February 2026

arXiv:2602.03878v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has become a mainstream representation for real-time 3D scene synthesis, enabling applications in virtual and augmented reality, robotics, and 3D content creation. Its rising commercial value and explicit parametric structure raise emerging intellectual property (IP) protection concerns, prompting a surge of research on 3DGS IP protection. However, current progress remains fragmented, lacking a unified view of the underlying mechanisms, protection paradigms, and robustness challenges. To address this gap, we […]

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arXiv:2602.03876v1 Announce Type: new Abstract: Standard reinforcement learning from human feedback (RLHF) trains a reward model on pairwise preference data and then uses it for policy optimization. However, while reward models are optimized to capture relative preferences, existing policy optimization techniques rely on absolute reward magnitudes during training. In settings where the rewards are non-verifiable such as summarization, instruction following, and chat completion, this misalignment often leads to suboptimal performance. We introduce Group Ordinal Policy Optimization (GOPO), a […]

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arXiv:2602.03875v1 Announce Type: new Abstract: We introduce a reversible deep learning model for 13C NMR that uses a single conditional invertible neural network for both directions between molecular structures and spectra. The network is built from i-RevNet style bijective blocks, so the forward map and its inverse are available by construction. We train the model to predict a 128-bit binned spectrum code from a graph-based structure encoding, while the remaining latent dimensions capture residual variability. At inference time, […]

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arXiv:2602.03873v1 Announce Type: new Abstract: Emotion recognition from human speech is a critical enabler for socially aware conversational AI. However, while most prior work frames emotion recognition as a categorical classification problem, real-world affective states are often ambiguous, overlapping, and context-dependent, posing significant challenges for both annotation and automatic modeling. Recent large-scale audio language models (ALMs) offer new opportunities for nuanced affective reasoning without explicit emotion supervision, but their capacity to handle ambiguous emotions remains underexplored. At the […]

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arXiv:2602.03872v1 Announce Type: new Abstract: Recent research shows that modern deep learning models achieve high predictive accuracy partly by memorizing individual training samples. Such memorization raises serious privacy concerns, motivating the widespread adoption of differentially private training algorithms such as DP-SGD. However, a growing body of empirical work shows that DP-SGD often leads to suboptimal generalization performance, particularly on long-tailed data that contain a large number of rare or atypical samples. Despite these observations, a theoretical understanding of […]

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arXiv:2602.03866v1 Announce Type: new Abstract: Transforming scientific papers into multimodal presentation content is essential for research dissemination but remains labor intensive. Existing automated solutions typically treat each format as an isolated downstream task, leading to redundant processing and semantic inconsistency. We introduce PaperX, a unified framework that models academic presentation generation as a structural transformation and rendering process. Central to our approach is the Scholar DAG, an intermediate representation that decouples the paper’s logical structure from its final […]

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arXiv:2602.03864v1 Announce Type: new Abstract: Large language models (LLMs) have rapidly emerged in civil and environmental engineering (CEE) research, education, and practice as a tool for project ideation, execution, and communication. However, it is unknown how prevalent LLM adoption is across CEE scholarship and whether it meaningfully alters research prose. Inspired by a recent analysis of biomedical abstracts, this study adapts a vocabulary-based frequency-shift methodology to estimate the incidence of LLM-written abstracts in the field of CEE scholarship […]

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arXiv:2602.03863v1 Announce Type: new Abstract: Computational reproducibility, the possibility for independent researchers to exactly reproduce published empirical results, is fundamental to science. Despite its importance, the proportion of research articles aiming for reproducibility remains low and uneven across disciplines. Barriers include a perceived lack of incentives for researchers and journals, practical challenges in preparing reproducible materials, and the absence of harmonised standards of reproducibility processes and requirements by journals. Existing guidance is often highly technical, reaching mainly those […]

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arXiv:2602.03854v1 Announce Type: new Abstract: Autonomous vehicles (AVs) are emerging as a transformative innovation in transportation, offering potential benefits in safety, sustainability, and efficiency. Saudi Arabian adoption of AVs aligns with Vision 2030, emphasizing smart mobility through initiatives such as the Riyadh Autonomous Metro and self-driving cars. This study explores Saudi citizens perceptions of AVs before and after exposure to these technologies and examines whether demographic factors age, gender, education level, and driving habits affect acceptance. Using quantitative […]

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arXiv:2602.03853v1 Announce Type: new Abstract: Since the COVID-19 pandemic, online lectures have spread rapidly and many students are satisfied with them. However, one challenge remains the loss of concentration due to the lack of students’ copresence. Our previous work suggests that presenting 3D characters with appropriate actions has the potential to improve concentration in online lectures. Nevertheless, an effective combination of actions has not yet been identified. In this study, we developed a lecture watching system that presents […]

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