Search: “models”

arXiv:2605.09269v1 Announce Type: new Abstract: Aligning Multimodal Large Language Models (MLLMs) requires reliable reward models, yet existing single-step evaluators can suffer from lazy judging, exp

arXiv:2605.09832v1 Announce Type: new Abstract: Knowledge of a protein's atomic conformational ensemble is critical to determining its function, yet state-of-the-art ensemble prediction models are lim

arXiv:2605.09519v1 Announce Type: new Abstract: We introduce the concept of weighted rules under the stable model semantics following the log-linear models of Markov Logic. This provides versatile met

arXiv:2511.18085v4 Announce Type: replace-cross Abstract: Vision-language-action (VLA) models show promising knowledge accumulation ability from pretraining, yet continual learning in VLA remains chal

arXiv:2605.07319v1 Announce Type: cross Abstract: We introduce Flux Matching, a new paradigm for generative modeling that generalizes existing score-based models to a broader family of vector fields t

arXiv:2605.07520v1 Announce Type: new Abstract: Differentiable planning enables gradient-based optimization of decision-making problems by leveraging differentiable models of system dynamics. However,

arXiv:2605.06728v1 Announce Type: cross Abstract: Interpreting transcriptomic data is one of the most common analytical tasks in modern biology. Yet most current models either consume expression profi

arXiv:2605.06898v1 Announce Type: new Abstract: At the heart of existing language model agents is a fixed orchestrator program responsible for the state transition between consecutive turns. This pape

arXiv:2603.11911v3 Announce Type: replace Abstract: We present InSpatio-WorldFM, an open-source real-time frame model for spatial intelligence. Unlike video-based world models that rely on sequential

arXiv:2605.05049v1 Announce Type: cross Abstract: Frontier models increasingly adopt Mixture-of-Experts (MoE) architectures to achieve large-model performance at reduced cost. However, training MoE mo

arXiv:2506.19714v2 Announce Type: replace-cross Abstract: Offline model-based optimization (MBO) refers to the task of optimizing a black-box objective function using only a fixed set of prior input-o

arXiv:2506.08849v4 Announce Type: replace Abstract: Vision-Language Foundation Models (VLFMs) exhibit remarkable generalization, yet their direct application to medical ultrasound is severely hindered

arXiv:2601.08116v2 Announce Type: replace Abstract: Tropical cyclones are dangerous natural hazards, but their hazard is challenging to quantify directly from historical datasets due to limited datase

arXiv:2605.02714v1 Announce Type: new Abstract: The advent of foundation models has heralded a new era in medical artificial intelligence (AI), enabling the extraction of generalizable representations

arXiv:2602.11543v2 Announce Type: replace Abstract: Pretraining large language models (LLMs) typically requires centralized clusters with thousands of high-memory GPUs (e.g., H100/A100). Recent decent

arXiv:2604.28196v1 Announce Type: new Abstract: Driving world models serve as a pivotal technology for autonomous driving by simulating environmental dynamics. However, existing approaches predominant

arXiv:2604.27351v1 Announce Type: new Abstract: Agentic large language model systems have demonstrated strong capabilities. However, their reliance on language as the universal interface fundamentally

arXiv:2604.27093v1 Announce Type: cross Abstract: Current LLM safety alignment techniques improve model robustness against adversarial attacks, but overlook whether and how LLMs can recover helpfulnes

arXiv:2604.28185v1 Announce Type: new Abstract: Recent visual generation models have made major progress in photorealism, typography, instruction following, and interactive editing, yet they still str

arXiv:2604.27082v1 Announce Type: new Abstract: We present a framework for migrating production Large Language Model (LLM) based systems when the underlying model reaches end-of-life or requires repla

arXiv:2604.25200v1 Announce Type: cross Abstract: Public agencies are beginning to consider large language models (LLMs) as decision-support tools for grant evaluation. This creates a practical govern

arXiv:2604.25685v1 Announce Type: cross Abstract: Foundation segmentation models such as the Segment Anything Model (SAM) have demonstrated strong generalization across natural images; however, their

arXiv:2604.25781v1 Announce Type: new Abstract: Articulation modeling aims to infer movable parts and their motion parameters for a 3D object, enabling interactive animation, simulation, and shape edi

arXiv:2603.19124v2 Announce Type: replace Abstract: This paper presents the design, modeling, and fabrication of 3D-printed, tendon-actuated continuum robots featuring a flexible, tapered backbone con

arXiv:2604.22823v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) rely on multimodal pre-training over diverse data sources, where different datasets often induce complementar

arXiv:2604.22748v1 Announce Type: new Abstract: As AI systems move from generating text to accomplishing goals through sustained interaction, the ability to model environment dynamics becomes a centra

arXiv:2604.22230v1 Announce Type: cross Abstract: Benchmark hacking refers to tuning a machine learning model to score highly on certain evaluation criteria without improving true generalization or fa

arXiv:2604.22464v1 Announce Type: new Abstract: Continual Model Merging (CMM) sequentially integrates task-specific models into a unified architecture without intensive retraining. However, existing C

arXiv:2508.19353v2 Announce Type: replace-cross Abstract: While transfer learning is an effective strategy, it often overlooks the opportunity to leverage knowledge from numerous available models onli

arXiv:2604.21127v1 Announce Type: new Abstract: The NASA PACE mission provides unprecedented hyperspectral observations of ocean color, aerosols, and clouds, offering new insights into how these compo

arXiv:2507.01829v2 Announce Type: replace-cross Abstract: Multi-timescale sequence modeling relies on capturing both local fast dynamics and global slow context; yet, maintaining these capabilities un

arXiv:2604.20867v1 Announce Type: cross Abstract: Recent events surrounding the relationship between frontier AI suppliers and national-security customers have made a structural problem newly visible:

arXiv:2604.21052v1 Announce Type: cross Abstract: We build on the Visual Autoregressive Modeling (VAR) framework and formulate style transfer as conditional discrete sequence modeling in a learned lat

arXiv:2604.21223v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated remarkable capabilities across various tasks. However, their ability to generate human-like text has ra

arXiv:2604.20263v1 Announce Type: cross Abstract: Virtual cell modeling predicts molecular state changes under genetic perturbations in silico, which is essential for biological mechanism studies. How

arXiv:2509.25844v3 Announce Type: replace Abstract: When people query Vision-Language Models (VLMs) but cannot see the accompanying visual context (e.g. for blind and low-vision users), augmenting VLM

arXiv:2604.20796v1 Announce Type: new Abstract: We present LLaDA2.0-Uni, a unified discrete diffusion large language model (dLLM) that supports multimodal understanding and generation within a nativel

arXiv:2604.20111v1 Announce Type: cross Abstract: Sparse additive models have attracted much attention in high-dimensional data analysis due to their flexible representation and strong interpretabilit

arXiv:2511.14311v2 Announce Type: replace-cross Abstract: Model Predictive Control (MPC) has established itself as the primary methodology for constrained control, enabling autonomy across diverse app

arXiv:2604.20834v1 Announce Type: new Abstract: Recent advances in Vision-Language-Action (VLA) models have opened new avenues for robot manipulation, yet existing methods exhibit limited efficiency a

arXiv:2604.20127v1 Announce Type: new Abstract: Failures in complex systems often emerge through gradual degradation and the propagation of stress across interacting components rather than through iso

arXiv:2604.18701v1 Announce Type: cross Abstract: Local prediction-error-based curiosity rewards focus on the current transition without considering the world model's cumulative prediction error acros

arXiv:2505.09803v3 Announce Type: replace-cross Abstract: In many applications, we wish to fit a parametric statistical model to a small ensemble of spatially distributed random variables ('fields').

arXiv:2604.19683v1 Announce Type: new Abstract: World models derived from large-scale video generative pre-training have emerged as a promising paradigm for generalist robot policy learning. However,

arXiv:2604.19596v1 Announce Type: new Abstract: Point cloud registration involves aligning one point cloud with another or with a three-dimensional (3D) model, enabling the integration of multimodal d

arXiv:2604.17293v1 Announce Type: new Abstract: Reliable Large Language Models (LLMs) should abstain when confidence is insufficient. However, prior studies often treat refusal as a generic 'I don't k