Knowledge catalogue
arXiv:2605.06699v1 Announce Type: cross Abstract: We propose a multimodal latent diffusion model that jointly synthesizes volumetric magnetic resonance imaging (MRI) and tabular clinical data within a
arXiv:2605.06940v1 Announce Type: new Abstract: Annotation automation via Large Language Models (LLMs) is the core approach for scaling NLP datasets; however, LLM behavior with respect to closed-set i
arXiv:2309.01751v3 Announce Type: replace-cross Abstract: The increasing frequency and severity of wildfires necessitates advanced methods for effective surveillance and management, as traditional gro
arXiv:2604.04891v2 Announce Type: replace-cross Abstract: Gradient normalization stabilizes deep-learning optimization, and spectral normalizations are especially natural for matrix-shaped parameter b
arXiv:2605.06884v1 Announce Type: cross Abstract: Most first-order optimizers treat matrix-valued parameters as vectors, ignoring the intrinsic geometry of hidden-layer weights in neural networks. Muo
arXiv:2605.06846v1 Announce Type: cross Abstract: Recent work identifies secret loyalties as a distinct threat from standard backdoors. A secret loyalty causes a model to covertly advance the interest
arXiv:2605.06317v2 Announce Type: replace-cross Abstract: Existing Vision-Language Navigation (VLN) methods typically adopt an egocentric, step-by-step paradigm, which struggles with error accumulatio
arXiv:2603.08256v2 Announce Type: replace Abstract: Word sense plausibility rating requires predicting the human-perceived plausibility of a given word sense on a 1-5 scale in the context of short nar
arXiv:2512.12116v3 Announce Type: replace Abstract: Learned time-series models, whether continuous or discrete, are widely used for forecasting the states of dynamical systems but suffer from error ac
arXiv:2601.19831v2 Announce Type: replace-cross Abstract: Neural scaling laws predict how language model performance improves with increased training inputs. While aggregate metrics like validation lo
arXiv:2605.07792v1 Announce Type: cross Abstract: Neural operators (NOs) are designed to learn maps between infinite-dimensional function spaces. We propose a novel reframing of their use. By introduc
arXiv:2605.07140v1 Announce Type: cross Abstract: Skeleton-based human activity recognition has achieved strong empirical performance, yet most existing models remain black boxes and difficult to inte
arXiv:2605.07166v1 Announce Type: new Abstract: Imitation learning is widely used for learning to act in complex environments. While pure neural-based methods handle high dimensional data effectively,
arXiv:2503.04638v3 Announce Type: replace Abstract: Most Continual Learning (CL) methods maintain performance on earlier tasks by storing exemplars in a replay buffer, introducing memory overhead that
arXiv:2605.07794v1 Announce Type: new Abstract: World Action Models (WAMs) are an emerging family of policies that tie robot action generation to future-observation modeling. In this work, we focus on
arXiv:2508.21466v2 Announce Type: replace Abstract: In recent years, with the large-scale expansion of graph data, there has been an increased focus on Riemannian manifold data spaces other than Eucli
arXiv:2605.08078v1 Announce Type: new Abstract: Diffusion-based models decompose sampling into many small Gaussian denoising steps -- an assumption that breaks down when generation is compressed to a
arXiv:2605.07660v1 Announce Type: new Abstract: Reinforcement-learning-based post-training has become a key approach for improving the reasoning ability of large language models, but its token-level l
arXiv:2605.06892v1 Announce Type: new Abstract: Diffusion Transformers (DiTs) have achieved state-of-the-art video generation quality, but they incur immense computational cost because standard infere
arXiv:2605.07476v1 Announce Type: new Abstract: Multivariate time series forecasting remains a challenge due to the complexity of local temporal dynamics and global dependencies across multiple variab
arXiv:2508.01248v4 Announce Type: replace Abstract: The rapid progress of generative models, such as GANs and diffusion models, has facilitated the creation of highly realistic images, raising growing
arXiv:2605.07051v1 Announce Type: new Abstract: Large Language Models (LLMs) have shown good performance on various science educational benchmarks, demonstrating their potential for use in science and
arXiv:2605.07828v1 Announce Type: cross Abstract: The convergence of Krylov-based linear iterative solvers applied to parametric partial differential equations (PDEs) is often highly sensitive to the
arXiv:2605.07606v1 Announce Type: cross Abstract: Detecting levels of psychological defence mechanisms in supportive conversations is inherently ambiguous. In the PsyDefDetect shared task at BioNLP 20
arXiv:2604.03675v2 Announce Type: replace Abstract: Agentic search enables language models to solve knowledge-intensive tasks by adaptively acquiring external evidence over multiple steps. Reinforceme
arXiv:2605.08031v1 Announce Type: new Abstract: Vision-language models (VLMs) raise growing concerns about privacy, copyright, and bias, motivating machine unlearning to remove sensitive knowledge. Ho
arXiv:2605.07741v1 Announce Type: new Abstract: 3D global relocalization is one of the key capabilities for mobile robots in practical applications. However, in large scale spaces, existing methods of
arXiv:2605.07393v1 Announce Type: new Abstract: A fundamental challenge in model-based offline reinforcement learning (RL) lies in the trade-off between generalization and robustness against exploitat
arXiv:2605.06728v1 Announce Type: cross Abstract: Interpreting transcriptomic data is one of the most common analytical tasks in modern biology. Yet most current models either consume expression profi
arXiv:2605.06835v1 Announce Type: cross Abstract: Tabular data plays an important role in many fields and industries, including those with elevated privacy considerations and risks. As such, there is
arXiv:2605.07289v1 Announce Type: cross Abstract: ReDoS is a well-known type of algorithmic complexity attack, where an adversary supplies maliciously crafted strings to a regular expression matching
arXiv:2605.06874v1 Announce Type: new Abstract: Learning rate is a critical component of reinforcement learning (RL). This work uses global and local clocks to distinguish two types of learning rates.
arXiv:2605.07546v1 Announce Type: new Abstract: Neural scaling laws establish a predictable relationship between model performance and data or compute, offering crucial guidance for resource allocatio
arXiv:2602.08786v4 Announce Type: replace-cross Abstract: There is an extensive literature that studies how to find optimal policies in resource allocation problems, taking the underlying design param
arXiv:2605.07220v1 Announce Type: new Abstract: Diffusion guidance is a powerful technique that enables controllable and high-fidelity sample generation with diffusion models. At a high level, it modi
arXiv:2605.06680v1 Announce Type: cross Abstract: Flow matching generates data by integrating a learned velocity field, where the number of integration steps (NFE) directly determines inference cost.
arXiv:2605.07860v1 Announce Type: cross Abstract: Federated Learning (FL) has emerged as a promising paradigm for preserving client data ownership and control over distributed Internet of Things (IoT)
arXiv:2605.06110v2 Announce Type: replace Abstract: Agentic systems increasingly solve complex user requests by executing orchestrated workflows, where subtasks are assigned to specialized models or t
arXiv:2605.06732v1 Announce Type: new Abstract: State-of-the-art model-based reinforcement learning methods train policies on imagined rollouts. These rollouts are trajectories generated by a learned
arXiv:2605.06873v1 Announce Type: cross Abstract: Probabilistic conditioning is concerned with the identification of a distribution of a random variable X given a random variable Y. It is a cornerston
arXiv:2605.07931v1 Announce Type: cross Abstract: Vision-language-action (VLA) models increasingly rely on auxiliary world modules to plan over long horizons, yet how such modules should be parameteri
arXiv:2605.07910v1 Announce Type: new Abstract: Reconstructing dynamic scenes from Vehicle-to-Infrastructure Cooperative Autonomous Driving (VICAD) data is fundamentally complicated by temporal asynch
arXiv:2605.07023v1 Announce Type: new Abstract: In many practical 6D object pose estimation scenarios, we often have access to only a single real-world RGB-D reference view per object, typically witho
arXiv:2605.07080v1 Announce Type: new Abstract: Many real-world resource allocation systems, such as humanitarian logistics and vaccine distribution, must preposition limited supply across multiple lo
arXiv:2605.07736v1 Announce Type: new Abstract: Online goal recognition in continuous domains poses two central challenges: efficiently encoding large trajectories and effectively comparing them. Rece
arXiv:2605.05497v2 Announce Type: replace Abstract: Conformal prediction is a framework that provides valid uncertainty quantification for general models with exchangeable data. However, in the online
arXiv:2605.07572v1 Announce Type: new Abstract: When applying Bayesian optimization (BO) to scientific workflow, a major yet often overlooked source of uncertainty is the task itself -- namely, what t
arXiv:2605.07649v1 Announce Type: cross Abstract: Over the last few years, research on autonomous systems has matured to such a degree that the field is increasingly well-positioned to translate resea
arXiv:2605.07695v1 Announce Type: new Abstract: High-fidelity surgical video generation can greatly improve medical training and the development of AI, adapting these generative models for precise vid
arXiv:2605.06993v1 Announce Type: new Abstract: Causal queries are often only partially identifiable from observational data, and experiments that could tighten the resulting bounds are typically cost
arXiv:2605.07598v1 Announce Type: new Abstract: Actionable Recourse provides individuals with actions they can take to change an unfavorable classifier outcome. While useful at the instance level, it
arXiv:2408.07522v2 Announce Type: replace-cross Abstract: Voice signals originating from the respiratory tract are utilized as valuable acoustic biomarkers for the diagnosis and assessment of respirat
arXiv:2603.04678v2 Announce Type: replace-cross Abstract: Large language models are known to often exhibit inconsistent knowledge. This is particularly problematic in multilingual scenarios, where mod
arXiv:2605.07414v1 Announce Type: cross Abstract: Tool-calling text-to-image (T2I) agents can plan and execute multi-step tool chains to accomplish complex generation and editing queries. However, thi
arXiv:2605.07815v1 Announce Type: cross Abstract: Muon improves neural-network training by orthogonalizing matrix-valued updates, but it leaves each layer's update magnitude controlled mostly by a glo
arXiv:2511.22316v2 Announce Type: replace Abstract: Large Language Models (LLMs) quantization facilitates deploying LLMs in resource-limited settings, but existing methods that combine incompatible gr
arXiv:2605.06820v1 Announce Type: cross Abstract: Deep learning-based organs/structures-at-risk(OARs) auto-contouring models can improve radiotherapy workflows, but models trained on adult data often
arXiv:2602.10024v2 Announce Type: replace-cross Abstract: The principal goal of the RAG TREC Instrument for Multilingual Evaluation (RAGTIME) track at TREC is to study report generation from multiling
arXiv:2605.07039v1 Announce Type: new Abstract: Large language models have become drivers of evolutionary search, but most systems rely on a fixed, prompt-elicited policy to sample next candidates. Th
arXiv:2602.00465v3 Announce Type: replace-cross Abstract: Functional miRNA--mRNA targeting is a large-bag prediction problem where each transcript yields a heavy-tailed pool of candidate target sites