Knowledge catalogue
arXiv:2606.02482v1 Announce Type: new Abstract: While video streaming understanding has made significant strides, real-world applications, such as live sports broadcasting, autonomous driving, and mul
arXiv:2606.00134v1 Announce Type: cross Abstract: Intrusion Detection Systems (IDS) in Internet of Things (IoT) environments face significant challenges due to data heterogeneity, lack of labeled data
arXiv:2510.13565v2 Announce Type: replace Abstract: Depth estimation remains central to autonomous driving, and radar-camera fusion offers robustness in adverse conditions by providing complementary g
arXiv:2602.13940v2 Announce Type: replace-cross Abstract: Tokenization is a hardcoded compression step which remains in the training pipeline of Large Language Models (LLMs), despite a general trend t
arXiv:2603.00133v2 Announce Type: replace-cross Abstract: Generative models have been shown to 'memorize' certain training data, leading to verbatim or near-verbatim generating images, which may cause
arXiv:2606.00390v1 Announce Type: cross Abstract: We present Zamba2-VL, a suite of vision-language models built on Zamba2, a hybrid language-model architecture combining Mamba2 state-space layers with
arXiv:2511.02591v2 Announce Type: replace Abstract: Multi-animal tracking is crucial for understanding animal ecology and behavior, yet remains challenging due to variations in habitat, motion pattern
arXiv:2602.01962v2 Announce Type: replace-cross Abstract: Off-policy learning methods seek to derive an optimal policy directly from a fixed dataset of prior interactions. This objective presents sign
arXiv:2605.30469v1 Announce Type: cross Abstract: 3D audio and novel-view acoustic synthesis models are usually evaluated with global metrics.However, global metrics often hide where and why binaural
arXiv:2603.07751v2 Announce Type: replace-cross Abstract: Current Large Language Models have achieved Olympiad-level logic, yet Vision-Language Models paradoxically falter on elementary spatial tasks
arXiv:2602.08964v2 Announce Type: replace-cross Abstract: Understanding an agent's goals helps explain and predict its behaviour, yet there is no established methodology for reliably attributing goals
arXiv:2605.30699v1 Announce Type: cross Abstract: This work proposes a context-aware middleware for medical workflow organization and efficiency improvement. In hospitals, laboratories and teleradiolo
arXiv:2602.03896v2 Announce Type: replace-cross Abstract: Poisson-distributed latent variable models are widely used in computational neuroscience, but differentiating through discrete stochastic samp
arXiv:2605.31231v1 Announce Type: cross Abstract: We present a neural-network-based framework for the solution of three-dimensional boundary value problems where the solution is expressible in terms o
arXiv:2602.07928v2 Announce Type: replace-cross Abstract: Flow-based generative models can be viewed through a physics lens: sampling transports a particle from noise to data by integrating a learned
arXiv:2605.30843v1 Announce Type: new Abstract: In the forward reinforcement-learning problem, the reward is fixed and known; the learner is asked to find a good policy or value function. Here we turn
arXiv:2509.10114v2 Announce Type: replace Abstract: Face image quality assessment (FIQA) plays a critical role in face recognition and verification systems, especially in uncontrolled, real-world envi
arXiv:2605.30399v1 Announce Type: cross Abstract: The aquaculture industry needs to address several challenges to secure sustainable seafood production that can serve an increasing global demand. One
arXiv:2605.30388v1 Announce Type: new Abstract: This paper introduces a new systematic framework for detecting anomalies in maritime Automatic Identification System (AIS) datasets. These anomalies inc
arXiv:2605.30510v1 Announce Type: cross Abstract: Brain cancer's severity necessitates precise brain tumor segmentation, which is crucial for effective brain tumor diagnosis. Manual identification, bu
arXiv:2605.30743v1 Announce Type: cross Abstract: Designing novel inorganic materials through generative models remains an important challenge for material science, driven by the complexity and divers
arXiv:2605.31021v1 Announce Type: new Abstract: Current alignment paradigms for generative artificial intelligence rely predominantly on monolithic benchmarking frameworks that reduce the plurality of
arXiv:2603.28201v2 Announce Type: replace Abstract: We revisit the standard perturbation-based approach of Abernethy et al. (2008) in the context of unconstrained Bandit Linear Optimization (uBLO). We
arXiv:2605.31080v1 Announce Type: cross Abstract: Blind and low-vision (BLV) audiences remain underserved by visual art descriptions, particularly across languages and in museum settings where privacy
arXiv:2605.30989v1 Announce Type: new Abstract: Robot teleoperation enables safe, non-contact task execution in hazardous environments where direct human access is difficult, and its application has e
arXiv:2601.22202v2 Announce Type: replace-cross Abstract: Semantic communication (SemCom) emerges as a transformative paradigm for traffic-intensive visual data transmission, shifting focus from raw d
arXiv:2605.31594v1 Announce Type: new Abstract: Communication costs are a major bottleneck in distributed learning and first-order optimization. A common approach to alleviate this issue is to compres
arXiv:2605.30899v1 Announce Type: cross Abstract: Speech foundation models and Speech LLMs have advanced speech understanding, yet deployment-oriented model selection is hindered by non-comparable eva
arXiv:2605.30452v1 Announce Type: cross Abstract: Many machine learning problems involve multiple inherent trade-offs that are best addressed by gradient-based multi-objective optimization (MOO) algor
arXiv:2605.30905v1 Announce Type: cross Abstract: Anchored fixed point and monotone equation methods, including Halpern iteration, extra anchored gradient, and their relatives, add a vanishing pull to
arXiv:2605.31369v1 Announce Type: cross Abstract: Many modern generative models can be viewed as minimizing divergences between probability distributions, yet they rely on different algorithmic and ge
arXiv:2605.31351v1 Announce Type: new Abstract: AI-based Visually Impaired Assistance (VIA) remains challenging, largely due to the high cost of human evaluation. The VLM-as-a-Judge paradigm may offer
arXiv:2605.30786v1 Announce Type: new Abstract: Graph classification is a core task in graph data mining with widespread real-world applications. Recent advances in graph neural networks (GNNs) have l
arXiv:2601.19220v2 Announce Type: replace Abstract: We study multi-objective optimization over probability distributions in Wasserstein space. Recently, Nguyen et al. (2025) introduced Multiple Wasser
arXiv:2605.30625v1 Announce Type: cross Abstract: Inferring continuous probability paths from sparse snapshots is a fundamental challenge in domains like single-cell biology, where high-fidelity data
arXiv:2605.31436v1 Announce Type: new Abstract: This paper proposes actuator-aware inverse kinematics for torque-controlled redundant robots under joint-limit constraints. In the considered architectu
arXiv:2605.31405v1 Announce Type: new Abstract: This paper addresses the challenge of simultaneously compensating for state-dependent uncertainties and enforcing time-varying state constraints in Eule
arXiv:2410.22967v5 Announce Type: replace Abstract: The widespread usage of the Internet of Things (IoT) has raised the risks of cyber threats; thus, developing Anomaly Detection Systems (ADSs) that c
arXiv:2602.11208v2 Announce Type: replace Abstract: The Earth's subsurface is a cornerstone of modern society, providing essential energy resources like hydrocarbons, geothermal, and minerals while se
arXiv:2605.31062v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved remarkable performance in complex reasoning tasks through Chain-of-Thought (CoT) prompting. However, this app
arXiv:2307.04722v2 Announce Type: replace Abstract: Meta-learning empowers learning systems with the ability to acquire knowledge from multiple tasks, enabling faster adaptation and generalization to
arXiv:2605.30578v1 Announce Type: cross Abstract: Adversarial patches are physical patterns attached to real objects to mislead AI vision systems. Their real-world risk is not determined by a single s
arXiv:2505.20840v2 Announce Type: replace Abstract: We revisit DropEdge, a data augmentation technique for GNNs which randomly removes edges to expose diverse graph structures during training. While b
arXiv:2605.30582v1 Announce Type: new Abstract: While platforms like Google Scholar and Semantic Scholar track citations for academic papers, no comparable infrastructure exists for monitoring dataset
arXiv:2605.30406v1 Announce Type: cross Abstract: Recent research demonstrating AI systems exhibiting deception and shutdown resistance suggests that AI loss of control (LOC) is an urgent policy conce
arXiv:2605.31272v1 Announce Type: new Abstract: As predictive models are increasingly deployed in high-stakes settings such as credit approval, there is a growing need for post-hoc methods that provid
arXiv:2605.30963v1 Announce Type: cross Abstract: We present AMix-2, a protein-text foundation model that establishes protein as a native modality in large language models (LLMs), unifying protein und
arXiv:2605.30599v1 Announce Type: cross Abstract: Medical knowledge is continuously evolving. This creates a need to update or selectively forget information encoded in already-trained medical LLMs. M
arXiv:2605.31016v1 Announce Type: new Abstract: Graph condensation (GC) is pivotal for enabling Graph Neural Networks (GNNs) deployment in resource-constrained scenarios by compressing large-scale gra
arXiv:2602.01399v2 Announce Type: replace-cross Abstract: The Shapley value is a ubiquitous framework for attribution in machine learning, encompassing feature importance, data valuation, and causal i
arXiv:2605.30604v1 Announce Type: cross Abstract: Regulated cybersecurity workflows lack a runtime substrate that enforces organization-level scope across retrieval, tool calls, memory, findings, repo
arXiv:2605.30804v1 Announce Type: new Abstract: We audit six large language models (LLMs) for gender stereotyping across English, Korean, Chinese, and Japanese. Three were developed primarily for Engl
arXiv:2605.31053v1 Announce Type: cross Abstract: Controllable music editing is to modify high-level attributes while strictly preserving rhythmic and melodic structures. However, this task is challen
arXiv:2605.31034v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) and group-based policy optimization methods such as GRPO update a stochastic policy by sampling
arXiv:2605.30742v1 Announce Type: new Abstract: This paper addresses the task of temporal sentence grounding (TSG). Although many respectable works have made decent achievements in this important topi
arXiv:2605.31444v1 Announce Type: new Abstract: Reinforcement Learning (RL) enables autonomous agents to learn policies from experience, but realistic problems often involve enormous state spaces, mak
arXiv:2605.30569v1 Announce Type: new Abstract: Robotic manipulation dexterity is often pursued by building increasingly complex high-DoF multifingered hands. While many robotic hands are designed to
arXiv:2605.31340v1 Announce Type: cross Abstract: The appropriateness of empathy in AI has emerged as a critical concern, as excessive empathy risks seeming manipulative while insufficient empathy app
arXiv:2605.31152v1 Announce Type: cross Abstract: This paper studies how efficiently deep ReLU neural networks can approximate and learn smooth functions. When the error is measured in L^p([0,1]^d) no
arXiv:2605.31314v1 Announce Type: new Abstract: The diffusion based robot navigation world models are typically trained using parallel supervision, while autoregressive inference is employed during pa