Search: “papers”

arXiv:2405.13729v3 Announce Type: replace-cross Abstract: In this paper, we study an under-explored but important factor of diffusion generative models, i.e., the combinatorial complexity. Data sample

arXiv:2604.26558v1 Announce Type: cross Abstract: Deep learning methods have proved highly effective for classification and image recognition problems. In this paper, we ask whether this success can b

Exciting direction - can see immediate gains for many teams trying to prevent lockins for cost/legal reasons - my mental model on this is shaped by the meta-harness paper that we need to think of harn

arXiv:2509.20265v3 Announce Type: replace-cross Abstract: In this paper, we show that Simple Preference Optimization (SimPO) can be derived as Maximum Entropy Reinforcement Learning, providing a theor

arXiv:2604.26578v1 Announce Type: cross Abstract: Reusing verification artefacts requires identifying structural and semantic similarities across programs and their specifications. In this paper, we f

arXiv:2604.26227v1 Announce Type: new Abstract: In this paper, we propose an HOI-aware adaptive network named AdaAct for weakly-supervised action segmentation. Most existing methods learn a fixed netw

arXiv:2604.25974v1 Announce Type: new Abstract: This paper addresses velocity estimation within robot-aided integrated sensing and communications (ISAC), where mobile robots act as sensing nodes but c

arXiv:2604.26888v1 Announce Type: new Abstract: This paper extends and explains the Multiple Additive Neural Networks (MANN) methodology, an enhancement to the traditional Gradient Boosting framework,

arXiv:2604.04135v2 Announce Type: replace Abstract: This paper presents a comprehensive review of the NTIRE 2026 3D Restoration and Reconstruction (3DRR) Challenge, detailing the proposed methods and

Ouch! Current AI assistants often corrupt documents. Sounds like an intern you can’t trust — once again. A trillion dollar investment in scaling hasn’t solved this. New Microsoft paper shows that curr

arXiv:2604.26318v1 Announce Type: new Abstract: Point cloud registration (PCR) is a fundamental task for integrating 3D observations in remote sensing applications. This paper proposes a fast and effe

arXiv:2604.26413v1 Announce Type: cross Abstract: This paper presents Quantum Gatekeeper, a context-bound image steganography framework where successful payload recovery depends on both cryptographic

arXiv:2604.26903v1 Announce Type: cross Abstract: This paper provides a concise yet comprehensive review of recent advancements in millimeter-wave (mm-wave) oscillators below 100 GHz and sub-terahertz

arXiv:2604.26833v1 Announce Type: cross Abstract: This paper presents a hierarchical decision-making framework for unmanned aerial vehicle (UAV) missions motivated by search-and-rescue (SAR) scenarios

arXiv:2604.26818v1 Announce Type: new Abstract: This paper proposes a novel algorithm for semisupervised learning. This algorithm learns graph cuts that maximize the margin with respect to the labels

arXiv:2604.26514v1 Announce Type: cross Abstract: This paper investigates efficient methods for utilizing text-only data to improve speech recognition, focusing on encoder-dominated models that facili

✨🧠 Tribe v2, our latest model of human brain responses to sound, sight and language can now be (partly) explored on your phone📱: ▶️demo: https://aidemos.atmeta.com/tribev2/ 📄paper: https://ai.meta.com

arXiv:2604.26216v1 Announce Type: new Abstract: This paper addresses the problem of anomaly detection in accounting subject association structures, proposing a structured modeling and unsupervised dis

arXiv:2603.05811v2 Announce Type: replace Abstract: Current video generation models suffer from high computational latency, making real-time applications prohibitively costly. In this paper, we addres

arXiv:2505.13766v5 Announce Type: replace-cross Abstract: Software Quality Assurance (SQA) is critical for delivering reliable, secure, and efficient software products. The Software Quality Assurance

arXiv:2604.25392v1 Announce Type: new Abstract: This paper benchmarks a classical machine learning approach based on PyCaret AutoML against a deep learning approach based on IndoBERT fine-tuning for b

arXiv:2509.17625v2 Announce Type: replace Abstract: In this paper, we present the first systematic comparison of Data Assimilation (DA) and Likelihood-Based Inference (LBI) in the context of an Agent-

arXiv:2512.17111v2 Announce Type: replace Abstract: This paper presents the first end-to-end pipeline for Handwritten Text Recognition (HTR) for Old Nepali, a historically significant but low-resource

arXiv:2603.16877v2 Announce Type: replace Abstract: Financial analysts face significant challenges extracting information from lengthy 10-K reports, which often exceed 100 pages. This paper presents a

arXiv:2604.25110v1 Announce Type: new Abstract: This position paper argues that knowledge distillation must account for what it loses: student models should be judged not only by retained task scores,

arXiv:2604.25765v1 Announce Type: new Abstract: The quality of training data is critical to the performance of machine learning models. In this paper, the Error Sensitivity Profile (ESP) is proposed.

arXiv:2503.07768v2 Announce Type: replace Abstract: This paper presents NimbleReg, a light-weight deep-learning (DL) framework for diffeomorphic image registration leveraging surface representation of

arXiv:2604.25367v1 Announce Type: new Abstract: In this paper, we present Self-DACE++, an improved unsupervised and lightweight framework for Low-Light Image Enhancement (LLIE), building upon our prev

arXiv:2604.25766v1 Announce Type: new Abstract: This paper presents a sensitivity-based tube Nonlinear Model Predictive Control (NMPC) framework for cooperative aerial chains under bounded parametric

arXiv:2604.25292v1 Announce Type: new Abstract: This paper addresses the problem of traffic congestion management in fixed-wing unmanned aerial vehicle (UAV) corridors by further developing a recently

arXiv:2604.04973v2 Announce Type: replace-cross Abstract: This paper presents StrADiff, a Structured Source-Wise Adaptive Diffusion Framework for unsupervised blind source separation under linear and

arXiv:2603.19124v2 Announce Type: replace Abstract: This paper presents the design, modeling, and fabrication of 3D-printed, tendon-actuated continuum robots featuring a flexible, tapered backbone con

arXiv:2604.24172v1 Announce Type: cross Abstract: This paper uses a minimum divergence framework to introduce a new way of calculating model weights that can be used to average probabilistic predictio

arXiv:2604.23225v1 Announce Type: new Abstract: This paper investigates the deep learning optimization problem with softmax cross-entropy loss. We propose a layer separation strategy to alleviate the

arXiv:2604.15184v2 Announce Type: replace Abstract: In the past year, researchers have created agentic systems that can design real-world CAD-style objects in a training-free setting, a new variety of

arXiv:2604.23615v1 Announce Type: cross Abstract: This paper studies interpretable and fair artificial intelligence architectures for understanding English reading. Introduced transformer-based models

arXiv:2604.22856v1 Announce Type: new Abstract: Accurate vehicle detection is a critical component of autonomous driving, traffic surveillance, and intelligent transportation systems. This paper prese

arXiv:2604.24665v1 Announce Type: cross Abstract: This paper investigates whether source trustworthiness shapes Turkish evidential morphology and whether large language models (LLMs) track this sensit

arXiv:2604.24018v1 Announce Type: new Abstract: This paper studies the problem of robot performance evaluation, focusing on how to obtain accurate and efficient estimates of real-world behavior under

arXiv:2604.23003v1 Announce Type: new Abstract: In this paper, we propose a Physics-Informed Neural Network framework for time-dependent simulations of pollution propagation originating from moving em

arXiv:2604.24745v1 Announce Type: new Abstract: In this paper, we propose a harmonized rotational gradient method, termed HRGrad, for simultaneously tackling multiscale time-dependent kinetic problems

arXiv:2604.24575v1 Announce Type: new Abstract: Diffusion models are primarily trained for image synthesis, yet their denoising trajectories encode rich, spatially aligned visual priors. In this paper

arXiv:2511.21715v2 Announce Type: replace-cross Abstract: This paper argues that dataset structure is important in image recognition tasks (among other tasks). Specifically, we focus on the nature and

arXiv:2604.23815v1 Announce Type: new Abstract: Scientific Deep Research (DR) agents answer user queries by synthesizing research papers into multi-section reports. User feedback can improve their uti

arXiv:2504.18576v2 Announce Type: replace Abstract: This paper presents DriVerse, a generative model for simulating navigation-driven driving scenes from a single image and a future trajectory. Previo

arXiv:2406.05984v2 Announce Type: replace-cross Abstract: Mental health constitutes a complex and pervasive global challenge, affecting millions of lives and often leading to severe consequences. In t