Search: “models”

arXiv:2609.14833v1 Announce Type: new Abstract: World models, systems that generate what happens next given current environmental conditions, are increasingly being implemented with multi-modal genera

arXiv:2609.08958v1 Announce Type: new Abstract: Tensegrity robots offer lightweight, compliant mobility over challenging terrain but remain difficult to model and control due to complex contact-rich d

arXiv:2608.30237v1 Announce Type: cross Abstract: General embodied agents should perceive, predict, act, evaluate, and improve within a unified system. World models have shown great promise in buildin

arXiv:2608.27856v1 Announce Type: new Abstract: Recent advances in large language models are enabling autonomous clinical agents to perform increasingly complex electronic health record (EHR) modeling

arXiv:2608.27345v1 Announce Type: cross Abstract: Recent video generation models are increasingly framed as world models. Many physical processes can unfold in more than one valid way. Therefore, a wo

arXiv:2607.22951v1 Announce Type: cross Abstract: Reliability assessment of large language models (LLMs) seeks to estimate the probability that a model produces correct responses under a specified ope

arXiv:2606.29879v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) provide powerful semantic understanding and commonsense reasoning for End-to-End Autonomous Driving (E2E-AD) planning. H

arXiv:2602.12643v2 Announce Type: replace-cross Abstract: We present Unified Latent Dynamics (ULD), a novel reinforcement learning algorithm that unifies the efficiency of model-free methods with the

arXiv:2605.01311v1 Announce Type: new Abstract: Offline evaluation of language models from usage logs is biased when model choice is confounded: the same user-side factors that influence which model i

arXiv:2604.23905v1 Announce Type: cross Abstract: Threat modeling for cyber-physical systems (CPS) remains a largely manual exercise. This project presents SMSI (System Model Security Inference), a hy

arXiv:2507.03014v2 Announce Type: replace-cross Abstract: Large language models (LLMs) face significant copyright and intellectual property challenges as the cost of training increases and model reuse

arXiv:2604.09665v1 Announce Type: cross Abstract: While the wide adoption of refusal training in large language models (LLMs) has showcased improvements in model safety, recent works have highlighted

arXiv:2610.18298v1 Announce Type: new Abstract: What binds large language models on auditable, hypothesis-driven decision tasks, where every candidate can be checked against the evidence? On the publi

arXiv:2610.11358v1 Announce Type: new Abstract: Cross-model KV-cache reuse remains a key challenge in modern LLM serving. Coding agents and multi-model systems increasingly route a shared context acro

arXiv:2610.04558v1 Announce Type: cross Abstract: We present ClimateBench v2, a standardized protocol for evaluating climate models on diagnostics expected to be informative for their skill in project

arXiv:2610.06509v1 Announce Type: new Abstract: Deep learning has revolutionised weather forecasting in recent years, especially through atmospheric foundation models, which offer competitive skill fo

arXiv:2606.17454v3 Announce Type: replace Abstract: AI agent performance is not just a modeling problem, it is fundamentally a systems problem. The advanced capabilities of models are realized through

arXiv:2511.21739v2 Announce Type: replace-cross Abstract: We present the first large-scale analysis of AI foundation model usage in science -- not just citations or keywords. We find that adoption has

arXiv:2609.18441v1 Announce Type: cross Abstract: Discrete choice model specification is a time-consuming task in which modellers often specify and estimate multiple models while balancing goodness-of

arXiv:2609.12441v1 Announce Type: cross Abstract: A world model asked what happens if an object is pushed at several speeds produces several futures. If the model has the object in mind, those futures

arXiv:2609.11656v1 Announce Type: new Abstract: The transition from file storage to database management systems transformed stored data into managed resources. AI now faces an analogous transition fro

arXiv:2602.19471v2 Announce Type: replace Abstract: Source-free domain adaptation (SFDA) aims to adapt a model trained in the source domain to perform well in the target domain, with only unlabeled ta

arXiv:2607.24465v1 Announce Type: new Abstract: Model merging aims to combine multiple domain-specialized experts trained from a shared foundation model into a single multi-task model. Existing approa

arXiv:2602.06837v2 Announce Type: replace Abstract: Hybrid modeling, the combination of machine learning models and scientific mathematical models, enables flexible and robust data-driven prediction w

arXiv:2604.22548v1 Announce Type: cross Abstract: Problem definition: Data-driven models in machine learning have enabled efficient management of production systems. However, a majority of machine lea

arXiv:2604.13618v1 Announce Type: new Abstract: Rubric-augmented verification guides reward models with explicit evaluation criteria, yielding more reliable judgments than single-model verification. H

arXiv:2405.04108v2 Announce Type: replace-cross Abstract: Recent booming development of Generative Artificial Intelligence (GenAI) has facilitated model commercialization to reinforce the model perfor

arXiv:2505.23209v3 Announce Type: replace Abstract: Model merging has emerged as an efficient method to combine multiple single-task fine-tuned models. The merged model can enjoy multi-task capabiliti

arXiv:2610.11344v1 Announce Type: new Abstract: Physics-based models connect scientific explanation with quantitative prediction. Constructing them requires selecting physical processes, defining stat

arXiv:2610.00382v1 Announce Type: cross Abstract: Model quantization reduces the numerical precision of neural network weights and activations to lower storage and computational costs. Model inversion

arXiv:2604.02881v2 Announce Type: replace-cross Abstract: Weight-space model merging combines independently fine-tuned checkpoints without access to the original training data. While merging has shown

arXiv:2608.26771v1 Announce Type: new Abstract: Automated tea leaf disease classification supports precision agriculture, yet deploying accurate models on edge devices remains challenging under tight

arXiv:2509.18751v4 Announce Type: replace Abstract: Recently reconstruction-based deep models have been widely used for time series anomaly detection, but as their capacity and generalization capabili

arXiv:2512.05462v2 Announce Type: replace-cross Abstract: Pharmaceutical drug discovery demands machine learning (ML) infrastructure that goes beyond general-purpose Machine Learning Operations (MLOps

arXiv:2607.01033v1 Announce Type: new Abstract: Model organisms (MOs) - language models trained to exhibit undesired or unnatural behaviours - are frequently used as testbeds for evaluating white-box

arXiv:2603.12433v3 Announce Type: replace-cross Abstract: Model stitching, connecting early layers of one model (source) to later layers of another (target) via a light stitch layer, has served as a p

arXiv:2605.13013v1 Announce Type: new Abstract: Diffusion world models have recently become competitive for online model-based reinforcement learning, but current approaches expose a tension: pixel di

arXiv:2604.27039v1 Announce Type: new Abstract: Token serves as the fundamental unit of computation in modern autoregressive models, and generation length directly influences both inference cost and r

arXiv:2604.22672v1 Announce Type: new Abstract: Batch processes are inherently transient and typically nonlinear, motivating nonlinear model predictive control (NMPC). However, adopting NMPC is hinder

arXiv:2603.13467v2 Announce Type: replace-cross Abstract: Model merging has shown that multitask models can be created by directly combining the parameters of different models that are each specialize

arXiv:2610.21025v1 Announce Type: cross Abstract: When does a language model acquire a capability? Developmental studies usually answer this by tracking when models start producing the correct token.

arXiv:2610.20179v1 Announce Type: new Abstract: Probes on activation vectors are widely used to detect misbehavior in language models, but their success relies on the assumption that models cannot con

arXiv:2610.07388v1 Announce Type: cross Abstract: Joint Models simultaneously model longitudinal and survival outcomes, leveraging patterns in patients' longitudinal trajectory to improve the predicti

arXiv:2610.07381v1 Announce Type: new Abstract: Modeling 3D scene geometry and its evolution over time is essential for autonomous driving and robotics. A common paradigm is to use world models to pre

arXiv:2512.23065v4 Announce Type: replace Abstract: The introduction of BERT established encoder-only transformer models as a foundational paradigm in natural language processing. Encoder-only models

arXiv:2610.04336v1 Announce Type: new Abstract: Recent advances in generative world models have increased interest in digital models that reproduce both the appearance of real objects and their respon