Knowledge catalogue
arXiv:2609.34900v1 Announce Type: new Abstract: Scaling image-to-3D generation to ultra-high resolutions requires controlling rapidly growing computational costs without sacrificing fine geometric det
arXiv:2606.17014v2 Announce Type: replace Abstract: Joint prediction sets for multivariate time series should control a single event while adapting to cross-coordinate dependence. We study filtered co
arXiv:2609.32069v1 Announce Type: new Abstract: Vision--language--action (VLA) models provide strong priors for robotic manipulation but are typically deployed as frozen policies, unable to improve fr
arXiv:2609.32855v1 Announce Type: cross Abstract: Adapting vision-language navigation (VLN) policies to new environments is expensive because every additional route and instruction requires an embodie
arXiv:2609.34009v1 Announce Type: new Abstract: Feedback-based on-policy self-distillation has emerged as a promising approach for enabling foundation models, more specifically Large Language Models (
arXiv:2609.34070v1 Announce Type: new Abstract: Long-horizon micromagnetic simulation remains expensive because conventional and learned solvers typically propagate Landau--Lifshitz--Gilbert (LLG) dyn
arXiv:2609.32696v1 Announce Type: cross Abstract: Data shape determines how features are structured, how patterns are separated, and how distributions cover the underlying domain. Poor data shape can
arXiv:2609.34488v1 Announce Type: new Abstract: Looped architectures scale computation by reusing the same parameters across recurrent steps, and recent work shows that they substantially improve deep
arXiv:2609.35673v1 Announce Type: new Abstract: Tool-based image editing (image retouching) is commonly formulated with autoregressive multimodal large language models (MLLMs) that sequentially genera
arXiv:2609.33353v1 Announce Type: new Abstract: Multi-class anomaly classification in industrial vision remains challenging due to noisy/incomplete anomaly representations and the unknown number of an
arXiv:2609.33158v1 Announce Type: new Abstract: Progress in AI-based retinal image analysis has advanced with foundation models, yet evaluating their reliability remains challenging. Performance repor
arXiv:2609.33190v1 Announce Type: new Abstract: Driving decisions depend on both where to focus and how to act on what is seen. Effective driving reasoning must establish which objects matter, where t
arXiv:2609.33551v1 Announce Type: new Abstract: Transferring human demonstrations to dexterous robots remains challenging because differences in hand morphology and contact dynamics often cause retarg
arXiv:2609.34115v1 Announce Type: cross Abstract: A non-significant coefficient in a dynamic panel model need not imply the absence of a relationship. It may instead reflect heterogeneous effects aver
arXiv:2602.22673v3 Announce Type: replace Abstract: Background: Antimicrobial resistance (AMR) is a global health threat. While the WHO Global Antimicrobial Resistance and Use Surveillance System (GLA
arXiv:2609.31661v1 Announce Type: new Abstract: Reliable face forgery detection is critical to the security of online identity verification systems, where missed attacks compromise security and excess
arXiv:2609.34358v1 Announce Type: new Abstract: In agentic AI systems, frozen foundation models are increasingly deployed as closed-weight API endpoints, making downstream adaptation possible only thr
arXiv:2609.33649v1 Announce Type: new Abstract: Learning mappings between probability distributions arises naturally when inputs and outputs are represented by populations of samples rather than indiv
arXiv:2609.34679v1 Announce Type: new Abstract: Bipartite matching is a fundamental problem in game theory and market design. Classical approaches such as Gale--Shapley assume complete preferences and
arXiv:2609.35491v1 Announce Type: new Abstract: Few-step autoregressive video generation commonly relies on Distribution Matching Distillation (DMD), requiring a bidirectional diffusion teacher and an
arXiv:2609.34371v1 Announce Type: new Abstract: On-policy distillation (OPD) specializes pretrained video diffusion models through teacher supervision along the student's own generation trajectory. Al
arXiv:2609.34414v1 Announce Type: new Abstract: Predicting the next state is a core paradigm of World Models for modeling physical dynamics, emphasizing prediction fidelity. As World Models evolve int
arXiv:2609.32273v1 Announce Type: new Abstract: Accurate delineation of whole tumor (WT), tumor core (TC), and enhancing tumor (ET) from multimodal magnetic resonance imaging remains challenging under
arXiv:2609.34207v1 Announce Type: cross Abstract: Functional data are intrinsically infinite-dimensional, and often exhibit phase variation, where corresponding events occur at different times across
arXiv:2609.34149v1 Announce Type: new Abstract: Current methods for egocentric view action recognition often face challenges in perceiving dynamic hand movements relying solely on geometrical or physi
arXiv:2609.33981v1 Announce Type: new Abstract: Exploration--exploitation is a central trade-off in bandit learning. While classical algorithms such as upper confidence bound methods and Thompson Samp
arXiv:2609.31715v1 Announce Type: new Abstract: Agentic recognition requires visual perception to move beyond static scene understanding and produce structured scene representations that support the p
arXiv:2609.34233v1 Announce Type: new Abstract: Humanoid avatars extend human physical presence beyond the body, enabling people to participate in social, service, and labor activities through remotel
arXiv:2609.31755v1 Announce Type: new Abstract: Panoramic 360^irc scene understanding increasingly relies on icosphere transformers, but a state-of-the-art spherical model loses more than half of its
arXiv:2609.35226v1 Announce Type: new Abstract: Early detection of oral cancer via photographic imaging presents a promising avenue for large-scale oral cavity screening. However, the development of r
arXiv:2609.34579v1 Announce Type: new Abstract: Single-image novel view synthesis remains challenging because the underlying 3D geometry is highly ambiguous. Recent diffusion-based approaches produce
arXiv:2609.33363v1 Announce Type: cross Abstract: Generating differentially private synthetic data with meaningful Wasserstein utility guarantees is challenging in high dimensions. For datasets of siz
arXiv:2609.34722v1 Announce Type: new Abstract: Long-horizon camera-controlled video generation requires recovering previously observed content from an ever-growing visual history. Existing approaches
arXiv:2609.32222v1 Announce Type: new Abstract: Raw 3D point clouds are a core geometric representation. Establishing their ownership is challenging because point sets are irregular, unstructured, and
arXiv:2609.35734v1 Announce Type: new Abstract: Novel view synthesis from sparse images must reconcile faithful reconstruction of observed regions with plausible completion of unseen content, while ma
arXiv:2609.34564v1 Announce Type: new Abstract: Post-training quantization (PTQ) efficiently compresses Vision Transformers (ViTs) without retraining, yet suffers severe accuracy degradation at low bi
arXiv:2412.08911v4 Announce Type: replace Abstract: Multi-objective learning endeavors to concurrently optimize multiple objectives using a single model, aiming to achieve high and balanced performanc
arXiv:2609.32737v1 Announce Type: new Abstract: Existing geospatial vision-language models (Geo-VLMs) typically optimize diverse geospatial tasks through a unified multi-task adaptation paradigm witho
arXiv:2609.35042v1 Announce Type: cross Abstract: Martian weather forecasting is important for future exploration, but atmospheric behaviour on Mars combines spatial, temporal, vertical, and dust-driv
arXiv:2609.32365v1 Announce Type: new Abstract: Dense associative memories have traditionally focused on storing and retrieving vector-valued patterns. Many modern machine learning problems, however,
arXiv:2508.14859v2 Announce Type: replace Abstract: Temporal graph learning is crucial for dynamic networks where nodes and edges evolve over time and new nodes continuously join the system. Inductive
arXiv:2609.35545v1 Announce Type: new Abstract: Epidemic policy planning often requires coordination between geographical regions, taking into account mobility-driven spillovers and how to make use of
arXiv:2609.32577v1 Announce Type: cross Abstract: Reinforcement learning (RL) for code agents often uses executable tests to provide binary rewards. With these rewards, Group Relative Policy Optimizat
arXiv:2609.31904v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have shown strong performance on robotic manipulation, but they often struggle to generalize to unseen tasks, conf
arXiv:2606.18363v2 Announce Type: replace Abstract: Language models trained on large-scale vision-language data have demonstrated strong potential for embodied agents. Harnessing models through embodi
arXiv:2609.34511v1 Announce Type: new Abstract: Most existing time series generators rely on a two-stage modeling paradigm: the first stage learns discrete latent representations of time series; the s
arXiv:2609.33972v1 Announce Type: cross Abstract: Existing interpretable graph additive models still face limitations in either computational scalability or modeling flexibility. In terms of structura
arXiv:2609.32551v1 Announce Type: new Abstract: Text-conditioned human-object interaction (HOI) generation requires body motion, object trajectories & rotations, and hand articulation to remain coordi
arXiv:2601.13177v2 Announce Type: replace Abstract: Spinal cord stimulation (SCS) is widely used for pain management and has recently shown promise in promoting functional recovery following spinal co
arXiv:2609.32581v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) architectures have become a standard way to scale model capacity while keeping computation sparse, yet routing remains a key
arXiv:2505.17458v3 Announce Type: replace Abstract: Machine learning on heterogeneous graphs has advanced rapidly, driven by the diverse entities, relations, and semantics inherent in real-world data.
arXiv:2609.32280v1 Announce Type: new Abstract: Hero frames are in-film stills used as source imagery for theatrical posters, streaming cover art, film database listings, and other promotional placeme
arXiv:2609.32789v1 Announce Type: new Abstract: Large-scale point cloud representations of complex geome tries incur prohibitive computational and memory costs, necessitating compressed implicit repre
arXiv:2609.32195v1 Announce Type: cross Abstract: Stochastic gradient descent (SGD) is widely used to train machine learning models, but subsampling the training data introduces noise into its updates
arXiv:2609.34514v1 Announce Type: cross Abstract: As large language models (LLMs) become increasingly widespread, preventing unsafe responses to harmful prompts is essential for their safe deployment.
OpenAI’s “How we think about securing frontier RL training runs” (released September 29 2026) explains the company’s approach to safeguarding large‑scale reinforcement learning experiments. It details
arXiv:2609.34430v1 Announce Type: new Abstract: Accurate retrieval of human motions is a crucial first step in text-guided human motion modeling and synthesis, as it selects semantically relevant sequ
arXiv:2609.31840v1 Announce Type: new Abstract: High-speed racket sports provide a demanding testbed for humanoid robots, requiring time-critical decisions, precise striking, and dynamic whole-body co
arXiv:2609.33096v1 Announce Type: new Abstract: Rapid advances in humanoid robotics have motivated growing interest in the application of humanoids for healthcare and clinical tasks. However, it remai
arXiv:2607.09710v2 Announce Type: replace Abstract: Tabular prediction is central to a wide range of real-world applications. Tabular data typically contain heterogeneous features as well as rich and