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arXiv:2602.21372v1 Announce Type: new Abstract: Model merging under unseen test-time distribution shifts often renders naive strategies, such as mean averaging unreliable. This challenge is especially...
arXiv:2602.21374v1 Announce Type: new Abstract: Extracting clinical information from medical transcripts in low-resource languages remains a significant challenge in healthcare natural language processing (NLP). This...
arXiv:2602.21377v1 Announce Type: new Abstract: Tokenization and sub-tokenization based models like word2vec, BERT and the GPTs are the state-of-the-art in natural language processing. Typically, these...
arXiv:2602.21379v1 Announce Type: new Abstract: We introduce MrBERT, a family of 150M-300M parameter encoders built on the ModernBERT architecture and pre-trained on 35 languages and...
arXiv:2602.21381v1 Announce Type: new Abstract: Time series causal discovery is essential for understanding dynamic systems, yet many existing methods remain sensitive to noise, non-stationarity, and...
arXiv:2602.21386v1 Announce Type: new Abstract: Logic locking as a solution for semiconductor intellectual property (IP) confidentiality has received considerable attention in academia, but has yet...
arXiv:2602.21389v1 Announce Type: new Abstract: Autonomous robots can transform how we observe marine ecosystems, but close-range operation in reefs and other cluttered habitats remains difficult....
arXiv:2602.21390v1 Announce Type: new Abstract: We study the problem of efficiently producing, in an online fashion, generative models of scalar, multiclass, and vector-valued outcomes that...
arXiv:2602.21394v1 Announce Type: new Abstract: Traditional phishing website detection relies on static heuristics or reference lists, which lag behind rapidly evolving attacks. While recent systems...
arXiv:2602.21395v1 Announce Type: new Abstract: Multimodal learning that integrates genomics and histopathology has shown strong potential in cancer diagnosis, yet its clinical translation is hindered...
arXiv:2602.21397v1 Announce Type: new Abstract: Prompt learning has become a dominant paradigm for adapting vision-language models (VLMs) such as CLIP to downstream tasks without modifying...
arXiv:2602.21399v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative model training across multiple clients without sharing their private data. However, data heterogeneity across clients...
arXiv:2602.21401v1 Announce Type: new Abstract: The boundary of the firm is determined by coordination cost. We argue that agentic AI induces a structural change in...
arXiv:2602.21402v1 Announce Type: new Abstract: We present FlowFixer, a refinement framework for subject-driven generation (SDG) that restores fine details lost during generation caused by changes...
arXiv:2602.21404v1 Announce Type: new Abstract: A central premise in evolutionary biology is that individual variation can generate information asymmetries that facilitate the emergence of hierarchical...
arXiv:2602.21406v1 Announce Type: new Abstract: Temporal Action Segmentation (TAS) requires dividing videos into action segments, yet the vast space of activities and alternative breakdowns makes...
arXiv:2602.21408v1 Announce Type: new Abstract: Gaussian process (GP) surrogates are the default tool for emulating expensive computer experiments, but cubic cost, stationarity assumptions, and Gaussian...
arXiv:2602.21411v1 Announce Type: new Abstract: Convex Agreement (CA) strengthens Byzantine Agreement (BA) by requiring the output agreed upon to lie in the convex hull of...
arXiv:2602.21415v1 Announce Type: new Abstract: Selecting the right deep learning model for power grid forecasting is challenging, as performance heavily depends on the data available...
arXiv:2602.21416v1 Announce Type: new Abstract: We introduce the task of SVG extraction, which consists in translating specific visual inputs from an image into scalable vector...
arXiv:2602.21418v1 Announce Type: new Abstract: This work presents a wearable human activity recognition (HAR) system that performs real-time inference directly inside a shank-mounted inertial measurement...
arXiv:2602.21420v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has become the leading paradigm for enhancing reasoning in Large Language Models (LLMs). However,...
arXiv:2602.21421v1 Announce Type: new Abstract: Forest monitoring is critical for climate change mitigation. However, existing global tree height maps provide only static snapshots and do...
arXiv:2602.21424v1 Announce Type: new Abstract: Reinforcement learning (RL) agents under partial observability often condition actions on internally accumulated information such as memory or inferred latent...