Knowledge catalogue
arXiv:2609.37125v1 Announce Type: new Abstract: Prospective memory allows an agent to retain an intention tied to a future condition, but the stored intention does not reveal whether that condition cu
arXiv:2609.37602v1 Announce Type: cross Abstract: Robust and reliable perception is essential for autonomous robots operating in real-world environments, particularly in long-term missions where envir
arXiv:2609.36855v1 Announce Type: new Abstract: Multi-agent LLM systems rely on message passing among specialized agents to accomplish complex tasks. However, an upstream agent may provide useful info
arXiv:2609.36830v1 Announce Type: new Abstract: Fully asynchronous reinforcement learning (RL) improves resource utilization in large language model post-training by overlapping rollout generation wit
arXiv:2609.37755v1 Announce Type: new Abstract: Purpose: Most Greek papyri remain unpublished and undigitised; a handwritten text recognition (HTR) pipeline that transcribes them automatically would l
arXiv:2609.37096v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) consistently struggle with fine-grained visual counting, yet the underlying causes remain poorly understood. In
arXiv:2609.37004v1 Announce Type: new Abstract: We present World2Motion, a framework that generates scene-aware 3D human motion and corresponding video from a single image and a text prompt. While exi
arXiv:2609.36438v1 Announce Type: new Abstract: Autonomous driving requires choosing a safe and efficient plan as surrounding traffic evolves. Generate-and-select planners propose multiple trajectorie
arXiv:2609.36136v1 Announce Type: new Abstract: Compact OCR-specific vision-language models achieve strong document parsing performance, but often rely on costly supervision and focus primarily on vis
arXiv:2602.02848v2 Announce Type: replace Abstract: Advances in large language models have driven strong performance across many tasks, but their memory and compute costs still hinder deployment. SVD-
arXiv:2609.31686v1 Announce Type: new Abstract: Existing aviation emissions inventories lack accurate trajectory data for high-resolution social cost and health impact assessment. This paper develops
arXiv:2609.34035v1 Announce Type: new Abstract: Tracking any point of a dynamic scene in metric 3D - in absolute meters, not up to an unknown scale - underpins 3D and 4D reconstruction, robot navigati
arXiv:2609.34982v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have shown great promise for robotic manipulation by mapping multi-modal semantics to physical actions. However, thi
arXiv:2609.34085v1 Announce Type: cross Abstract: Autonomous driving requires extit{world models} that can understand the physical world, reason and plan, and operate safely. In this paper, we first s
arXiv:2609.32464v1 Announce Type: new Abstract: Multi-objective Bayesian optimization (MOBO) commonly relies on independent Gaussian processes (GPs) with stationary kernels, limiting its ability to re
arXiv:2609.31690v1 Announce Type: new Abstract: Explainable Artificial Intelligence (XAI) solutions are essential for building trust in AI technologies and their integration in real manufacturing line
arXiv:2609.32976v1 Announce Type: new Abstract: Incorrect or corrupted labels in tabular datasets can significantly degrade supervised learning performance, particularly when mislabeling is subtle and
arXiv:2609.35021v1 Announce Type: new Abstract: Spatiotemporal prediction aims to learn discriminative representations from correlated temporal signals over spatial structures for accurate future infe
arXiv:2609.35090v1 Announce Type: new Abstract: Video-text pretraining has achieved remarkable progress through the scaling of models and datasets, yet the quality of language supervision remains unde
arXiv:2609.35212v1 Announce Type: new Abstract: Multi-view clustering aims to capture cross-view consistency while exploiting view-specific information. However, shared representations learned to capt
arXiv:2609.34281v1 Announce Type: new Abstract: High-dimensional Bayesian optimization (HDBO) seeks sample-efficient optimization when the number of variables is large relative to the evaluation budge
arXiv:2609.33323v1 Announce Type: cross Abstract: Recent advances in Large Language Models (LLMs) have enabled agentic systems capable of solving complex tasks through multi-turn planning, tool use, v
arXiv:2609.31713v1 Announce Type: new Abstract: As large language models (LLMs) become capable of processing increasingly diverse modalities and longer temporal contexts, an emerging line of work is m
arXiv:2609.34586v1 Announce Type: cross Abstract: Deploying LLM-enabled agentic applications across the Edge-to-Cloud continuum remains challenging due to hardware heterogeneity, deployment complexity
arXiv:2609.34176v1 Announce Type: new Abstract: Radiance fields need hundreds of views, and their placement matters as much as their number. Next-best-view (NBV) selection for 3D Gaussian Splatting (3
arXiv:2509.01019v3 Announce Type: replace Abstract: Coral reefs are on the brink of collapse, with climate change, ocean acidification, and pollution leading to a projected 70-90% loss of coral specie
arXiv:2609.33728v1 Announce Type: new Abstract: Reliable world models should not only predict future states but express how actions change the world in an explicit, transparent and testable form, such
arXiv:2609.34962v1 Announce Type: new Abstract: Estimating mutual information (MI) from samples is a central objective in a variety of scientific fields. Modern neural estimators are accurate in the l
arXiv:2609.34467v1 Announce Type: new Abstract: Recent advances in Vision-Language-Action (VLA) models point toward general-purpose robotic intelligence by unifying perception, instruction, and contro
arXiv:2609.35505v1 Announce Type: new Abstract: We study on-policy distillation (OPD) through the lens of reinforcement learning, establishing a connection between the reverse-KL objective in OPD and
arXiv:2609.32178v1 Announce Type: new Abstract: Rotary position encodings make attention sensitive to relative position, but extending them to graphs requires choosing how graph structure enters the r
arXiv:2609.33814v1 Announce Type: cross Abstract: We characterize Bregman Douglas-Rachford splitting (BDRS) for unregularized discrete optimal transport and develop an anytime primal-dual certificate
arXiv:2609.33299v1 Announce Type: new Abstract: World Action Models (WAMs) are becoming increasingly important and useful for embodied intelligence, as they enable robots to anticipate the consequence
arXiv:2609.35620v1 Announce Type: new Abstract: Graph Transformers produce, for each attention head, a dense nimes n matrix of learned pairwise interactions. We ask a fundamental question: do these at
arXiv:2509.25570v2 Announce Type: replace Abstract: Vision Graph Neural Networks (ViGs) have demonstrated promising performance in image recognition tasks against Convolutional Neural Networks (CNNs)
arXiv:2609.33662v1 Announce Type: cross Abstract: Imperfect verifiers can assign a harmful update direction even when clipping and regularization bound its magnitude. We introduce Audit-First VAPO, wh
arXiv:2609.33818v1 Announce Type: new Abstract: Visual anomaly detectors identify deviations from known-normal data, but their anomaly signals may mix evidence of actual anomalies with benign visual v
arXiv:2609.33392v1 Announce Type: new Abstract: Hypergraph neural networks have achieved significant success in recent years. However, manual architecture crafting is labor-intensive and often fails t
arXiv:2609.33802v1 Announce Type: cross Abstract: Understanding quantum phases of matter has long relied on physicists' intuition and mathematical tools such as symmetry and topology. Remarkably succe
arXiv:2506.08140v2 Announce Type: replace Abstract: Despite long-standing efforts in accelerating scientific discovery with AI, building AI co-scientists remains challenging due to limited high-qualit
arXiv:2609.34641v1 Announce Type: new Abstract: Test-time adversarial defense improves the robustness of vision-language foundation models such as CLIP without retraining. However, adversarial activat
arXiv:2609.35615v1 Announce Type: new Abstract: Behavioral Foundation Models (BFMs) are an emerging paradigm in reinforcement learning, playing a role analogous to large language models in natural lan
arXiv:2609.33254v1 Announce Type: new Abstract: Understanding how drugs interact with protein targets is fundamental to drug discovery, drug repurposing and the early identification of promising thera
arXiv:2609.35060v1 Announce Type: new Abstract: Inferring dynamics from snapshots of evolving distributions is fundamentally underdetermined: the Fokker-Planck equation constrains the drift F only thr
arXiv:2609.31753v1 Announce Type: cross Abstract: Video anomaly detection for patrol robots and surveillance systems must recognize abnormal interactions among familiar entities. Existing pixel-recons
arXiv:2609.34083v1 Announce Type: new Abstract: Recommendation models with sparse embeddings and a shared consumer often exhibit the one-epoch phenomenon: a second epoch lowers training loss while sha
arXiv:2609.35347v1 Announce Type: new Abstract: Reinforcement learning can turn one language model into several specialists, each excellent at a single skill such as mathematics, coding or following i
arXiv:2609.31740v1 Announce Type: new Abstract: Accurate coronary artery segmentation on coronary computed tomography angiography (CCTA) is essential for diagnosing coronary artery disease. Deep netwo
arXiv:2609.32720v1 Announce Type: new Abstract: Reward models score responses from large language models (LLMs) and guide LLM training toward human preferences. However, reward models can favor superf
arXiv:2505.11294v3 Announce Type: replace Abstract: Hierarchical Gaussian Process (H-GP) models divide problems into different subtasks, allowing different components to address each part, making them
arXiv:2308.01184v4 Announce Type: replace Abstract: Although noisy-label learning is often approached with discriminative methods for simplicity and speed, generative modeling offers a principled alte
GPT‑6.1 Sol is now generally available on Amazon Bedrock, providing enhanced reasoning for coding, computer use, and professional workflows. It runs on a high‑performance inference engine optimized fo
arXiv:2609.32570v1 Announce Type: new Abstract: Clustering algorithms that operate on nearest-neighbor graphs, such as FINCH (First Integer Neighbor Clustering Hierarchy), depend heavily on the qualit
arXiv:2609.33342v1 Announce Type: new Abstract: Molecular property prediction is central to drug development and materials discovery, but experiments are costly and labeled data are scarce. Context-aw
arXiv:2609.31996v1 Announce Type: new Abstract: Can out-of-distribution (OOD) generalization be predicted from a trained model's weights alone, without any target-domain data? Existing representationa
arXiv:2609.33114v1 Announce Type: cross Abstract: For decades, statistical inference has largely been developed one problem at a time. Given a scientific target, such as a treatment effect or a regres
arXiv:2609.31706v1 Announce Type: new Abstract: Visual Language Models (VLMs) have achieved remarkable success across diverse tasks, yet they struggle with high-resolution inputs where critical inform
arXiv:2609.31957v1 Announce Type: new Abstract: Interactive CAPTCHAs remain challenging for computer-use agents, while existing datasets face trade-offs among type coverage, interaction fidelity, and
arXiv:2609.35195v1 Announce Type: new Abstract: Accurate segmentation of post-treatment brain metastases is essential for treatment planning, longitudinal disease monitoring, and quantitative assessme
arXiv:2609.34246v1 Announce Type: new Abstract: Autoregressive neural emulators can drift or diverge over long rollouts despite accurate short-term predictions. We introduce Cascaded Emulation (CasEm)