Ito maps for any-step SDEs
arXiv:2606.11156v1 Announce Type: cross Abstract: Recent one-step generative models accelerate sampling by learning deterministic flow maps of the underlying dynamics. These methods rely on learning f
Knowledge catalogue
arXiv:2606.11156v1 Announce Type: cross Abstract: Recent one-step generative models accelerate sampling by learning deterministic flow maps of the underlying dynamics. These methods rely on learning f
arXiv:2606.10852v1 Announce Type: cross Abstract: LLM deception is often evaluated through direct markers such as fabricated claims, explicit lies, or strategic concealment. However, many real-world m
arXiv:2606.09964v1 Announce Type: cross Abstract: The NISQ era places stringent constraints on quantum computation, where noise and decoherence fundamentally limit performance. In classical deep learn
arXiv:2606.11151v1 Announce Type: new Abstract: Assistive mobility and manipulation platforms have received increasing attention as a means of restoring independence to individuals with disabilities.
arXiv:2606.10820v1 Announce Type: cross Abstract: Autoregressive (AR) language modeling is the dominant paradigm for text generation, yet its sequential token-by-token decoding makes inference memory-
arXiv:2606.10295v1 Announce Type: cross Abstract: The Gromov--Wasserstein (GW) distance provides a framework for comparing metric measure spaces, regardless of their underlying structure or geometry.
arXiv:2606.10403v1 Announce Type: new Abstract: Math reasoning benchmarks have proliferated, yet most lack a per-item difficulty signal grounded in actual human performance. We introduce KCSAT-ML, a d
arXiv:2606.10358v1 Announce Type: cross Abstract: Learning Bayesian network (BN) structure from sparse discrete data is hard: when each instance records only a few variables, most variable pairs lack
arXiv:2606.10651v1 Announce Type: new Abstract: We introduce Kwai Keye-VL-2.0-30B-A3B, an open-source Mixture-of-Experts (MoE) multimodal foundation model designed to advance long-video understanding
arXiv:2606.10517v1 Announce Type: new Abstract: Learning high-quality latent actions from large-scale unlabeled videos, coupled with limited real-world interaction data for training an action decoder,
arXiv:2606.10460v1 Announce Type: cross Abstract: Recent large language models (LLMs) have shown rapid progress in reading-based question answering (QA), where evidence is explicitly provided or can b
arXiv:2606.10974v1 Announce Type: new Abstract: The driving behavior of autonomous vehicles is typically governed by the cost function of their motion planner, which encodes objectives such as speed t
arXiv:2606.10467v1 Announce Type: new Abstract: The rapid advancement of large language models (LLMs) has intensified debates about their significance for linguistic theory. These debates are commonly
arXiv:2606.10881v1 Announce Type: new Abstract: Learner agency and autonomy are foundational to personal development, yet a pervasive 'jingle-jangle' fallacy (i.e. identical terms denoting different c
arXiv:2504.18424v2 Announce Type: replace Abstract: We present Layered Ray Intersections (LaRI), a fully supervised method for occluded geometry reasoning from a single image. Unlike conventional dept
arXiv:2506.03672v2 Announce Type: replace-cross Abstract: Combinatorial Optimization problems are widespread in domains such as logistics, manufacturing, and drug discovery, yet their NP-hard nature m
arXiv:2606.10531v1 Announce Type: cross Abstract: Quantization-aware training (QAT) is essential for extremely low-bit large language models (LLMs). Current QAT methods are mainly based on scalar quan
arXiv:2606.10975v1 Announce Type: new Abstract: Finding convenient spaces in which certain hypotheses regarding an assumed sparse structure of natural signals hold true has become a desirable result i
arXiv:2606.10170v1 Announce Type: new Abstract: This paper extends the concept of Learning Entropy (LE) from temporal adaptive systems to spatial learning in multilayer perceptron networks (MLPs) appl
arXiv:2511.01927v2 Announce Type: replace-cross Abstract: Solving large-scale Generalized Eigenvalue Problems (GEPs) is a fundamental yet computationally prohibitive task in science and engineering. A
arXiv:2606.10023v1 Announce Type: cross Abstract: Accurate posterior estimation is central to scientific inference, as uncertainties determine what can be reliably learned from observational data. Whi
arXiv:2606.10616v1 Announce Type: new Abstract: Long-horizon language agents accumulate observations, reasoning traces, and retrieved facts that exceed their finite context windows, making memory rete
arXiv:2606.09949v1 Announce Type: cross Abstract: Data-driven PDE surrogates are trained with data produced by numerical PDE solvers. However, when the surrogate's goal is to generalize across a wide
arXiv:2606.10209v1 Announce Type: new Abstract: Large language models deployed as autonomous agents for enterprise workflows face a key challenge: verbose tool responses from enterprise systems can ca
arXiv:2606.09900v1 Announce Type: cross Abstract: Long-term memory is the missing layer for LLM agents: across sessions they forget, and the common workaround -- replaying the whole history into the p
arXiv:2606.10628v1 Announce Type: new Abstract: We present our solution to the 2025 SoccerNet Monocular Depth Estimation Competition Challenge. Predicting the relative depth in football scenarios is c
arXiv:2606.10459v1 Announce Type: cross Abstract: Nowadays, social media networks have become widely preferred sources of information. Especially during the time of the Coronavirus disease 2019 COVID
arXiv:2606.10862v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models achieve strong performance on standard manipulation benchmarks, but most evaluations assume that task-relevant obj
arXiv:2606.10579v1 Announce Type: new Abstract: Designing dynamically feasible trajectories for rigid bodies is a fundamental problem in robotics. While direct methods are widely used, the existing co
arXiv:2512.02240v2 Announce Type: replace Abstract: Large language models (LLMs) tackle complex tasks by generating long chains of thought or 'reasoning traces' that act as latent variables in the gen
arXiv:2606.11104v1 Announce Type: new Abstract: We investigate limitations of learning anh neural networks from point evaluations under finite-precision computations and L^p accuracy guarantees, build
arXiv:2606.10246v1 Announce Type: cross Abstract: Maliciously-created fake speech, including deepfaked and spoofed audio, is proliferating at an alarming rate, and detection models are racing to stay
arXiv:2606.11180v1 Announce Type: new Abstract: Diffusion-based lip synchronization models achieve strong visual quality and audio-visual alignment, but full-sequence bidirectional attention and many
arXiv:2606.10887v1 Announce Type: new Abstract: Class-Incremental Learning (CIL) aims to continuously learn new classes without forgetting previously acquired knowledge. While recent CIL advances have
arXiv:2507.17188v2 Announce Type: replace-cross Abstract: This paper investigates secure communications in rate-splitting multiple access (RSMA) enabled heterogeneous UAV networks, where multiple UAVs
arXiv:2606.09865v1 Announce Type: new Abstract: Privacy and data sharing are often in tension. Many organizations use synthetic data to reduce privacy risk and still share useful data. For tabular dat
arXiv:2606.09852v1 Announce Type: cross Abstract: High-quality source code documentation is vital yet often neglected, especially in critical domains like healthcare where reliability and maintainabil
arXiv:2606.10294v1 Announce Type: cross Abstract: Deploying neural networks on unconventional hardware demands architectures that co-optimize task accuracy and platform-specific constraints such as en
arXiv:2606.09892v1 Announce Type: new Abstract: Textual event records, such as alarm logs, have become an increasingly common data source in engineering and manufacturing systems. Beyond identifying c
arXiv:2606.10173v1 Announce Type: cross Abstract: As AI systems move into operating systems, privacy no longer turns only on whether a model runs locally. A local assistant may assemble email, calenda
arXiv:2606.10273v1 Announce Type: new Abstract: Deploying legged robots in extra-terrestrial environments includes many challenges due to complex terrain interactions, energy, and thermal constraints.
arXiv:2606.09907v1 Announce Type: cross Abstract: Multimodal clinical learning is increasingly important for integrating diverse patient data, including imaging, text, and personalised health records.
arXiv:2511.10234v3 Announce Type: replace-cross Abstract: While promising, graph reasoners based on Large Language Models (LLMs) lack built-in invariance to symmetries in graph representations. Operat
arXiv:2606.10530v1 Announce Type: cross Abstract: Recent developments in brain recording are driving a demand for machine learning tools capable of decoding the latent structure of large populations o
arXiv:2509.24710v2 Announce Type: replace-cross Abstract: Score-based diffusion models are a highly effective method for generating samples from a distribution of images. We consider scenarios where t
arXiv:2606.10349v1 Announce Type: cross Abstract: We present a magnetic extension of the Hierarchically Interacting Particle Neural Network (HIP-NN) that enables large-scale simulations of electron-me
arXiv:2606.10183v1 Announce Type: cross Abstract: Modern Diffusion Transformers for video generation provide limited control over the progression of time and the editing of temporal dynamics. We propo
arXiv:2606.10645v1 Announce Type: new Abstract: Reconstructing dynamic and interactive 3D scenes from real-world observations remains a fundamental challenge in computer vision and robotics. While rec
arXiv:2606.10288v1 Announce Type: new Abstract: Perceptive bipedal locomotion over sparse terrain remains a difficult challenge: model-based methods are precise but brittle to uncertainty, while model
arXiv:2606.10107v1 Announce Type: new Abstract: Reliable evaluation of instance segmentation models requires metrics that accurately and consistently reflect segmentation quality. However, the metrics
arXiv:2606.11155v1 Announce Type: new Abstract: Flow Matching models have demonstrated strong performance across a wide range of generative tasks. However, their reliance on ODE-based iterative sampli
arXiv:2606.11018v1 Announce Type: new Abstract: Measuring subjective constructs in naturally occurring social media text requires annotation procedures that are theoretically grounded, empirically val
arXiv:2606.09850v1 Announce Type: cross Abstract: Post-training alignment algorithms are predominantly evaluated as black boxes, obscuring how they reshape language models' internal computations. We p
arXiv:2603.02221v2 Announce Type: replace-cross Abstract: In clinical tabular prediction, classical machine learning models with feature engineering often outperform neural methods. LLMs are increasin
arXiv:2602.03164v2 Announce Type: replace-cross Abstract: Time series forecasting (TSF) plays a critical role in decision-making for many real-world applications. Recently, large language model (LLM)-
arXiv:2606.10742v1 Announce Type: cross Abstract: External memory has become a core component of modern web agents, enabling long-horizon reasoning through the retrieval of past experiences. However,
arXiv:2606.10120v1 Announce Type: cross Abstract: Postprandial hyperglycemia is a key risk factor for metabolic disorders; however, existing dietary guidance is often static, impractical, and insuffic
arXiv:2606.10956v1 Announce Type: new Abstract: The deployment of Large Language Model (LLM) agents for computer automation is accelerating, yet their ability to navigate complex, professional-grade p
arXiv:2512.06628v3 Announce Type: replace-cross Abstract: Scalable embodied intelligence is constrained by the scarcity of diverse, long-horizon robotic manipulation data. Existing video world models
arXiv:2606.09855v1 Announce Type: cross Abstract: Korean folk painting (minhwa) is built from a small vocabulary of auspicious symbols, a tiger for protection, a pair of birds for marital harmony, a p