Knowledge catalogue
arXiv:2511.22565v2 Announce Type: replace Abstract: Neural methods for Complex Query Answering (CQA) over knowledge graphs (KGs) are widely believed to learn patterns that generalize beyond explicit g
arXiv:2605.08960v1 Announce Type: cross Abstract: Crystal generative models mainly learn what stable crystals look like, with little explicit supervision for what makes them stable. We reveal a substa
arXiv:2605.09864v1 Announce Type: new Abstract: Rapid and accurate damage assessment following natural disasters is critical for effective emergency response. However, identifying fine-grained damage
arXiv:2605.10166v1 Announce Type: new Abstract: Robotic imitation learning typically assumes access to optimal demonstrations, yet real-world data collection often yields suboptimal, exploratory, or e
arXiv:2605.10933v1 Announce Type: cross Abstract: While Mixture-of-Experts (MoE) scales model capacity without proportionally increasing computation, its massive total parameter footprint creates sign
arXiv:2605.10364v1 Announce Type: new Abstract: Modeling uncertainty in heavy-tailed time series remains a critical challenge for deep probabilistic forecasting models, which often struggle to capture
arXiv:2605.09586v1 Announce Type: new Abstract: World models for deformable objects should recover not only geometry and appearance, but also underlying physical dynamics, interaction grounding, and m
arXiv:2605.09065v1 Announce Type: new Abstract: Scene graphs (SGs) represent objects and their relationships as structured graphs, enabling applications in image generation, robotics, and 3D understan
arXiv:2605.08446v1 Announce Type: new Abstract: We propose training Bayesian neural networks by directly minimizing the Bethe free energy rather than maximizing a variational lower bound. On tree-stru
arXiv:2605.08293v1 Announce Type: new Abstract: 3D semantic scene understanding has broad applications in digital twins, autonomous driving, smart agriculture, and embodied perception. However, dense
arXiv:2605.09719v1 Announce Type: cross Abstract: Large-scale 3D vision-language models (VLMs) like LLaVA-3D offer strong spatial reasoning but are difficult to deploy due to high computational costs.
arXiv:2605.08961v1 Announce Type: new Abstract: We present Dolphin-CN-Dialect, a streaming-capable ASR model with a focus on Chinese and dialect-rich scenarios. Compared to the previous version, Dolph
arXiv:2605.09701v1 Announce Type: new Abstract: Existing latent world models for autonomous driving have opened a promising path toward future-aware driving intelligence. However, they typically treat
arXiv:2512.23964v2 Announce Type: replace-cross Abstract: Flood models inform strategic disaster management by simulating the spatiotemporal hydrodynamics of flooding. While physics-based numerical fl
arXiv:2605.10923v1 Announce Type: cross Abstract: Large language model agents increasingly rely on external skills to solve complex tasks, where skills act as modular units that extend their capabilit
arXiv:2605.10050v1 Announce Type: new Abstract: Long-form video understanding remains challenging for Video Large Language Models (VideoLLMs), as the dense frame sampling introduces massive visual tok
arXiv:2602.05391v2 Announce Type: replace Abstract: Dataset distillation seeks to synthesize a highly compact dataset that achieves performance comparable to the original dataset on downstream tasks.
arXiv:2605.09848v1 Announce Type: new Abstract: Electrocardiogram (ECG) interpretation is essential for diagnosing a wide range of cardiac abnormalities. While deep learning has shown strong potential
arXiv:2605.08799v1 Announce Type: new Abstract: Diffusion policies have demonstrated exceptional performance in embodied AI. However, their iterative denoising process results in high latency, and exi
arXiv:2605.09187v1 Announce Type: new Abstract: Understanding how linguistic structure emerges in language models is central to interpreting what these systems learn from data and how much supervision
arXiv:2602.01194v2 Announce Type: replace Abstract: Long-term weather forecasting is critical for socioeconomic planning and disaster preparedness. While recent approaches employ finetuning to extend
arXiv:2605.06663v2 Announce Type: replace Abstract: Large language models are typically deployed as monolithic systems, requiring the full model even when applications need only a narrow subset of cap
arXiv:2605.09495v1 Announce Type: cross Abstract: Machine learning-based simulators offer the potential to model the dynamics of complex systems more efficiently than classical approaches, while retai
arXiv:2605.09570v1 Announce Type: new Abstract: With the rapid growth of mobile robotics and embedded intelligence, there is an increasing demand for efficient on-device data processing on edge platfo
arXiv:2511.08644v3 Announce Type: replace-cross Abstract: This paper presents a detailed comparative analysis of the performance of three major Python data manipulation libraries - Pandas, Polars, and
arXiv:2602.08617v2 Announce Type: replace Abstract: Scaling Federated Learning (FL) to billion-parameter models forces a challenging trade-off between privacy, scalability, and model utility. Existing
arXiv:2509.18484v2 Announce Type: replace-cross Abstract: Estimating causal effects on networks is challenging because treatments may affect both treated units and their neighbors, while network homop
arXiv:2605.09429v1 Announce Type: cross Abstract: Are low-attention visual tokens truly redundant in vision-language reasoning? Existing pruning methods often assume so, ranking visual tokens by shall
arXiv:2605.09042v1 Announce Type: new Abstract: Evaluating pragmatic reasoning in large language models (LLMs) remains challenging because model behavior can vary depending on evaluation methods. Prev
arXiv:2605.08685v1 Announce Type: cross Abstract: We propose a new class of waveform foundation models that departs from conventional sequence based representations by modeling physiological time seri
arXiv:2605.09192v1 Announce Type: new Abstract: Agent skills can remarkably improve task success rates by using human-written procedural documents, but their quality is difficult to assess without env
arXiv:2605.10343v1 Announce Type: cross Abstract: Streaming video understanding demands more than watching longer videos: assistants must decide when to speak in real time, balancing responsiveness ag
arXiv:2605.10680v1 Announce Type: new Abstract: This paper proposes a paradigm shift linking machine unlearning directly to the structure of the data distributions rather than a mere update of the neu
arXiv:2605.09923v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has become the standard paradigm for LLM mathematical reasoning, where Group Relative Policy Optim
arXiv:2605.09827v1 Announce Type: cross Abstract: We present Fashion Florence, a Florence-2 vision-language model fine-tuned with LoRA to extract structured fashion attributes from clothing images. Gi
Anthropic has released a fast mode for Claude Opus 4.7, now available in research preview for both the API and Claude Code, offering improved performance for compatible workloads. This feature allows
arXiv:2602.04093v2 Announce Type: replace Abstract: Concept-based Models (CMs) enhance interpretability in deep learning by grounding predictions in human-understandable concepts. However, concept ann
arXiv:2605.10439v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) has become a widely used mechanism for customizing diffusion models, enabling users to inject new visual concepts or styles t
arXiv:2605.09003v1 Announce Type: new Abstract: Recently, diffusion-based object removal models have achieved impressive results in eliminating objects and their associated visual effects. However, th
arXiv:2605.10302v1 Announce Type: new Abstract: Existing approaches to controllable generation typically rely on fine-tuning, auxiliary networks, or test-time search. We show that flow matching admits
arXiv:2512.02010v5 Announce Type: replace Abstract: As large language models have grown larger, interest has grown in low-precision numerical formats such as NVFP4 as a way to improve speed and reduce
arXiv:2605.08712v1 Announce Type: new Abstract: Action-conditioned surgical video generation is a critical yet highly challenging problem for robotic surgery. The core difficulty is that low-dimension
arXiv:2405.09570v2 Announce Type: replace-cross Abstract: Heart murmurs are abnormal sounds caused by turbulent blood flow in the heart. Several diagnostic methods are available to detect heart murmur
arXiv:2605.08702v1 Announce Type: cross Abstract: This paper tackles compositional personalization of vision-language models (VLMs). In this problem, multiple user-defined concepts must be recognized
arXiv:2605.08613v1 Announce Type: new Abstract: The evolution of 6G networking toward agentic AI networking (AgentNet) systems requires a shift from traditional data pipelines to task-aware, agentic A
arXiv:2605.08733v1 Announce Type: new Abstract: Expressive generative policies such as diffusion and flow models are appealing for MaxEnt online reinforcement learning because of their ability to mode
GLM models are now live on @tensorix_ai We’re partnering to bring cost-efficient frontier AI models to developers, startups, and enterprises across Europe and beyond — and to back the Sovereign AI eco
arXiv:2605.10762v1 Announce Type: cross Abstract: Long-video understanding in VLMs is bottlenecked by a single monolithic forward pass over thousands of frames at quadratic attention cost. A common mi
arXiv:2605.09308v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) excel at relational reasoning but face two persistent challenges: the lack of interpretable attribution for heterogeneous
arXiv:2602.09524v3 Announce Type: replace Abstract: Unsupervised industrial anomaly detection (UAD) is essential for modern manufacturing inspection, where defect samples are scarce and reliable detec
arXiv:2605.09093v1 Announce Type: new Abstract: A Remotely Operated Vehicle (ROV) is a tethered underwater robot used for tasks like inspection and intervention. While essential tools for underwater s
arXiv:2605.10149v1 Announce Type: new Abstract: Temporal action segmentation (TAS) divides untrimmed videos into labeled action segments. While fully supervised methods have advanced the field, challe
arXiv:2601.21926v4 Announce Type: replace Abstract: Diffusion-based visuomotor policies built on 3D visual representations have achieved strong performance in learning complex robotic skills. However,
arXiv:2605.08131v1 Announce Type: new Abstract: Inverse reinforcement learning (IRL) learns a reward function and a corresponding policy that best fit the demonstration data of an expert. However, in
arXiv:2605.09439v1 Announce Type: new Abstract: Generative models are powerful tools for sampling from a learned distribution P(Y mid X), and inverse-design methods invert this map to find an input x
arXiv:2605.09487v1 Announce Type: new Abstract: Modern embodied agents achieve impressive performance, but their task knowledge is often stored in neural weights, latent state, or prompt-bound memory,
arXiv:2605.09060v1 Announce Type: new Abstract: Multilingual vision-language models exhibit systematic performance gaps across languages, but the mechanism remains ambiguous: cross-language divergence
arXiv:2605.08732v1 Announce Type: cross Abstract: Modern vision-based world models can represent observations as compact yet expressive latent manifolds, but fast goal-oriented planning in these space
arXiv:2605.10292v1 Announce Type: cross Abstract: Time series forecasting serves as an essential tool for many real-world applications, supporting tasks such as resource optimization and decision-maki
arXiv:2605.09718v1 Announce Type: cross Abstract: Many systems in physics, engineering, and biology exhibit multiscale stochastic dynamics, where low-dimensional slow variables evolve under the influe