Search: “models”

arXiv:2604.07303v2 Announce Type: replace Abstract: Understanding and modeling animal behavior is essential for studying collective motion, decision-making, and bio-inspired robotics. Yet, evaluating

arXiv:2605.17276v1 Announce Type: cross Abstract: While scaling laws have established a fundamental framework for foundation models in natural language processing, their applicability to electrocardio

arXiv:2510.02453v3 Announce Type: replace-cross Abstract: Frontier language models are deployed as black-box services, where model weights cannot be modified and customization is limited to prompting.

arXiv:2605.14028v1 Announce Type: new Abstract: Since the emergence of Vision Transformer (ViT), it has been widely used in generative language model and generative visual model. Especially in the cur

arXiv:2605.12843v1 Announce Type: cross Abstract: Model merging aims to combine multiple task-specific expert models into a single model without joint retraining, offering a practical alternative to m

arXiv:2605.13568v1 Announce Type: cross Abstract: Myocardial infarction (MI) is a leading cause of death, and its adverse outcomes are urgent to predict. Yet ECG-based prognostic models underperform b

arXiv:2605.13160v1 Announce Type: cross Abstract: Modern Bayesian optimization and adaptive sampling methods increasingly rely on nonlinear parametric models, yet theoretical guarantees for such model

arXiv:2605.10956v1 Announce Type: cross Abstract: Machine-learned surrogate modeling of advection may accelerate geoscientific models, but existing approaches have either achieved limited speedup or h

arXiv:2509.24244v4 Announce Type: replace Abstract: We study empirical scaling laws for language model merging measured by cross-entropy. Despite its wide practical use, merging lacks a quantitative r

arXiv:2605.08301v1 Announce Type: cross Abstract: Hybrid State-Space models combine Attention with recurrent State-Space Model (SSM) layers, balancing eidetic memory from Attention with compressed fad

arXiv:2512.13618v3 Announce Type: replace Abstract: Representing continuous time is a critical and under-explored challenge in modeling temporal event sequences with large language models (LLMs). Vari

arXiv:2605.10405v1 Announce Type: new Abstract: Selecting the best large language model (LLM) for a fixed benchmark is often expensive, since exhaustive evaluation requires running every model on ever

arXiv:2605.07301v1 Announce Type: new Abstract: Accurately predicting opponents' behavior from interactions is a fundamental capability for large language model (LLM)-based agents in multi-agent and g

arXiv:2605.01185v1 Announce Type: new Abstract: Accelerated magnetic resonance imaging (MRI) enabled by the training of deep learning (DL)-based image recon. models requires large and diverse raw k-sp

arXiv:2604.25159v1 Announce Type: new Abstract: Landslide investigation relies on sufficient and well-balanced observational data influenced by geological, hydrological, and anthropogenic factors. Ava

arXiv:2604.25416v1 Announce Type: new Abstract: Model-Based Reinforcement Learning distinguishes between physical dynamics models operating on proprioceptive inputs and latent dynamics models operatin

arXiv:2604.22981v1 Announce Type: new Abstract: Reward models in RLHF are trained to score only the final token of a response - a choice that discards rich signal from every intermediate position and

arXiv:2604.22167v1 Announce Type: cross Abstract: Language models are increasingly capable and are being rapidly deployed on a population-level scale. As a result, the safety of these models is increa

arXiv:2510.21285v4 Announce Type: replace Abstract: Large Reasoning Models (LRMs) achieve strong performance on complex multi-step reasoning, yet they still exhibit severe safety failures such as harm

arXiv:2604.21686v1 Announce Type: new Abstract: Interactive video generation models such as Genie, YUME, HY-World, and Matrix-Game are advancing rapidly, yet every model is evaluated on its own benchm

arXiv:2506.02132v5 Announce Type: replace Abstract: Large transformer-based language models dominate modern NLP, yet our understanding of how they encode linguistic information relies primarily on stu

arXiv:2604.17153v1 Announce Type: new Abstract: Transforming legal text into executable decision logic is a longstanding challenge in legal informatics. With the rise of LLMs, this task has gained ren

arXiv:2509.12539v2 Announce Type: replace-cross Abstract: We present LEAF ('Lightweight Embedding Alignment Framework'), a knowledge distillation framework for text embedding models. A key distinguish

arXiv:2604.17830v1 Announce Type: new Abstract: Traditional Task and Motion Planning (TAMP) systems depend on physics models for motion planning and discrete symbolic models for task planning. Althoug

arXiv:2604.15611v1 Announce Type: cross Abstract: Latent diffusion models have emerged as powerful generative models in medical imaging, enabling the synthesis of high quality brain magnetic resonance

arXiv:2511.05476v4 Announce Type: replace-cross Abstract: Transformer-based language models of code have achieved state-of-the-art performance across a wide range of software analytics tasks, but thei

arXiv:2604.11748v1 Announce Type: new Abstract: Continuous diffusion models have achieved strong performance across domains such as images. However, in language modeling, prior continuous diffusion la

arXiv:2604.11290v1 Announce Type: new Abstract: Synthesizing supervised finetuning (SFT) data from language models (LMs) to teach smaller models multilingual tasks has become increasingly common. Howe

arXiv:2604.08627v1 Announce Type: cross Abstract: Pretrained models have become standard in both vision and language, yet they typically do not provide reliable measures of confidence. Existing uncert

arXiv:2604.08995v1 Announce Type: new Abstract: With the advancement of interactive video generation, diffusion models have increasingly demonstrated their potential as world models. However, existing

arXiv:2604.08070v1 Announce Type: new Abstract: Darija, the Moroccan Arabic dialect, is rich in visual content yet lacks specialized Optical Character Recognition (OCR) tools. This paper introduces At

arXiv:2603.12658v2 Announce Type: replace-cross Abstract: Continual learning (CL) has emerged as a pivotal paradigm to enable large language models (LLMs) to dynamically adapt to evolving knowledge an

arXiv:2608.02845v1 Announce Type: new Abstract: Tabular model performance degrades when feature distributions change over time or the relationship between features and outcome variables change over ti

arXiv:2510.01925v3 Announce Type: replace Abstract: Reward models (RMs) play a critical role in enhancing the reasoning performance of LLMs. For example, they can provide training signals to finetune

arXiv:2607.29071v1 Announce Type: cross Abstract: Federated learning of foundation models faces a fundamental resource-asymmetry challenge: the institutions holding the most valuable domain-specific d

arXiv:2607.29431v1 Announce Type: new Abstract: Large language models increasingly generate optimization models from natural language, but existing evaluation often reduces a generated model and its g

arXiv:2607.27350v1 Announce Type: new Abstract: Sybil bots are Ethereum actors that imitate legitimate users to extract airdrop rewards or influence governance. Recent Sybil detection methods increasi

arXiv:2607.28287v1 Announce Type: cross Abstract: ARC-AGI-3 turns abstraction into an interactive problem of skill acquisition. A player must infer an unfamiliar game's rules, hidden state, and goal w

arXiv:2607.25292v1 Announce Type: new Abstract: Silicon sampling uses language models as proxies for human survey respondents, treating each model call as an independent draw from the persona's respon

arXiv:2607.24440v1 Announce Type: cross Abstract: Vision-language models (VLMs) deployed on consumer hardware must decide when to answer and when to defer, and that decision depends on having a confid

arXiv:2607.23147v1 Announce Type: cross Abstract: Large language models now power autonomous agents capable of complex, multi-step tasks in different environments. Accurate and reliable execution of t

arXiv:2602.07643v2 Announce Type: replace Abstract: Foundation models have emerged as a transformative paradigm in 3D medical imaging, with the promise of unified quantitative analysis across diverse

arXiv:2607.13239v1 Announce Type: new Abstract: Foundation models, including large language models (LLMs) and vision-language models (VLMs), are increasingly used for transportation management center

arXiv:2607.12739v1 Announce Type: new Abstract: A language model may be asked either what experts believe about a contested claim or what it believes about the claim itself. A trustworthy conversation

arXiv:2607.05571v1 Announce Type: new Abstract: Large language models are increasingly explored as AI tutors, yet deploying them in K-12 settings raises concerns around privacy, cost, and reliance on

arXiv:2607.04409v1 Announce Type: new Abstract: Learning and planning in imagination using world models provides an effective paradigm for training agents for decision-making. However, existing approa