Knowledge catalogue
arXiv:2606.22574v1 Announce Type: new Abstract: While synthetic data generation resolves the manual labeling bottleneck in computer vision, minimizing the syn-to-real domain gap requires optimizing re
arXiv:2606.15678v2 Announce Type: replace Abstract: A feasibility and dynamics study of the Reservoir Attention Network (RAN), an architecture that injects a fixed, randomly-initialized reservoir into
arXiv:2606.22516v1 Announce Type: cross Abstract: Input Diversity (DI), which applies random resizing and padding at each attack iteration, is a near-default ingredient of transfer-based adversarial a
arXiv:2506.23921v5 Announce Type: replace-cross Abstract: The public often attributes human-like qualities to large language models (LLMs), assuming that they 'know' certain things. In reality, LLMs e
arXiv:2606.21611v1 Announce Type: new Abstract: Mathematical search problems present a unique challenge for Reinforcement Learning (RL) due to vast search spaces and sparse rewards. In previous works,
arXiv:2606.21579v1 Announce Type: new Abstract: Procedural mistake detection is important for quality control and user assistance across many disciplines. Recent work in this field has achieved signif
arXiv:2606.22858v1 Announce Type: new Abstract: As machine learning models grow more influential and opaque, algorithmic fairness and explainability are critical for ensuring accountability. However,
arXiv:2606.22911v1 Announce Type: cross Abstract: Multi-zone HVAC control is a spatial decision problem in which indoor thermal evolution and control decisions depend not only on outdoor conditions an
arXiv:2606.21792v1 Announce Type: new Abstract: Manipulation in confined environments, such as threading a manipulator through narrow apertures, remains a fundamental challenge, especially for convent
arXiv:2606.23058v1 Announce Type: new Abstract: Pedestrian trajectory prediction requires modeling temporal dynamics, multimodal cues, and social interactions in crowded environments. Existing methods
arXiv:2606.19813v1 Announce Type: cross Abstract: Thermal infrared (TIR) imaging has been a popular choice for field robotics due to its robust perception capability under low light visual degradation
arXiv:2606.23472v1 Announce Type: cross Abstract: Time series classification involves learning a mapping from a continuous, temporally ordered sequence of real-valued observations to a discrete respon
arXiv:2506.08026v3 Announce Type: replace-cross Abstract: Real-time market prediction services need correct predictions before a decision deadline; a correct prediction delivered late is not a usable
arXiv:2606.19635v2 Announce Type: replace-cross Abstract: Large Recommendation Models (LRMs) have demonstrated promising capabilities in industry-scale recommendation tasks. However, holistically inte
arXiv:2606.23362v1 Announce Type: cross Abstract: Diffusion models (DMs), despite their impressive capabilities across a wide range of generative tasks, have been shown to be vulnerable to backdoor at
arXiv:2606.21295v1 Announce Type: new Abstract: Existing sequence models, including RNNs, LSTMs, continuous-time networks, and Transformers, share a common structural principle: layer-wise dynamics, w
arXiv:2606.22969v1 Announce Type: new Abstract: Predicting the behavior of dynamical systems (DS) beyond the dynamical and parameter regimes observed in training is a pivotal and essentially unresolve
arXiv:2606.22029v1 Announce Type: new Abstract: Fingerprints are the most widely deployed biometric. Verifying whether two impressions come from the same finger typically relies on minutiae, small lan
arXiv:2606.16272v2 Announce Type: replace Abstract: Human hand-object demonstrations provide dense reference motions for training dexterous manipulation reinforcement learning (RL) policies through re
arXiv:2606.20748v1 Announce Type: new Abstract: Humanoid robots are emerging as co-workers in smart manufacturing, yet their dynamic, human-like movements introduce safety risks that differ fundamenta
arXiv:2601.22461v2 Announce Type: replace-cross Abstract: General-purpose congestion control algorithms (CCAs) are designed to achieve general congestion control goals, but they may not meet the speci
arXiv:2606.20886v1 Announce Type: new Abstract: As urban areas expand, automatic monitoring of parking lots becomes essential for efficient and sustainable cities. This work proposes a self-supervised
arXiv:2602.01448v2 Announce Type: replace Abstract: This paper introduces a novel robotic system designed to manage severe bleeding in emergency scenarios, including unique environments like space sta
arXiv:2606.22299v1 Announce Type: new Abstract: Idling Vehicle Detection (IVD) seeks to determine, at the final frame of a video clip, whether any vehicle is idling, meaning the vehicle is stationary
arXiv:2505.11579v3 Announce Type: replace-cross Abstract: As AI systems evolve from static tools to dynamic agents, traditional categorical governance frameworks -- based on fixed risk tiers, levels o
arXiv:2604.04374v2 Announce Type: replace Abstract: The rapid advancement of robotics is reshaping what it means for humans and robots to coexist -- through expanded capabilities, more intuitive inter
arXiv:2606.20670v1 Announce Type: new Abstract: Wireless foundation models offer a path toward reusable channel state information (CSI) intelligence for sixth-generation (6G) systems. However, existin
arXiv:2606.22370v1 Announce Type: new Abstract: Recent advances in video generation have made minute-level synthesis possible; however, generating long videos remains challenging due to error accumula
arXiv:2603.25260v2 Announce Type: replace Abstract: LiDAR point clouds are fundamental to various applications, yet the extreme sparsity of high-precision geometric details hinders efficient context m
arXiv:2606.22782v1 Announce Type: new Abstract: The proliferation of IoT devices has fueled distributed edge systems to collect vast amounts of sensitive data, creating fertile ground for on-device ma
arXiv:2606.20933v1 Announce Type: new Abstract: The choice of loss function and optimizer is an important decision, that shapes further model training. Yet automated architecture search pipelines (Aut
arXiv:2606.21514v1 Announce Type: new Abstract: Recently, Muon has gained substantial attention as an appealing alternative to Adam-like optimizers, with many works highlighting its advantages through
arXiv:2606.22333v1 Announce Type: cross Abstract: A-mode ultrasound (US) has emerged as a promising modality for hand and wrist motion tracking. Prior works have mainly addressed static gesture classi
arXiv:2503.23332v2 Announce Type: replace Abstract: Image generation algorithms are increasingly integral to diverse aspects of human society, driven by their practical applications. However, insuffic
arXiv:2512.02581v3 Announce Type: replace Abstract: Reinforcement learning (RL) faces a persistent tension: policies that are stable to optimize (e.g., Gaussians) are often too simple to represent the
arXiv:2606.22550v1 Announce Type: new Abstract: Autoregressive visual models (AVMs) based on next-scale prediction have emerged as a prominent paradigm for image and video synthesis. However, decompos
arXiv:2606.22589v1 Announce Type: new Abstract: Ever since the advent of foundation models and the pre-training-finetuning paradigm, there have been numerous efforts to merge multiple task-specific ex
arXiv:2606.22527v1 Announce Type: new Abstract: Diffusion and flow-based generative models produce strong images, yet their controllability remains largely endpoint-centric: users specify conditions a
arXiv:2403.17407v4 Announce Type: replace-cross Abstract: Accurate transcription of Bengali text to the International Phonetic Alphabet (IPA) is a challenging task due to the complex phonology of the
arXiv:2606.23286v1 Announce Type: new Abstract: Sorting a huge stream of waste accurately within a short period can be done with the support of digitalization, particularly Artificial Intelligence, in
arXiv:2606.20852v1 Announce Type: new Abstract: Inference-time engineering can alter model behavior without fine-tuning. However, its utility for improving diagnostic performance in medical vision-lan
arXiv:2606.20131v2 Announce Type: replace Abstract: We present TriFlow, a new generative approach for producing compact 3D meshes with artist-like triangle topology directly from input geometry condit
arXiv:2509.14641v2 Announce Type: replace Abstract: Dense 3D convolutions provide high accuracy for perception but are too computationally expensive for real-time robotic systems. Existing tri-plane m
arXiv:2606.20774v1 Announce Type: new Abstract: Camera motion control is essential for directing viewpoint changes in generative systems. However, existing methods typically condition the generation p
arXiv:2606.23496v1 Announce Type: new Abstract: Discrete text-trigger optimization -- searching for text sequences that, when ingested by a model, steer it toward a specified objective -- underpins mo
arXiv:2606.17343v2 Announce Type: replace Abstract: We propose a novel Bayesian framework for joint image reconstruction and uncertainty quantification from compressed sensing magnetic resonance imagi
arXiv:2606.13714v2 Announce Type: replace Abstract: Unsupervised video object-centric learning aims to decompose dynamic scenes into temporally persistent entity representations. Existing recurrent vi
arXiv:2606.23371v1 Announce Type: new Abstract: For imitation learning in robotic manipulation, high data collection costs result in the scarcity of high quality data. In this paper, we leverage the i
arXiv:2508.20906v3 Announce Type: replace Abstract: While foundation models have revolutionized fields such as natural language processing and computer vision, their potential in graph machine learnin
arXiv:2603.06608v2 Announce Type: replace-cross Abstract: The research community lacks a middle ground between StarCraft II full game and its mini-games. The full-game's sprawling state-action space r
arXiv:2606.21185v1 Announce Type: cross Abstract: There is a precise sense in which drawing causal inferences from observational data is hard, even when identifiability is assumed. In particular, Robi
arXiv:2606.19328v2 Announce Type: replace Abstract: Preference-based RL provides an approach to learning reward models from pairwise comparisons of behaviors, bypassing the need for explicit reward de
arXiv:2606.23046v1 Announce Type: new Abstract: Collaborative perception serves as a pivotal solution to enhance the perception capability of individual agents in autonomous driving, where a core chal
arXiv:2606.21223v1 Announce Type: new Abstract: Reliable localization is essential for intelligent transportation systems (ITS), including autonomous vehicles, quadruped last-mile carriers, and infras
arXiv:2606.23353v1 Announce Type: cross Abstract: Precise knowledge of nuclear structure is essential across fundamental physics, yet probing these structures is notoriously difficult. To address this
arXiv:2606.20474v2 Announce Type: replace Abstract: Context-heavy agents place unusual pressure on the key-value (KV) cache: long prefixes are reused across many short turns, while concurrency determi
arXiv:2512.11791v2 Announce Type: replace Abstract: Accurately quantifying vitiligo extent in routine clinical photographs is crucial for longitudinal monitoring of treatment response. We propose a tr
arXiv:2606.20878v1 Announce Type: new Abstract: Synthetic tabular data enables microdata sharing in regulated domains, yet deploying continuous-time generative models requires balancing analytical uti
arXiv:2606.22976v1 Announce Type: new Abstract: In this paper, we propose using random walks on graphs as a verifiable sandbox to study different parallel sampling strategies in masked diffusion model
arXiv:2508.05762v3 Announce Type: replace-cross Abstract: Universal machine learning force fields (UMLFFs) promise to revolutionize materials science by enabling rapid atomistic simulations across the