Search: “models”

arXiv:2505.20142v2 Announce Type: replace Abstract: In deep learning, functional similarity evaluation quantifies the extent to which independently trained models learn similar input--output relations

arXiv:2602.02890v2 Announce Type: replace Abstract: Model soups are strange and strangely effective combinations of parameters. They take a model (the stock), fine-tune it into multiple models (the in

Automatic behind the scene routing in user interfaces (instead of model picker) will redistribute value capture and usage towards many more models than just frontier ones (especially towards open-sour

arXiv:2606.01468v1 Announce Type: cross Abstract: Due to their explicit priors and ability to model uncertainty, Bayesian methods have played a major role in dynamical latent variable modeling of sing

arXiv:2310.10322v3 Announce Type: replace Abstract: Large language models (LLMs) are prone to hallucinate unintended text due to false or outdated knowledge. Since retraining LLMs is resource intensiv

arXiv:2512.05277v3 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) are increasingly deployed as the perception and reasoning backbone of autonomous agents acting in the wild, with

arXiv:2606.01904v1 Announce Type: cross Abstract: The increasing application of Natural Language Processing (NLP) in healthcare demands language models specifically attuned to the complexities of clin

arXiv:2602.03318v3 Announce Type: replace Abstract: Operations Research (OR) relies on expert-driven modeling-a slow and fragile process ill-suited to novel scenarios. While large language models (LLM

arXiv:2602.10623v2 Announce Type: replace-cross Abstract: Reward models learned from human preferences are central to aligning large language models (LLMs) via reinforcement learning from human feedba

arXiv:2606.00285v1 Announce Type: new Abstract: Large-scale multilingual bitext often contains two distinct problems: non-parallel sentence pairs and low-quality translations. We decompose model-based

arXiv:2606.00467v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used for zero-shot annotation and LLM-as-a-judge tasks, yet their reliability hinges on how model-intern

arXiv:2409.01062v4 Announce Type: replace-cross Abstract: Model Inversion (MI) attacks pose a significant privacy threat by reconstructing private training data from machine learning models. While exi

arXiv:2606.01416v1 Announce Type: new Abstract: Tool-augmented large language model (LLM) agents rely on orchestration layers that coordinate planning, retrieval, tool invocation, validation, memory,

arXiv:2606.01456v1 Announce Type: cross Abstract: Large language models are increasingly deployed as advisors whose objective is not aligned with the user's: recommenders optimize for engagement, sale

arXiv:2601.22787v2 Announce Type: replace Abstract: Post-training compression is currently divided into two contrasting regimes. On the one hand, fast, data-free, and model-agnostic methods (e.g., NF4

arXiv:2605.30589v1 Announce Type: cross Abstract: U.S. immigration law spans thousands of pages of official policy, federal regulations, and procedural guidance that change frequently and carry high s

arXiv:2510.05115v3 Announce Type: replace Abstract: Large language models (LLMs) have opened new paradigms in optimization modeling by enabling the generation of executable solver code from natural la

arXiv:2603.00377v2 Announce Type: replace Abstract: Full Waveform Inversion (FWI) is a highly nonlinear and ill-posed problem that aims to recover subsurface velocity maps from surface-recorded seismi

arXiv:2605.30323v1 Announce Type: cross Abstract: Reinforcement Learning from Human Feedback (RLHF) typically relies on static reward models to align Large Language Models with human preferences. Howe

arXiv:2605.29280v1 Announce Type: cross Abstract: Knowledge distillation (KD) transfers a single scalar prediction from a large foundation model (FM) to compact vertical models (VMs), suffering from d

arXiv:2605.29101v1 Announce Type: new Abstract: Model merging combines fine-tuned checkpoints into a single multi-task model without retraining. Existing methods - such as task arithmetic, model soups

arXiv:2605.30280v1 Announce Type: cross Abstract: Embodied intelligence is often studied through specialized models for individual tasks such as manipulation or navigation, resulting in fragmented cap

arXiv:2605.30346v1 Announce Type: new Abstract: As video diffusion models (VDMs) advance toward world models, a key question arises: do they truly understand causality, or merely overfit to statistica

arXiv:2605.27725v1 Announce Type: cross Abstract: High-fidelity computational fluid dynamics (CFD) is widely used for thermal-fluid design, but repeated CFD solves remain expensive for design optimiza

arXiv:2605.28526v1 Announce Type: new Abstract: Masked language modeling has become a standard pretraining objective for training encoder-based language models. In this approach, certain tokens in the

arXiv:2605.28174v1 Announce Type: cross Abstract: Foundation models offer a promising route to transferable remote sensing representations, but many current approaches depend on very large pretraining

arXiv:2605.28816v1 Announce Type: new Abstract: World models for interactive video generation have largely focused on single-agent settings, where future observations are generated from a single contr

Organizations are unlocking entirely new ways to use image generation and editing across their industries. To drive next-generation experiences, businesses are embedding AI directly into creative, age

We're releasing Paris 2.0, which, to our knowledge, is the world's first decentralized trained video generation model. We benchmarked it against a monolithic model trained on the same data and compute

A closer look at what we released today 🧵 ESMC is a language model trained on billions of protein sequences spanning the full diversity of life. Trained across 2.8 billion sequences, the model is expo

arXiv:2009.11997v3 Announce Type: replace-cross Abstract: Effective planning in model-based reinforcement learning (MBRL) and model-predictive control (MPC) relies on the accuracy of the learned dynam

arXiv:2605.26520v1 Announce Type: cross Abstract: While vision-language models (VLMs) have exhibited multi-turn visual reasoning capabilities, their reasoning trajectories remain relatively shallow an

arXiv:2507.13762v4 Announce Type: replace Abstract: Motivation: Structure-based drug design (SBDD) has advanced with deep generative models, but bridging the gap between continuous atomic coordinates

arXiv:2603.07523v2 Announce Type: replace Abstract: Transferring knowledge by fine-tuning large-scale pre-trained networks has become a standard paradigm for downstream tasks, yet the knowledge of a p

arXiv:2605.23934v1 Announce Type: new Abstract: Quantum computing devices are recognized as powerful tools for solving NP-complete problems. However, the intricacy of their modeling presents notable b

arXiv:2605.24852v1 Announce Type: new Abstract: Recent advances in learning-based model predictive control (MPC) have leveraged neural networks for online model learning, achieving strong performance

arXiv:2605.24411v1 Announce Type: new Abstract: Existing language model applications struggle to meet the demand for emotionally oriented support, primarily due to their inability to maintain deep, pe

arXiv:2605.24025v1 Announce Type: cross Abstract: Large models have delivered remarkable performance across a wide range of perception and generation tasks, yet practical deployment is increasingly co

arXiv:2605.23790v1 Announce Type: new Abstract: Saliency prediction has been extensively studied in RGB images and videos as a computational model of human visual attention. In contrast, predicting sa

arXiv:2505.13893v2 Announce Type: replace Abstract: Recent advances in large language models (LLMs) have intensified efforts to fuse heterogeneous open-source models into a unified system that inherit

arXiv:2605.22842v1 Announce Type: cross Abstract: Multi-agent AI pipelines typically assume that agent misconduct originates from model misalignment. We identify a structural failure in this assumptio

arXiv:2601.21306v2 Announce Type: replace-cross Abstract: This paper investigates search in model-based reinforcement learning (RL). Conventional wisdom holds that long-term predictions and compoundin

arXiv:2506.04708v3 Announce Type: replace Abstract: Language models have demonstrated remarkable capabilities in reasoning tasks through test-time scaling techniques like best-of-N sampling and tree s

arXiv:2605.21728v1 Announce Type: cross Abstract: Image captioning evaluation remains a significant challenge, as vision-language models evolve toward more challenging capabilities such as generating

After 15 months of incremental updates, leaks, and rumored leaks, DeepSeek released version 4. It arrived without the fanfare R1 and R1-preview commanded in early 2025. That quiet reception is the mos

arXiv:2605.22089v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have emerged as a promising framework for end-to-end autonomous driving. However, existing VLAs typically rely on sp