Search: “models”

arXiv:2606.28823v1 Announce Type: new Abstract: Recent large language models (LLMs) achieve strong performance on entity matching without requiring task-specific training data. However, applying these

arXiv:2606.30461v1 Announce Type: new Abstract: State space models (SSMs) have emerged as efficient linear-time alternatives to attention for long-sequence modeling. However, existing SSMs often suffe

arXiv:2606.30423v1 Announce Type: new Abstract: With the increasing adoption of Machine Learning, protecting model ownership has become an essential challenge. We initiate a formal study of Proof of O

arXiv:2606.30104v1 Announce Type: new Abstract: Electroencephalography (EEG) foundation models aim to learn generalizable representations from large-scale brain recordings. However, the role of tempor

arXiv:2606.23079v1 Announce Type: new Abstract: Neural world models coupled with model predictive control (MPC) replan at every environment step to bound accumulated prediction error, but this incurs

arXiv:2602.20208v2 Announce Type: replace Abstract: Model merging aims to integrate multiple task-specific fine-tuned models derived from a shared pre-trained checkpoint into a single multi-task model

arXiv:2606.22601v1 Announce Type: cross Abstract: Gaussian Processes (GPs) are a powerful tool for Bayesian time-series modeling, yet their cubic computational cost remains a severe barrier for applic

arXiv:2606.11482v1 Announce Type: cross Abstract: Understanding and predicting how social beliefs evolve in response to events -- from policy changes to scientific breakthroughs -- remains a fundament

arXiv:2606.11698v1 Announce Type: cross Abstract: Model watermarking safeguards AI model intellectual property by embedding distinctive knowledge that induces unique behavioral signatures. The primary

arXiv:2606.07524v1 Announce Type: cross Abstract: The explosive growth of large language models (LLMs) has created a heterogeneous and poorly documented ecosystem, making systematic model comparison i

arXiv:2501.15509v5 Announce Type: replace-cross Abstract: Model fingerprinting has emerged as a crucial mechanism for safeguarding the intellectual property of open-source models, offering a non-intru

arXiv:2606.06881v1 Announce Type: new Abstract: Blood glucose forecasting models are foundational for modern diabetes management systems, as reliable short-term predictions can enable proactive interv

arXiv:2512.24698v2 Announce Type: replace Abstract: Generating dynamic motions for legged robots remains a challenging problem. While reinforcement learning has achieved notable success in various leg

arXiv:2605.30542v1 Announce Type: new Abstract: World models for embodied AI must be physically viable: constructed to answer intervention queries by representing the physical structure governing acti

arXiv:2605.29809v1 Announce Type: cross Abstract: Large-scale text-to-image (T2I) diffusion models have enabled unprecedented creative applications, but their unauthorized use has raised serious intel

arXiv:2605.29267v1 Announce Type: new Abstract: Foundation models are increasingly trained on synthetic data generated by prior model iterations rather than exclusively on real data. This self-consumi

arXiv:2603.23085v2 Announce Type: replace Abstract: Vision-Language Models (VLMs) have enabled interpretable medical diagnosis by integrating visual perception with linguistic reasoning. Yet, existing

arXiv:2605.28488v1 Announce Type: cross Abstract: We study inference in stochastic block models (SBMs) through the lens of optimal transport (OT). We first establish that maximum likelihood variationa

arXiv:2605.27013v1 Announce Type: new Abstract: Mathematical optimization is a powerful tool for structured decision-making across domains such as resource allocation and planning. Formulating optimiz

arXiv:2505.18728v2 Announce Type: replace-cross Abstract: The recent success of State-Space Models (SSMs) in sequence modeling has motivated their adaptation to graph learning, giving rise to Graph St

arXiv:2605.23918v1 Announce Type: cross Abstract: The AI inference industry keeps models loaded in GPU memory around the clock to avoid cold-start latency, implicitly treating idle power as a fixed co

arXiv:2605.23595v1 Announce Type: cross Abstract: The rapid growth of machine learning has produced an ever-expanding ecosystem of models, making it increasingly challenging to verify the reliability

arXiv:2605.23656v1 Announce Type: new Abstract: Training high-capacity vision models from scratch requires substantial computational resources. To improve training efficiency of a wide target model, e

arXiv:2401.00139v3 Announce Type: replace-cross Abstract: This paper introduces a causal attribution model to enhance the interpretability of large language models (LLMs) and improve their causal reas

arXiv:2506.16950v2 Announce Type: replace Abstract: Out-of-distribution (OOD) robustness is a desired property of computer vision models. Improving model robustness requires high-quality signals from

arXiv:2602.13466v2 Announce Type: replace-cross Abstract: The ability of machine learning models to store input information in hidden layer vector embeddings, analogous to the concept of `memory', is

arXiv:2605.20151v1 Announce Type: new Abstract: The proliferation of generative artificial intelligence has given rise to an interactive learning environment, where model parameters are continuously u

arXiv:2605.18013v1 Announce Type: cross Abstract: Segment Anything Model 2 (SAM 2) serves as a core foundation model in the field of video segmentation. Building upon the original SAM model, it introd

arXiv:2510.08398v4 Announce Type: replace Abstract: The recent rapid advancement of Text-to-Video (T2V) generation technologies are engaging the trained models with more world model ability, making th

arXiv:2605.12850v1 Announce Type: cross Abstract: Fine-tuning large language models on narrow data with harmful content produces broadly misaligned behavior on unrelated prompts, a phenomenon known as

arXiv:2411.00471v3 Announce Type: replace-cross Abstract: This paper introduces Dirichlet process mixtures of block g priors for model selection and prediction in linear models. These priors are exten

arXiv:2605.07533v1 Announce Type: new Abstract: Large Language Models (LLMs) have recently demonstrated strong performance in machine translation (MT). However, most prior work focuses on improving or

arXiv:2605.04051v1 Announce Type: cross Abstract: In complex real-world settings, optimization is challenged by the presence of diverse models of differing fidelity. In many optimization problems, a s

arXiv:2605.04447v1 Announce Type: new Abstract: Medical foundation models pre-trained on large-scale datasets have shown powerful versatile performance. However, when adapting medical foundation model

arXiv:2605.04454v1 Announce Type: cross Abstract: Alignment evaluation in machine learning has largely become evaluation of models. Influential benchmarks score model outputs under fixed inputs, such

arXiv:2601.06395v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly multilingual, yet open models continue to underperform relative to proprietary systems, with the gap m

arXiv:2605.03299v1 Announce Type: new Abstract: Cross-lingual topic modeling aims to discover shared semantic structures across languages, yet existing models depend on sparse bilingual resources and

arXiv:2603.28489v2 Announce Type: cross Abstract: The rapid evolution of video generation has enabled models to simulate complex physical dynamics and long-horizon causalities, positioning them as pot

arXiv:2605.01870v1 Announce Type: new Abstract: Large Language Models (LLMs) have substantially advanced the field of Natural Language Processing (NLP), achieving state-of-the-art performance across a

arXiv:2601.03423v3 Announce Type: replace-cross Abstract: Adapting language models to the clinical domain through continued pretraining and instruction tuning requires costly retraining for each new m

arXiv:2604.23952v1 Announce Type: cross Abstract: Stochastic reduced-order models are widely used to represent the effective dynamics of complex systems, but estimating their drift and diffusion coeff

arXiv:2604.23530v1 Announce Type: cross Abstract: Multi-turn, long-horizon tasks are increasingly common for large language models (LLMs), but solving them typically requires many sequential model inv

arXiv:2604.23586v1 Announce Type: cross Abstract: Joint audio-video generation models have shown that unified generation yields stronger cross-modal coherence than cascaded approaches. However, existi

arXiv:2510.07632v2 Announce Type: replace Abstract: Frontier AI models have achieved remarkable progress, yet recent studies suggest they struggle with compositional reasoning, often performing at or

arXiv:2406.11354v3 Announce Type: replace-cross Abstract: Humans can retain old knowledge while learning new information, but Large Language Models (LLMs) often suffer from catastrophic forgetting whe

arXiv:2506.01937v2 Announce Type: replace Abstract: Reward models are used throughout the post-training of language models to capture nuanced signals from preference data and provide a training target