Knowledge catalogue
arXiv:2609.23753v1 Announce Type: new Abstract: Generative world models aim to predict future states conditioned on actions, where action controllability is fundamental for reliable dynamics modeling.
arXiv:2609.23968v1 Announce Type: new Abstract: Humanoid robots are expected to perform diverse human-level tasks in daily environments, many of which require precise regulation of interaction forces.
arXiv:2609.23308v1 Announce Type: new Abstract: Online controlled experiments, or A/B tests, are widely used to estimate causal effects on digital platforms. A central challenge is to improve experime
arXiv:2302.07477v4 Announce Type: replace Abstract: We study the optimal sample complexity of tabular reinforcement learning for infinite-horizon discounted Markov decision processes. The unrestricted
arXiv:2408.08823v2 Announce Type: replace Abstract: We develop a theoretical foundation for designing group-equivariant neural networks that align the choice of symmetries with the underlying probabil
arXiv:2609.23157v1 Announce Type: cross Abstract: Aerial Reconfigurable Intelligent Surfaces (ARISs) can enhance wireless connectivity in obstructed environments by combining reconfigurable metasurfac
arXiv:2609.23950v1 Announce Type: cross Abstract: Cardiovascular magnetic resonance (CMR) provides comprehensive cardiac assessment but remains underutilized because of the complexity of acquisition,
arXiv:2609.24128v1 Announce Type: cross Abstract: Judge-specific sensitivity is useful for aggregating pairwise LLM evaluations, but its interpretation depends on which systematic presentation effects
arXiv:2609.24586v1 Announce Type: new Abstract: Traditional evaluation metrics provides numerical values but often lack comprehensibility, hindering effective differentiation of model performances. Ou
arXiv:2609.24138v1 Announce Type: cross Abstract: Generative models have recently demonstrated considerable promise in speech super-resolution (SSR). Nevertheless, the majority of existing work has co
arXiv:2609.24117v1 Announce Type: new Abstract: Identifying linear time-invariant (LTI) dynamical systems is challenging when trajectories are short, noisy, or high-dimensional. Traditional system ide
arXiv:2609.22771v1 Announce Type: cross Abstract: Recent advances in Audio LLMs have achieved human-level speech recognition, yet existing systems struggle to capture paralinguistic aspects such as sp
arXiv:2609.24116v1 Announce Type: new Abstract: Although deep learning has advanced Whole Slide Image (WSI) Analysis, tissue artifacts like bubbles and folds often cause silent failures by concealing
arXiv:2609.22281v1 Announce Type: new Abstract: Foundation models have recently demonstrated strong capabilities across a wide range of medical imaging tasks. However, their performance in structured
arXiv:2609.24133v1 Announce Type: new Abstract: Recent advances in humanoid robotics and embodied intelligence have enabled robots to perform increasingly complex manipulation tasks. However, musical
arXiv:2609.23442v1 Announce Type: new Abstract: Diffusion models generate high-quality images, yet often violate the physical laws governing mirror reflections. Reflections often suffer from geometric
arXiv:2609.23534v1 Announce Type: new Abstract: Language-based 3D localization retrieves the point-cloud submap containing a target position from descriptions of nearby objects and their spatial relat
arXiv:2609.24125v1 Announce Type: new Abstract: Contrastive self-supervised learning has achieved strong performance by learning representations from multiple augmented views of the same image. Howeve
arXiv:2609.23601v1 Announce Type: new Abstract: Long-video understanding must capture transient visual evidence under strict token budgets, yet existing methods compress frames, append memory tokens,
arXiv:2609.24524v1 Announce Type: new Abstract: Background: Microvascular invasion (MVI) predicts recurrence and survival in hepatocellular carcinoma (HCC) but requires postoperative histopathology fo
arXiv:2609.24370v1 Announce Type: cross Abstract: Self-attention is central to modern Transformer architectures, but its dense dot-product formulation makes it difficult to identify which internal dir
arXiv:2609.23547v1 Announce Type: new Abstract: Graph prompt learning enables parameter-efficient adaptation of frozen Graph Neural Networks to downstream tasks through lightweight prompt parameters.
arXiv:2609.22603v1 Announce Type: new Abstract: Summarization ships in countless production systems, making model selection a routine decision that depends on measuring summary quality. Existing metri
arXiv:2609.24768v1 Announce Type: new Abstract: Professional photo finishing relies on both global adjustments and region-specific local edits guided by semantic masks, yet current automated methods h
arXiv:2609.22112v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated the ability to generate user-specific text with high stylistic fidelity. However, the personal data that
arXiv:2609.23386v1 Announce Type: new Abstract: Text-guided 3D building generation holds tremendous application potential, yet existing generative models typically output inseparable single meshes or
arXiv:2609.22106v1 Announce Type: cross Abstract: Accuracy of Low-bit quantization of linear layers is often dominated by a small number of outliers. Although existing methods, such as smoothing, rota
arXiv:2609.23449v1 Announce Type: cross Abstract: Memory systems are becoming a core component of LLM agents, but constructing and maintaining memory remains expensive because it relies on repeated ca
arXiv:2609.23139v1 Announce Type: new Abstract: Most medical vision-language models (VLMs) excel at open-ended report generation and VQA but provide limited support for structured, fine-grained clinic
arXiv:2609.23439v1 Announce Type: new Abstract: Non-prehensile manipulation is practical for relocating large, heavy, or geometrically ungraspable objects. Yet, long-horizon pushing of arbitrarily-sha
arXiv:2509.22650v3 Announce Type: replace Abstract: Most existing approaches to referring segmentation achieve strong performance only through fine-tuning or by composing multiple pre-trained models,
arXiv:2609.22325v1 Announce Type: new Abstract: Large language models have shown considerable potential for natural-language-driven parametric CAD modeling. However, a fundamental contradiction exists
arXiv:2609.24158v1 Announce Type: new Abstract: Reconstructing expressive and relightable 3D head avatars from monocular videos remains challenging in computer vision, as it requires accurate modeling
arXiv:2609.24057v1 Announce Type: cross Abstract: Medical image interpretation is central to diagnosis and care, yet adapting general-purpose multimodal large language models (MLLMs) often requires re
arXiv:2502.07977v3 Announce Type: replace Abstract: Empirical risk minimization (ERM) is a cornerstone of modern machine learning. This paper focuses on the man-in-the-middle (MITM) attack, wherein ad
arXiv:2609.22947v1 Announce Type: new Abstract: Reinforcement learning (RL) is vital for optimizing video generation models, with a robust reward model (RM) serving as the cornerstone. However, existi
arXiv:2609.23423v1 Announce Type: new Abstract: Vision-language navigation (VLN) has largely been developed for indoor and terrestrial robots, where language can often be treated as a static goal and
arXiv:2609.22785v1 Announce Type: new Abstract: Market-making strategies in real limit order book markets face substantial model uncertainty and regime-shift risk. Existing adversarial reinforcement l
arXiv:2609.23432v1 Announce Type: new Abstract: Dynamic rope manipulation is highly sensitive to unknown object dynamics: the same robot motion can produce substantially different responses across rop
arXiv:2609.23466v1 Announce Type: new Abstract: Long-running LLM agents require memory that persists and evolves across sessions. Text-based memory retrieves and reconstructs past interactions at ever
arXiv:2609.22258v1 Announce Type: cross Abstract: Large language model-driven remote sensing (RS) agents offer a promising approach to automating geospatial analysis. However, lightweight RS agents ba
arXiv:2609.24062v1 Announce Type: new Abstract: Hyper-redundant robots are well suited for confined-space manipulation due to their high dexterity, but safe operation in cluttered environments remains
arXiv:2603.08269v2 Announce Type: replace Abstract: In-context imitation learning allows robots to acquire skills from demonstrations, yet one-shot trajectory generation remains fragile under environm
arXiv:2508.03351v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have demonstrated remarkable capabilities across diverse language tasks, motivating their extension to vision-lan
arXiv:2609.22210v1 Announce Type: new Abstract: SALSA (Semi-Autonomous Literature Summarization Assistant) is an open- source, human-in-the-loop platform for extracting structured scientific datasets
arXiv:2609.22834v1 Announce Type: new Abstract: Open-vocabulary semantic segmentation (OVS) of remote sensing imagery is a challenging pixel-level task requiring strong generalization and adaptation t
arXiv:2609.22233v1 Announce Type: new Abstract: Energy systems face converging pressures for security, affordability, resilience, and sustainability, creating a need for faster discovery of deployable
arXiv:2609.22213v1 Announce Type: new Abstract: Temporal Knowledge Graph Question Answering (TKGQA) requires answer inference from evidence that is both structurally valid and temporally admissible. E
arXiv:2609.23206v1 Announce Type: cross Abstract: Green's function based operator approximation offers an efficient route for solving linear partial differential equations under varying boundary condi
arXiv:2606.13279v2 Announce Type: replace Abstract: In bimanual robotic manipulation, task-relevant visual information varies with the task stage and context, while the interaction of the two arms shi
arXiv:2609.23548v1 Announce Type: new Abstract: Generating sewing patterns from images and text requires modeling a heterogeneous representation composed of discrete topology and continuous geometry.
arXiv:2609.24198v1 Announce Type: cross Abstract: This paper describes the SKstars submission to SHROOM-Visions 2026, a shared task on fine-grained hallucination detection in large vision-language mod
arXiv:2601.01352v2 Announce Type: replace Abstract: Human identity-preserving text-to-video generation remains challenging under large changes in viewpoint, facial expression, illumination, and motion
arXiv:2603.14598v2 Announce Type: replace Abstract: Microgravity rendezvous and close proximity operations (RPO) is a growing area of interest for applications spanning in-space assembly and manufactu
Krystal Hu / Reuters: Snorkel AI, which offers an “agentic data development platform” with humans and AI agents to create and vet data, raised 350M at a 3.5B valuation — Data startup Snorkel AI has ra
arXiv:2609.22126v1 Announce Type: cross Abstract: High-resolution surface solar radiation (SSR) is important for solar forecasting and grid operation. However, physically consistent reanalysis product
arXiv:2609.23937v1 Announce Type: cross Abstract: Robust linear fits can resist response contamination yet remain too dense or unstable for useful global explanations. We propose penalized distillatio
arXiv:2609.24708v1 Announce Type: new Abstract: Robotic perception often requires solving large nonlinear least-squares (NLS) problems. While sparsity has been widely exploited to scale solvers, a com
arXiv:2609.23038v1 Announce Type: cross Abstract: Spatial reasoning is essential for vision-language models (VLMs) to understand and act in the physical world. Reasoning in dynamic environments requir
arXiv:2609.24875v1 Announce Type: new Abstract: Recent foundation models are moving toward native multimodal Vision-Language Models (VLMs), making VLMs a central form of next-generation foundation mod