Knowledge catalogue
arXiv:2605.24367v1 Announce Type: cross Abstract: The exponential growth of data has intensified the gap between the availability of unlabeled data and the high cost of manual annotation. Graph Neural
arXiv:2501.18196v3 Announce Type: replace Abstract: Unsupervised anomaly detection of multivariate time series is a challenging task, given the requirements of deriving a compact detection criterion w
arXiv:2605.24370v1 Announce Type: new Abstract: Behavioral phenotyping of genetic animal models currently requires labor-intensive manual feature engineering that limits reproducibility and scalabilit
arXiv:2603.10250v2 Announce Type: replace Abstract: A commonly used family of RL algorithms for diffusion policies conducts softmax reweighting over samples from the behavior policy, which often induc
arXiv:2505.11758v2 Announce Type: replace-cross Abstract: Few-shot adaptation of vision-language models remains fundamentally limited by how negative class signals are handled at inference. Existing m
arXiv:2605.25599v1 Announce Type: new Abstract: Evidential Deep Learning (EDL) has emerged as an efficient, sampling-free strategy for uncertainty estimation. A series of EDL variants have been propos
arXiv:2605.25418v1 Announce Type: cross Abstract: Generating 3D models from face sketches is an active topic of research in Computer Graphics due to its potential to tremendously facilitate the modeli
arXiv:2605.24534v1 Announce Type: new Abstract: We present a fully automated pipeline that transforms large collections of court decisions into legal commentaries for statutes - without providing any
arXiv:2601.21406v3 Announce Type: replace-cross Abstract: Unified Multimodal Models (UMMs) integrate both visual understanding and generation within a single framework. Their ultimate aspiration is to
arXiv:2605.25505v1 Announce Type: cross Abstract: Generative artificial intelligence (GenAI) is the first automation wave to reach high-cognitive tasks at scale, yet its effects on intra-urban inequal
arXiv:2604.04453v2 Announce Type: replace-cross Abstract: Granular flows govern many natural and industrial processes, yet their interior kinematics and mechanics remain largely unobservable, as exper
arXiv:2602.04139v2 Announce Type: replace Abstract: Neural operators provide a powerful framework for learning discretization invariant mappings between function spaces, but standard deterministic mod
arXiv:2605.24405v1 Announce Type: cross Abstract: We study sequential decision-making with offline reinforcement learning (RL). Traditional offline RL policies may result in out-of-distribution (OOD)
arXiv:2605.24064v1 Announce Type: cross Abstract: Hyper-relational knowledge graphs (HKGs) effectively represent complex facts. While inferring new knowledge in HKGs is a critical problem, current met
arXiv:2602.01576v2 Announce Type: replace-cross Abstract: Mobile Graphical User Interface (GUI) World Models (WMs) offer a promising path for improving mobile GUI agent performance at train- and infer
arXiv:2605.24844v1 Announce Type: new Abstract: While general-purpose Large Language Models (LLMs) applied to Geology often hallucinate when reasoning about subsurface structures and deep-time evoluti
arXiv:2605.25384v1 Announce Type: new Abstract: Mathematical reasoning is a hallmark of human intelligence, requiring logical deduction, symbolic manipulation, and abstract thinking. Recent multimodal
arXiv:2605.25848v1 Announce Type: cross Abstract: Concept probes extracted from transformer residual streams are only as reliable as the layer from which they are extracted. The common practice of pro
arXiv:2605.25577v1 Announce Type: cross Abstract: The generation of accurate 3D molecular conformations is a pivotal challenge in computational chemistry and drug discovery. Recently, diffusion and fl
arXiv:2605.24760v1 Announce Type: new Abstract: We present a kinematic and transmission-aware design framework for a serial spherical mechanism with an additional translational degree of freedom for m
arXiv:2605.25811v1 Announce Type: cross Abstract: We study counterfactual distribution learning for high-dimensional outcomes whose counterfactual law may concentrate near lower-dimensional structure.
arXiv:2605.25447v1 Announce Type: new Abstract: Generating structured, editable diagrams remains a significant challenge for contemporary large language models, despite their proficiency in general-pu
arXiv:2605.24243v1 Announce Type: cross Abstract: In 3D scene understanding, deep learning models rely on large models and extensive training to capture basic geometric structures that are present in
arXiv:2605.25061v1 Announce Type: cross Abstract: EEG-based emotion recognition holds significant promise for objective diagnosis of mood disorders. Graph neural networks (GNNs) have emerged as the do
arXiv:2605.26078v1 Announce Type: new Abstract: Wasserstein policy gradient (WPG) is a policy optimization method for reinforcement learning (RL) that exploits the optimal-transport geometry of action
arXiv:2605.24939v1 Announce Type: new Abstract: We study the global convergence of policy gradient for infinite-horizon entropy-regularized Markov decision processes (MDPs) with continuous state and a
arXiv:2602.21479v2 Announce Type: replace-cross Abstract: Across many risk-sensitive areas, it is critical to continuously audit machine learning systems as we receive more data to quickly determine i
arXiv:2605.24636v1 Announce Type: new Abstract: While large language models (LLMs) hold transformative potential for medicine, their reasoning robustness and safety in real-world clinical scenarios re
arXiv:2510.02171v3 Announce Type: replace-cross Abstract: Music performance is a distinctly human activity, intrinsically linked to the performer's ability to convey, evoke, or express emotion. Machin
arXiv:2605.26093v1 Announce Type: new Abstract: Bayesian optimal experimental design (BOED) selects experiments to maximize information gain about model parameters. However, in decision-critical setti
arXiv:2605.24784v1 Announce Type: new Abstract: Domain scientists increasingly develop Python scripts to analyze satellite imagery but they lack scalability to large-scale data. This paper demonstrate
arXiv:2605.24518v1 Announce Type: cross Abstract: The quadratic complexity of self-attention in Transformer models remains a significant bottleneck for processing long sequences and deploying large la
arXiv:2605.25279v1 Announce Type: new Abstract: Greenhouse agriculture in the Mediterranean region faces significant automation challenges due to its unique structural and environmental constraints. T
arXiv:2605.25200v1 Announce Type: new Abstract: Travel planning is a realistic task for evaluating the planning and tool-use abilities of LLM agents. However, existing benchmarks typically assume only
arXiv:2603.06626v2 Announce Type: replace-cross Abstract: Traditional Mixture-of-Experts (MoE) training typically proceeds without any structural priors, effectively requiring the model to simultaneou
arXiv:2605.25170v1 Announce Type: cross Abstract: Training data for olfaction is scattered through disparate, non-standardized datasets that limit the ability to build representative world models. Olf
arXiv:2605.25174v1 Announce Type: cross Abstract: Spatial and temporal resource constraints are critical for both biological and artificial intelligent systems. Here we define differentiable cost term
arXiv:2511.09048v2 Announce Type: replace Abstract: We propose a novel projection method that guarantees the conservation of integral quantities in Physics-Informed Neural Networks (PINNs). While the
arXiv:2605.24613v1 Announce Type: cross Abstract: Post-hoc repair of LLM mathematical reasoning introduces an asymmetric risk: fixing an incorrect reasoning trace is useful, but replacing a trace that
arXiv:2605.25254v1 Announce Type: cross Abstract: With unified model-generated images now widespread online, attributing their model of origin offers a path toward transparency and deeper insight into
arXiv:2605.25509v1 Announce Type: cross Abstract: Reconstructing PDE solutions from sparse observations is a core challenge in scientific computing. We present FM4PDE, a flow-matching generative frame
arXiv:2605.24930v1 Announce Type: new Abstract: Transformer-based LLMs achieve strong results on many language tasks; however, long inputs remain challenging because context windows are finite, and pr
arXiv:2406.09079v5 Announce Type: replace Abstract: Deep reinforcement learning agents progressively lose representational capacity during training: neurons become dormant, removing active capacity fr
arXiv:2605.24712v1 Announce Type: new Abstract: Federated learning (FL) enables privacy-preserving collaborative training across distributed edge devices, but real deployments involve heterogeneous cl
arXiv:2605.25443v1 Announce Type: new Abstract: Post-training has significantly enhanced the reasoning capability of Large Reasoning Models (LRMs), especially with Reinforcement Learning (RL) like Gro
arXiv:2605.24002v1 Announce Type: cross Abstract: Computational materials science and chemistry span vast knowledge domains and fractured software ecosystems. Although large language models (LLMs) hav
arXiv:2509.22299v3 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) architectures in large language models (LLMs) deliver exceptional performance and reduced inference costs compared to
arXiv:2605.24588v1 Announce Type: new Abstract: While Deep Learning (DL) enhances automated electrocardiogram (ECG) analysis, clinical deployment is hindered by class imbalance and the generalization
arXiv:2509.23651v3 Announce Type: replace Abstract: In nature, animals often need to move/manipulate objects comparable in weight/size to their own bodies. Compared to grasping and carrying, pushing p
arXiv:2605.24598v1 Announce Type: new Abstract: Large language model (LLM) agents excel at solving complex long-horizon tasks through autonomous interaction with environments. However, their real-worl
arXiv:2605.24774v1 Announce Type: new Abstract: We propose Hermite-NGP, a gradient-augmented multi-resolution hash encoding designed to enable fast and accurate computation of spatial derivatives for
arXiv:2605.25967v1 Announce Type: new Abstract: As policy catches up with the capabilities of generative AI, watermarking is central to content provenance efforts. Inference-time watermarks for autore
arXiv:2605.24042v1 Announce Type: cross Abstract: Of 1{,}536 Gaussian release covariances we tested for single-layer hidden-state privacy, zero achieve both moderate utility and moderate privacy again
arXiv:2507.09179v3 Announce Type: replace Abstract: Decentralized finance (DeFi) has introduced a new era of permissionless financial innovation but also led to unprecedented market manipulation. With
arXiv:2605.25198v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a powerful paradigm for improving language models on reasoning-intensive tasks, but i
arXiv:2208.14882v2 Announce Type: replace-cross Abstract: This paper studies the multimedia problem of temporal sentence grounding (TSG), which aims to accurately determine the specific video segment
arXiv:2605.24763v1 Announce Type: new Abstract: This work presents a high-fidelity computational fluid dynamics (CFD) and data-driven modeling framework for assembly-level flow characterization in a f
arXiv:2605.23922v1 Announce Type: cross Abstract: The EU Artificial Intelligence Act (AIA) establishes a lifecycle governance regime for high-risk AI systems built around ex-ante conformity assessment
arXiv:2509.02113v2 Announce Type: replace-cross Abstract: The advancement of graph-based malware analysis is critically limited by the absence of large-scale datasets that capture the inherent hierarc
arXiv:2605.24635v1 Announce Type: new Abstract: Medical large language models hold promise for reducing healthcare disparities, yet Hindi remains severely underrepresented. While medical LLMs excel in