arXiv Computer Science @arxiv_cs@qoto.org

Understanding the Impact of Differentially Private Training on Memorization of Long-Tailed Data https://arxiv.org/abs/2602.03872 #cs.LG #cs.AI

Unifying Adversarial Robustness and Training Across Text Scoring Models https://arxiv.org/abs/2602.00857 #cs.CL #cs.IR

ReACT-TTC: Capacity-Aware Top Trading Cycles for Post-Choice Reassignment in Shared CPS https://arxiv.org/abs/2602.00859 #cs.GT

Multi-Head Attention Is a Multi-Player Game https://arxiv.org/abs/2602.00861 #cs.AI #cs.CL #cs.GT #cs.LG

Towards Multiscale Graph-based Protein Learning with Geometric Secondary Structural Motifs https://arxiv.org/abs/2602.00862 #math.NA #cs.LG #cs.AI #cs.NA

Distill3R: A Pipeline for Democratizing 3D Foundation Models on Commodity Hardware https://arxiv.org/abs/2602.00865 #cs.CV

Foundation CAN LM: A Pretrained Language Model For Automotive CAN Data https://arxiv.org/abs/2602.00866 #cs.AI #cs.CL

Safe Stochastic Explorer: Enabling Safe Goal Driven Exploration in Stochastic Environments and Safe Interaction with Unknown Objects https://arxiv.org/abs/2602.00868 #cs.RO

Improving Flow Matching by Aligning Flow Divergence https://arxiv.org/abs/2602.00869 #math.NA #cs.LG #cs.AI #cs.NA

Finite Element Eigenfunction Network (FEENet): A Hybrid Framework for Solving PDEs on Complex Geometries https://arxiv.org/abs/2602.00870 #math.NA #cs.NA

Beyond Output Critique: Self-Correction via Task Distillation https://arxiv.org/abs/2602.00871 #cs.AI #cs.CL

Screen, Match, and Cache: A Training-Free Causality-Consistent Reference Frame Framework for Human Animation https://arxiv.org/abs/2601.22160 #cs.GR #cs.AI

Attention Isn't All You Need for Emotion Recognition:Domain Features Outperform Transformers on the EAV Dataset https://arxiv.org/abs/2601.22161 #eess.AS #cs.LG #cs.CV #cs.SD

Do Open-Vocabulary Detectors Transfer to Aerial Imagery? A Comparative Evaluation https://arxiv.org/abs/2601.22164 #cs.CV #cs.LG #cs.RO

In Vino Veritas and Vulnerabilities: Examining LLM Safety via Drunk Language Inducement https://arxiv.org/abs/2601.22169 #cs.CL #cs.AI #cs.CR #cs.LG

Large Language Models: A Mathematical Formulation https://arxiv.org/abs/2601.22170 #math.NA #stat.ML #cs.LG #cs.NA

On the $L^p$-Convergence and Denoising Performance of Durrmeyer-Type Max-Min Neural Network Operators https://arxiv.org/abs/2601.22174 #math.NA #cs.NA

An innovating approach to teaching applied to database design. Improvement of Action Learning in Lifelong Learning https://arxiv.org/abs/2601.22175 #cs.DB

Discovering High-utility Sequential Rules with Increasing Utility Ratio https://arxiv.org/abs/2601.22178 #cs.DB

High-utility Sequential Rule Mining Utilizing Segmentation Guided by Confidence https://arxiv.org/abs/2601.22179 #cs.DB