Search: “research”

arXiv:2604.11587v1 Announce Type: new Abstract: This paper introduces Bidirectional Tight Informed Trees (BTIT*), an asymptotically optimal kinodynamic sampling-based motion planning algorithm that in

arXiv:2601.13844v2 Announce Type: replace Abstract: We study generalization in an overparameterized continual linear regression setting, where a model is trained with L2 (isotropic) regularization acr

arXiv:2510.02779v3 Announce Type: replace Abstract: Recent advances have significantly improved our understanding of the generalization performance of gradient descent (GD) methods in deep neural netw

arXiv:2604.11026v1 Announce Type: cross Abstract: We study the problem of characterizing the stability of Kullback-Leibler (KL) divergence under Gaussian perturbations beyond Gaussian families. Existi

arXiv:2601.07447v2 Announce Type: replace Abstract: Existing image foundation models are not optimized for spherical images having been trained primarily on perspective images. PanoSAMic integrates th

arXiv:2601.15593v2 Announce Type: replace-cross Abstract: Masked Diffusion Language Models (MDLMs) promise parallel token generation and arbitrary-order decoding, yet it remains unclear to what extent

arXiv:2604.09863v1 Announce Type: cross Abstract: The goal of domain adaptation is to make predictions for unlabeled samples from a target domain with the help of labeled samples from a different but

arXiv:2604.10481v1 Announce Type: cross Abstract: Retrieving the correct set of files from a large codebase is a crucial step in Automated Program Repair (APR). High recall is necessary to ensure that

arXiv:2512.07222v3 Announce Type: replace-cross Abstract: To address the trade-off between robustness and performance for robust VLM, we observe that function words could incur vulnerability of VLMs a

arXiv:2604.10439v1 Announce Type: new Abstract: Purpose: Existing deep learning-based MRI artifact correction models exhibit poor clinical generalization due to inherent artifact-tissue confusion, fai

arXiv:2604.09829v1 Announce Type: new Abstract: Gaze-following in child-robot interaction improves attention, recall, and learning, but requires expensive platforms ($30,000+), sensors, algorithms, an

arXiv:2604.11645v1 Announce Type: cross Abstract: Frequency-selective wireless power transfer provides a feasible route to enable independent actuation and control of multiple untethered robots in a c

arXiv:2602.13847v3 Announce Type: replace-cross Abstract: We present a framework for constructing physics and causally constrained neural models of turbulent dynamical systems from data. We first form

arXiv:2604.10610v1 Announce Type: cross Abstract: High-speed quantitative phase imaging enables non-intrusive visualization of transient compressible gas flows and energetic phenomena. However, phase

arXiv:2604.09903v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has recently unlocked real-time, high-fidelity novel view synthesis by representing scenes using explicit 3D primitives. Ho

arXiv:2407.15389v2 Announce Type: replace Abstract: Without direct access to the client's data, federated learning (FL) is well-known for its unique strength in data privacy protection among existing

arXiv:2604.10791v1 Announce Type: new Abstract: We propose two complementary modifications to transformer attention blocks. First, a non-linear pre-projection MLP is inserted between layer norm and Q/

arXiv:2602.09295v2 Announce Type: replace Abstract: This work presents the largest curation of Southern Resident Killer Whale (SRKW) acoustic data to date, also containing other marine mammals in thei

arXiv:2604.10016v1 Announce Type: cross Abstract: Solar eruptions, including flares and coronal mass ejections (CMEs), have a significant impact on Earth. Some flares are associated with CMEs, and som

arXiv:2506.21834v2 Announce Type: replace Abstract: Inpainting, the process of filling missing or corrupted image parts, has broad applications in medical imaging. However, generating anatomically acc

arXiv:2604.09657v1 Announce Type: new Abstract: Digitizing magnetic media containing computer data is only the first step towards the preservation of early home computing era artifacts. The audio tape

arXiv:2604.07486v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have emerged as a powerful tool for synthetic data generation. A particularly important use case is producing syn

arXiv:2604.10885v1 Announce Type: cross Abstract: This paper proposes a method to review public acceptance of products based on their brand by analyzing the facial expression of the customer intending

arXiv:2604.02653v2 Announce Type: replace Abstract: Empirically, modern deep learning training often occurs at the Edge of Stability (EoS), where the sharpness of the loss exceeds the threshold below

arXiv:2604.10945v1 Announce Type: new Abstract: Accurate assessment of spheno-occipital synchondrosis (SOS) maturation is a key indicator of craniofacial growth and a critical determinant for orthodon

arXiv:2604.11559v1 Announce Type: new Abstract: Diffusion-based sparse-view CT (SVCT) imaging has achieved remarkable advancements in recent years, thanks to its more stable generative capability. How

arXiv:2604.10949v1 Announce Type: cross Abstract: Unified multimodal models (UMMs) were designed to combine the reasoning ability of large language models (LLMs) with the generation capability of visi

arXiv:2505.04842v2 Announce Type: replace-cross Abstract: Prevalent reinforcement learning~(RL) methods for fine-tuning LLM reasoners, such as GRPO or Leave-one-out PPO, abandon the learned value func

arXiv:2602.19509v3 Announce Type: replace-cross Abstract: We observe that LLM cascading and routing implicitly solves an anytime computation problem -- a class of algorithms, well-studied in classical

arXiv:2511.03636v2 Announce Type: replace-cross Abstract: We perform a simulation-based forecasting analysis to compare the cosmological constraining power of higher-order summary statistics of the la

arXiv:2604.10857v1 Announce Type: cross Abstract: Diffusion models generate samples by iteratively querying learned score estimates. A rapidly growing literature focuses on accelerating sampling by mi

arXiv:2503.23001v5 Announce Type: replace Abstract: The deployment of large language models (LLMs) for next-generation network optimization introduces novel data governance challenges. mobile network

arXiv:2604.09681v1 Announce Type: cross Abstract: With the rapid growth of large-scale video analytics applications, edge-cloud collaborative systems have become the dominant paradigm for real-time in

arXiv:2604.11164v1 Announce Type: new Abstract: Deep learning has greatly advanced medical image segmentation, but its success relies heavily on fully supervised learning, which requires dense annotat

arXiv:2604.10568v1 Announce Type: new Abstract: Systematic chemical names, such as IUPAC-style nomenclature for metal-organic frameworks (MOFs), contain rich structural and compositional information i

arXiv:2604.10259v1 Announce Type: new Abstract: We present a generalizable feed-forward Gaussian splatting framework for human 3D reconstruction and real-time animation that operates directly on multi

arXiv:2604.10213v1 Announce Type: cross Abstract: Reliable LiDAR perception requires robustness across sensors, environments, and adverse weather. However, existing datasets rarely provide physically

arXiv:2604.10072v1 Announce Type: new Abstract: Recent advancements in the Generative Reward Model (GRM) have demonstrated its potential to enhance the reasoning abilities of LLMs through Chain-of-Tho

arXiv:2604.10908v1 Announce Type: new Abstract: Every existing knowledge system separates storage from computation. We show this separation is unnecessary and eliminate it. In a standard triple is_a(A

arXiv:2604.11229v1 Announce Type: cross Abstract: Retrieving procedure-oriented evidence from materials science papers is difficult because key synthesis details are often scattered across long, conte

arXiv:2603.16630v2 Announce Type: replace Abstract: High-performance closed-loop control of truly soft continuum manipulators has remained elusive. Experimental demonstrations have largely relied on s

Software engineering has experienced two seismic shifts this century. First was the rise of the open source movement, which gradually made code accessible to developers and engineers everywhere. Secon

arXiv:2509.22055v2 Announce Type: replace Abstract: We introduce RedNote-Vibe, a dataset spanning five years (pre-LLM to July 2025) sourced from lifestyle platform RedNote (Xiaohongshu), capturing the

arXiv:2604.10423v1 Announce Type: new Abstract: Replicability requires that algorithmic conclusions remain consistent when rerun on independently drawn data. A central structural question is compositi

arXiv:2604.11278v1 Announce Type: new Abstract: In federated learning (FL), accommodating clients with diverse resource constraints remains a significant challenge. A widely adopted approach is to use

arXiv:2604.09982v1 Announce Type: cross Abstract: Reproducibility must validate architectural robustness, not just numerical accuracy. We evaluate ColBERT-v2 and ConstBERT across five dimensions, find