arXiv:2604.21011v1 Announce Type: new
Abstract: Micro-actions are subtle, localized movements lasting 1-3 seconds such as scratching one’s head or tapping fingers. Such subtle actions are essential for social communication, ubiquitously used in natura…
arXiv:2604.21102v1 Announce Type: new
Abstract: We present a novel framework for automatically evaluating building conditions nationwide in the United States by leveraging large language models (LLMs) and Google Street View (GSV) imagery. By fine-tuni…
arXiv:2604.21028v1 Announce Type: cross
Abstract: The increasing frequency and severity of global flood events highlights the need for the development of rapid and reliable flood prediction tools. This process traditionally relies on computationally e…
arXiv:2604.21221v1 Announce Type: new
Abstract: We introduce Sparse Forcing, a training-and-inference paradigm for autoregressive video diffusion models that improves long-horizon generation quality while reducing decoding latency. Sparse Forcing is m…
arXiv:2604.21691v1 Announce Type: new
Abstract: In this paper, we make the case that a scientific theory of deep learning is emerging. By this we mean a theory which characterizes important properties and statistics of the training process, hidden rep…
arXiv:2604.08016v2 Announce Type: replace
Abstract: Regardless of its foundational role in human discovery and sense-making, abductive reasoning–the inference of the most plausible explanation for an observation–has been relatively underexplored in …
arXiv:2511.11439v2 Announce Type: replace-cross
Abstract: Binary security has increasingly relied on deep learning to reason about malware behavior and program semantics. However, the performance often degrades as threat landscapes evolve and code rep…
arXiv:2604.09000v2 Announce Type: replace
Abstract: Vision agent memory has shown remarkable effectiveness in streaming video understanding. However, storing such memory for videos incurs substantial memory overhead, leading to high costs in both stor…
arXiv:2604.21251v1 Announce Type: cross
Abstract: Large language models (LLMs) trained on unfiltered corpora inherently risk retaining sensitive information, necessitating selective knowledge unlearning for regulatory compliance and ethical safety. Ho…
arXiv:2604.21060v1 Announce Type: new
Abstract: Accurate diagnosis of pediatric brain tumors, starting with histopathology, presents unique challenges for deep learning, including severe data scarcity, class imbalance, and fine-grained morphologic ove…