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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...
arXiv:2602.19139v1 Announce Type: new Abstract: As the aging population faces a chronic care deficit, domestic care is increasingly recast as spectral governance. This paper presents...
arXiv:2602.19140v1 Announce Type: new Abstract: Modality gap significantly restricts the effectiveness of multimodal fusion. Previous methods often use techniques such as diffusion models and adversarial...
arXiv:2602.19141v1 Announce Type: new Abstract: "AI psychosis" or "delusional spiraling" is an emerging phenomenon where AI chatbot users find themselves dangerously confident in outlandish beliefs...
arXiv:2602.19142v1 Announce Type: new Abstract: Learned optimizers are powerful alternatives to hand-designed update rules like Adam, yet they have seen limited practical adoption since they...
arXiv:2602.19143v1 Announce Type: new Abstract: This paper introduces a high-order Markov chain task to investigate how transformers learn to integrate information from multiple past positions...
arXiv:2602.19146v1 Announce Type: new Abstract: We introduce VIGiA, a novel multimodal dialogue model designed to understand and reason over complex, multi-step instructional video action plans....
arXiv:2602.19149v1 Announce Type: new Abstract: Image-generative models are widely deployed across industries. Recent studies show that they can be exploited to produce policy-violating content. Existing...
arXiv:2602.19155v1 Announce Type: new Abstract: We present a robust and efficient numerical framework based on a median filter scheme for solving a broad class of...
arXiv:2602.19156v1 Announce Type: new Abstract: Dermatophytosis is commonly assessed using potassium hydroxide (KOH) microscopy, yet accurate recognition of fungal hyphae is hindered by artefacts, heterogeneous...
arXiv:2602.19157v1 Announce Type: new Abstract: Personality control in Role-Playing Agents (RPAs) is commonly achieved via training-free methods that inject persona descriptions and memory through prompts...
arXiv:2602.19158v1 Announce Type: new Abstract: Medical foundation models generate narrative explanations but cannot quantify intervention effects, detect evidence conflicts, or validate literature claims, limiting clinical...
arXiv:2602.19159v1 Announce Type: new Abstract: Prior behavioural work suggests that some LLMs alter choices when options are framed as causing pain or pleasure, and that...
arXiv:2602.19160v1 Announce Type: new Abstract: This paper examines the reasoning capabilities of Large Language Models (LLMs) from a novel perspective, focusing on their ability to...
arXiv:2602.19161v1 Announce Type: new Abstract: Latent diffusion models have enabled high-quality video synthesis, yet their inference remains costly and time-consuming. As diffusion transformers become increasingly...
arXiv:2602.19163v1 Announce Type: new Abstract: AIGC has rapidly expanded from text-to-image generation toward high-quality multimodal synthesis across video and audio. Within this context, joint audio-video...