Knowledge catalogue
arXiv:2606.22091v1 Announce Type: new Abstract: Per-scene 3D Gaussian Splatting (3DGS) enables high-fidelity rendering, but practical robotic and AR scene capture pipelines often depend on external ge
arXiv:2603.20819v2 Announce Type: replace Abstract: This paper studies finite-sample set-membership identification for discrete-time bilinear systems under bounded symmetric log-concave disturbances.
arXiv:2606.23662v1 Announce Type: cross Abstract: Bayesian experimental design (BED) has traditionally been based on maximising expected uncertainty reductions from prior to posterior. A major shortfa
arXiv:2503.01290v3 Announce Type: replace Abstract: Predicting post-intervention distributions from observational data is central to many scientific and decision-making problems, but remains challengi
arXiv:2602.02451v2 Announce Type: replace Abstract: Discovering causal relationships requires controlled experiments, but experimentalists face a sequential decision problem: each intervention reveals
arXiv:2606.22277v1 Announce Type: cross Abstract: We study trajectory optimization in mobile sensing systems that must identify which member of a finite candidate set is the true target, while maintai
arXiv:2602.14257v2 Announce Type: replace-cross Abstract: While Large Language Model (LLM) agents have made remarkable progress on complex reasoning, evaluating them in real-world environments remains
arXiv:2606.22326v1 Announce Type: cross Abstract: Adam is one of the most widely implemented and influential modern optimizers. Why is it effective across different optimization problems in practice?
arXiv:2511.06675v2 Announce Type: replace-cross Abstract: Beside the standard stochastic gradient descent (SGD) method, the Adam optimizer due to Kingma & Ba (2014) is currently probably the best-know
arXiv:2606.21757v1 Announce Type: new Abstract: We present AdaPrivate-TS, a differentially private contextual bandit algorithm that combines Thompson Sampling with batched zCDP composition. Our key in
arXiv:2606.21713v1 Announce Type: cross Abstract: In X-ray tomography, reconstruction quality generally improves with larger numbers of projections. However, more projections increase experiment costs
arXiv:2606.23359v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) provide an effective way to solve partial differential equations (PDEs) by embedding physical principles into t
arXiv:2606.22462v1 Announce Type: new Abstract: Pre-trained foundation models have demonstrated remarkable success in many domains, enabling a unified backbone to generalize across diverse downstream
arXiv:2606.23079v1 Announce Type: new Abstract: Neural world models coupled with model predictive control (MPC) replan at every environment step to bound accumulated prediction error, but this incurs
arXiv:2606.19891v2 Announce Type: replace Abstract: We study adversarial bandit optimization in which the loss functions may be non-convex and non-smooth. In each round, the learner selects an action
arXiv:2606.21736v1 Announce Type: new Abstract: Single domain generalization (SDG) aims to learn a robust model, which could perform well on many unseen domains while there is only one single domain a
arXiv:2606.20880v1 Announce Type: cross Abstract: Decision-making under partial or adversarial observability requires accurate inference of the environment's latent state and its associated uncertaint
arXiv:2606.20697v1 Announce Type: new Abstract: AlphaEarth Foundations (AEF) unify global remote sensing foundation embeddings through multimodal self-supervised learning, but their pretraining focuse
arXiv:2606.21013v1 Announce Type: cross Abstract: Forecasting future events is a critical challenge for large language model (LLM) agents, spanning domains from elections and monetary policy to financ
arXiv:2506.04018v3 Announce Type: replace-cross Abstract: As Large Language Model (LLM) agents become more widespread, associated misalignment risks increase. While prior research has studied agents'
arXiv:2606.21892v1 Announce Type: cross Abstract: Crop recommendation systems in precision agriculture have long suffered from a fundamental modality gap: visual soil characterization and chemical nut
arXiv:2603.20179v3 Announce Type: replace-cross Abstract: Large language model-based AI agents are now able to autonomously execute substantial portions of a high energy physics (HEP) analysis pipelin
arXiv:2503.00366v3 Announce Type: replace-cross Abstract: Thyroid scintigraphy is vital for diagnosing thyroid disorders, yet deep learning (DL) models in this domain often struggle with limited, imba
arXiv:2606.23678v1 Announce Type: new Abstract: Following the paradigm shift initiated by OpenAI o3, interleaved reasoning with code to enhance multimodal large language models (MLLMs) has become a pi
arXiv:2410.07397v2 Announce Type: replace Abstract: As data-driven modeling of physical dynamical systems becomes more prevalent, a new challenge is emerging: making these models more compatible and a
arXiv:2606.22475v1 Announce Type: cross Abstract: Modern web applications increasingly combine three ingredients that are hard to test: output from large language models, multi-market internationaliza
arXiv:2606.22325v1 Announce Type: new Abstract: Attention sinks, representation collapse, and norm stratification are treated as transformer-specific pathologies. We show they are not specific to atte
arXiv:2602.12691v3 Announce Type: replace Abstract: We study how to improve large foundation vision-language-action (VLA) systems through human-in-the-loop reinforcement learning (RL) in real-world en
arXiv:2606.20625v1 Announce Type: cross Abstract: LLM agents are promising for alpha mining via combining financial priors, symbolic reasoning, executable factor generation, and feedback-driven refine
arXiv:2606.22218v1 Announce Type: cross Abstract: In this paper, we investigate untrained recurrent models from the Reservoir Computing (RC) paradigm for audio surveillance, focusing on bidirectional
arXiv:2606.20738v1 Announce Type: new Abstract: This article presents a complementary approach for integrating multimodal medical data in cancer classification, based on state space models represented
arXiv:2509.03372v3 Announce Type: replace-cross Abstract: A recent line of research on automated speaking assessment (ASA) has benefited from self-supervised learning (SSL) representations, which capt
arXiv:2606.21072v1 Announce Type: new Abstract: Traffic prediction is a core task in intelligent transportation systems and urban-scale decision making. Despite the effectiveness of mainstream neural-
arXiv:2606.21257v1 Announce Type: new Abstract: OpenPangu models are attractive targets for private and domestic large-language-model deployment, yet their robustness under aggressive post-training qu
arXiv:2603.05544v2 Announce Type: replace-cross Abstract: Proper scoring rules are essential for evaluating probabilistic forecasts. We propose a simple algebraic rearrangement of the Yates covariance
arXiv:2606.20640v1 Announce Type: cross Abstract: Autonomous vehicles offer the potential for safer and more efficient mobility, yet public trust remains limited due to the lack of transparency in the
arXiv:2606.20617v1 Announce Type: cross Abstract: Student attrition represents a significant challenge for higher education institutions because it impacts both academic results and financial viabilit
arXiv:2606.21177v1 Announce Type: cross Abstract: Segmenting the temporomandibular joint (TMJ) disc from MRI is essential for accurate diagnosis of internal derangement, yet it remains unreliable in p
arXiv:2606.22054v1 Announce Type: cross Abstract: Detectors for GNSS radio-frequency impairments (jamming, spoofing, multipath) are usually reported with a single AUC measured on the distribution they
arXiv:2606.22278v1 Announce Type: cross Abstract: Ensuring safety of learning-enabled robotic manipulation across diverse embodiments and tasks still requires significant manual engineering. Existing
arXiv:2505.10022v4 Announce Type: replace Abstract: Learning natural, animal-like locomotion from demonstrations has become a core paradigm in legged robotics. While motion tracking can reproduce refe
arXiv:2606.23550v1 Announce Type: cross Abstract: In this work, Neural ODEs equipped with a curated collection of equilibrium points have been successfully employed for classification tasks.The plante
arXiv:2606.23514v1 Announce Type: new Abstract: Text and image conditioned 3D models now generate convincing assets, but they still offer little direct control over the space an object should occupy o
arXiv:2606.21209v1 Announce Type: cross Abstract: We present an iterative algorithm to compute an arc-length parameterized spline interpolating a set of points. This differs from other methods where t
arXiv:2606.21701v1 Announce Type: cross Abstract: Long-term records of the Martian atmosphere based on general circulation models and reanalysis of atmospheric state variables are important to underst
arXiv:2606.20687v1 Announce Type: new Abstract: Multi-Camera Multi-Target (MCMT) tracking has emerged as a critical capability for applications ranging from autonomous driving to animal behavior monit
arXiv:2606.22480v1 Announce Type: new Abstract: Learning visuomotor policies for long-horizon manipulation remains a fundamental challenge. Recent skill-based imitation learning methods based on discr
arXiv:2606.21938v1 Announce Type: new Abstract: Reconstructing articulated objects from sparse images requires recovering complete geometry, movable parts, and motion parameters. Recent methods typica
arXiv:2408.02153v2 Announce Type: replace-cross Abstract: Achieving reproducibility, quantity, and diversity in vulnerability datasets has long been viewed as an inherent three-way trade-off, where im
arXiv:2507.12744v4 Announce Type: replace Abstract: Detecting deformable linear objects (DLOs), such as floor cables, is essential for safe mobile robot navigation but remains challenging due to obliq
arXiv:2606.21470v1 Announce Type: cross Abstract: Vision--Language--Action (VLA) controllers are often built by extending vision--language models (VLMs) with action supervision, relying on multimodal
arXiv:2606.23147v1 Announce Type: new Abstract: We propose Assistron, a shared autonomy model that leverages Vision-Language-Action (VLA) models to assist the user in daily activities. Our approach is
arXiv:2507.10443v3 Announce Type: replace-cross Abstract: The Hopfield network made associative memory (AM) the model system of neural computation, but it solves the problem only for a stationary worl
arXiv:2606.18319v2 Announce Type: replace Abstract: Air Traffic Control Operators (ATCOs) are vital in ensuring the safe, orderly, and efficient flow of air traffic, yet training capacity is constrain
arXiv:2606.23153v1 Announce Type: new Abstract: Animal collectives navigate cluttered environments through local coordination, yet robot swarms still struggle to reproduce this capability in the physi
arXiv:2606.22406v1 Announce Type: new Abstract: Attention mechanisms have demonstrated remarkable empirical success in identifying relevant information from large collections of tokens, yet the theore
arXiv:2606.21395v1 Announce Type: new Abstract: Atomistic structure and natural language have long been modeled separately, with language models either calling atomistic models as tools or being fine-
arXiv:2606.22966v1 Announce Type: new Abstract: Many recent vision-language-action (VLA) policies adopt an imagine-then-act design. A world-action model (WAM) first imagines a short future as a latent
arXiv:2604.01989v3 Announce Type: replace Abstract: Like a body at rest that stays at rest, we find that visual attention in multimodal large language models (MLLMs) exhibits pronounced inertia, remai
arXiv:2606.23251v1 Announce Type: cross Abstract: High-fidelity simulations of free-surface flows using Lagrangian methods such as the Particle Finite Element Method (PFEM) are computationally demandi