Knowledge catalogue
arXiv:2609.27513v1 Announce Type: new Abstract: Autoregressive robot policies learn continuous control by predicting discrete action tokens from observations. Different tasks often share local motions
arXiv:2609.27650v1 Announce Type: new Abstract: Brain-to-language decoding translates neural activity associated with language production, internal speech and perception into linguistic or expressive
arXiv:2609.28328v1 Announce Type: new Abstract: Accurate localization of the bronchoscope within the bronchial tree is essential for clinicians to be able to reach target lesions, perform biopsies and
arXiv:2609.27607v1 Announce Type: cross Abstract: An AI-generated radiology report can resemble a physician's report while omitting an abnormality, adding an unsupported finding, or reversing its pres
arXiv:2609.27327v1 Announce Type: new Abstract: Video provides a rich record of human behavior, interaction, and situated contexts, offering important evidence for understanding people and conducting
arXiv:2609.27745v1 Announce Type: new Abstract: This paper advocates category theory as a practical framework for structuring and improving rein- forcement learning in high-dimensional, partially obse
arXiv:2609.27067v1 Announce Type: cross Abstract: Large language model (LLM)-based agents use Electronic Design Automation (EDA) tools to generate and revise register-transfer-level (RTL) designs unde
arXiv:2505.10083v2 Announce Type: replace Abstract: Conventional forecasting methods are trained end-to-end on unimodal time series, which limits their ability to exploit textual information and under
arXiv:2609.26913v1 Announce Type: cross Abstract: No single Large Language Model (LLM) is uniformly reliable across queries, motivating multi-model inference systems that either route among models or
arXiv:2609.26860v1 Announce Type: cross Abstract: Web applications are increasingly targeted by cyberattacks that exploit HTTP requests to evade security mechanisms. Traditional web application firewa
arXiv:2412.02575v3 Announce Type: replace Abstract: Driven by practical demands in land resource monitoring and national defense security, this paper introduces the Remote Sensing Copy-Move Question A
arXiv:2609.26905v1 Announce Type: cross Abstract: Stacking heterogeneous vision backbones (CNNs, ViTs, and hybrids) is the de facto recipe for accuracy, calibration, and robustness, yet two coupled pa
arXiv:2609.27165v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to measure public value orientations from long social media posts, yet such posts often mix backgro
arXiv:2605.06043v2 Announce Type: replace Abstract: Domain generalization requires identifying stable representations that support reliable classification across domains. Domains may differ in low-lev
arXiv:2609.28131v1 Announce Type: new Abstract: Synthesizing realistic articulated hand-object interactions is a fundamental problem in virtual reality, embodied intelligence, and digital human applic
arXiv:2609.27930v1 Announce Type: cross Abstract: We propose a Bayesian nonparametric mixture of regression trees with a Dirichlet process prior over tree-parameter pairs, enabling data-driven selecti
arXiv:2609.27234v1 Announce Type: new Abstract: AI virtual cells aim to predict cellular responses to specified interventions, yet held-out predictive performance alone does not establish use of the s
arXiv:2609.28087v1 Announce Type: new Abstract: The integration of model-based and data-driven paradigms provides a powerful framework for fault diagnosis by combining the interpretability of analytic
arXiv:2609.28107v1 Announce Type: cross Abstract: Advances in generative modeling have recently been extensively employed in robotics for policy learning. In particular, Conditional Flow Matching (CFM
arXiv:2609.28231v1 Announce Type: cross Abstract: Pathology foundation models (PFMs) have transformed computational pathology through powerful representation learning from histopathological images. PF
arXiv:2609.27338v1 Announce Type: new Abstract: Learned local navigation in crowded indoor environments is sensitive to how dynamic obstacle motion is represented, while collision-prone behaviour may
arXiv:2605.06192v2 Announce Type: replace-cross Abstract: Pretrained video diffusion models provide powerful spatiotemporal generative priors, making them a natural foundation for robotic world models
arXiv:2609.27547v1 Announce Type: new Abstract: Embodied reinforcement learning (RL) improves model capabilities with a pipeline of environment simulation, action generation, and model updates. These
Mixture‑of‑Experts (MoE) models scale capacity more efficiently than dense transformers by activating only a subset of expert subnetworks per token. NVIDIA’s Transformer Engine optimizes MoE training
arXiv:2609.27279v1 Announce Type: new Abstract: An agent that interacts with users over long periods must recall facts, preferences, events, and changes from a continuously growing interaction history
arXiv:2609.28026v1 Announce Type: cross Abstract: We investigate whether state-of-the-art large language models (LLMs) generate feedback that reflects the pedagogical practices of expert teachers in t
arXiv:2609.27657v1 Announce Type: cross Abstract: Solutions based on large language models (LLMs) often rely on temperature sampling to improve accuracy and stability by aggregating multiple samples f
arXiv:2609.27363v1 Announce Type: new Abstract: Camera localization in a prior LiDAR map provides a persistent geometric reference for long-term robotic navigation, yet remains challenging because of
arXiv:2609.27370v1 Announce Type: cross Abstract: Visual Place Recognition (VPR) in natural environments remains challenging due to repetitive vegetation, sparse distinctive landmarks, and substantial
arXiv:2609.27821v1 Announce Type: new Abstract: Bounding-box scores on RefCOCO-family grounding leave little room to distinguish frontier vision-language systems, yet boxes discard object shape. We in
arXiv:2609.27353v1 Announce Type: new Abstract: Web agents are usually evaluated in live environments, where environment state and judge models drift between runs, so the same checkpoint rarely reprod
arXiv:2609.28041v1 Announce Type: new Abstract: Conference policies distinguish using Large Language Models (LLMs) to polish one's own review from delegating the critique, but current Artificial Text
arXiv:2609.27533v1 Announce Type: new Abstract: Unsupervised domain adaptation (UDA) for semantic segmentation remains challenging under adverse weather conditions because severe appearance changes en
arXiv:2609.27770v1 Announce Type: new Abstract: Autonomous web agents, powered by Large Language Models (LLMs), have garnered significant attention for automating various web-based tasks with multi-st
arXiv:2609.27620v1 Announce Type: cross Abstract: Modern text-to-image (T2I) models generate high-quality images from arbitrary user prompts, yet they can just as easily produce not-safe-for-work (NSF
Interesting new paper from Google DeepMind. Studies agents that follow bad advice from users. Users often suggest a fix that sounds right and is wrong. XYEval adds one confident, misleading hint to ta
arXiv:2609.27656v1 Announce Type: cross Abstract: Physical intelligence requires more than predicting how the world may evolve: predictions must remain actionable as the world continues to change. We
arXiv:2601.22029v2 Announce Type: replace Abstract: We introduce a new multivariate statistical problem that we refer to as the Ensemble-conditioned Inverse Problem (EIP). The aim of EIP is to invert
Just trained Tev1 0.8B, a tiny Jev-like classifier. Here it is running completely locally on my mac with @ollama & classifying some tasks. It's extremely fast: only ~50ms E2E latency. Video is not spe
arXiv:2609.27216v1 Announce Type: cross Abstract: Topology optimization (TO) remains computationally intensive due to repeated finite element analysis (FEA) evaluations required at each iteration. Whi
arXiv:2609.28409v1 Announce Type: cross Abstract: Vector Symbolic Algebras project data structures into a hyperdimensional vector space through the application of their vector algebras to randomly gen
arXiv:2609.27637v1 Announce Type: cross Abstract: Traffic flow forecasting is essential to intelligent transportation systems. Large-scale traffic forecasting requires jointly modeling local spatial d
arXiv:2609.27144v1 Announce Type: new Abstract: We consider the problem of learning risk scores to prioritize individuals for scarce resources or interventions, from historical observational data affe
arXiv:2609.27217v1 Announce Type: cross Abstract: We address adaptive computation in 3D medical image segmentation: instead of designing another backbone, we ask how much spectral mixing each network
arXiv:2609.27009v1 Announce Type: new Abstract: Large language models are increasingly applied to high-risk domains such as law, yet complex legal reasoning remains limited by two structural challenge
arXiv:2609.27747v1 Announce Type: cross Abstract: Vision-language-action models (VLA) promise human-steerable autonomous driving, but their training is bottlenecked by the scarcity of frames paired wi
arXiv:2609.28078v1 Announce Type: new Abstract: Segmentation-based multi-object tracking (MOT) with foundation video models such as SAM2 offers strong localization quality, yet remains fragile in crow
arXiv:2609.27741v1 Announce Type: new Abstract: In value-based reinforcement learning, improving the accuracy of policy evaluation has been shown to improve downstream policy optimization performance.
arXiv:2609.27220v1 Announce Type: new Abstract: Diffusion language models generate text through iterative denoising, exposing intermediate trajectories before final answers are produced. We identify a
arXiv:2609.27225v1 Announce Type: cross Abstract: Probabilistic question-answering systems -- whether large language models (LLMs) themselves, retrieval-augmented generation (RAG), or trained multi-ho
arXiv:2609.27739v1 Announce Type: new Abstract: We propose the Memory-Efficient Neural Operator (MENO) as a high-performance PDE neural solver based on the Manifold Function Encoder (MFE). MENO featur
arXiv:2603.16868v2 Announce Type: replace-cross Abstract: Monocular 3D scene reconstruction has recently seen significant progress. Powered by the modern neural architectures and large-scale data, rec
arXiv:2609.27142v1 Announce Type: new Abstract: Text-to-image retrieval with frozen dual encoders degrades when the query names a small, visually subordinate object in a cluttered scene: a single glob
arXiv:2609.27380v1 Announce Type: new Abstract: Likelihood-based context compression can account for cross-context redundancy through sequential scoring, but this makes compression outcomes sensitive
arXiv:2609.27086v1 Announce Type: new Abstract: NADI 2026 is the seventh edition of the Nuanced Arabic Dialect Identification (NADI) shared task series and the second dedicated to multidialectal Arabi
arXiv:2609.27511v1 Announce Type: cross Abstract: We present NV-Reason-CT, a generative vision--language model for chest and abdominal CT combining native 3D visual encoding with radiologist-guided re
arXiv:2509.18088v2 Announce Type: replace-cross Abstract: Efficient resource allocation in multi-agent systems requires autonomous agents to coordinate their decisions while balancing system-wide obje
arXiv:2609.28286v1 Announce Type: new Abstract: Estimating the Planetary Boundary Layer Height (PBLH) from satellite observations is a challenging regression problem due to the indirect relationship b
arXiv:2609.27987v1 Announce Type: new Abstract: Large language models (LLMs) have made substantial progress on medical question-answering, yet effective medical dialogue also requires learning to ask
arXiv:2609.27633v1 Announce Type: new Abstract: High-throughput single-cell data is now collected across large patient cohorts. Understanding patient-level heterogeneity from cellular-level data motiv