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arXiv:2602.19096v1 Announce Type: new Abstract: Crafting adversarial examples can be formulated as an optimization problem. While sign-based optimizers such as I-FGSM and MI-FGSM have become...
arXiv:2602.19097v1 Announce Type: new Abstract: Robust reversible watermarking (RRW) enables copyright protection for images while overcoming the limitation of distortion introduced by watermark itself. Current...
arXiv:2602.19098v1 Announce Type: new Abstract: Test flakiness is a significant issue in industry, affecting test efficiency and product quality. While extensive research has examined the...
arXiv:2602.19101v1 Announce Type: new Abstract: Value alignment of Large Language Models (LLMs) requires us to empirically measure these models' actual, acquired representation of value. Among...
arXiv:2602.19102v1 Announce Type: new Abstract: Satisfiability Modulo Theories (SMT) specifications often rely on quantifiers to remain concise and declarative. However, checking the satisfiability of such...
arXiv:2602.19107v1 Announce Type: new Abstract: Robotaxis are emerging as a promising form of urban mobility, yet research has largely emphasized technical driving performance while leaving...
arXiv:2602.19108v1 Announce Type: new Abstract: Safely moving through environments affected by fire is a critical capability for autonomous mobile robots deployed in disaster response. In...
arXiv:2602.19109v1 Announce Type: new Abstract: We study three-digit addition in Meta-Llama-3-8B (base) under a one-token readout to characterize how arithmetic answers are finalized after cross-token...
arXiv:2602.19111v1 Announce Type: new Abstract: Parameter-Efficient Fine-Tuning (PEFT) methods, especially LoRA, are widely used for adapting pre-trained models to downstream tasks due to their computational...
arXiv:2602.19112v1 Announce Type: new Abstract: Establishing dense correspondences between shapes is a crucial task in computer vision and graphics, while prior approaches depend on near-isometric...
arXiv:2602.19113v1 Announce Type: new Abstract: Spatio-temporal forecasting is fundamental to intelligent systems in transportation, climate science, and urban planning. However, training deep learning models on...
arXiv:2602.19115v1 Announce Type: new Abstract: In recent years, there has been a growing use of generative AI, and large language models (LLMs) in particular, to...
arXiv:2602.19117v1 Announce Type: new Abstract: Perspective-aware spatial reasoning involves understanding spatial relationships from specific viewpoints-either egocentric (observer-centered) or allocentric (object-centered). While vision-language models (VLMs) perform...
arXiv:2602.19121v1 Announce Type: new Abstract: We introduce the problem of asymptotic subspace consensus, which requires the outputs of processes to converge onto a common subspace...
arXiv:2602.19123v1 Announce Type: new Abstract: The fine-grained classification of street trees is a crucial task for urban planning, streetscape management, and the assessment of urban...
arXiv:2602.19124v1 Announce Type: new Abstract: Red-teaming, where adversarial prompts are crafted to expose harmful behaviors and assess risks, offers a dynamic approach to surfacing underlying...
arXiv:2602.19126v1 Announce Type: new Abstract: We propose a robust Bayesian formulation of random feature (RF) regression that accounts explicitly for prior and likelihood misspecification via...
arXiv:2602.19127v1 Announce Type: new Abstract: With the rapid advancement of agent-based methods in recent years, Agentic RAG has undoubtedly become an important research direction. Multi-hop...
arXiv:2602.19128v1 Announce Type: new Abstract: Optimizing GPU kernels is critical for efficient modern machine learning systems yet remains challenging due to the complex interplay of...
arXiv:2602.19130v1 Announce Type: new Abstract: Labeling bias arises during data collection due to resource limitations or unconscious bias, leading to unequal label error rates across...
arXiv:2602.19131v1 Announce Type: new Abstract: Supervised causal learning has shown promise in causal discovery, yet it often struggles with generalization across diverse interventional settings, particularly...
arXiv:2602.19133v1 Announce Type: new Abstract: This paper introduces FRAME (Fine-grained Recognition of Art-historical Metadata and Entities), a manually annotated dataset of art-historical image descriptions for...
arXiv:2602.19134v1 Announce Type: new Abstract: The escalating parameter counts in modern deep learning models pose a fundamental challenge to efficient training and resolution of overfitting....
arXiv:2602.19137v1 Announce Type: new Abstract: We introduce derivation depth-a computable metric of the reasoning effort needed to answer a query based on a given set...