Search: “models”

arXiv:2608.02050v1 Announce Type: new Abstract: Can a strictly local, iterated, weight-shared computation primitive support language modelling, and which of those three properties actually drives the

arXiv:2608.00105v1 Announce Type: new Abstract: Pathology foundation models are reported to encode molecular programmes in tissue morphology, but the evidence is usually a cohort-wide ranked gene list

arXiv:2607.28982v1 Announce Type: cross Abstract: Recent language models achieve strong performance across a variety of tasks, but conventional adaptation applies updates uniformly across training sam

arXiv:2607.27433v1 Announce Type: cross Abstract: We develop two parametric data-driven reduced models: a physics-informed neural network (PINN) and a non-intrusive tensorial reduced-order model (TROM

arXiv:2607.28624v1 Announce Type: new Abstract: We introduce PhiZero, a physical world model built around physical language, a compact discrete representation of world-state transitions. Existing phys

arXiv:2607.27023v1 Announce Type: new Abstract: Evaluating large generative models across benchmarks is time-consuming and computationally expensive. This drives the need for methods that can estimate

arXiv:2607.23183v1 Announce Type: cross Abstract: Neurological disorders are a leading cause of global disability and are increasingly linked to environmental chemical exposures. Yet neurotoxicity ass

arXiv:2607.22583v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved widespread adoption because of their strong reasoning and query-response capabilities. However, deploying the

arXiv:2607.22708v1 Announce Type: new Abstract: Deploying a vision-language model with full UI understanding on end devices has long been trapped between accuracy and efficiency: on one side is the ac

arXiv:2607.24032v1 Announce Type: new Abstract: Generative-AI evaluations can become historical before publication, yet calendar age does not affect every conclusion equally. This paper has two linked

arXiv:2607.06216v1 Announce Type: new Abstract: The future of World Models depends not only on scaling model capability, but also on scaling practicality and inference efficiency. High-frame-rate infe

arXiv:2607.04755v1 Announce Type: new Abstract: Model merging offers a practical alternative to conventional continual learning by integrating independently fine-tuned models without retaining previou

arXiv:2607.01986v1 Announce Type: new Abstract: Multivariate time-series models for prognostics are often evaluated by point prediction accuracy, yet their internal states rarely expose a coherent deg

arXiv:2607.00083v1 Announce Type: cross Abstract: Language models have changed from unreliable text generators to highly-capable large models with trillions of parameters. Capability increases come ha

arXiv:2606.29059v1 Announce Type: cross Abstract: World modeling requires forecasting uncertain futures while preserving information useful for downstream perception. Existing visual world models ofte

arXiv:2606.26578v1 Announce Type: new Abstract: Automating optimization modeling from natural language with large language models (LLMs) faces two key challenges. First, training corpora lack structur

arXiv:2606.27325v1 Announce Type: new Abstract: Recent advances in action-conditioned world models show promising progress in modeling complex interactions and forecasting future states under diverse

arXiv:2606.24256v1 Announce Type: new Abstract: Physical interactions follow a long-tailed distribution: a set of common and regular interactions dominates human experience and visual data, while a br

arXiv:2606.22317v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) is widely viewed as a promising path toward continuously improving large language models. Recent w

arXiv:2509.21489v3 Announce Type: replace Abstract: Graph foundation models face several fundamental challenges including transferability across diverse domains and data scarcity, which calls into que

arXiv:2606.20726v1 Announce Type: new Abstract: We introduce a compact empirical model that quantifies how answer accuracy degrades as a function of frame budget B and temporal distance D in long vide

arXiv:2606.18627v2 Announce Type: replace Abstract: Model merging has emerged as a training-free alternative to multi-task learning, aiming to combine multiple task-specific fine-tuned models into a s

arXiv:2511.22699v4 Announce Type: replace Abstract: The landscape of high-performance image generation models is currently dominated by proprietary systems, such as Nano Banana Pro and Seedream 4.0. L

arXiv:2606.11270v1 Announce Type: cross Abstract: Distillation of a language model intended to transfer benign behavior to a student model may also transfer undesirable characteristics, if they are pr

arXiv:2606.09936v1 Announce Type: cross Abstract: World models are now built on substantially different computational substrates. Latent recurrent state-space models such as PlaNet and the Dreamer fam

arXiv:2606.09932v1 Announce Type: cross Abstract: Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL) has become a standard pipeline for Large Language Model (LLM) post-training. SFT

arXiv:2606.08525v1 Announce Type: new Abstract: Reward models play a pivotal role in reinforcement learning (RL) and multi-modal trajectory selection for autonomous driving. However, acquiring such re

arXiv:2603.24963v3 Announce Type: replace Abstract: Modern computational advertising platforms typically rely on recommendation systems to predict user responses, such as click-through rates, conversi

arXiv:2511.04567v2 Announce Type: replace-cross Abstract: Constructing reduced models for turbulent transport is essential for accelerating profile predictions and enabling many-query tasks such as pa

arXiv:2509.25522v3 Announce Type: replace Abstract: Recent advancements in generative models have allowed the emergence of a promising paradigm for recommender systems (RS), known as Generative Recomm

arXiv:2512.21917v3 Announce Type: replace-cross Abstract: Policy alignment to preference data typically assumes a known link function between observed preferences and latent rewards (e.g., Bradley-Ter

arXiv:2606.04459v1 Announce Type: cross Abstract: Language model parameters are known to impose unique (to each model) geometric constraints on their logit outputs, which serves as a signature that id

arXiv:2606.04264v1 Announce Type: new Abstract: Recent years have seen remarkable progress in unified vision-language models handling both multimodal understanding and generation within a single archi

arXiv:2606.02969v1 Announce Type: new Abstract: This paper examines three approaches for modeling the dynamics of a flexible-link 2-DoF robotic arm to address unmodeled dynamics not captured by rigid-

arXiv:2606.01057v1 Announce Type: cross Abstract: Procedural 3D modeling through code is emerging as a versatile paradigm, offering deterministic, engine-ready, and precisely editable assets that neur

arXiv:2603.02346v2 Announce Type: replace-cross Abstract: We introduce Large Electron Model, a single neural network model that produces variational wavefunctions of interacting electrons over the ent

arXiv:2606.01075v1 Announce Type: new Abstract: Recent work suggests that large language models (LLMs) can improve through self-evolution (SE), using supervision signals generated by the model itself.

arXiv:2606.00511v1 Announce Type: cross Abstract: Model merging aims to consolidate multiple task-specific models fine-tuned on different datasets into a unified architecture that performs cross-domai

arXiv:2511.04981v2 Announce Type: replace Abstract: Model depth is a double-edged sword in deep learning: deeper models achieve higher accuracy but require higher computational cost. To efficiently tr

arXiv:2509.23074v3 Announce Type: replace-cross Abstract: In the era of increasingly complex AI models for time series forecasting, progress is often measured by marginal improvements on benchmark lea

arXiv:2605.28544v1 Announce Type: new Abstract: Pretrained foundation models have become an important basis for end-to-end autonomous driving. In contrast to vision-language models pretrained primaril

arXiv:2510.08932v2 Announce Type: replace Abstract: Recently, a growing body of research has focused on either optimizing CTR model architectures to better model feature interactions or refining train

arXiv:2605.24414v1 Announce Type: new Abstract: We introduce JT-Safe-V2, a large language model designed to advance the safety and trustworthiness of foundation models, extending our previous JT-Safe

arXiv:2605.22177v1 Announce Type: cross Abstract: The proliferation of large language models (LLMs) and modular skills has endowed autonomous agents with increasingly powerful capabilities. Existing f

arXiv:2605.22446v1 Announce Type: new Abstract: While large vision-language-action (VLA) models and generative world models (WM) have advanced long-horizon embodied intelligence, their practical deplo

arXiv:2512.23292v3 Announce Type: replace-cross Abstract: The prevailing paradigm in AI for physical systems (scaling general-purpose foundation models toward universal multimodal reasoning) confronts