METRO: Metric-Enhanced Token Routing Operator
arXiv:2610.05100v1 Announce Type: new Abstract: State-of-the-art neural operators scale to complex meshes via slice-and-process architectures, yet many rely on linear compatibility scores for latent t
Knowledge catalogue
arXiv:2610.05100v1 Announce Type: new Abstract: State-of-the-art neural operators scale to complex meshes via slice-and-process architectures, yet many rely on linear compatibility scores for latent t
arXiv:2610.06559v1 Announce Type: cross Abstract: Modern online platforms commonly rank ads and organic content separately before blending them into a feed displayed to the user, overlooking externali
arXiv:2510.03614v2 Announce Type: replace Abstract: Sequential estimation under partial observability requires tracking beliefs that may be high-dimensional, multimodal, and non-Gaussian. Classical Ba
arXiv:2610.06259v1 Announce Type: new Abstract: Partial differential equation (PDE) inverse problems are often ill-posed, making fine-scale details difficult to recover. We address this problem by int
arXiv:2610.04281v1 Announce Type: new Abstract: Lossless triangle mesh compression must preserve both vertex positions and connectivity. Octrees support learned point cloud geometry coding and progres
arXiv:2610.04967v1 Announce Type: new Abstract: Prompting guides language model behavior through the initial context, whereas activation steering often intervenes throughout generation. A natural ques
arXiv:2503.19945v4 Announce Type: replace-cross Abstract: Mammography, an X-ray-based imaging technique, remains central to the early detection of breast cancer. Recent advances in artificial intellig
arXiv:2610.06709v1 Announce Type: cross Abstract: Learning the Hamiltonian of a many-body system from its dynamics is a central task in quantum science, yet the algorithms with the strongest provable
arXiv:2610.06062v1 Announce Type: new Abstract: Federated multi-player multi-armed bandit problems model collaborative sequential decision-making where multiple players interact with a common bandit e
arXiv:2610.05416v1 Announce Type: new Abstract: Natively training joint video-audio generation models at higher resolutions empowers them to learn richer visual details and sharper motion dynamics. Ho
arXiv:2610.04100v1 Announce Type: new Abstract: Model compression strategies are widely employed to reduce memory footprint and network complexity, particularly for devices with constrained computatio
arXiv:2610.04038v1 Announce Type: new Abstract: Synthetic data are increasingly used as an alternative to sharing sensitive records. However, synthetic data generation does not guarantee privacy, as d
arXiv:2610.05240v1 Announce Type: new Abstract: Time-series foundation models offer a unified approach to forecasting across heterogeneous domains. Textual context and auxiliary observations provide c
arXiv:2610.04299v1 Announce Type: new Abstract: Self-evolving reasoning models learn from their own generated questions, yet repeated self-training can lead to performance collapse. In this paper, we
Hey everyone! I am trying to go really deep into Machine Learning and Artificial Intelligence. Not just learning how to use the latest models and frameworks, but understanding how we reached here from
arXiv:2503.21969v4 Announce Type: replace Abstract: Robotic long-horizon manipulation requires robots to compose perception, reasoning, and action over extended task sequences, yet existing language-c
arXiv:2610.04338v1 Announce Type: new Abstract: Machine learning-based weather prediction (MLWP) has achieved strong performance in global weather forecasting. Recent Hierarchical Equal Area isoLatitu
arXiv:2610.06257v1 Announce Type: new Abstract: Foundation models can generate scientific code, but authoring a scientific simulator (an executable program encoding hypotheses about how mechanisms gen
arXiv:2610.04547v1 Announce Type: new Abstract: Cold-start forecasting, the task of forecasting a time series with little to no historical data, is a common challenge. Addressing it requires approache
arXiv:2610.06694v1 Announce Type: new Abstract: Graph foundation models aim to transfer across graphs, feature spaces, relational schemas, and prediction tasks, yet existing approaches typically gener
arXiv:2610.06833v1 Announce Type: new Abstract: Every recurrence of a looped language model adds cost in training, decoding, prefill, and reinforcement learning (RL). The closer recurrent states get t
arXiv:2610.03872v1 Announce Type: cross Abstract: AI agents are becoming increasingly capable of generating scientific code, but generating code is not the same as improving the algorithms behind it.
arXiv:2610.06008v1 Announce Type: cross Abstract: Ultrasound is widely used, but acquisition quality is heavily dependent on the operator's knowledge and expertise. With demand for examinations outpac
arXiv:2610.04766v1 Announce Type: new Abstract: Modern models increasingly learn through black-box oracles such as humans, optimization solvers, and external tools that provide feedback without exposi
arXiv:2610.04588v1 Announce Type: cross Abstract: Molecular property prediction is central to computational drug discovery, where graph neural networks learn to weight neighboring atomic environments
arXiv:2610.06666v1 Announce Type: new Abstract: Latent reasoning lets a large language model (LLM) think in a continuous space and verbalize only the answer. We argue that an effective latent thought
arXiv:2605.05592v2 Announce Type: replace Abstract: Increasing a voting budget can improve or reduce population accuracy, and an initial gain or loss need not persist. We give a complete structural cl
arXiv:2610.05645v1 Announce Type: new Abstract: A diffusion model starts from pure noise and removes it step by step. Somewhere along the way it stops being able to become 'anything' and becomes commi
arXiv:2610.03452v1 Announce Type: new Abstract: Causal representation learning for time-series data aims to identify latent states and their causal relations from observations. In this setting, an imp
arXiv:2610.02278v1 Announce Type: new Abstract: Cognitive diagnosis (CD) is a fundamental task in intelligent education that profiles learner proficiency over knowledge concepts. In real-world learnin
arXiv:2610.03698v1 Announce Type: new Abstract: Self-supervised Vision Transformers (ViTs), such as DINOv2, learn rich visual representations, but the functions of their internal tokens remain poorly
arXiv:2610.02960v1 Announce Type: new Abstract: Deep learning has shown remarkable success in the data-driven modeling of dynamical systems. Much of its success is attributed not to the flexibility of
arXiv:2603.19307v2 Announce Type: replace-cross Abstract: Modeling the complex interactions among functional subnetworks is crucial for the diagnosis of mental disorders and the identification of func
arXiv:2610.03123v1 Announce Type: cross Abstract: Existing streaming vision-language models (VLMs) continuously perceive and reason over visual streams, but their computational pathways remain fixed t
arXiv:2610.03403v1 Announce Type: cross Abstract: Forest point cloud segmentation is fundamental for fine-grained 3D forest scene understanding, yet remains challenging due to irregular tree structure
arXiv:2610.02427v1 Announce Type: cross Abstract: Flow-map distillation enables one- and few-step generation by learning finite-time transitions of a pretrained generative ODE. We investigate whether
arXiv:2610.03363v1 Announce Type: new Abstract: Neural surrogate models for Partial Differential Equations (PDEs) on unstructured 3D geometries are often limited by poor generalization and the high co
arXiv:2610.03119v1 Announce Type: cross Abstract: In lifelong reinforcement learning, retaining previously learned policies is not sufficient for effective transfer to a new task. Useful knowledge may
arXiv:2610.02557v1 Announce Type: new Abstract: As powerful AI systems reach and sometimes surpass the abilities of human experts across a range of cognitively demanding tasks, the problem of accurate
arXiv:2610.02542v1 Announce Type: new Abstract: World models (WMs) simulate the transition dynamics of environments, enabling agents to plan over the consequences of their actions. In text-based envir
arXiv:2610.02772v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly serve as general-purpose interfaces to factual knowledge, but their parameters do not automatically reflect
arXiv:2506.06557v3 Announce Type: replace-cross Abstract: An ultrametric space or infinity-metric space is defined by a dissimilarity function that satisfies a strong triangle inequality in which ever
Jacob Coxon @hilbertspaess and @DKokotajlo next at the NYC City Council meeting, hosted by @SpeakerMenin. Coxon cpens with “We do not know how to control any AI system yet”, which, importantly, isn’t
arXiv:2610.03296v1 Announce Type: new Abstract: Complex reasoning queries can be decomposed into directed acyclic task graphs and distributed across heterogeneous LLMs, reducing latency through parall
arXiv:2610.03071v1 Announce Type: new Abstract: Chance-constrained programs (CCPs) optimize decisions under uncertainty by limiting the probability of constraint violation. Despite advances in traditi
arXiv:2610.03251v1 Announce Type: new Abstract: A model trained on 'The capital of X is Y' may produce 'Y' after 'The capital of X is' but fail after 'The capital of X:'. We call these different ways
arXiv:2610.02886v1 Announce Type: cross Abstract: Language models learn from web documents, some of them false, and false content can reach a model's answer to a factual question and the summaries and
arXiv:2610.03316v1 Announce Type: new Abstract: Designing effective heuristics for diverse combinatorial optimization problems requires substantial expertise and repeated search. Large language models
arXiv:2610.02768v1 Announce Type: new Abstract: Multimodal graph learning has recently emerged as an effective paradigm for in corporating inter-entity relationships into multimodal representations. E
arXiv:2610.02793v1 Announce Type: new Abstract: Research on LLMs continually uncovers model limitations, their causes, and potential solutions. Yet these human discoveries remain largely disconnected
AI is moving beyond the data centers where models are trained and onto factory floors where machines must handle constant change. But traditional industrial robots work behind a cage, repeat a single
arXiv:2610.02840v1 Announce Type: cross Abstract: World action models jointly learn to forecast world dynamics and predict robot actions, such that the learned internal world dynamics guide accurate a
arXiv:2610.02967v1 Announce Type: cross Abstract: Recent text-to-image generation models have achieved remarkable visual quality, but improving them through post-training remains challenging because n
arXiv:2610.02975v1 Announce Type: new Abstract: Large language model (LLM)-based self-evolving search is a promising approach to scientific discovery. However, high-fidelity evaluation of every candid
arXiv:2610.03313v1 Announce Type: cross Abstract: Existing machine learning weather forecasting models typically generate forecasts through autoregressive rollouts at a fixed temporal resolution. Whil
arXiv:2610.02281v1 Announce Type: new Abstract: Societal resilience research relies on access to useful and actionable data, which motivates our main research question: Can annual reports, processed a
arXiv:2610.03124v1 Announce Type: cross Abstract: Open-weight language models can be downloaded, modified, and deployed beyond their developers' control, limiting the effectiveness of centrally enforc
arXiv:2610.02247v1 Announce Type: cross Abstract: Artificial intelligence increasingly serves as a natural-language interface to complex technical systems, letting people accomplish sophisticated task
arXiv:2610.02881v1 Announce Type: new Abstract: Multimodal graph foundation models (MGFMs) seek to learn generalizable representations from large-scale graphs with heterogeneous node modalities. Howev
arXiv:2610.02397v1 Announce Type: new Abstract: Electric utilities and grid operators increasingly rely on machine-learning models to forecast next-day demand, and those models learn from meter data t