-critical perception systems require both reliable uncertainty quantification and principled abstention mechanisms to maintain safety under diverse operational conditions. We present a novel dual-threshold conformalization framework that provides statistically-guaranteed uncertainty estimates while enabling selec...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 1, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07259	Flat U-Net: An Efficient Ultralightweight Model for Solar Filament Segmentation in Full-disk H$\alpha$ Images	[ "astro-ph.IM", "astro-ph.SR", "cs.CV", "cs.LG" ]	Solar filaments are one of the most prominent features observed on the Sun, and their evolutions are closely related to various solar activities, such as flares and coronal mass ejections. Real-time automated identification of solar filaments is the most effective approach to managing large volumes of data. Existing mo...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07263	Hidden Division of Labor in Scientific Teams Revealed Through 1.6 Million LaTeX Files	[ "cs.SI", "cs.CL", "cs.DL" ]	Recognition of individual contributions is fundamental to the scientific reward system, yet coauthored papers obscure who did what. Traditional proxies-author order and career stage-reinforce biases, while contribution statements remain self-reported and limited to select journals. We construct the first large-scale da...	{ "Other": 1, "cs.AI": 0, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 1, "cs.SY": 0 }
2502.07265	Riemannian Proximal Sampler for High-accuracy Sampling on Manifolds	[ "stat.ML", "cs.LG", "math.ST", "stat.TH" ]	We introduce the Riemannian Proximal Sampler, a method for sampling from densities defined on Riemannian manifolds. The performance of this sampler critically depends on two key oracles: the Manifold Brownian Increments (MBI) oracle and the Riemannian Heat-kernel (RHK) oracle. We establish high-accuracy sampling guaran...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07266	When More is Less: Understanding Chain-of-Thought Length in LLMs	[ "cs.AI", "cs.CL", "cs.LG" ]	Chain-of-thought (CoT) reasoning enhances the multi-step reasoning capabilities of large language models (LLMs) by breaking complex tasks into smaller, manageable sub-tasks. Researchers have been exploring ways to guide models to generate more complex CoT processes to improve the reasoning ability of LLMs, such as long...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07269	Exploring Active Data Selection Strategies for Continuous Training in Deepfake Detection	[ "cs.CV" ]	In deepfake detection, it is essential to maintain high performance by adjusting the parameters of the detector as new deepfake methods emerge. In this paper, we propose a method to automatically and actively select the small amount of additional data required for the continuous training of deepfake detection models in...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07272	GENERator: A Long-Context Generative Genomic Foundation Model	[ "cs.CL", "q-bio.GN" ]	Advancements in DNA sequencing technologies have significantly improved our ability to decode genomic sequences. However, the prediction and interpretation of these sequences remain challenging due to the intricate nature of genetic material. Large language models (LLMs) have introduced new opportunities for biological...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07273	Variational Learning Induces Adaptive Label Smoothing	[ "cs.LG", "cs.AI" ]	We show that variational learning naturally induces an adaptive label smoothing where label noise is specialized for each example. Such label-smoothing is useful to handle examples with labeling errors and distribution shifts, but designing a good adaptivity strategy is not always easy. We propose to skip this step and...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07274	Cost-Efficient Continual Learning with Sufficient Exemplar Memory	[ "cs.LG", "cs.AI" ]	Continual learning (CL) research typically assumes highly constrained exemplar memory resources. However, in many real-world scenarios-especially in the era of large foundation models-memory is abundant, while GPU computational costs are the primary bottleneck. In this work, we investigate CL in a novel setting where e...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07276	Dataset Ownership Verification in Contrastive Pre-trained Models	[ "cs.LG", "cs.AI", "cs.CV" ]	High-quality open-source datasets, which necessitate substantial efforts for curation, has become the primary catalyst for the swift progress of deep learning. Concurrently, protecting these datasets is paramount for the well-being of the data owner. Dataset ownership verification emerges as a crucial method in this do...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07277	Enhancing Video Understanding: Deep Neural Networks for Spatiotemporal Analysis	[ "cs.CV", "cs.AI" ]	It's no secret that video has become the primary way we share information online. That's why there's been a surge in demand for algorithms that can analyze and understand video content. It's a trend going to continue as video continues to dominate the digital landscape. These algorithms will extract and classify relate...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07278	Articulate That Object Part (ATOP): 3D Part Articulation from Text and Motion Personalization	[ "cs.CV" ]	We present ATOP (Articulate That Object Part), a novel method based on motion personalization to articulate a 3D object with respect to a part and its motion as prescribed in a text prompt. Specifically, the text input allows us to tap into the power of modern-day video diffusion to generate plausible motion samples fo...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07279	Exploratory Diffusion Policy for Unsupervised Reinforcement Learning	[ "cs.LG", "cs.AI" ]	Unsupervised reinforcement learning (RL) aims to pre-train agents by exploring states or skills in reward-free environments, facilitating the adaptation to downstream tasks. However, existing methods often overlook the fitting ability of pre-trained policies and struggle to handle the heterogeneous pre-training data, w...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07280	MIGT: Memory Instance Gated Transformer Framework for Financial Portfolio Management	[ "cs.LG", "cs.AI" ]	Deep reinforcement learning (DRL) has been applied in financial portfolio management to improve returns in changing market conditions. However, unlike most fields where DRL is widely used, the stock market is more volatile and dynamic as it is affected by several factors such as global events and investor sentiment. Th...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07281	Supervised Contrastive Block Disentanglement	[ "cs.LG" ]	Real-world datasets often combine data collected under different experimental conditions. This yields larger datasets, but also introduces spurious correlations that make it difficult to model the phenomena of interest. We address this by learning two embeddings to independently represent the phenomena of interest and ...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07282	Leader-follower formation enabled by pressure sensing in free-swimming undulatory robotic fish	[ "cs.RO" ]	Fish use their lateral lines to sense flows and pressure gradients, enabling them to detect nearby objects and organisms. Towards replicating this capability, we demonstrated successful leader-follower formation swimming using flow pressure sensing in our undulatory robotic fish ($\mu$Bot/MUBot). The follower $\mu$Bot ...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 1, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07285	Negative Dependence as a toolbox for machine learning : review and new developments	[ "stat.ML", "cs.LG", "math.PR" ]	Negative dependence is becoming a key driver in advancing learning capabilities beyond the limits of traditional independence. Recent developments have evidenced support towards negatively dependent systems as a learning paradigm in a broad range of fundamental machine learning challenges including optimization, sampli...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07286	Small Language Model Makes an Effective Long Text Extractor	[ "cs.CL", "cs.AI" ]	Named Entity Recognition (NER) is a fundamental problem in natural language processing (NLP). However, the task of extracting longer entity spans (e.g., awards) from extended texts (e.g., homepages) is barely explored. Current NER methods predominantly fall into two categories: span-based methods and generation-based m...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07288	KPIs 2024 Challenge: Advancing Glomerular Segmentation from Patch- to Slide-Level	[ "cs.CV", "cs.AI" ]	Chronic kidney disease (CKD) is a major global health issue, affecting over 10% of the population and causing significant mortality. While kidney biopsy remains the gold standard for CKD diagnosis and treatment, the lack of comprehensive benchmarks for kidney pathology segmentation hinders progress in the field. To add...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07289	Learning Inverse Laplacian Pyramid for Progressive Depth Completion	[ "cs.CV" ]	Depth completion endeavors to reconstruct a dense depth map from sparse depth measurements, leveraging the information provided by a corresponding color image. Existing approaches mostly hinge on single-scale propagation strategies that iteratively ameliorate initial coarse depth estimates through pixel-level message p...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07293	Global Universal Scaling and Ultra-Small Parameterization in Machine Learning Interatomic Potentials with Super-Linearity	[ "cond-mat.mtrl-sci", "cs.LG" ]	Using machine learning (ML) to construct interatomic interactions and thus potential energy surface (PES) has become a common strategy for materials design and simulations. However, those current models of machine learning interatomic potential (MLIP) provide no relevant physical constrains, and thus may owe intrinsic ...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07295	Treatment Effect Estimation for Exponential Family Outcomes using Neural Networks with Targeted Regularization	[ "cs.LG" ]	Neural Networks (NNs) have became a natural choice for treatment effect estimation due to their strong approximation capabilities. Nevertheless, how to design NN-based estimators with desirable properties, such as low bias and doubly robustness, still remains a significant challenge. A common approach to address this i...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07297	Generation of Drug-Induced Cardiac Reactions towards Virtual Clinical Trials	[ "cs.LG", "q-bio.QM" ]	Clinical trials are pivotal in cardiac drug development, yet they often fail due to inadequate efficacy and unexpected safety issues, leading to significant financial losses. Using in-silico trials to replace a part of physical clinical trials, e.g., leveraging advanced generative models to generate drug-influenced ele...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07299	Life-Code: Central Dogma Modeling with Multi-Omics Sequence Unification	[ "cs.LG", "cs.AI", "cs.CL", "q-bio.GN" ]	The interactions between DNA, RNA, and proteins are fundamental to biological processes, as illustrated by the central dogma of molecular biology. While modern biological pre-trained models have achieved great success in analyzing these macromolecules individually, their interconnected nature remains under-explored. In...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07302	CASC-AI: Consensus-aware Self-corrective AI Agents for Noise Cell Segmentation	[ "cs.CV" ]	Multi-class cell segmentation in high-resolution gigapixel whole slide images (WSI) is crucial for various clinical applications. However, training such models typically requires labor-intensive, pixel-wise annotations by domain experts. Recent efforts have democratized this process by involving lay annotators without ...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07303	Flow Matching for Collaborative Filtering	[ "cs.IR" ]	Generative models have shown great promise in collaborative filtering by capturing the underlying distribution of user interests and preferences. However, existing approaches struggle with inaccurate posterior approximations and misalignment with the discrete nature of recommendation data, limiting their expressiveness...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 1, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07306	TRAVEL: Training-Free Retrieval and Alignment for Vision-and-Language Navigation	[ "cs.CV", "cs.AI", "cs.CL", "cs.LG", "cs.RO" ]	In this work, we propose a modular approach for the Vision-Language Navigation (VLN) task by decomposing the problem into four sub-modules that use state-of-the-art Large Language Models (LLMs) and Vision-Language Models (VLMs) in a zero-shot setting. Given navigation instruction in natural language, we first prompt LL...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 1, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07307	CreAgent: Towards Long-Term Evaluation of Recommender System under Platform-Creator Information Asymmetry	[ "cs.IR" ]	Ensuring the long-term sustainability of recommender systems (RS) emerges as a crucial issue. Traditional offline evaluation methods for RS typically focus on immediate user feedback, such as clicks, but they often neglect the long-term impact of content creators. On real-world content platforms, creators can strategic...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 1, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07308	Explicit Codes approaching Generalized Singleton Bound using Expanders	[ "cs.IT", "cs.CC", "math.IT" ]	We construct a new family of explicit codes that are list decodable to capacity and achieve an optimal list size of $O(\frac{1}{\epsilon})$. In contrast to existing explicit constructions of codes achieving list decoding capacity, our arguments do not rely on algebraic structure but utilize simple combinatorial propert...	{ "Other": 1, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 1, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07309	Semi-Supervised Vision-Centric 3D Occupancy World Model for Autonomous Driving	[ "cs.CV" ]	Understanding world dynamics is crucial for planning in autonomous driving. Recent methods attempt to achieve this by learning a 3D occupancy world model that forecasts future surrounding scenes based on current observation. However, 3D occupancy labels are still required to produce promising results. Considering the h...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07312	OpenGrok: Enhancing SNS Data Processing with Distilled Knowledge and Mask-like Mechanisms	[ "cs.LG", "cs.AI" ]	This report details Lumen Labs' novel approach to processing Social Networking Service (SNS) data. We leverage knowledge distillation, specifically a simple distillation method inspired by DeepSeek-R1's CoT acquisition, combined with prompt hacking, to extract valuable training data from the Grok model. This data is th...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07315	Prompt-Based Document Modifications In Ranking Competitions	[ "cs.IR", "cs.GT" ]	We study prompting-based approaches with Large Language Models (LLMs) for modifying documents so as to promote their ranking in a competitive search setting. Our methods are inspired by prior work on leveraging LLMs as rankers. We evaluate our approach by deploying it as a bot in previous ranking competitions and in co...	{ "Other": 1, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 1, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07316	CodeI/O: Condensing Reasoning Patterns via Code Input-Output Prediction	[ "cs.CL", "cs.AI" ]	Reasoning is a fundamental capability of Large Language Models. While prior research predominantly focuses on enhancing narrow skills like math or code generation, improving performance on many other reasoning tasks remains challenging due to sparse and fragmented training data. To address this issue, we propose CodeI/...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07318	Beamfocusing Capabilities of a Uniform Linear Array in the Holographic Regime	[ "cs.IT", "eess.SP", "math.IT" ]	The use of multiantenna technologies in the near field offers the possibility of focusing the energy in spatial regions rather than just in angle. The objective of this paper is to provide a formal framework that allows to establish the region in space where this effect can take place and how efficient this focusing ca...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 1, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07319	Learnable Residual-based Latent Denoising in Semantic Communication	[ "cs.LG", "cs.IT", "math.IT" ]	A latent denoising semantic communication (SemCom) framework is proposed for robust image transmission over noisy channels. By incorporating a learnable latent denoiser into the receiver, the received signals are preprocessed to effectively remove the channel noise and recover the semantic information, thereby enhancin...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 1, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07322	MEMIT-Merge: Addressing MEMIT's Key-Value Conflicts in Same-Subject Batch Editing for LLMs	[ "cs.CL", "cs.LG" ]	As large language models continue to scale up, knowledge editing techniques that modify models' internal knowledge without full retraining have gained significant attention. MEMIT, a prominent batch editing algorithm, stands out for its capability to perform mass knowledge modifications. However, we uncover a critical ...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07323	Semantic to Structure: Learning Structural Representations for Infringement Detection	[ "cs.CV" ]	Structural information in images is crucial for aesthetic assessment, and it is widely recognized in the artistic field that imitating the structure of other works significantly infringes on creators' rights. The advancement of diffusion models has led to AI-generated content imitating artists' structural creations, ye...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07325	Long-term simulation of physical and mechanical behaviors using curriculum-transfer-learning based physics-informed neural networks	[ "cs.LG", "cs.NA", "math.NA" ]	This paper proposes a Curriculum-Transfer-Learning based physics-informed neural network (CTL-PINN) for long-term simulation of physical and mechanical behaviors. The main innovation of CTL-PINN lies in decomposing long-term problems into a sequence of short-term subproblems. Initially, the standard PINN is employed to...	{ "Other": 1, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07326	PICTS: A Novel Deep Reinforcement Learning Approach for Dynamic P-I Control in Scanning Probe Microscopy	[ "cond-mat.mtrl-sci", "cs.LG", "physics.app-ph" ]	We have developed a Parallel Integrated Control and Training System, leveraging the deep reinforcement learning to dynamically adjust the control strategies in real time for scanning probe microscopy techniques.	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07327	Generative Ghost: Investigating Ranking Bias Hidden in AI-Generated Videos	[ "cs.IR", "cs.CV" ]	With the rapid development of AI-generated content (AIGC), the creation of high-quality AI-generated videos has become faster and easier, resulting in the Internet being flooded with all kinds of video content. However, the impact of these videos on the content ecosystem remains largely unexplored. Video information re...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 1, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07328	Music for All: Exploring Multicultural Representations in Music Generation Models	[ "cs.SD", "cs.AI", "cs.CL", "cs.LG", "cs.MM" ]	The advent of Music-Language Models has greatly enhanced the automatic music generation capability of AI systems, but they are also limited in their coverage of the musical genres and cultures of the world. We present a study of the datasets and research papers for music generation and quantify the bias and under-repre...	{ "Other": 1, "cs.AI": 1, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 1, "cs.SI": 0, "cs.SY": 0 }
2502.07331	ERANet: Edge Replacement Augmentation for Semi-Supervised Meniscus Segmentation with Prototype Consistency Alignment and Conditional Self-Training	[ "cs.CV" ]	Manual segmentation is labor-intensive, and automatic segmentation remains challenging due to the inherent variability in meniscal morphology, partial volume effects, and low contrast between the meniscus and surrounding tissues. To address these challenges, we propose ERANet, an innovative semi-supervised framework fo...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07332	The Combined Problem of Online Task Assignment and Lifelong Path Finding in Logistics Warehouses: A Case Study	[ "cs.MA", "cs.RO" ]	We study the combined problem of online task assignment and lifelong path finding, which is crucial for the logistics industries. However, most literature either (1) focuses on lifelong path finding assuming a given task assigner, or (2) studies the offline version of this problem where tasks are known in advance. We a...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 1, "cs.NE": 0, "cs.RO": 1, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07336	Frequency-selective Dynamic Scattering Arrays for Over-the-air EM Processing	[ "eess.SP", "cs.IT", "math.IT" ]	In this paper, we investigate frequency-selective dynamic scattering array (DSA), a versatile antenna structure capable of performing joint wave-based computing and radiation by transitioning signal processing tasks from the digital domain to the electromagnetic (EM) domain. The numerical results demonstrate the potent...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 1, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07337	Neural Flow Samplers with Shortcut Models	[ "cs.LG" ]	Sampling from unnormalized densities is a fundamental task across various domains. Flow-based samplers generate samples by learning a velocity field that satisfies the continuity equation, but this requires estimating the intractable time derivative of the partition function. While importance sampling provides an appro...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07340	Aligning Large Language Models to Follow Instructions and Hallucinate Less via Effective Data Filtering	[ "cs.CL", "cs.AI" ]	Training LLMs on data containing unfamiliar knowledge during the instruction tuning stage can encourage hallucinations. To address this challenge, we introduce NOVA, a novel framework designed to identify high-quality data that aligns well with the LLM's learned knowledge to reduce hallucinations. NOVA includes Interna...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07343	DEG: Efficient Hybrid Vector Search Using the Dynamic Edge Navigation Graph	[ "cs.DB" ]	Bimodal data, such as image-text pairs, has become increasingly prevalent in the digital era. The Hybrid Vector Query (HVQ) is an effective approach for querying such data and has recently garnered considerable attention from researchers. It calculates similarity scores for objects represented by two vectors using a we...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 1, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07344	Integrating Physics and Data-Driven Approaches: An Explainable and Uncertainty-Aware Hybrid Model for Wind Turbine Power Prediction	[ "cs.LG", "cs.AI", "cs.CE" ]	The rapid growth of the wind energy sector underscores the urgent need to optimize turbine operations and ensure effective maintenance through early fault detection systems. While traditional empirical and physics-based models offer approximate predictions of power generation based on wind speed, they often fail to cap...	{ "Other": 0, "cs.AI": 1, "cs.CE": 1, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07346	BenchMAX: A Comprehensive Multilingual Evaluation Suite for Large Language Models	[ "cs.CL" ]	Previous multilingual benchmarks focus primarily on simple understanding tasks, but for large language models(LLMs), we emphasize proficiency in instruction following, reasoning, long context understanding, code generation, and so on. However, measuring these advanced capabilities across languages is underexplored. To ...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07347	Coarse Set Theory: A Mathematical Foundation for Coarse Ethics	[ "cs.AI", "cs.IT", "math.IT", "math.LO", "math.PR" ]	In ethical decision-making, individuals are often evaluated based on generalized assessments rather than precise individual performance. This concept, known as Coarse Ethics (CE), has primarily been discussed in natural language without a formal mathematical foundation. This paper introduces Coarse Set Theory (CST) to ...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 1, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07350	KABB: Knowledge-Aware Bayesian Bandits for Dynamic Expert Coordination in Multi-Agent Systems	[ "cs.AI" ]	As scaling large language models faces prohibitive costs, multi-agent systems emerge as a promising alternative, though challenged by static knowledge assumptions and coordination inefficiencies. We introduces Knowledge-Aware Bayesian Bandits (KABB), a novel framework that enhances multi-agent system coordination throu...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07351	Multi-Task-oriented Nighttime Haze Imaging Enhancer for Vision-driven Measurement Systems	[ "cs.CV", "cs.AI" ]	Salient object detection (SOD) plays a critical role in vision-driven measurement systems (VMS), facilitating the detection and segmentation of key visual elements in an image. However, adverse imaging conditions such as haze during the day, low light, and haze at night severely degrade image quality, and complicating ...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07352	Bridging the Evaluation Gap: Leveraging Large Language Models for Topic Model Evaluation	[ "cs.CL", "cs.AI", "cs.DL" ]	This study presents a framework for automated evaluation of dynamically evolving topic taxonomies in scientific literature using Large Language Models (LLMs). In digital library systems, topic modeling plays a crucial role in efficiently organizing and retrieving scholarly content, guiding researchers through complex k...	{ "Other": 1, "cs.AI": 1, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07355	Performance Bounds and Degree-Distribution Optimization of Finite-Length BATS Codes	[ "cs.IT", "math.IT" ]	Batched sparse (BATS) codes were proposed as a reliable communication solution for networks with packet loss. In the finite-length regime, the error probability of BATS codes under belief propagation (BP) decoding has been studied in the literature and can be analyzed by recursive formulae. However, all existing analys...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 1, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07358	SymbioSim: Human-in-the-loop Simulation Platform for Bidirectional Continuing Learning in Human-Robot Interaction	[ "cs.RO" ]	The development of intelligent robots seeks to seamlessly integrate them into the human world, providing assistance and companionship in daily life and work, with the ultimate goal of achieving human-robot symbiosis. To realize this vision, robots must continuously learn and evolve through consistent interaction and co...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 1, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07360	Supervised contrastive learning for cell stage classification of animal embryos	[ "q-bio.QM", "cs.CV" ]	Video microscopy, when combined with machine learning, offers a promising approach for studying the early development of in vitro produced (IVP) embryos. However, manually annotating developmental events, and more specifically cell divisions, is time-consuming for a biologist and cannot scale up for practical applicati...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07364	Effects of Random Edge-Dropping on Over-Squashing in Graph Neural Networks	[ "cs.LG" ]	Message Passing Neural Networks (MPNNs) are a class of Graph Neural Networks (GNNs) that leverage the graph topology to propagate messages across increasingly larger neighborhoods. The message-passing scheme leads to two distinct challenges: over-smoothing and over-squashing. While several algorithms, e.g. DropEdge and...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07365	LongReD: Mitigating Short-Text Degradation of Long-Context Large Language Models via Restoration Distillation	[ "cs.CL", "cs.LG" ]	Large language models (LLMs) have gained extended context windows through scaling positional encodings and lightweight continual pre-training. However, this often leads to degraded performance on short-text tasks, while the reasons for this degradation remain insufficiently explored. In this work, we identify two prima...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07368	Bidirectional Piggybacking Design for Systematic Nodes with Sub-Packetization $l=2$	[ "cs.IT", "math.IT" ]	In 2013, Rashmi et al. proposed the piggybacking design framework to reduce the repair bandwidth of $(n,k;l)$ MDS array codes with small sub-packetization $l$ and it has been studied extensively in recent years. In this work, we propose an explicit bidirectional piggybacking design (BPD) with sub-packetization $l=2$ an...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 1, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07369	Uniform Kernel Prober	[ "stat.ML", "cs.LG", "math.ST", "stat.TH" ]	The ability to identify useful features or representations of the input data based on training data that achieves low prediction error on test data across multiple prediction tasks is considered the key to multitask learning success. In practice, however, one faces the issue of the choice of prediction tasks and the av...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07371	Mixed Integer Linear Programming for Active Contact Selection in Deep Brain Stimulation	[ "eess.SY", "cs.SY" ]	Deep brain stimulation (DBS) programming remains a complex and time-consuming process, requiring manual selection of stimulation parameters to achieve therapeutic effects while minimizing adverse side-effects. This study explores mathematical optimization for DBS programming, using functional subdivisions of the subtha...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 1 }
2502.07372	USRNet: Unified Scene Recovery Network for Enhancing Traffic Imaging under Multiple Adverse Weather Conditions	[ "cs.CV" ]	Advancements in computer vision technology have facilitated the extensive deployment of intelligent transportation systems and visual surveillance systems across various applications, including autonomous driving, public safety, and environmental monitoring. However, adverse weather conditions such as haze, rain, snow,...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07373	EvoFlow: Evolving Diverse Agentic Workflows On The Fly	[ "cs.LG", "cs.CL", "cs.MA", "cs.NE" ]	The past two years have witnessed the evolution of large language model (LLM)-based multi-agent systems from labor-intensive manual design to partial automation (\textit{e.g.}, prompt engineering, communication topology) and eventually to fully automated design. However, existing agentic automation pipelines often lack...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 1, "cs.NE": 1, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07374	LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!	[ "cs.AI" ]	Large reasoning models (LRMs) tackle complex reasoning problems by following long chain-of-thoughts (Long CoT) that incorporate reflection, backtracking, and self-validation. However, the training techniques and data requirements to elicit Long CoT remain poorly understood. In this work, we find that a Large Language m...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07377	Reddit's Appetite: Predicting User Engagement with Nutritional Content	[ "cs.SI", "cs.CY" ]	The increased popularity of food communities on social media shapes the way people engage with food-related content. Due to the extensive consequences of such content on users' eating behavior, researchers have started studying the factors that drive user engagement with food in online platforms. However, while most st...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 1, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 1, "cs.SY": 0 }
2502.07380	Demonstrating Wheeled Lab: Modern Sim2Real for Low-cost, Open-source Wheeled Robotics	[ "cs.RO" ]	Simulation has been pivotal in recent robotics milestones and is poised to play a prominent role in the field's future. However, recent robotic advances often rely on expensive and high-maintenance platforms, limiting access to broader robotics audiences. This work introduces Wheeled Lab, a framework for the low-cost, ...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 1, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07381	Spatial Degradation-Aware and Temporal Consistent Diffusion Model for Compressed Video Super-Resolution	[ "cs.CV" ]	Due to limitations of storage and bandwidth, videos stored and transmitted on the Internet are usually low-quality with low-resolution and compression noise. Although video super-resolution (VSR) is an efficient technique to enhance video resolution, relatively VSR methods focus on compressed videos. Directly applying ...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07384	SAGEPhos: Sage Bio-Coupled and Augmented Fusion for Phosphorylation Site Detection	[ "cs.CE" ]	Phosphorylation site prediction based on kinase-substrate interaction plays a vital role in understanding cellular signaling pathways and disease mechanisms. Computational methods for this task can be categorized into kinase-family-focused and individual kinase-targeted approaches. Individual kinase-targeted methods ha...	{ "Other": 0, "cs.AI": 0, "cs.CE": 1, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07386	Parametric type design in the era of variable and color fonts	[ "cs.CL", "cs.GR" ]	Parametric fonts are programatically defined fonts with variable parameters, pioneered by Donald Kunth with his MetaFont technology in the 1980s. While Donald Knuth's ideas in MetaFont and subsequently in MetaPost are often seen as legacy techniques from the pre-graphical user interface (GUI) era of type design, recent...	{ "Other": 1, "cs.AI": 0, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07388	UAV-assisted Joint Mobile Edge Computing and Data Collection via Matching-enabled Deep Reinforcement Learning	[ "cs.NE" ]	Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) and data collection (DC) have been popular research issues. Different from existing works that consider MEC and DC scenarios separately, this paper investigates a multi-UAV-assisted joint MEC-DC system. Specifically, we formulate a joint optimization pr...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 1, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07389	FADE: Forecasting for Anomaly Detection on ECG	[ "cs.CV" ]	Cardiovascular diseases, a leading cause of noncommunicable disease-related deaths, require early and accurate detection to improve patient outcomes. Taking advantage of advances in machine learning and deep learning, multiple approaches have been proposed in the literature to address the challenge of detecting ECG ano...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07391	Target-Augmented Shared Fusion-based Multimodal Sarcasm Explanation Generation	[ "cs.CL" ]	Sarcasm is a linguistic phenomenon that intends to ridicule a target (e.g., entity, event, or person) in an inherent way. Multimodal Sarcasm Explanation (MuSE) aims at revealing the intended irony in a sarcastic post using a natural language explanation. Though important, existing systems overlooked the significance of...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07394	Interpretable Rules for Online Failure Prediction: A Case Study on the Metro do Porto dataset	[ "cs.LG" ]	Due to their high predictive performance, predictive maintenance applications have increasingly been approached with Deep Learning techniques in recent years. However, as in other real-world application scenarios, the need for explainability is often stated but not sufficiently addressed. This study will focus on predi...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07396	Optimality in importance sampling: a gentle survey	[ "stat.CO", "cs.CE", "stat.ML" ]	The performance of the Monte Carlo sampling methods relies on the crucial choice of a proposal density. The notion of optimality is fundamental to design suitable adaptive procedures of the proposal density within Monte Carlo schemes. This work is an exhaustive review around the concept of optimality in importance samp...	{ "Other": 0, "cs.AI": 0, "cs.CE": 1, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07397	Bandit Optimal Transport	[ "stat.ML", "cs.LG" ]	Despite the impressive progress in statistical Optimal Transport (OT) in recent years, there has been little interest in the study of the \emph{sequential learning} of OT. Surprisingly so, as this problem is both practically motivated and a challenging extension of existing settings such as linear bandits. This article...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07399	On Iterative Evaluation and Enhancement of Code Quality Using GPT-4o	[ "cs.SE", "cs.AI" ]	This paper introduces CodeQUEST, a novel framework leveraging Large Language Models (LLMs) to iteratively evaluate and enhance code quality across multiple dimensions, including readability, maintainability, efficiency, and security. The framework is divided into two main components: an Evaluator that assesses code qua...	{ "Other": 1, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07400	Explainable Multimodal Machine Learning for Revealing Structure-Property Relationships in Carbon Nanotube Fibers	[ "cond-mat.mtrl-sci", "cond-mat.soft", "cs.AI", "cs.LG", "physics.data-an" ]	In this study, we propose Explainable Multimodal Machine Learning (EMML), which integrates the analysis of diverse data types (multimodal data) using factor analysis for feature extraction with Explainable AI (XAI), for carbon nanotube (CNT) fibers prepared from aqueous dispersions. This method is a powerful approach t...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07401	Enhancing Higher Education with Generative AI: A Multimodal Approach for Personalised Learning	[ "cs.HC", "cs.AI" ]	This research explores the opportunities of Generative AI (GenAI) in the realm of higher education through the design and development of a multimodal chatbot for an undergraduate course. Leveraging the ChatGPT API for nuanced text-based interactions and Google Bard for advanced image analysis and diagram-to-code conver...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 1, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07403	Extended monocular 3D imaging	[ "cs.CV", "physics.optics" ]	3D vision is of paramount importance for numerous applications ranging from machine intelligence to precision metrology. Despite much recent progress, the majority of 3D imaging hardware remains bulky and complicated and provides much lower image resolution compared to their 2D counterparts. Moreover, there are many we...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07404	Human-in-the-Loop Annotation for Image-Based Engagement Estimation: Assessing the Impact of Model Reliability on Annotation Accuracy	[ "cs.HC", "cs.AI", "cs.CV" ]	Human-in-the-loop (HITL) frameworks are increasingly recognized for their potential to improve annotation accuracy in emotion estimation systems by combining machine predictions with human expertise. This study focuses on integrating a high-performing image-based emotion model into a HITL annotation framework to evalua...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 1, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07405	Coupling Agent-Based Simulations and VR universes: the case of GAMA and Unity	[ "cs.MA" ]	Agent-based models (ABMs) and video games, including those taking advantage of virtual reality (VR), have undergone a remarkable parallel evolution, achieving impressive levels of complexity and sophistication. This paper argues that while ABMs prioritize scientific analysis and understanding and VR aims for immersive ...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 1, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07408	No Data, No Optimization: A Lightweight Method To Disrupt Neural Networks With Sign-Flips	[ "cs.LG", "cs.AI", "cs.CV" ]	Deep Neural Networks (DNNs) can be catastrophically disrupted by flipping only a handful of sign bits in their parameters. We introduce Deep Neural Lesion (DNL), a data-free, lightweight method that locates these critical parameters and triggers massive accuracy drops. We validate its efficacy on a wide variety of comp...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07409	MGPATH: Vision-Language Model with Multi-Granular Prompt Learning for Few-Shot WSI Classification	[ "cs.CV", "cs.LG" ]	Whole slide pathology image classification presents challenges due to gigapixel image sizes and limited annotation labels, hindering model generalization. This paper introduces a prompt learning method to adapt large vision-language models for few-shot pathology classification. We first extend the Prov-GigaPath vision ...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07411	EgoTextVQA: Towards Egocentric Scene-Text Aware Video Question Answering	[ "cs.CV", "cs.MM" ]	We introduce EgoTextVQA, a novel and rigorously constructed benchmark for egocentric QA assistance involving scene text. EgoTextVQA contains 1.5K ego-view videos and 7K scene-text aware questions that reflect real-user needs in outdoor driving and indoor house-keeping activities. The questions are designed to elicit id...	{ "Other": 1, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07412	Mapping the Intellectual Structure of Social Network Research: A Comparative Bibliometric Analysis	[ "cs.SI" ]	Network science is an interdisciplinary field that transcends traditional academic boundaries, offering profound insights into complex systems across disciplines. This study conducts a bibliometric analysis of three leading journals, Social Networks, Network Science, and the Journal of Complex Networks, each representi...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 1, "cs.SY": 0 }
2502.07414	Sample Weight Averaging for Stable Prediction	[ "cs.LG" ]	The challenge of Out-of-Distribution (OOD) generalization poses a foundational concern for the application of machine learning algorithms to risk-sensitive areas. Inspired by traditional importance weighting and propensity weighting methods, prior approaches employ an independence-based sample reweighting procedure. Th...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07415	Quantification of model error for inverse problems in the Weak Neural Variational Inference framework	[ "stat.ML", "cs.LG" ]	We present a novel extension of the Weak Neural Variational Inference (WNVI) framework for probabilistic material property estimation that explicitly quantifies model errors in PDE-based inverse problems. Traditional approaches assume the correctness of all governing equations, including potentially unreliable constitu...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07417	Fast-COS: A Fast One-Stage Object Detector Based on Reparameterized Attention Vision Transformer for Autonomous Driving	[ "cs.CV" ]	The perception system is a a critical role of an autonomous driving system for ensuring safety. The driving scene perception system fundamentally represents an object detection task that requires achieving a balance between accuracy and processing speed. Many contemporary methods focus on improving detection accuracy b...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07418	Entity Linking using LLMs for Automated Product Carbon Footprint Estimation	[ "cs.CL" ]	Growing concerns about climate change and sustainability are driving manufacturers to take significant steps toward reducing their carbon footprints. For these manufacturers, a first step towards this goal is to identify the environmental impact of the individual components of their products. We propose a system levera...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07422	MoENAS: Mixture-of-Expert based Neural Architecture Search for jointly Accurate, Fair, and Robust Edge Deep Neural Networks	[ "cs.LG", "cs.CV" ]	There has been a surge in optimizing edge Deep Neural Networks (DNNs) for accuracy and efficiency using traditional optimization techniques such as pruning, and more recently, employing automatic design methodologies. However, the focus of these design techniques has often overlooked critical metrics such as fairness, ...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2502.07423	Towards a Formal Theory of the Need for Competence via Computational Intrinsic Motivation	[ "cs.AI" ]	Computational models offer powerful tools for formalising psychological theories, making them both testable and applicable in digital contexts. However, they remain little used in the study of motivation within psychology. We focus on the "need for competence", postulated as a key basic human need within Self-Determina...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }

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