Knowledge catalogue
arXiv:2609.06316v1 Announce Type: cross Abstract: Aims. The objective of this work is to adapt a deep neural network model to perform galaxy morphological classification trained from crowd annotations
arXiv:2507.05134v2 Announce Type: replace Abstract: We present a deep learning approach to extract physical parameters (e.g., mobility, Schottky contact barrier height, defect profiles) of two-dimensi
arXiv:2607.17138v2 Announce Type: replace Abstract: Deep neural networks trained on natural images are shown to produce outputs consistent with human observers for brightness illusions. While this phe
arXiv:2609.07264v1 Announce Type: new Abstract: Inference energy per token drives the cost and carbon footprint of deployed transformers. It is dominated by dense matrix products that incur fused mult
arXiv:2609.09001v1 Announce Type: cross Abstract: Multi-step LLM reasoning lacks a machine-recheckable ledger: discarded reasoning paths leave no auditable record. We propose the Deposon scattering la
arXiv:2609.06007v1 Announce Type: new Abstract: Guided depth completion methods heavily depend on RGB quality and alignment, while unguided ones often suffer from limited precision due to the absence
arXiv:2609.07072v1 Announce Type: new Abstract: Graphic designs, such as posters, advertisements, and infographics, are an important medium for communicating information and shaping understanding. Unl
arXiv:2507.03656v2 Announce Type: replace Abstract: Discrepancies between clinical diagnoses and omics profiles within a characterized cohort may reflect misdiagnosis, hidden subgroups or prodromal di
arXiv:2609.07747v1 Announce Type: new Abstract: Human videos are an abundant source of dexterous manipulation behaviors, but they lack tactile information that is crucial for contact-rich interaction.
arXiv:2609.07369v1 Announce Type: new Abstract: We introduce DF26, a novel benchmark for detecting AI-generated videos containing fully synthetic clips produced by recent text-to-video and image-to-vi
arXiv:2609.06820v1 Announce Type: cross Abstract: Active mapping requires a robot to select camera viewpoints that efficiently reconstruct an unknown 3D scene. To reason about unobserved regions, rece
arXiv:2602.18613v2 Announce Type: replace Abstract: Standard reranking evaluations study how a reranker orders candidates returned by an upstream retriever. This setup couples ranking behavior with re
arXiv:2609.05796v1 Announce Type: cross Abstract: Principal component analysis (PCA) can rotate away from its population target when a covariance matrix is estimated from limited data. We introduce di
arXiv:2601.01368v2 Announce Type: replace Abstract: Score-based causal discovery in the presence of unobserved confounders requires both a consistent scoring criterion and an efficient search over gra
arXiv:2508.04800v2 Announce Type: replace-cross Abstract: We introduce a novel privatization framework for high-dimensional controlled variable selection. Our framework enables rigorous False Discover
arXiv:2609.08650v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has been central to the recent success of Large Reasoning Models. However, while RLVR significantl
arXiv:2609.06491v1 Announce Type: new Abstract: Open-world entity segmentation aims to predict masks for arbitrary objects across domains and at multiple granularities, from parts to whole objects. In
arXiv:2512.01292v4 Announce Type: replace Abstract: Medical image segmentation is crucial for clinical diagnosis and treatment planning. Traditional methods typically produce a single segmentation mas
arXiv:2609.05522v1 Announce Type: new Abstract: Eye-tracking data are expensive to collect, requiring specialized hardware and controlled laboratory conditions, and difficult to share because of priva
arXiv:2101.02307v4 Announce Type: replace-cross Abstract: Mixed membership modeling for undirected networks has been extensively explored in network science over the past few years. Despite the substa
arXiv:2609.07110v1 Announce Type: new Abstract: Natural adversarial examples (NAEs) reveal that vision models can fail under realistic semantic changes beyond norm-bounded perturbations. However, gene
arXiv:2609.06593v1 Announce Type: cross Abstract: Choosing when to translate multilingual documents is a central routing problem in text classification: translation can improve predictions for some la
arXiv:2609.08123v1 Announce Type: cross Abstract: A robot that can be taught a new task from a handful of demonstrations has to work out for itself what it still cannot do, and then ask for exactly th
arXiv:2609.07037v1 Announce Type: new Abstract: Activation steering has emerged as a lightweight, inference-time approach to control the behavior of Large Language Models (LLMs). However, traditional
arXiv:2609.06809v1 Announce Type: new Abstract: Modern stereo matching models fail when disparity crosses zero, with end-point error (EPE) rising by 4.6-37imes. Yet stereoscopic content, from cinema 3
arXiv:2609.08337v1 Announce Type: new Abstract: On-policy distillation (OPD) transfers teacher knowledge on student-generated trajectories, but efficient sampled objectives reduce the teacher distribu
arXiv:2609.06930v1 Announce Type: cross Abstract: Distributed Dexterous Manipulation (DDM) is a novel paradigm that presents significant control challenges due to high action-space redundancy, inter-r
arXiv:2609.07381v1 Announce Type: cross Abstract: We introduce Distributed Lag Neural Additive Models (DLNAMs), neural-additive analogues of Distributed Lag Non-linear Models (DLNMs) for learning nonl
arXiv:2609.06896v1 Announce Type: new Abstract: Distributed learning control for multirobot systems (MRS) offers significant flexibility in presence of uncertainties but lacks provable performance gua
arXiv:2609.08234v1 Announce Type: cross Abstract: Suppose we are given an ordered sequence of independent data whose distribution changes K times at unknown locations, for some unknown K geq 0. In thi
arXiv:2609.05525v1 Announce Type: new Abstract: Large vision-language models (VLMs) are increasingly deployed in safety-critical settings, yet existing visual jailbreak research has focused almost exc
arXiv:2512.13742v3 Announce Type: replace Abstract: Medical image classifiers detect gastrointestinal diseases well, but they do not explain their decisions. Large language models can generate clinica
arXiv:2609.05543v1 Announce Type: new Abstract: Automated assessment of depression from facial dynamics holds promise for scalable mental health monitoring, yet cross-corpus generalization of learned
arXiv:2609.08331v1 Announce Type: new Abstract: Video Large Language Models (VideoLLMs) are increasingly deployed in safety-critical applications such as content moderation and video analytics. To pro
arXiv:2609.05842v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards enables large language models to think slowly, but the same training can induce policy collapse: probab
arXiv:2609.09038v1 Announce Type: new Abstract: Reasoning representations are increasingly used as explanations for large language model outputs. Yet they are typically evaluated with model-centric cr
arXiv:2609.08475v1 Announce Type: cross Abstract: Large language models have collapsed the cost of producing lexically elaborate prose, and whether peer reviewers still reward it is a question about t
arXiv:2609.06962v1 Announce Type: new Abstract: Real-world remote sensing image dehazing (RSID) remains challenging because atmospheric scattering, spatially non-uniform haze and colour distortion joi
arXiv:2609.08282v1 Announce Type: new Abstract: Unified multimodal models integrate visual understanding and generation within a single network, yet the two capabilities are commonly optimized as sepa
arXiv:2609.08213v1 Announce Type: cross Abstract: Diffusion watermarking embeds verifiable signals into the generative process and commonly verifies them by recovering trajectory-dependent evidence, m
arXiv:2609.06923v1 Announce Type: new Abstract: Automated parking requires generating complete and executable trajectories in highly constrained spaces with low tolerance for goal pose error. Existing
arXiv:2609.08217v1 Announce Type: new Abstract: VLMs have shown promise for autonomous driving, yet still suffer from hallucination, weak spatio-temporal perception, and limited generalization. Recent
arXiv:2609.08117v1 Announce Type: new Abstract: Driver motion can provide cues to ongoing behavior, attention, and near-term driving intent. However, most existing driver-centric datasets focus on rec
arXiv:2609.06055v1 Announce Type: new Abstract: Most end-to-end autonomous-driving systems learn by imitating human driving logs, leaving their learned behavior constrained by the quality and behavior
arXiv:2512.03936v2 Announce Type: replace Abstract: Autonomous driving planning systems perform nearly perfectly in routine scenarios using lightweight, rule-based methods but still struggle in dense
arXiv:2609.07780v1 Announce Type: new Abstract: Automated drone surveillance has become increasingly important for public safety, critical infrastructure protection,and restricted airspace monitoring.
arXiv:2609.06779v1 Announce Type: new Abstract: Drug repurposing aims to identify new therapeutic uses for existing compounds and, compared with de novo drug discovery, offers a faster and more cost-e
arXiv:2609.08850v1 Announce Type: new Abstract: Text-guided Video Temporal Grounding (VTG) aims to localize the relevant segments in an untrimmed video based on text queries, yet collecting dense temp
arXiv:2603.21461v2 Announce Type: replace Abstract: Preference alignment is usually achieved by weight-updating training on preference data, which adds substantial alignment-stage compute and provides
arXiv:2411.15876v3 Announce Type: replace-cross Abstract: Overfitting remains a significant challenge in deep learning, often arising from data outliers, noise, and limited training data. To address t
arXiv:2609.05539v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have recently made strong progress in vision-language reasoning, yet their performance often degrades as genera
arXiv:2609.08164v1 Announce Type: new Abstract: Aerial Object Goal Navigation (ObjectNav) requires an unmanned aerial vehicle (UAV) to locate a described target in an unknown outdoor environment using
arXiv:2609.06370v1 Announce Type: new Abstract: Predicting 3D occupancy from multi-view images requires preserving geometric detail during 2D-to-3D lifting while reasoning over sparse, volumetric scen
arXiv:2609.09005v1 Announce Type: new Abstract: We present DXPR, a depth-based cross-modal place recognition (CMPR) framework that uses vision foundation models (VFMs) to match monocular camera querie
arXiv:2609.09023v1 Announce Type: new Abstract: An embodied assistant working beside a person must track task state, recognize help seeking, choose how to intervene, and produce an appropriate respons
arXiv:2609.06898v1 Announce Type: cross Abstract: Byte Pair Encoding (BPE) constructs vocabularies through greedy pair merging, but the resulting merge order does not necessarily allocate a fixed mode
arXiv:2609.06520v1 Announce Type: cross Abstract: This work presents a control framework that enables a multicopter equipped with a two-axis gimbal to emulate the flight dynamics of a fixed-wing aircr
arXiv:2507.10383v5 Announce Type: replace-cross Abstract: Recurrent neural networks are canonical models of biological memory. In these models, memories are represented by distributed patterns of neur
arXiv:2609.08855v1 Announce Type: new Abstract: Machine learning emulators have become essential for accelerating expensive Earth-system simulations, but most existing approaches remain passive foreca
arXiv:2609.05506v1 Announce Type: cross Abstract: We propose ECHO for dyadic 3D facial motion generation under a strict dual-stream audio-only setting, formulating the problem as an asymmetric task in